Coarse-Grained Clustering Dynamics of Heterogeneously Coupled Neurons.

PubMed

Moon, Sung Joon; Cook, Katherine A; Rajendran, Karthikeyan; Kevrekidis, Ioannis G; Cisternas, Jaime; Laing, Carlo R

2015-12-01

The formation of oscillating phase clusters in a network of identical Hodgkin-Huxley neurons is studied, along with their dynamic behavior. The neurons are synaptically coupled in an all-to-all manner, yet the synaptic coupling characteristic time is heterogeneous across the connections. In a network of N neurons where this heterogeneity is characterized by a prescribed random variable, the oscillatory single-cluster state can transition-through [Formula: see text] (possibly perturbed) period-doubling and subsequent bifurcations-to a variety of multiple-cluster states. The clustering dynamic behavior is computationally studied both at the detailed and the coarse-grained levels, and a numerical approach that can enable studying the coarse-grained dynamics in a network of arbitrarily large size is suggested. Among a number of cluster states formed, double clusters, composed of nearly equal sub-network sizes are seen to be stable; interestingly, the heterogeneity parameter in each of the double-cluster components tends to be consistent with the random variable over the entire network: Given a double-cluster state, permuting the dynamical variables of the neurons can lead to a combinatorially large number of different, yet similar "fine" states that appear practically identical at the coarse-grained level. For weak heterogeneity we find that correlations rapidly develop, within each cluster, between the neuron's "identity" (its own value of the heterogeneity parameter) and its dynamical state. For single- and double-cluster states we demonstrate an effective coarse-graining approach that uses the Polynomial Chaos expansion to succinctly describe the dynamics by these quickly established "identity-state" correlations. This coarse-graining approach is utilized, within the equation-free framework, to perform efficient computations of the neuron ensemble dynamics.

Network-induced chaos in integrate-and-fire neuronal ensembles.

PubMed

Zhou, Douglas; Rangan, Aaditya V; Sun, Yi; Cai, David

2009-09-01

It has been shown that a single standard linear integrate-and-fire (IF) neuron under a general time-dependent stimulus cannot possess chaotic dynamics despite the firing-reset discontinuity. Here we address the issue of whether conductance-based, pulsed-coupled network interactions can induce chaos in an IF neuronal ensemble. Using numerical methods, we demonstrate that all-to-all, homogeneously pulse-coupled IF neuronal networks can indeed give rise to chaotic dynamics under an external periodic current drive. We also provide a precise characterization of the largest Lyapunov exponent for these high dimensional nonsmooth dynamical systems. In addition, we present a stable and accurate numerical algorithm for evaluating the largest Lyapunov exponent, which can overcome difficulties encountered by traditional methods for these nonsmooth dynamical systems with degeneracy induced by, e.g., refractoriness of neurons.

Interplay between population firing stability and single neuron dynamics in hippocampal networks

PubMed Central

Slomowitz, Edden; Styr, Boaz; Vertkin, Irena; Milshtein-Parush, Hila; Nelken, Israel; Slutsky, Michael; Slutsky, Inna

2015-01-01

Neuronal circuits' ability to maintain the delicate balance between stability and flexibility in changing environments is critical for normal neuronal functioning. However, to what extent individual neurons and neuronal populations maintain internal firing properties remains largely unknown. In this study, we show that distributions of spontaneous population firing rates and synchrony are subject to accurate homeostatic control following increase of synaptic inhibition in cultured hippocampal networks. Reduction in firing rate triggered synaptic and intrinsic adaptive responses operating as global homeostatic mechanisms to maintain firing macro-stability, without achieving local homeostasis at the single-neuron level. Adaptive mechanisms, while stabilizing population firing properties, reduced short-term facilitation essential for synaptic discrimination of input patterns. Thus, invariant ongoing population dynamics emerge from intrinsically unstable activity patterns of individual neurons and synapses. The observed differences in the precision of homeostatic control at different spatial scales challenge cell-autonomous theory of network homeostasis and suggest the existence of network-wide regulation rules. DOI: http://dx.doi.org/10.7554/eLife.04378.001 PMID:25556699

Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons

PubMed Central

Mensi, Skander; Hagens, Olivier; Gerstner, Wulfram; Pozzorini, Christian

2016-01-01

The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong inputs, neurons modify their coding strategy by progressively reducing their selective sensitivity to rapid input fluctuations. Combining mathematical modeling with in vitro experiments, we demonstrate that, in L5 pyramidal neurons, the firing threshold dynamics adaptively adjust the effective timescale of somatic integration in order to preserve sensitivity to rapid signals over a broad range of input statistics. For that, a new Generalized Integrate-and-Fire model featuring nonlinear firing threshold dynamics and conductance-based adaptation is introduced that outperforms state-of-the-art neuron models in predicting the spiking activity of neurons responding to a variety of in vivo-like fluctuating currents. Our model allows for efficient parameter extraction and can be analytically mapped to a Generalized Linear Model in which both the input filter—describing somatic integration—and the spike-history filter—accounting for spike-frequency adaptation—dynamically adapt to the input statistics, as experimentally observed. Overall, our results provide new insights on the computational role of different biophysical processes known to underlie adaptive coding in single neurons and support previous theoretical findings indicating that the nonlinear dynamics of the firing threshold due to Na+-channel inactivation regulate the sensitivity to rapid input fluctuations. PMID:26907675

A stochastic-field description of finite-size spiking neural networks

PubMed Central

Longtin, André

2017-01-01

Neural network dynamics are governed by the interaction of spiking neurons. Stochastic aspects of single-neuron dynamics propagate up to the network level and shape the dynamical and informational properties of the population. Mean-field models of population activity disregard the finite-size stochastic fluctuations of network dynamics and thus offer a deterministic description of the system. Here, we derive a stochastic partial differential equation (SPDE) describing the temporal evolution of the finite-size refractory density, which represents the proportion of neurons in a given refractory state at any given time. The population activity—the density of active neurons per unit time—is easily extracted from this refractory density. The SPDE includes finite-size effects through a two-dimensional Gaussian white noise that acts both in time and along the refractory dimension. For an infinite number of neurons the standard mean-field theory is recovered. A discretization of the SPDE along its characteristic curves allows direct simulations of the activity of large but finite spiking networks; this constitutes the main advantage of our approach. Linearizing the SPDE with respect to the deterministic asynchronous state allows the theoretical investigation of finite-size activity fluctuations. In particular, analytical expressions for the power spectrum and autocorrelation of activity fluctuations are obtained. Moreover, our approach can be adapted to incorporate multiple interacting populations and quasi-renewal single-neuron dynamics. PMID:28787447

Visualization of Cortical Dynamics

NASA Astrophysics Data System (ADS)

Grinvald, Amiram

2003-03-01

Recent progress in studies of cortical dynamics will be reviewed including the combination of real time optical imaging based on voltage sensitive dyes, single and multi- unit recordings, LFP, intracellular recordings and microstimulation. To image the flow of neuronal activity from one cortical site to the next, in real time, we have used optical imaging based on newly designed voltage sensitive dyes and a Fuji 128x 128 fast camera which we modified. A factor of 20-40 fold improvement in the signal to noise ratio was obtained with the new dye during in vivo imaging experiments. This improvements has facilitates the exploration of cortical dynamics without signal averaging in the millisecond time domain. We confirmed that the voltage sensitive dye signal indeed reflects membrane potential changes in populations of neurons by showing that the time course of the intracellular activity recorded intracellularly from a single neuron was highly correlated in many cases with the optical signal from a small patch of cortex recorded nearby. We showed that the firing of single cortical neurons is not a random process but occurs when the on-going pattern of million of neurons is similar to the functional architecture map which correspond to the tuning properties of that neuron. Chronic optical imaging, combined with electrical recordings and microstimulation, over a long period of times of more than a year, was successfully applied also to the study of higher brain functions in the behaving macaque monkey.

On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs

PubMed Central

Truccolo, Wilson

2017-01-01

Point process generalized linear models (PP-GLMs) provide an important statistical framework for modeling spiking activity in single-neurons and neuronal networks. Stochastic stability is essential when sampling from these models, as done in computational neuroscience to analyze statistical properties of neuronal dynamics and in neuro-engineering to implement closed-loop applications. Here we show, however, that despite passing common goodness-of-fit tests, PP-GLMs estimated from data are often unstable, leading to divergent firing rates. The inclusion of absolute refractory periods is not a satisfactory solution since the activity then typically settles into unphysiological rates. To address these issues, we derive a framework for determining the existence and stability of fixed points of the expected conditional intensity function (CIF) for general PP-GLMs. Specifically, in nonlinear Hawkes PP-GLMs, the CIF is expressed as a function of the previous spike history and exogenous inputs. We use a mean-field quasi-renewal (QR) approximation that decomposes spike history effects into the contribution of the last spike and an average of the CIF over all spike histories prior to the last spike. Fixed points for stationary rates are derived as self-consistent solutions of integral equations. Bifurcation analysis and the number of fixed points predict that the original models can show stable, divergent, and metastable (fragile) dynamics. For fragile models, fluctuations of the single-neuron dynamics predict expected divergence times after which rates approach unphysiologically high values. This metric can be used to estimate the probability of rates to remain physiological for given time periods, e.g., for simulation purposes. We demonstrate the use of the stability framework using simulated single-neuron examples and neurophysiological recordings. Finally, we show how to adapt PP-GLM estimation procedures to guarantee model stability. Overall, our results provide a stability framework for data-driven PP-GLMs and shed new light on the stochastic dynamics of state-of-the-art statistical models of neuronal spiking activity. PMID:28234899

On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs.

PubMed

Gerhard, Felipe; Deger, Moritz; Truccolo, Wilson

2017-02-01

Point process generalized linear models (PP-GLMs) provide an important statistical framework for modeling spiking activity in single-neurons and neuronal networks. Stochastic stability is essential when sampling from these models, as done in computational neuroscience to analyze statistical properties of neuronal dynamics and in neuro-engineering to implement closed-loop applications. Here we show, however, that despite passing common goodness-of-fit tests, PP-GLMs estimated from data are often unstable, leading to divergent firing rates. The inclusion of absolute refractory periods is not a satisfactory solution since the activity then typically settles into unphysiological rates. To address these issues, we derive a framework for determining the existence and stability of fixed points of the expected conditional intensity function (CIF) for general PP-GLMs. Specifically, in nonlinear Hawkes PP-GLMs, the CIF is expressed as a function of the previous spike history and exogenous inputs. We use a mean-field quasi-renewal (QR) approximation that decomposes spike history effects into the contribution of the last spike and an average of the CIF over all spike histories prior to the last spike. Fixed points for stationary rates are derived as self-consistent solutions of integral equations. Bifurcation analysis and the number of fixed points predict that the original models can show stable, divergent, and metastable (fragile) dynamics. For fragile models, fluctuations of the single-neuron dynamics predict expected divergence times after which rates approach unphysiologically high values. This metric can be used to estimate the probability of rates to remain physiological for given time periods, e.g., for simulation purposes. We demonstrate the use of the stability framework using simulated single-neuron examples and neurophysiological recordings. Finally, we show how to adapt PP-GLM estimation procedures to guarantee model stability. Overall, our results provide a stability framework for data-driven PP-GLMs and shed new light on the stochastic dynamics of state-of-the-art statistical models of neuronal spiking activity.

Elevated correlations in neuronal ensembles of mouse auditory cortex following parturition.

PubMed

Rothschild, Gideon; Cohen, Lior; Mizrahi, Adi; Nelken, Israel

2013-07-31

The auditory cortex is malleable by experience. Previous studies of auditory plasticity have described experience-dependent changes in response profiles of single neurons or changes in global tonotopic organization. However, experience-dependent changes in the dynamics of local neural populations have remained unexplored. In this study, we examined the influence of a dramatic yet natural experience in the life of female mice, giving birth and becoming a mother on single neurons and neuronal ensembles in the primary auditory cortex (A1). Using in vivo two-photon calcium imaging and electrophysiological recordings from layer 2/3 in A1 of mothers and age-matched virgin mice, we monitored changes in the responses to a set of artificial and natural sounds. Population dynamics underwent large changes as measured by pairwise and higher-order correlations, with noise correlations increasing as much as twofold in lactating mothers. Concomitantly, changes in response properties of single neurons were modest and selective. Remarkably, despite the large changes in correlations, information about stimulus identity remained essentially the same in the two groups. Our results demonstrate changes in the correlation structure of neuronal activity as a result of a natural life event.

Polarization and dynamical properties of VCSELs-based photonic neuron subject to optical pulse injection

NASA Astrophysics Data System (ADS)

Xiang, Shuiying; Wen, Aijun; Zhang, Hao; Li, Jiafu; Guo, Xingxing; Shang, Lei; Lin, Lin

2016-11-01

The polarization-resolved nonlinear dynamics of vertical-cavity surface-emitting lasers (VCSELs) subject to orthogonally polarized optical pulse injection are investigated numerically based on the spin flip model. By extensive numerical bifurcation analysis, the responses dynamics of photonic neuron based on VCSELs under the arrival of external stimuli of orthogonally polarized optical pulse injection are mainly discussed. It is found that, several neuron-like dynamics, such as phasic spiking of a single abrupt large amplitude pulse followed with or without subthreshold oscillation, and tonic spiking with multiple periodic pulses, are successfully reproduced in the numerical model of VCSELs. Besides, the effects of stimuli strength, pump current, frequency detuning, as well as the linewidth enhancement factor on the neuron-like response dynamics are examined carefully. The operating parameters ranges corresponding to different neuron-like dynamics are further identified. Thus, the numerical model and simulation results are very useful and interesting for the ultrafast brain-inspired neuromorphic photonics systems based on VCSELs.

Fast two-photon imaging of subcellular voltage dynamics in neuronal tissue with genetically encoded indicators.

PubMed

Chamberland, Simon; Yang, Helen H; Pan, Michael M; Evans, Stephen W; Guan, Sihui; Chavarha, Mariya; Yang, Ying; Salesse, Charleen; Wu, Haodi; Wu, Joseph C; Clandinin, Thomas R; Toth, Katalin; Lin, Michael Z; St-Pierre, François

2017-07-27

Monitoring voltage dynamics in defined neurons deep in the brain is critical for unraveling the function of neuronal circuits but is challenging due to the limited performance of existing tools. In particular, while genetically encoded voltage indicators have shown promise for optical detection of voltage transients, many indicators exhibit low sensitivity when imaged under two-photon illumination. Previous studies thus fell short of visualizing voltage dynamics in individual neurons in single trials. Here, we report ASAP2s, a novel voltage indicator with improved sensitivity. By imaging ASAP2s using random-access multi-photon microscopy, we demonstrate robust single-trial detection of action potentials in organotypic slice cultures. We also show that ASAP2s enables two-photon imaging of graded potentials in organotypic slice cultures and in Drosophila . These results demonstrate that the combination of ASAP2s and fast two-photon imaging methods enables detection of neural electrical activity with subcellular spatial resolution and millisecond-timescale precision.

Contribution of LFP dynamics to single-neuron spiking variability in motor cortex during movement execution

PubMed Central

Rule, Michael E.; Vargas-Irwin, Carlos; Donoghue, John P.; Truccolo, Wilson

2015-01-01

Understanding the sources of variability in single-neuron spiking responses is an important open problem for the theory of neural coding. This variability is thought to result primarily from spontaneous collective dynamics in neuronal networks. Here, we investigate how well collective dynamics reflected in motor cortex local field potentials (LFPs) can account for spiking variability during motor behavior. Neural activity was recorded via microelectrode arrays implanted in ventral and dorsal premotor and primary motor cortices of non-human primates performing naturalistic 3-D reaching and grasping actions. Point process models were used to quantify how well LFP features accounted for spiking variability not explained by the measured 3-D reach and grasp kinematics. LFP features included the instantaneous magnitude, phase and analytic-signal components of narrow band-pass filtered (δ,θ,α,β) LFPs, and analytic signal and amplitude envelope features in higher-frequency bands. Multiband LFP features predicted single-neuron spiking (1ms resolution) with substantial accuracy as assessed via ROC analysis. Notably, however, models including both LFP and kinematics features displayed marginal improvement over kinematics-only models. Furthermore, the small predictive information added by LFP features to kinematic models was redundant to information available in fast-timescale (<100 ms) spiking history. Overall, information in multiband LFP features, although predictive of single-neuron spiking during movement execution, was redundant to information available in movement parameters and spiking history. Our findings suggest that, during movement execution, collective dynamics reflected in motor cortex LFPs primarily relate to sensorimotor processes directly controlling movement output, adding little explanatory power to variability not accounted by movement parameters. PMID:26157365

M-type potassium conductance controls the emergence of neural phase codes: a combined experimental and neuron modelling study

PubMed Central

Kwag, Jeehyun; Jang, Hyun Jae; Kim, Mincheol; Lee, Sujeong

2014-01-01

Rate and phase codes are believed to be important in neural information processing. Hippocampal place cells provide a good example where both coding schemes coexist during spatial information processing. Spike rate increases in the place field, whereas spike phase precesses relative to the ongoing theta oscillation. However, what intrinsic mechanism allows for a single neuron to generate spike output patterns that contain both neural codes is unknown. Using dynamic clamp, we simulate an in vivo-like subthreshold dynamics of place cells to in vitro CA1 pyramidal neurons to establish an in vitro model of spike phase precession. Using this in vitro model, we show that membrane potential oscillation (MPO) dynamics is important in the emergence of spike phase codes: blocking the slowly activating, non-inactivating K+ current (IM), which is known to control subthreshold MPO, disrupts MPO and abolishes spike phase precession. We verify the importance of adaptive IM in the generation of phase codes using both an adaptive integrate-and-fire and a Hodgkin–Huxley (HH) neuron model. Especially, using the HH model, we further show that it is the perisomatically located IM with slow activation kinetics that is crucial for the generation of phase codes. These results suggest an important functional role of IM in single neuron computation, where IM serves as an intrinsic mechanism allowing for dual rate and phase coding in single neurons. PMID:25100320

Transition to Chaos in Random Neuronal Networks

NASA Astrophysics Data System (ADS)

Kadmon, Jonathan; Sompolinsky, Haim

2015-10-01

Firing patterns in the central nervous system often exhibit strong temporal irregularity and considerable heterogeneity in time-averaged response properties. Previous studies suggested that these properties are the outcome of the intrinsic chaotic dynamics of the neural circuits. Indeed, simplified rate-based neuronal networks with synaptic connections drawn from Gaussian distribution and sigmoidal nonlinearity are known to exhibit chaotic dynamics when the synaptic gain (i.e., connection variance) is sufficiently large. In the limit of an infinitely large network, there is a sharp transition from a fixed point to chaos, as the synaptic gain reaches a critical value. Near the onset, chaotic fluctuations are slow, analogous to the ubiquitous, slow irregular fluctuations observed in the firing rates of many cortical circuits. However, the existence of a transition from a fixed point to chaos in neuronal circuit models with more realistic architectures and firing dynamics has not been established. In this work, we investigate rate-based dynamics of neuronal circuits composed of several subpopulations with randomly diluted connections. Nonzero connections are either positive for excitatory neurons or negative for inhibitory ones, while single neuron output is strictly positive with output rates rising as a power law above threshold, in line with known constraints in many biological systems. Using dynamic mean field theory, we find the phase diagram depicting the regimes of stable fixed-point, unstable-dynamic, and chaotic-rate fluctuations. We focus on the latter and characterize the properties of systems near this transition. We show that dilute excitatory-inhibitory architectures exhibit the same onset to chaos as the single population with Gaussian connectivity. In these architectures, the large mean excitatory and inhibitory inputs dynamically balance each other, amplifying the effect of the residual fluctuations. Importantly, the existence of a transition to chaos and its critical properties depend on the shape of the single-neuron nonlinear input-output transfer function, near firing threshold. In particular, for nonlinear transfer functions with a sharp rise near threshold, the transition to chaos disappears in the limit of a large network; instead, the system exhibits chaotic fluctuations even for small synaptic gain. Finally, we investigate transition to chaos in network models with spiking dynamics. We show that when synaptic time constants are slow relative to the mean inverse firing rates, the network undergoes a transition from fast spiking fluctuations with constant rates to a state where the firing rates exhibit chaotic fluctuations, similar to the transition predicted by rate-based dynamics. Systems with finite synaptic time constants and firing rates exhibit a smooth transition from a regime dominated by stationary firing rates to a regime of slow rate fluctuations. This smooth crossover obeys scaling properties, similar to crossover phenomena in statistical mechanics. The theoretical results are supported by computer simulations of several neuronal architectures and dynamics. Consequences for cortical circuit dynamics are discussed. These results advance our understanding of the properties of intrinsic dynamics in realistic neuronal networks and their functional consequences.

Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks

PubMed Central

Cavallari, Stefano; Panzeri, Stefano; Mazzoni, Alberto

2014-01-01

Models of networks of Leaky Integrate-and-Fire (LIF) neurons are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single neuron and neural population dynamics of conductance-based networks (COBNs) and current-based networks (CUBNs) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-modulated in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, the network activity of COBN showed stronger synchronization in the gamma band, and spectral information about the input higher and spread over a broader range of frequencies. These results suggest that the second order statistics of network dynamics depend strongly on the choice of synaptic model. PMID:24634645

Comparison of the dynamics of neural interactions between current-based and conductance-based integrate-and-fire recurrent networks.

PubMed

Cavallari, Stefano; Panzeri, Stefano; Mazzoni, Alberto

2014-01-01

Models of networks of Leaky Integrate-and-Fire (LIF) neurons are a widely used tool for theoretical investigations of brain function. These models have been used both with current- and conductance-based synapses. However, the differences in the dynamics expressed by these two approaches have been so far mainly studied at the single neuron level. To investigate how these synaptic models affect network activity, we compared the single neuron and neural population dynamics of conductance-based networks (COBNs) and current-based networks (CUBNs) of LIF neurons. These networks were endowed with sparse excitatory and inhibitory recurrent connections, and were tested in conditions including both low- and high-conductance states. We developed a novel procedure to obtain comparable networks by properly tuning the synaptic parameters not shared by the models. The so defined comparable networks displayed an excellent and robust match of first order statistics (average single neuron firing rates and average frequency spectrum of network activity). However, these comparable networks showed profound differences in the second order statistics of neural population interactions and in the modulation of these properties by external inputs. The correlation between inhibitory and excitatory synaptic currents and the cross-neuron correlation between synaptic inputs, membrane potentials and spike trains were stronger and more stimulus-modulated in the COBN. Because of these properties, the spike train correlation carried more information about the strength of the input in the COBN, although the firing rates were equally informative in both network models. Moreover, the network activity of COBN showed stronger synchronization in the gamma band, and spectral information about the input higher and spread over a broader range of frequencies. These results suggest that the second order statistics of network dynamics depend strongly on the choice of synaptic model.

Neuronal activity determines distinct gliotransmitter release from a single astrocyte

PubMed Central

Covelo, Ana

2018-01-01

Accumulating evidence indicates that astrocytes are actively involved in brain function by regulating synaptic activity and plasticity. Different gliotransmitters, such as glutamate, ATP, GABA or D-serine, released form astrocytes have been shown to induce different forms of synaptic regulation. However, whether a single astrocyte may release different gliotransmitters is unknown. Here we show that mouse hippocampal astrocytes activated by endogenous (neuron-released endocannabinoids or GABA) or exogenous (single astrocyte Ca2+ uncaging) stimuli modulate putative single CA3-CA1 hippocampal synapses. The astrocyte-mediated synaptic modulation was biphasic and consisted of an initial glutamate-mediated potentiation followed by a purinergic-mediated depression of neurotransmitter release. The temporal dynamic properties of this biphasic synaptic regulation depended on the firing frequency and duration of the neuronal activity that stimulated astrocytes. Present results indicate that single astrocytes can decode neuronal activity and, in response, release distinct gliotransmitters to differentially regulate neurotransmission at putative single synapses. PMID:29380725

Molecular dynamics in an optical trap of glutamate receptors labeled with quantum-dots on living neurons

NASA Astrophysics Data System (ADS)

Kishimoto, Tatsunori; Maezawa, Yasuyo; Kudoh, Suguru N.; Taguchi, Takahisa; Hosokawa, Chie

2017-04-01

Molecular dynamics of glutamate receptor, which is major neurotransmitter receptor at excitatory synapse located on neuron, is essential for synaptic plasticity in the complex neuronal networks. Here we studied molecular dynamics in an optical trap of AMPA-type glutamate receptor (AMPAR) labeled with quantum-dot (QD) on living neuronal cells with fluorescence imaging and fluorescence correlation spectroscopy (FCS). When a 1064-nm laser beam for optical trapping was focused on QD-AMPARs located on neuronal cells, the fluorescence intensity of QD-AMPARs gradually increased at the focal spot. Using single-particle tracking of QD-AMPARs on neurons, the average diffusion coefficient decreased in an optical trap. Moreover, the decay time obtained from FCS analysis increased with the laser power and the initial assembling state of AMPARs depended on culturing day, suggesting that the motion of QD-AMPAR was constrained in an optical trap.

Two-photon imaging of spatially extended neuronal network dynamics with high temporal resolution.

PubMed

Lillis, Kyle P; Eng, Alfred; White, John A; Mertz, Jerome

2008-07-30

We describe a simple two-photon fluorescence imaging strategy, called targeted path scanning (TPS), to monitor the dynamics of spatially extended neuronal networks with high spatiotemporal resolution. Our strategy combines the advantages of mirror-based scanning, minimized dead time, ease of implementation, and compatibility with high-resolution low-magnification objectives. To demonstrate the performance of TPS, we monitor the calcium dynamics distributed across an entire juvenile rat hippocampus (>1.5mm), at scan rates of 100 Hz, with single cell resolution and single action potential sensitivity. Our strategy for fast, efficient two-photon microscopy over spatially extended regions provides a particularly attractive solution for monitoring neuronal population activity in thick tissue, without sacrificing the signal-to-noise ratio or high spatial resolution associated with standard two-photon microscopy. Finally, we provide the code to make our technique generally available.

Universal Critical Dynamics in High Resolution Neuronal Avalanche Data

NASA Astrophysics Data System (ADS)

Friedman, Nir; Ito, Shinya; Brinkman, Braden A. W.; Shimono, Masanori; DeVille, R. E. Lee; Dahmen, Karin A.; Beggs, John M.; Butler, Thomas C.

2012-05-01

The tasks of neural computation are remarkably diverse. To function optimally, neuronal networks have been hypothesized to operate near a nonequilibrium critical point. However, experimental evidence for critical dynamics has been inconclusive. Here, we show that the dynamics of cultured cortical networks are critical. We analyze neuronal network data collected at the individual neuron level using the framework of nonequilibrium phase transitions. Among the most striking predictions confirmed is that the mean temporal profiles of avalanches of widely varying durations are quantitatively described by a single universal scaling function. We also show that the data have three additional features predicted by critical phenomena: approximate power law distributions of avalanche sizes and durations, samples in subcritical and supercritical phases, and scaling laws between anomalous exponents.

Neural plasticity explored by correlative two-photon and electron/SPIM microscopy

NASA Astrophysics Data System (ADS)

Allegra Mascaro, A. L.; Silvestri, L.; Costantini, I.; Sacconi, L.; Maco, B.; Knott, G. W.; Pavone, F. S.

2013-06-01

Plasticity of the central nervous system is a complex process which involves the remodeling of neuronal processes and synaptic contacts. However, a single imaging technique can reveal only a small part of this complex machinery. To obtain a more complete view, complementary approaches should be combined. Two-photon fluorescence microscopy, combined with multi-photon laser nanosurgery, allow following the real-time dynamics of single neuronal processes in the cerebral cortex of living mice. The structural rearrangement elicited by this highly confined paradigm of injury can be imaged in vivo first, and then the same neuron could be retrieved ex-vivo and characterized in terms of ultrastructural features of the damaged neuronal branch by means of electron microscopy. Afterwards, we describe a method to integrate data from in vivo two-photon fluorescence imaging and ex vivo light sheet microscopy, based on the use of major blood vessels as reference chart. We show how the apical dendritic arbor of a single cortical pyramidal neuron imaged in living mice can be found in the large-scale brain reconstruction obtained with light sheet microscopy. Starting from its apical portion, the whole pyramidal neuron can then be segmented and located in the correct cortical layer. With the correlative approach presented here, researchers will be able to place in a three-dimensional anatomic context the neurons whose dynamics have been observed with high detail in vivo.

The metabolic response to excitotoxicity - lessons from single-cell imaging.

PubMed

Connolly, Niamh M C; Prehn, Jochen H M

2015-04-01

Excitotoxicity is a pathological process implicated in neuronal death during ischaemia, traumatic brain injuries and neurodegenerative diseases. Excitotoxicity is caused by excess levels of glutamate and over-activation of NMDA or calcium-permeable AMPA receptors on neuronal membranes, leading to ionic influx, energetic stress and potential neuronal death. The metabolic response of neurons to excitotoxicity is complex and plays a key role in the ability of the neuron to adapt and recover from such an insult. Single-cell imaging is a powerful experimental technique that can be used to study the neuronal metabolic response to excitotoxicity in vitro and, increasingly, in vivo. Here, we review some of the knowledge of the neuronal metabolic response to excitotoxicity gained from in vitro single-cell imaging, including calcium and ATP dynamics and their effects on mitochondrial function, along with the contribution of glucose metabolism, oxidative stress and additional neuroprotective signalling mechanisms. Future work will combine knowledge gained from single-cell imaging with data from biochemical and computational techniques to garner holistic information about the metabolic response to excitotoxicity at the whole brain level and transfer this knowledge to a clinical setting.

From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining

PubMed Central

Truccolo, Wilson

2017-01-01

This review presents a perspective on capturing collective dynamics in recorded neuronal ensembles based on multivariate point process models, inference of low-dimensional dynamics and coarse graining of spatiotemporal measurements. A general probabilistic framework for continuous time point processes reviewed, with an emphasis on multivariate nonlinear Hawkes processes with exogenous inputs. A point process generalized linear model (PP-GLM) framework for the estimation of discrete time multivariate nonlinear Hawkes processes is described. The approach is illustrated with the modeling of collective dynamics in neocortical neuronal ensembles recorded in human and non-human primates, and prediction of single-neuron spiking. A complementary approach to capture collective dynamics based on low-dimensional dynamics (“order parameters”) inferred via latent state-space models with point process observations is presented. The approach is illustrated by inferring and decoding low-dimensional dynamics in primate motor cortex during naturalistic reach and grasp movements. Finally, we briefly review hypothesis tests based on conditional inference and spatiotemporal coarse graining for assessing collective dynamics in recorded neuronal ensembles. PMID:28336305

From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining.

PubMed

Truccolo, Wilson

2016-11-01

This review presents a perspective on capturing collective dynamics in recorded neuronal ensembles based on multivariate point process models, inference of low-dimensional dynamics and coarse graining of spatiotemporal measurements. A general probabilistic framework for continuous time point processes reviewed, with an emphasis on multivariate nonlinear Hawkes processes with exogenous inputs. A point process generalized linear model (PP-GLM) framework for the estimation of discrete time multivariate nonlinear Hawkes processes is described. The approach is illustrated with the modeling of collective dynamics in neocortical neuronal ensembles recorded in human and non-human primates, and prediction of single-neuron spiking. A complementary approach to capture collective dynamics based on low-dimensional dynamics ("order parameters") inferred via latent state-space models with point process observations is presented. The approach is illustrated by inferring and decoding low-dimensional dynamics in primate motor cortex during naturalistic reach and grasp movements. Finally, we briefly review hypothesis tests based on conditional inference and spatiotemporal coarse graining for assessing collective dynamics in recorded neuronal ensembles. Published by Elsevier Ltd.

Fuzzy decoupling controller based on multimode control algorithm of PI-single neuron and its application

NASA Astrophysics Data System (ADS)

Zhang, Xianxia; Wang, Jian; Qin, Tinggao

2003-09-01

Intelligent control algorithms are introduced into the control system of temperature and humidity. A multi-mode control algorithm of PI-Single Neuron is proposed for single loop control of temperature and humidity. In order to remove the coupling between temperature and humidity, a new decoupling method is presented, which is called fuzzy decoupling. The decoupling is achieved by using a fuzzy controller that dynamically modifies the static decoupling coefficient. Taking the control algorithm of PI-Single Neuron as the single loop control of temperature and humidity, the paper provides the simulated output response curves with no decoupling control, static decoupling control and fuzzy decoupling control. Those control algorithms are easily implemented in singlechip-based hardware systems.

Fast two-photon imaging of subcellular voltage dynamics in neuronal tissue with genetically encoded indicators

PubMed Central

Chamberland, Simon; Yang, Helen H; Pan, Michael M; Evans, Stephen W; Guan, Sihui; Chavarha, Mariya; Yang, Ying; Salesse, Charleen; Wu, Haodi; Wu, Joseph C; Clandinin, Thomas R; Toth, Katalin; Lin, Michael Z; St-Pierre, François

2017-01-01

Monitoring voltage dynamics in defined neurons deep in the brain is critical for unraveling the function of neuronal circuits but is challenging due to the limited performance of existing tools. In particular, while genetically encoded voltage indicators have shown promise for optical detection of voltage transients, many indicators exhibit low sensitivity when imaged under two-photon illumination. Previous studies thus fell short of visualizing voltage dynamics in individual neurons in single trials. Here, we report ASAP2s, a novel voltage indicator with improved sensitivity. By imaging ASAP2s using random-access multi-photon microscopy, we demonstrate robust single-trial detection of action potentials in organotypic slice cultures. We also show that ASAP2s enables two-photon imaging of graded potentials in organotypic slice cultures and in Drosophila. These results demonstrate that the combination of ASAP2s and fast two-photon imaging methods enables detection of neural electrical activity with subcellular spatial resolution and millisecond-timescale precision. DOI: http://dx.doi.org/10.7554/eLife.25690.001 PMID:28749338

Heterogeneous Intracellular Trafficking Dynamics of Brain-Derived Neurotrophic Factor Complexes in the Neuronal Soma Revealed by Single Quantum Dot Tracking

PubMed Central

Vermehren-Schmaedick, Anke; Krueger, Wesley; Jacob, Thomas; Ramunno-Johnson, Damien; Balkowiec, Agnieszka; Lidke, Keith A.; Vu, Tania Q.

2014-01-01

Accumulating evidence underscores the importance of ligand-receptor dynamics in shaping cellular signaling. In the nervous system, growth factor-activated Trk receptor trafficking serves to convey biochemical signaling that underlies fundamental neural functions. Focus has been placed on axonal trafficking but little is known about growth factor-activated Trk dynamics in the neuronal soma, particularly at the molecular scale, due in large part to technical hurdles in observing individual growth factor-Trk complexes for long periods of time inside live cells. Quantum dots (QDs) are intensely fluorescent nanoparticles that have been used to study the dynamics of ligand-receptor complexes at the plasma membrane but the value of QDs for investigating ligand-receptor intracellular dynamics has not been well exploited. The current study establishes that QD conjugated brain-derived neurotrophic factor (QD-BDNF) binds to TrkB receptors with high specificity, activates TrkB downstream signaling, and allows single QD tracking capability for long recording durations deep within the soma of live neurons. QD-BDNF complexes undergo internalization, recycling, and intracellular trafficking in the neuronal soma. These trafficking events exhibit little time-synchrony and diverse heterogeneity in underlying dynamics that include phases of sustained rapid motor transport without pause as well as immobility of surprisingly long-lasting duration (several minutes). Moreover, the trajectories formed by dynamic individual BDNF complexes show no apparent end destination; BDNF complexes can be found meandering over long distances of several microns throughout the expanse of the neuronal soma in a circuitous fashion. The complex, heterogeneous nature of neuronal soma trafficking dynamics contrasts the reported linear nature of axonal transport data and calls for models that surpass our generally limited notions of nuclear-directed transport in the soma. QD-ligand probes are poised to provide understanding of how the molecular mechanisms underlying intracellular ligand-receptor trafficking shape cell signaling under conditions of both healthy and dysfunctional neurological disease models. PMID:24732948

Dynamical estimation of neuron and network properties III: network analysis using neuron spike times.

PubMed

Knowlton, Chris; Meliza, C Daniel; Margoliash, Daniel; Abarbanel, Henry D I

2014-06-01

Estimating the behavior of a network of neurons requires accurate models of the individual neurons along with accurate characterizations of the connections among them. Whereas for a single cell, measurements of the intracellular voltage are technically feasible and sufficient to characterize a useful model of its behavior, making sufficient numbers of simultaneous intracellular measurements to characterize even small networks is infeasible. This paper builds on prior work on single neurons to explore whether knowledge of the time of spiking of neurons in a network, once the nodes (neurons) have been characterized biophysically, can provide enough information to usefully constrain the functional architecture of the network: the existence of synaptic links among neurons and their strength. Using standardized voltage and synaptic gating variable waveforms associated with a spike, we demonstrate that the functional architecture of a small network of model neurons can be established.

Collective Dynamics for Heterogeneous Networks of Theta Neurons

NASA Astrophysics Data System (ADS)

Luke, Tanushree

Collective behavior in neural networks has often been used as an indicator of communication between different brain areas. These collective synchronization and desynchronization patterns are also considered an important feature in understanding normal and abnormal brain function. To understand the emergence of these collective patterns, I create an analytic model that identifies all such macroscopic steady-states attainable by a network of Type-I neurons. This network, whose basic unit is the model "theta'' neuron, contains a mixture of excitable and spiking neurons coupled via a smooth pulse-like synapse. Applying the Ott-Antonsen reduction method in the thermodynamic limit, I obtain a low-dimensional evolution equation that describes the asymptotic dynamics of the macroscopic mean field of the network. This model can be used as the basis in understanding more complicated neuronal networks when additional dynamical features are included. From this reduced dynamical equation for the mean field, I show that the network exhibits three collective attracting steady-states. The first two are equilibrium states that both reflect partial synchronization in the network, whereas the third is a limit cycle in which the degree of network synchronization oscillates in time. In addition to a comprehensive identification of all possible attracting macro-states, this analytic model permits a complete bifurcation analysis of the collective behavior of the network with respect to three key network features: the degree of excitability of the neurons, the heterogeneity of the population, and the overall coupling strength. The network typically tends towards the two macroscopic equilibrium states when the neuron's intrinsic dynamics and the network interactions reinforce each other. In contrast, the limit cycle state, bifurcations, and multistability tend to occur when there is competition between these network features. I also outline here an extension of the above model where the neurons' excitability now varies in time sinuosoidally, thus simulating a parabolic bursting network. This time-varying excitability can lead to the emergence of macroscopic chaos and multistability in the collective behavior of the network. Finally, I expand the single population model described above to examine a two-population neuronal network where each population has its own unique mixture of excitable and spiking neurons, as well as its own coupling strength (either excitatory or inhibitory in nature). Specifically, I consider the situation where the first population is only allowed to influence the second population without any feedback, thus effectively creating a feed-forward "driver-response" system. In this special arrangement, the driver's asymptotic macroscopic dynamics are fully explored in the comprehensive analysis of the single population. Then, in the presence of an influence from the driver, the modified dynamics of the second population, which now acts as a response population, can also be fully analyzed. As in the time-varying model, these modifications give rise to richer dynamics to the response population than those found from the single population formalism, including multi-periodicity and chaos.

The response of cortical neurons to in vivo-like input current: theory and experiment: II. Time-varying and spatially distributed inputs.

PubMed

Giugliano, Michele; La Camera, Giancarlo; Fusi, Stefano; Senn, Walter

2008-11-01

The response of a population of neurons to time-varying synaptic inputs can show a rich phenomenology, hardly predictable from the dynamical properties of the membrane's inherent time constants. For example, a network of neurons in a state of spontaneous activity can respond significantly more rapidly than each single neuron taken individually. Under the assumption that the statistics of the synaptic input is the same for a population of similarly behaving neurons (mean field approximation), it is possible to greatly simplify the study of neural circuits, both in the case in which the statistics of the input are stationary (reviewed in La Camera et al. in Biol Cybern, 2008) and in the case in which they are time varying and unevenly distributed over the dendritic tree. Here, we review theoretical and experimental results on the single-neuron properties that are relevant for the dynamical collective behavior of a population of neurons. We focus on the response of integrate-and-fire neurons and real cortical neurons to long-lasting, noisy, in vivo-like stationary inputs and show how the theory can predict the observed rhythmic activity of cultures of neurons. We then show how cortical neurons adapt on multiple time scales in response to input with stationary statistics in vitro. Next, we review how it is possible to study the general response properties of a neural circuit to time-varying inputs by estimating the response of single neurons to noisy sinusoidal currents. Finally, we address the dendrite-soma interactions in cortical neurons leading to gain modulation and spike bursts, and show how these effects can be captured by a two-compartment integrate-and-fire neuron. Most of the experimental results reviewed in this article have been successfully reproduced by simple integrate-and-fire model neurons.

Complete Firing-Rate Response of Neurons with Complex Intrinsic Dynamics

PubMed Central

Puelma Touzel, Maximilian; Wolf, Fred

2015-01-01

The response of a neuronal population over a space of inputs depends on the intrinsic properties of its constituent neurons. Two main modes of single neuron dynamics–integration and resonance–have been distinguished. While resonator cell types exist in a variety of brain areas, few models incorporate this feature and fewer have investigated its effects. To understand better how a resonator’s frequency preference emerges from its intrinsic dynamics and contributes to its local area’s population firing rate dynamics, we analyze the dynamic gain of an analytically solvable two-degree of freedom neuron model. In the Fokker-Planck approach, the dynamic gain is intractable. The alternative Gauss-Rice approach lifts the resetting of the voltage after a spike. This allows us to derive a complete expression for the dynamic gain of a resonator neuron model in terms of a cascade of filters on the input. We find six distinct response types and use them to fully characterize the routes to resonance across all values of the relevant timescales. We find that resonance arises primarily due to slow adaptation with an intrinsic frequency acting to sharpen and adjust the location of the resonant peak. We determine the parameter regions for the existence of an intrinsic frequency and for subthreshold and spiking resonance, finding all possible intersections of the three. The expressions and analysis presented here provide an account of how intrinsic neuron dynamics shape dynamic population response properties and can facilitate the construction of an exact theory of correlations and stability of population activity in networks containing populations of resonator neurons. PMID:26720924

Response-dependent dynamics of cell-specific inhibition in cortical networks in vivo

PubMed Central

El-Boustani, Sami; Sur, Mriganka

2014-01-01

In the visual cortex, inhibitory neurons alter the computations performed by target cells via combination of two fundamental operations, division and subtraction. The origins of these operations have been variously ascribed to differences in neuron classes, synapse location or receptor conductances. Here, by utilizing specific visual stimuli and single optogenetic probe pulses, we show that the function of parvalbumin-expressing and somatostatin-expressing neurons in mice in vivo is governed by the overlap of response timing between these neurons and their targets. In particular, somatostatin-expressing neurons respond at longer latencies to small visual stimuli compared with their target neurons and provide subtractive inhibition. With large visual stimuli, however, they respond at short latencies coincident with their target cells and switch to provide divisive inhibition. These results indicate that inhibition mediated by these neurons is a dynamic property of cortical circuits rather than an immutable property of neuronal classes. PMID:25504329

Effects of Calcium Spikes in the Layer 5 Pyramidal Neuron on Coincidence Detection and Activity Propagation

PubMed Central

Chua, Yansong; Morrison, Abigail

2016-01-01

The role of dendritic spiking mechanisms in neural processing is so far poorly understood. To investigate the role of calcium spikes in the functional properties of the single neuron and recurrent networks, we investigated a three compartment neuron model of the layer 5 pyramidal neuron with calcium dynamics in the distal compartment. By performing single neuron simulations with noisy synaptic input and occasional large coincident input at either just the distal compartment or at both somatic and distal compartments, we show that the presence of calcium spikes confers a substantial advantage for coincidence detection in the former case and a lesser advantage in the latter. We further show that the experimentally observed critical frequency phenomenon, in which action potentials triggered by stimuli near the soma above a certain frequency trigger a calcium spike at distal dendrites, leading to further somatic depolarization, is not exhibited by a neuron receiving realistically noisy synaptic input, and so is unlikely to be a necessary component of coincidence detection. We next investigate the effect of calcium spikes in propagation of spiking activities in a feed-forward network (FFN) embedded in a balanced recurrent network. The excitatory neurons in the network are again connected to either just the distal, or both somatic and distal compartments. With purely distal connectivity, activity propagation is stable and distinguishable for a large range of recurrent synaptic strengths if the feed-forward connections are sufficiently strong, but propagation does not occur in the absence of calcium spikes. When connections are made to both the somatic and the distal compartments, activity propagation is achieved for neurons with active calcium dynamics at a much smaller number of neurons per pool, compared to a network of passive neurons, but quickly becomes unstable as the strength of recurrent synapses increases. Activity propagation at higher scaling factors can be stabilized by increasing network inhibition or introducing short term depression in the excitatory synapses, but the signal to noise ratio remains low. Our results demonstrate that the interaction of synchrony with dendritic spiking mechanisms can have profound consequences for the dynamics on the single neuron and network level. PMID:27499740

Effects of Calcium Spikes in the Layer 5 Pyramidal Neuron on Coincidence Detection and Activity Propagation.

PubMed

Chua, Yansong; Morrison, Abigail

2016-01-01

The role of dendritic spiking mechanisms in neural processing is so far poorly understood. To investigate the role of calcium spikes in the functional properties of the single neuron and recurrent networks, we investigated a three compartment neuron model of the layer 5 pyramidal neuron with calcium dynamics in the distal compartment. By performing single neuron simulations with noisy synaptic input and occasional large coincident input at either just the distal compartment or at both somatic and distal compartments, we show that the presence of calcium spikes confers a substantial advantage for coincidence detection in the former case and a lesser advantage in the latter. We further show that the experimentally observed critical frequency phenomenon, in which action potentials triggered by stimuli near the soma above a certain frequency trigger a calcium spike at distal dendrites, leading to further somatic depolarization, is not exhibited by a neuron receiving realistically noisy synaptic input, and so is unlikely to be a necessary component of coincidence detection. We next investigate the effect of calcium spikes in propagation of spiking activities in a feed-forward network (FFN) embedded in a balanced recurrent network. The excitatory neurons in the network are again connected to either just the distal, or both somatic and distal compartments. With purely distal connectivity, activity propagation is stable and distinguishable for a large range of recurrent synaptic strengths if the feed-forward connections are sufficiently strong, but propagation does not occur in the absence of calcium spikes. When connections are made to both the somatic and the distal compartments, activity propagation is achieved for neurons with active calcium dynamics at a much smaller number of neurons per pool, compared to a network of passive neurons, but quickly becomes unstable as the strength of recurrent synapses increases. Activity propagation at higher scaling factors can be stabilized by increasing network inhibition or introducing short term depression in the excitatory synapses, but the signal to noise ratio remains low. Our results demonstrate that the interaction of synchrony with dendritic spiking mechanisms can have profound consequences for the dynamics on the single neuron and network level.

Single neuron modeling and data assimilation in BNST neurons

NASA Astrophysics Data System (ADS)

Farsian, Reza

Neurons, although tiny in size, are vastly complicated systems, which are responsible for the most basic yet essential functions of any nervous system. Even the most simple models of single neurons are usually high dimensional, nonlinear, and contain many parameters and states which are unobservable in a typical neurophysiological experiment. One of the most fundamental problems in experimental neurophysiology is the estimation of these parameters and states, since knowing their values is essential in identification, model construction, and forward prediction of biological neurons. Common methods of parameter and state estimation do not perform well for neural models due to their high dimensionality and nonlinearity. In this dissertation, two alternative approaches for parameters and state estimation of biological neurons have been demonstrated: dynamical parameter estimation (DPE) and a Markov Chain Monte Carlo (MCMC) method. The first method uses elements of chaos control and synchronization theory for parameter and state estimation. MCMC is a statistical approach which uses a path integral formulation to evaluate a mean and an error bound for these unobserved parameters and states. These methods have been applied to biological system of neurons in Bed Nucleus of Stria Termialis neurons (BNST) of rats. State and parameters of neurons in both systems were estimated, and their value were used for recreating a realistic model and predicting the behavior of the neurons successfully. The knowledge of biological parameters can ultimately provide a better understanding of the internal dynamics of a neuron in order to build robust models of neuron networks.

State-space receptive fields of semicircular canal afferent neurons in the bullfrog

NASA Technical Reports Server (NTRS)

Paulin, M. G.; Hoffman, L. F.

2001-01-01

Receptive fields are commonly used to describe spatial characteristics of sensory neuron responses. They can be extended to characterize temporal or dynamical aspects by mapping neural responses in dynamical state spaces. The state-space receptive field of a neuron is the probability distribution of the dynamical state of the stimulus-generating system conditioned upon the occurrence of a spike. We have computed state-space receptive fields for semicircular canal afferent neurons in the bullfrog (Rana catesbeiana). We recorded spike times during broad-band Gaussian noise rotational velocity stimuli, computed the frequency distribution of head states at spike times, and normalized these to obtain conditional pdfs for the state. These state-space receptive fields quantify what the brain can deduce about the dynamical state of the head when a single spike arrives from the periphery. c2001 Elsevier Science B.V. All rights reserved.

Dendritic Slow Dynamics Enables Localized Cortical Activity to Switch between Mobile and Immobile Modes with Noisy Background Input

PubMed Central

Kurashige, Hiroki; Câteau, Hideyuki

2011-01-01

Mounting lines of evidence suggest the significant computational ability of a single neuron empowered by active dendritic dynamics. This motivates us to study what functionality can be acquired by a network of such neurons. The present paper studies how such rich single-neuron dendritic dynamics affects the network dynamics, a question which has scarcely been specifically studied to date. We simulate neurons with active dendrites networked locally like cortical pyramidal neurons, and find that naturally arising localized activity – called a bump – can be in two distinct modes, mobile or immobile. The mode can be switched back and forth by transient input to the cortical network. Interestingly, this functionality arises only if each neuron is equipped with the observed slow dendritic dynamics and with in vivo-like noisy background input. If the bump activity is considered to indicate a point of attention in the sensory areas or to indicate a representation of memory in the storage areas of the cortex, this would imply that the flexible mode switching would be of great potential use for the brain as an information processing device. We derive these conclusions using a natural extension of the conventional field model, which is defined by combining two distinct fields, one representing the somatic population and the other representing the dendritic population. With this tool, we analyze the spatial distribution of the degree of after-spike adaptation and explain how we can understand the presence of the two distinct modes and switching between the modes. We also discuss the possible functional impact of this mode-switching ability. PMID:21931635

Dynamic neural networking as a basis for plasticity in the control of heart rate.

PubMed

Kember, G; Armour, J A; Zamir, M

2013-01-21

A model is proposed in which the relationship between individual neurons within a neural network is dynamically changing to the effect of providing a measure of "plasticity" in the control of heart rate. The neural network on which the model is based consists of three populations of neurons residing in the central nervous system, the intrathoracic extracardiac nervous system, and the intrinsic cardiac nervous system. This hierarchy of neural centers is used to challenge the classical view that the control of heart rate, a key clinical index, resides entirely in central neuronal command (spinal cord, medulla oblongata, and higher centers). Our results indicate that dynamic networking allows for the possibility of an interplay among the three populations of neurons to the effect of altering the order of control of heart rate among them. This interplay among the three levels of control allows for different neural pathways for the control of heart rate to emerge under different blood flow demands or disease conditions and, as such, it has significant clinical implications because current understanding and treatment of heart rate anomalies are based largely on a single level of control and on neurons acting in unison as a single entity rather than individually within a (plastically) interconnected network. Copyright © 2012 Elsevier Ltd. All rights reserved.

Single-molecule tracking of tau reveals fast kiss-and-hop interaction with microtubules in living neurons

PubMed Central

Janning, Dennis; Igaev, Maxim; Sündermann, Frederik; Brühmann, Jörg; Beutel, Oliver; Heinisch, Jürgen J.; Bakota, Lidia; Piehler, Jacob; Junge, Wolfgang; Brandt, Roland

2014-01-01

The microtubule-associated phosphoprotein tau regulates microtubule dynamics and is involved in neurodegenerative diseases collectively called tauopathies. It is generally believed that the vast majority of tau molecules decorate axonal microtubules, thereby stabilizing them. However, it is an open question how tau can regulate microtubule dynamics without impeding microtubule-dependent transport and how tau is also available for interactions other than those with microtubules. Here we address this apparent paradox by fast single-molecule tracking of tau in living neurons and Monte Carlo simulations of tau dynamics. We find that tau dwells on a single microtubule for an unexpectedly short time of ∼40 ms before it hops to the next. This dwell time is 100-fold shorter than previously reported by ensemble measurements. Furthermore, we observed by quantitative imaging using fluorescence decay after photoactivation recordings of photoactivatable GFP–tagged tubulin that, despite this rapid dynamics, tau is capable of regulating the tubulin–microtubule balance. This indicates that tau's dwell time on microtubules is sufficiently long to influence the lifetime of a tubulin subunit in a GTP cap. Our data imply a novel kiss-and-hop mechanism by which tau promotes neuronal microtubule assembly. The rapid kiss-and-hop interaction explains why tau, although binding to microtubules, does not interfere with axonal transport. PMID:25165145

Noise effects on robust synchronization of a small pacemaker neuronal ensemble via nonlinear controller: electronic circuit design.

PubMed

Megam Ngouonkadi, Elie Bertrand; Fotsin, Hilaire Bertrand; Kabong Nono, Martial; Louodop Fotso, Patrick Herve

2016-10-01

In this paper, we report on the synchronization of a pacemaker neuronal ensemble constituted of an AB neuron electrically coupled to two PD neurons. By the virtue of this electrical coupling, they can fire synchronous bursts of action potential. An external master neuron is used to induce to the whole system the desired dynamics, via a nonlinear controller. Such controller is obtained by a combination of sliding mode and feedback control. The proposed controller is able to offset uncertainties in the synchronized systems. We show how noise affects the synchronization of the pacemaker neuronal ensemble, and briefly discuss its potential benefits in our synchronization scheme. An extended Hindmarsh-Rose neuronal model is used to represent a single cell dynamic of the network. Numerical simulations and Pspice implementation of the synchronization scheme are presented. We found that, the proposed controller reduces the stochastic resonance of the network when its gain increases.

Implementation of an integrated op-amp based chaotic neuron model and observation of its chaotic dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jung, Jinwoo; Lee, Jewon; Song, Hanjung

2011-03-15

This paper presents a fully integrated circuit implementation of an operational amplifier (op-amp) based chaotic neuron model with a bipolar output function, experimental measurements, and analyses of its chaotic behavior. The proposed chaotic neuron model integrated circuit consists of several op-amps, sample and hold circuits, a nonlinear function block for chaotic signal generation, a clock generator, a nonlinear output function, etc. Based on the HSPICE (circuit program) simulation results, approximated empirical equations for analyses were formulated. Then, the chaotic dynamical responses such as bifurcation diagrams, time series, and Lyapunov exponent were calculated using these empirical equations. In addition, we performedmore » simulations about two chaotic neuron systems with four synapses to confirm neural network connections and got normal behavior of the chaotic neuron such as internal state bifurcation diagram according to the synaptic weight variation. The proposed circuit was fabricated using a 0.8-{mu}m single poly complementary metal-oxide semiconductor technology. Measurements of the fabricated single chaotic neuron with {+-}2.5 V power supplies and a 10 kHz sampling clock frequency were carried out and compared with the simulated results.« less

Modeling of synchronization behavior of bursting neurons at nonlinearly coupled dynamical networks.

PubMed

Çakir, Yüksel

2016-01-01

Synchronization behaviors of bursting neurons coupled through electrical and dynamic chemical synapses are investigated. The Izhikevich model is used with random and small world network of bursting neurons. Various currents which consist of diffusive electrical and time-delayed dynamic chemical synapses are used in the simulations to investigate the influences of synaptic currents and couplings on synchronization behavior of bursting neurons. The effects of parameters, such as time delay, inhibitory synaptic strengths, and decay time on synchronization behavior are investigated. It is observed that in random networks with no delay, bursting synchrony is established with the electrical synapse alone, single spiking synchrony is observed with hybrid coupling. In small world network with no delay, periodic bursting behavior with multiple spikes is observed when only chemical and only electrical synapse exist. Single-spike and multiple-spike bursting are established with hybrid couplings. A decrease in the synchronization measure is observed with zero time delay, as the decay time is increased in random network. For synaptic delays which are above active phase period, synchronization measure increases with an increase in synaptic strength and time delay in small world network. However, in random network, it increases with only an increase in synaptic strength.

Analysis of neuronal cells of dissociated primary culture on high-density CMOS electrode array

PubMed Central

Matsuda, Eiko; Mita, Takeshi; Hubert, Julien; Bakkum, Douglas; Frey, Urs; Hierlemann, Andreas; Takahashi, Hirokazu; Ikegami, Takashi

2017-01-01

Spontaneous development of neuronal cells was recorded around 4–34 days in vitro (DIV) with high-density CMOS array, which enables detailed study of the spatio-temporal activity of neuronal culture. We used the CMOS array to characterize the evolution of the inter-spike interval (ISI) distribution from putative single neurons, and estimate the network structure based on transfer entropy analysis, where each node corresponds to a single neuron. We observed that the ISI distributions gradually obeyed the power law with maturation of the network. The amount of information transferred between neurons increased at the early stage of development, but decreased as the network matured. These results suggest that both ISI and transfer entropy were very useful for characterizing the dynamic development of cultured neural cells over a few weeks. PMID:24109870

From individual spiking neurons to population behavior: Systematic elimination of short-wavelength spatial modes

NASA Astrophysics Data System (ADS)

Steyn-Ross, Moira L.; Steyn-Ross, D. A.

2016-02-01

Mean-field models of the brain approximate spiking dynamics by assuming that each neuron responds to its neighbors via a naive spatial average that neglects local fluctuations and correlations in firing activity. In this paper we address this issue by introducing a rigorous formalism to enable spatial coarse-graining of spiking dynamics, scaling from the microscopic level of a single type 1 (integrator) neuron to a macroscopic assembly of spiking neurons that are interconnected by chemical synapses and nearest-neighbor gap junctions. Spiking behavior at the single-neuron scale ℓ ≈10 μ m is described by Wilson's two-variable conductance-based equations [H. R. Wilson, J. Theor. Biol. 200, 375 (1999), 10.1006/jtbi.1999.1002], driven by fields of incoming neural activity from neighboring neurons. We map these equations to a coarser spatial resolution of grid length B ℓ , with B ≫1 being the blocking ratio linking micro and macro scales. Our method systematically eliminates high-frequency (short-wavelength) spatial modes q ⃗ in favor of low-frequency spatial modes Q ⃗ using an adiabatic elimination procedure that has been shown to be equivalent to the path-integral coarse graining applied to renormalization group theory of critical phenomena. This bottom-up neural regridding allows us to track the percolation of synaptic and ion-channel noise from the single neuron up to the scale of macroscopic population-average variables. Anticipated applications of neural regridding include extraction of the current-to-firing-rate transfer function, investigation of fluctuation criticality near phase-transition tipping points, determination of spatial scaling laws for avalanche events, and prediction of the spatial extent of self-organized macrocolumnar structures. As a first-order exemplar of the method, we recover nonlinear corrections for a coarse-grained Wilson spiking neuron embedded in a network of identical diffusively coupled neurons whose chemical synapses have been disabled. Intriguingly, we find that reblocking transforms the original type 1 Wilson integrator into a type 2 resonator whose spike-rate transfer function exhibits abrupt spiking onset with near-vertical takeoff and chaotic dynamics just above threshold.

Neural correlates of motor learning in the vestibulo-ocular reflex: dynamic regulation of multimodal integration in the macaque vestibular system

PubMed Central

Sadeghi, Soroush G.; Minor, Lloyd B.; Cullen, Kathleen E.

2010-01-01

Motor learning is required for the reacquisition of skills that have been compromised as a result of brain lesion or disease, as well as for the acquisition of new skills. Behaviors with well-characterized anatomy and physiology are required to yield significant insight into changes that occur in the brain during motor learning. The vestibulo-ocular-reflex (VOR) is well suited to establish connections between neurons, neural circuits, and motor performance during learning. Here we examined the linkage between neuronal and behavioural VOR responses in alert behaving monkeys (macaca mulatta) during the impressive recovery that occurs after unilateral vestibular loss. We show, for the first time, that motor learning is characterized by the dynamic reweighting of inputs from different modalities (i.e., vestibular versus extra-vestibular) at the level of the single neurons which constitute the first central stage of vestibular processing. Specifically, two types of information, which did not influence neuronal responses prior to the lesion, had an important role during compensation. First, unmasked neck proprioceptive inputs played a critical role in the early stages of this process demonstrated by faster and more substantial recovery of vestibular responses in proprioceptive sensitive neurons. Second, neuronal and VOR responses were significantly enhanced during active relative to passive head motion later in the compensation process (>3 weeks). Taken together, our findings provide evidence linking the dynamic regulation of multimodal integration at the level of single neurons and behavioural recovery, suggesting a role for homeostatic mechanisms in VOR motor learning. PMID:20668199

Exact subthreshold integration with continuous spike times in discrete-time neural network simulations.

PubMed

Morrison, Abigail; Straube, Sirko; Plesser, Hans Ekkehard; Diesmann, Markus

2007-01-01

Very large networks of spiking neurons can be simulated efficiently in parallel under the constraint that spike times are bound to an equidistant time grid. Within this scheme, the subthreshold dynamics of a wide class of integrate-and-fire-type neuron models can be integrated exactly from one grid point to the next. However, the loss in accuracy caused by restricting spike times to the grid can have undesirable consequences, which has led to interest in interpolating spike times between the grid points to retrieve an adequate representation of network dynamics. We demonstrate that the exact integration scheme can be combined naturally with off-grid spike events found by interpolation. We show that by exploiting the existence of a minimal synaptic propagation delay, the need for a central event queue is removed, so that the precision of event-driven simulation on the level of single neurons is combined with the efficiency of time-driven global scheduling. Further, for neuron models with linear subthreshold dynamics, even local event queuing can be avoided, resulting in much greater efficiency on the single-neuron level. These ideas are exemplified by two implementations of a widely used neuron model. We present a measure for the efficiency of network simulations in terms of their integration error and show that for a wide range of input spike rates, the novel techniques we present are both more accurate and faster than standard techniques.

Elucidating distinct ion channel populations on the surface of hippocampal neurons via single-particle tracking recurrence analysis

NASA Astrophysics Data System (ADS)

Sikora, Grzegorz; Wyłomańska, Agnieszka; Gajda, Janusz; Solé, Laura; Akin, Elizabeth J.; Tamkun, Michael M.; Krapf, Diego

2017-12-01

Protein and lipid nanodomains are prevalent on the surface of mammalian cells. In particular, it has been recently recognized that ion channels assemble into surface nanoclusters in the soma of cultured neurons. However, the interactions of these molecules with surface nanodomains display a considerable degree of heterogeneity. Here, we investigate this heterogeneity and develop statistical tools based on the recurrence of individual trajectories to identify subpopulations within ion channels in the neuronal surface. We specifically study the dynamics of the K+ channel Kv1.4 and the Na+ channel Nav1.6 on the surface of cultured hippocampal neurons at the single-molecule level. We find that both these molecules are expressed in two different forms with distinct kinetics with regards to surface interactions, emphasizing the complex proteomic landscape of the neuronal surface. Further, the tools presented in this work provide new methods for the analysis of membrane nanodomains, transient confinement, and identification of populations within single-particle trajectories.

Criticality in the brain

NASA Astrophysics Data System (ADS)

de Arcangelis, L.; Lombardi, F.; Herrmann, H. J.

2014-03-01

Spontaneous brain activity has been recently characterized by avalanche dynamics with critical features for systems in vitro and in vivo. In this contribution we present a review of experimental results on neuronal avalanches in cortex slices, together with numerical results from a neuronal model implementing several physiological properties of living neurons. Numerical data reproduce experimental results for avalanche statistics. The temporal organization of avalanches can be characterized by the distribution of waiting times between successive avalanches. Experimental measurements exhibit a non-monotonic behaviour, not usually found in other natural processes. Numerical simulations provide evidence that this behaviour is a consequence of the alternation between states of high and low activity, leading to a balance between excitation and inhibition controlled by a single parameter. During these periods both the single neuron state and the network excitability level, keeping memory of past activity, are tuned by homoeostatic mechanisms. Interestingly, the same homoeostatic balance is detected for neuronal activity at the scale of the whole brain. We finally review the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules and the learning dynamics exhibits universal features as a function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.

Optical magnetic detection of single-neuron action potentials using quantum defects in diamond

PubMed Central

Barry, John F.; Turner, Matthew J.; Schloss, Jennifer M.; Glenn, David R.; Song, Yuyu; Lukin, Mikhail D.; Park, Hongkun; Walsworth, Ronald L.

2016-01-01

Magnetic fields from neuronal action potentials (APs) pass largely unperturbed through biological tissue, allowing magnetic measurements of AP dynamics to be performed extracellularly or even outside intact organisms. To date, however, magnetic techniques for sensing neuronal activity have either operated at the macroscale with coarse spatial and/or temporal resolution—e.g., magnetic resonance imaging methods and magnetoencephalography—or been restricted to biophysics studies of excised neurons probed with cryogenic or bulky detectors that do not provide single-neuron spatial resolution and are not scalable to functional networks or intact organisms. Here, we show that AP magnetic sensing can be realized with both single-neuron sensitivity and intact organism applicability using optically probed nitrogen-vacancy (NV) quantum defects in diamond, operated under ambient conditions and with the NV diamond sensor in close proximity (∼10 µm) to the biological sample. We demonstrate this method for excised single neurons from marine worm and squid, and then exterior to intact, optically opaque marine worms for extended periods and with no observed adverse effect on the animal. NV diamond magnetometry is noninvasive and label-free and does not cause photodamage. The method provides precise measurement of AP waveforms from individual neurons, as well as magnetic field correlates of the AP conduction velocity, and directly determines the AP propagation direction through the inherent sensitivity of NVs to the associated AP magnetic field vector. PMID:27911765

Optical magnetic detection of single-neuron action potentials using quantum defects in diamond.

PubMed

Barry, John F; Turner, Matthew J; Schloss, Jennifer M; Glenn, David R; Song, Yuyu; Lukin, Mikhail D; Park, Hongkun; Walsworth, Ronald L

2016-12-06

Magnetic fields from neuronal action potentials (APs) pass largely unperturbed through biological tissue, allowing magnetic measurements of AP dynamics to be performed extracellularly or even outside intact organisms. To date, however, magnetic techniques for sensing neuronal activity have either operated at the macroscale with coarse spatial and/or temporal resolution-e.g., magnetic resonance imaging methods and magnetoencephalography-or been restricted to biophysics studies of excised neurons probed with cryogenic or bulky detectors that do not provide single-neuron spatial resolution and are not scalable to functional networks or intact organisms. Here, we show that AP magnetic sensing can be realized with both single-neuron sensitivity and intact organism applicability using optically probed nitrogen-vacancy (NV) quantum defects in diamond, operated under ambient conditions and with the NV diamond sensor in close proximity (∼10 µm) to the biological sample. We demonstrate this method for excised single neurons from marine worm and squid, and then exterior to intact, optically opaque marine worms for extended periods and with no observed adverse effect on the animal. NV diamond magnetometry is noninvasive and label-free and does not cause photodamage. The method provides precise measurement of AP waveforms from individual neurons, as well as magnetic field correlates of the AP conduction velocity, and directly determines the AP propagation direction through the inherent sensitivity of NVs to the associated AP magnetic field vector.

Scientific basis for learning transfer from movements to urinary bladder functions for bladder repair in human patients with CNS injury.

PubMed

Schalow, G

2010-01-01

Coordination Dynamics Therapy (CDT) has been shown to be able to partly repair CNS injury. The repair is based on a movement-based re-learning theory which requires at least three levels of description: the movement or pattern (and anamnesis) level, the collective variable level, and the neuron level. Upon CDT not only the actually performed movement pattern itself is repaired, but the entire dynamics of CNS organization is improved, which is the theoretical basis for (re-) learning transfer. The transfer of learning for repair from jumping on springboard and exercising on a special CDT and recording device to urinary bladder functions is investigated at the neuron level. At the movement or pattern level, the improvement of central nervous system (CNS) functioning in human patients can be seen (or partly measured) by the improvement of the performance of the pattern. At the collective variable level, coordination tendencies can be measured by the so-called 'coordination dynamics' before, during and after treatment. At the neuron level, re-learning can additionally be assessed by surface electromyography (sEMG) as alterations of single motor unit firings and motor programs. But to express the ongoing interaction between the numerous neural, muscular, and metabolic elements involved in perception and action, it is relevant to inquire how the individual afferent and efferent neurons adjust their phase and frequency coordination to other neurons to satisfy learning task requirements. With the single-nerve fibre action potential recording method it was possible to measure that distributed single neurons communicate by phase and frequency coordination. It is shown that this timed firing of neurons is getting impaired upon injury and has to be improved by learning The stability of phase and frequency coordination among afferent and efferent neuron firings can be related to pattern stability. The stability of phase and frequency coordination at the neuron level can therefore be assessed integratively at the (non-invasive) collective variable level by the arrhythmicity of turning (coordination dynamics) when a patient is exercising on a special CDT device. Upon jumping on springboard and exercising on the special CDT device, the intertwined neuronal networks, subserving movements (somatic) and urinary bladder functions (autonomic and somatic) in the sacral spinal cord, are synchronously activated and entrained to give rise to learning transfer from movements to bladder functions. Jumping on springboard and other movements primarily repair the pattern dynamics, whereas the exactly coordinated performed movements, performed on the special CDT device for turning, primarily improve the preciseness of the timed firing of neurons. The synchronous learning of perceptuomotor and perceptuobladder functioning from a dynamical perspective (giving rise to learning transfer) can be understood at the neuron level. Especially the activated phase and frequency coordination upon natural stimulation under physiologic and pathophysiologic conditions among a and gamma-motoneurons, muscle spindle afferents, touch and pain afferents, and urinary bladder stretch and tension receptor afferents in the human sacral spinal cord make understandable that somatic and parasympathetic functions are integrated in their functioning and give rise to learning transfer from movements to bladder functions. The power of this human treatment research project lies in the unit of theory, diagnostic/measurement, and praxis, namely that CNS injury can partly be repaired, including urinary bladder functions, and the repair can partly be understood even at the neuron level of description in human.

Network synchronization in hippocampal neurons.

PubMed

Penn, Yaron; Segal, Menahem; Moses, Elisha

2016-03-22

Oscillatory activity is widespread in dynamic neuronal networks. The main paradigm for the origin of periodicity consists of specialized pacemaking elements that synchronize and drive the rest of the network; however, other models exist. Here, we studied the spontaneous emergence of synchronized periodic bursting in a network of cultured dissociated neurons from rat hippocampus and cortex. Surprisingly, about 60% of all active neurons were self-sustained oscillators when disconnected, each with its own natural frequency. The individual neuron's tendency to oscillate and the corresponding oscillation frequency are controlled by its excitability. The single neuron intrinsic oscillations were blocked by riluzole, and are thus dependent on persistent sodium leak currents. Upon a gradual retrieval of connectivity, the synchrony evolves: Loose synchrony appears already at weak connectivity, with the oscillators converging to one common oscillation frequency, yet shifted in phase across the population. Further strengthening of the connectivity causes a reduction in the mean phase shifts until zero-lag is achieved, manifested by synchronous periodic network bursts. Interestingly, the frequency of network bursting matches the average of the intrinsic frequencies. Overall, the network behaves like other universal systems, where order emerges spontaneously by entrainment of independent rhythmic units. Although simplified with respect to circuitry in the brain, our results attribute a basic functional role for intrinsic single neuron excitability mechanisms in driving the network's activity and dynamics, contributing to our understanding of developing neural circuits.

Irregular spiking of pyramidal neurons organizes as scale-invariant neuronal avalanches in the awake state

PubMed Central

Bellay, Timothy; Klaus, Andreas; Seshadri, Saurav; Plenz, Dietmar

2015-01-01

Spontaneous fluctuations in neuronal activity emerge at many spatial and temporal scales in cortex. Population measures found these fluctuations to organize as scale-invariant neuronal avalanches, suggesting cortical dynamics to be critical. Macroscopic dynamics, though, depend on physiological states and are ambiguous as to their cellular composition, spatiotemporal origin, and contributions from synaptic input or action potential (AP) output. Here, we study spontaneous firing in pyramidal neurons (PNs) from rat superficial cortical layers in vivo and in vitro using 2-photon imaging. As the animal transitions from the anesthetized to awake state, spontaneous single neuron firing increases in irregularity and assembles into scale-invariant avalanches at the group level. In vitro spike avalanches emerged naturally yet required balanced excitation and inhibition. This demonstrates that neuronal avalanches are linked to the global physiological state of wakefulness and that cortical resting activity organizes as avalanches from firing of local PN groups to global population activity. DOI: http://dx.doi.org/10.7554/eLife.07224.001 PMID:26151674

The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields

PubMed Central

Deco, Gustavo; Jirsa, Viktor K.; Robinson, Peter A.; Breakspear, Michael; Friston, Karl

2008-01-01

The cortex is a complex system, characterized by its dynamics and architecture, which underlie many functions such as action, perception, learning, language, and cognition. Its structural architecture has been studied for more than a hundred years; however, its dynamics have been addressed much less thoroughly. In this paper, we review and integrate, in a unifying framework, a variety of computational approaches that have been used to characterize the dynamics of the cortex, as evidenced at different levels of measurement. Computational models at different space–time scales help us understand the fundamental mechanisms that underpin neural processes and relate these processes to neuroscience data. Modeling at the single neuron level is necessary because this is the level at which information is exchanged between the computing elements of the brain; the neurons. Mesoscopic models tell us how neural elements interact to yield emergent behavior at the level of microcolumns and cortical columns. Macroscopic models can inform us about whole brain dynamics and interactions between large-scale neural systems such as cortical regions, the thalamus, and brain stem. Each level of description relates uniquely to neuroscience data, from single-unit recordings, through local field potentials to functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), and magnetoencephalogram (MEG). Models of the cortex can establish which types of large-scale neuronal networks can perform computations and characterize their emergent properties. Mean-field and related formulations of dynamics also play an essential and complementary role as forward models that can be inverted given empirical data. This makes dynamic models critical in integrating theory and experiments. We argue that elaborating principled and informed models is a prerequisite for grounding empirical neuroscience in a cogent theoretical framework, commensurate with the achievements in the physical sciences. PMID:18769680

A framework for analyzing the relationship between gene expression and morphological, topological, and dynamical patterns in neuronal networks.

PubMed

de Arruda, Henrique Ferraz; Comin, Cesar Henrique; Miazaki, Mauro; Viana, Matheus Palhares; Costa, Luciano da Fontoura

2015-04-30

A key point in developmental biology is to understand how gene expression influences the morphological and dynamical patterns that are observed in living beings. In this work we propose a methodology capable of addressing this problem that is based on estimating the mutual information and Pearson correlation between the intensity of gene expression and measurements of several morphological properties of the cells. A similar approach is applied in order to identify effects of gene expression over the system dynamics. Neuronal networks were artificially grown over a lattice by considering a reference model used to generate artificial neurons. The input parameters of the artificial neurons were determined according to two distinct patterns of gene expression and the dynamical response was assessed by considering the integrate-and-fire model. As far as single gene dependence is concerned, we found that the interaction between the gene expression and the network topology, as well as between the former and the dynamics response, is strongly affected by the gene expression pattern. In addition, we observed a high correlation between the gene expression and some topological measurements of the neuronal network for particular patterns of gene expression. To our best understanding, there are no similar analyses to compare with. A proper understanding of gene expression influence requires jointly studying the morphology, topology, and dynamics of neurons. The proposed framework represents a first step towards predicting gene expression patterns from morphology and connectivity. Copyright © 2015. Published by Elsevier B.V.

Temperature manipulation of neuronal dynamics in a forebrain motor control nucleus

PubMed Central

Mindlin, Gabriel B.

2017-01-01

Different neuronal types within brain motor areas contribute to the generation of complex motor behaviors. A widely studied songbird forebrain nucleus (HVC) has been recognized as fundamental in shaping the precise timing characteristics of birdsong. This is based, among other evidence, on the stretching and the “breaking” of song structure when HVC is cooled. However, little is known about the temperature effects that take place in its neurons. To address this, we investigated the dynamics of HVC both experimentally and computationally. We developed a technique where simultaneous electrophysiological recordings were performed during temperature manipulation of HVC. We recorded spontaneous activity and found three effects: widening of the spike shape, decrease of the firing rate and change in the interspike interval distribution. All these effects could be explained with a detailed conductance based model of all the neurons present in HVC. Temperature dependence of the ionic channel time constants explained the first effect, while the second was based in the changes of the maximal conductance using single synaptic excitatory inputs. The last phenomenon, only emerged after introducing a more realistic synaptic input to the inhibitory interneurons. Two timescales were present in the interspike distributions. The behavior of one timescale was reproduced with different input balances received form the excitatory neurons, whereas the other, which disappears with cooling, could not be found assuming poissonian synaptic inputs. Furthermore, the computational model shows that the bursting of the excitatory neurons arises naturally at normal brain temperature and that they have an intrinsic delay at low temperatures. The same effect occurs at single synapses, which may explain song stretching. These findings shed light on the temperature dependence of neuronal dynamics and present a comprehensive framework to study neuronal connectivity. This study, which is based on intrinsic neuronal characteristics, may help to understand emergent behavioral changes. PMID:28829769

From in silico astrocyte cell models to neuron-astrocyte network models: A review.

PubMed

Oschmann, Franziska; Berry, Hugues; Obermayer, Klaus; Lenk, Kerstin

2018-01-01

The idea that astrocytes may be active partners in synaptic information processing has recently emerged from abundant experimental reports. Because of their spatial proximity to neurons and their bidirectional communication with them, astrocytes are now considered as an important third element of the synapse. Astrocytes integrate and process synaptic information and by doing so generate cytosolic calcium signals that are believed to reflect neuronal transmitter release. Moreover, they regulate neuronal information transmission by releasing gliotransmitters into the synaptic cleft affecting both pre- and postsynaptic receptors. Concurrent with the first experimental reports of the astrocytic impact on neural network dynamics, computational models describing astrocytic functions have been developed. In this review, we give an overview over the published computational models of astrocytic functions, from single-cell dynamics to the tripartite synapse level and network models of astrocytes and neurons. Copyright © 2017 Elsevier Inc. All rights reserved.

A double-sided microscope to realize whole-ganglion imaging of membrane potential in the medicinal leech

PubMed Central

Wagenaar, Daniel A

2017-01-01

Studies of neuronal network emergence during sensory processing and motor control are greatly facilitated by technologies that allow us to simultaneously record the membrane potential dynamics of a large population of neurons in single cell resolution. To achieve whole-brain recording with the ability to detect both small synaptic potentials and action potentials, we developed a voltage-sensitive dye (VSD) imaging technique based on a double-sided microscope that can image two sides of a nervous system simultaneously. We applied this system to the segmental ganglia of the medicinal leech. Double-sided VSD imaging enabled simultaneous recording of membrane potential events from almost all of the identifiable neurons. Using data obtained from double-sided VSD imaging, we analyzed neuronal dynamics in both sensory processing and generation of behavior and constructed functional maps for identification of neurons contributing to these processes. PMID:28944754

Very long transients, irregular firing, and chaotic dynamics in networks of randomly connected inhibitory integrate-and-fire neurons.

PubMed

Zillmer, Rüdiger; Brunel, Nicolas; Hansel, David

2009-03-01

We present results of an extensive numerical study of the dynamics of networks of integrate-and-fire neurons connected randomly through inhibitory interactions. We first consider delayed interactions with infinitely fast rise and decay. Depending on the parameters, the network displays transients which are short or exponentially long in the network size. At the end of these transients, the dynamics settle on a periodic attractor. If the number of connections per neuron is large ( approximately 1000) , this attractor is a cluster state with a short period. In contrast, if the number of connections per neuron is small ( approximately 100) , the attractor has complex dynamics and very long period. During the long transients the neurons fire in a highly irregular manner. They can be viewed as quasistationary states in which, depending on the coupling strength, the pattern of activity is asynchronous or displays population oscillations. In the first case, the average firing rates and the variability of the single-neuron activity are well described by a mean-field theory valid in the thermodynamic limit. Bifurcations of the long transient dynamics from asynchronous to synchronous activity are also well predicted by this theory. The transient dynamics display features reminiscent of stable chaos. In particular, despite being linearly stable, the trajectories of the transient dynamics are destabilized by finite perturbations as small as O(1/N) . We further show that stable chaos is also observed for postsynaptic currents with finite decay time. However, we report in this type of network that chaotic dynamics characterized by positive Lyapunov exponents can also be observed. We show in fact that chaos occurs when the decay time of the synaptic currents is long compared to the synaptic delay, provided that the network is sufficiently large.

Collective dynamics in heterogeneous networks of neuronal cellular automata

NASA Astrophysics Data System (ADS)

Manchanda, Kaustubh; Bose, Amitabha; Ramaswamy, Ramakrishna

2017-12-01

We examine the collective dynamics of heterogeneous random networks of model neuronal cellular automata. Each automaton has b active states, a single silent state and r - b - 1 refractory states, and can show 'spiking' or 'bursting' behavior, depending on the values of b. We show that phase transitions that occur in the dynamical activity can be related to phase transitions in the structure of Erdõs-Rényi graphs as a function of edge probability. Different forms of heterogeneity allow distinct structural phase transitions to become relevant. We also show that the dynamics on the network can be described by a semi-annealed process and, as a result, can be related to the Boolean Lyapunov exponent.

Data collapse and critical dynamics in neuronal avalanche data

NASA Astrophysics Data System (ADS)

Butler, Thomas; Friedman, Nir; Dahmen, Karin; Beggs, John; Deville, Lee; Ito, Shinya

2012-02-01

The tasks of information processing, computation, and response to stimuli require neural computation to be remarkably flexible and diverse. To optimally satisfy the demands of neural computation, neuronal networks have been hypothesized to operate near a non-equilibrium critical point. In spite of their importance for neural dynamics, experimental evidence for critical dynamics has been primarily limited to power law statistics that can also emerge from non-critical mechanisms. By tracking the firing of large numbers of synaptically connected cortical neurons and comparing the resulting data to the predictions of critical phenomena, we show that cortical tissues in vitro can function near criticality. Among the most striking predictions of critical dynamics is that the mean temporal profiles of avalanches of widely varying durations are quantitatively described by a single universal scaling function (data collapse). We show for the first time that this prediction is confirmed in neuronal networks. We also show that the data have three additional features predicted by critical phenomena: approximate power law distributions of avalanche sizes and durations, samples in subcritical and supercritical phases, and scaling laws between anomalous exponents.

Biophysical Neural Spiking, Bursting, and Excitability Dynamics in Reconfigurable Analog VLSI.

PubMed

Yu, T; Sejnowski, T J; Cauwenberghs, G

2011-10-01

We study a range of neural dynamics under variations in biophysical parameters underlying extended Morris-Lecar and Hodgkin-Huxley models in three gating variables. The extended models are implemented in NeuroDyn, a four neuron, twelve synapse continuous-time analog VLSI programmable neural emulation platform with generalized channel kinetics and biophysical membrane dynamics. The dynamics exhibit a wide range of time scales extending beyond 100 ms neglected in typical silicon models of tonic spiking neurons. Circuit simulations and measurements show transition from tonic spiking to tonic bursting dynamics through variation of a single conductance parameter governing calcium recovery. We similarly demonstrate transition from graded to all-or-none neural excitability in the onset of spiking dynamics through the variation of channel kinetic parameters governing the speed of potassium activation. Other combinations of variations in conductance and channel kinetic parameters give rise to phasic spiking and spike frequency adaptation dynamics. The NeuroDyn chip consumes 1.29 mW and occupies 3 mm × 3 mm in 0.5 μm CMOS, supporting emerging developments in neuromorphic silicon-neuron interfaces.

Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning

PubMed Central

Dann, Benjamin

2016-01-01

Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity. PMID:27814352

Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning.

PubMed

Michaels, Jonathan A; Dann, Benjamin; Scherberger, Hansjörg

2016-11-01

Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity.

Subthreshold membrane potential oscillations in inferior olive neurons are dynamically regulated by P/Q- and T-type calcium channels: a study in mutant mice.

PubMed

Choi, Soonwook; Yu, Eunah; Kim, Daesoo; Urbano, Francisco J; Makarenko, Vladimir; Shin, Hee-Sup; Llinás, Rodolfo R

2010-08-15

The role of P/Q- and T-type calcium channels in the rhythmic oscillatory behaviour of inferior olive (IO) neurons was investigated in mutant mice. Mice lacking either the CaV2.1 gene of the pore-forming alpha1A subunit for P/Q-type calcium channel, or the CaV3.1 gene of the pore-forming alpha1G subunit for T-type calcium channel were used. In vitro intracellular recording from IO neurons reveals that the amplitude and frequency of sinusoidal subthreshold oscillations (SSTOs) were reduced in the CaV2.1-/- mice. In the CaV3.1-/- mice, IO neurons also showed altered patterns of SSTOs and the probability of SSTO generation was significantly lower (15%, 5 of 34 neurons) than that of wild-type (78%, 31 of 40 neurons) or CaV2.1-/- mice (73%, 22 of 30 neurons). In addition, the low-threshold calcium spike and the sustained endogenous oscillation following rebound potentials were absent in IO neurons from CaV3.1-/- mice. Moreover, the phase-reset dynamics of oscillatory properties of single neurons and neuronal clusters in IO were remarkably altered in both CaV2.1-/- and CaV3.1-/- mice. These results suggest that both alpha1A P/Q- and alpha1G T-type calcium channels are required for the dynamic control of neuronal oscillations in the IO. These findings were supported by results from a mathematical IO neuronal model that incorporated T and P/Q channel kinetics.

Visualizing the spinal neuronal dynamics of locomotion

NASA Astrophysics Data System (ADS)

Subramanian, Kalpathi R.; Bashor, D. P.; Miller, M. T.; Foster, J. A.

2004-06-01

Modern imaging and simulation techniques have enhanced system-level understanding of neural function. In this article, we present an application of interactive visualization to understanding neuronal dynamics causing locomotion of a single hip joint, based on pattern generator output of the spinal cord. Our earlier work visualized cell-level responses of multiple neuronal populations. However, the spatial relationships were abstract, making communication with colleagues difficult. We propose two approaches to overcome this: (1) building a 3D anatomical model of the spinal cord with neurons distributed inside, animated by the simulation and (2) adding limb movements predicted by neuronal activity. The new system was tested using a cat walking central pattern generator driving a pair of opposed spinal motoneuron pools. Output of opposing motoneuron pools was combined into a single metric, called "Net Neural Drive", which generated angular limb movement in proportion to its magnitude. Net neural drive constitutes a new description of limb movement control. The combination of spatial and temporal information in the visualizations elegantly conveys the neural activity of the output elements (motoneurons), as well as the resulting movement. The new system encompasses five biological levels of organization from ion channels to observed behavior. The system is easily scalable, and provides an efficient interactive platform for rapid hypothesis testing.

Task-Dependent Changes in Cross-Level Coupling between Single Neurons and Oscillatory Activity in Multiscale Networks

PubMed Central

Canolty, Ryan T.; Ganguly, Karunesh; Carmena, Jose M.

2012-01-01

Understanding the principles governing the dynamic coordination of functional brain networks remains an important unmet goal within neuroscience. How do distributed ensembles of neurons transiently coordinate their activity across a variety of spatial and temporal scales? While a complete mechanistic account of this process remains elusive, evidence suggests that neuronal oscillations may play a key role in this process, with different rhythms influencing both local computation and long-range communication. To investigate this question, we recorded multiple single unit and local field potential (LFP) activity from microelectrode arrays implanted bilaterally in macaque motor areas. Monkeys performed a delayed center-out reach task either manually using their natural arm (Manual Control, MC) or under direct neural control through a brain-machine interface (Brain Control, BC). In accord with prior work, we found that the spiking activity of individual neurons is coupled to multiple aspects of the ongoing motor beta rhythm (10–45 Hz) during both MC and BC, with neurons exhibiting a diversity of coupling preferences. However, here we show that for identified single neurons, this beta-to-rate mapping can change in a reversible and task-dependent way. For example, as beta power increases, a given neuron may increase spiking during MC but decrease spiking during BC, or exhibit a reversible shift in the preferred phase of firing. The within-task stability of coupling, combined with the reversible cross-task changes in coupling, suggest that task-dependent changes in the beta-to-rate mapping play a role in the transient functional reorganization of neural ensembles. We characterize the range of task-dependent changes in the mapping from beta amplitude, phase, and inter-hemispheric phase differences to the spike rates of an ensemble of simultaneously-recorded neurons, and discuss the potential implications that dynamic remapping from oscillatory activity to spike rate and timing may hold for models of computation and communication in distributed functional brain networks. PMID:23284276

Intrinsic Cellular Properties and Connectivity Density Determine Variable Clustering Patterns in Randomly Connected Inhibitory Neural Networks

PubMed Central

Rich, Scott; Booth, Victoria; Zochowski, Michal

2016-01-01

The plethora of inhibitory interneurons in the hippocampus and cortex play a pivotal role in generating rhythmic activity by clustering and synchronizing cell firing. Results of our simulations demonstrate that both the intrinsic cellular properties of neurons and the degree of network connectivity affect the characteristics of clustered dynamics exhibited in randomly connected, heterogeneous inhibitory networks. We quantify intrinsic cellular properties by the neuron's current-frequency relation (IF curve) and Phase Response Curve (PRC), a measure of how perturbations given at various phases of a neurons firing cycle affect subsequent spike timing. We analyze network bursting properties of networks of neurons with Type I or Type II properties in both excitability and PRC profile; Type I PRCs strictly show phase advances and IF curves that exhibit frequencies arbitrarily close to zero at firing threshold while Type II PRCs display both phase advances and delays and IF curves that have a non-zero frequency at threshold. Type II neurons whose properties arise with or without an M-type adaptation current are considered. We analyze network dynamics under different levels of cellular heterogeneity and as intrinsic cellular firing frequency and the time scale of decay of synaptic inhibition are varied. Many of the dynamics exhibited by these networks diverge from the predictions of the interneuron network gamma (ING) mechanism, as well as from results in all-to-all connected networks. Our results show that randomly connected networks of Type I neurons synchronize into a single cluster of active neurons while networks of Type II neurons organize into two mutually exclusive clusters segregated by the cells' intrinsic firing frequencies. Networks of Type II neurons containing the adaptation current behave similarly to networks of either Type I or Type II neurons depending on network parameters; however, the adaptation current creates differences in the cluster dynamics compared to those in networks of Type I or Type II neurons. To understand these results, we compute neuronal PRCs calculated with a perturbation matching the profile of the synaptic current in our networks. Differences in profiles of these PRCs across the different neuron types reveal mechanisms underlying the divergent network dynamics. PMID:27812323

[Functional organization and structure of the serotonergic neuronal network of terrestrial snail].

PubMed

Nikitin, E S; Balaban, P M

2011-01-01

The extension of knowledge how the brain works requires permanent improvement of methods of recording of neuronal activity and increase in the number of neurons recorded simultaneously to better understand the collective work of neuronal networks and assemblies. Conventional methods allow simultaneous intracellular recording up to 2-5 neurons and their membrane potentials, currents or monosynaptic connections or observation of spiking of neuronal groups with subsequent discrimination of individual spikes with loss of details of the dynamics of membrane potential. We recorded activity of a compact group of serotonergic neurons (up to 56 simultaneously) in the ganglion of a terrestrial mollusk using the method of optical recording of membrane potential that allowed to record individual action potentials in details with action potential parameters and to reveal morphology of the neurons rcorded. We demonstrated clear clustering in the group in relation with the dynamics of action potentials and phasic or tonic components in the neuronal responses to external electrophysiological and tactile stimuli. Also, we showed that identified neuron Pd2 could induce activation of a significant number of neurons in the group whereas neuron Pd4 did not induce any activation. However, its activation is delayed with regard to activation of the reacting group of neurons. Our data strongly support the concept of possible delegation of the integrative function by the network to a single neuron.

Nonlinear Dynamic Modeling of Neuron Action Potential Threshold During Synaptically Driven Broadband Intracellular Activity

PubMed Central

Roach, Shane M.; Song, Dong; Berger, Theodore W.

2012-01-01

Activity-dependent variation of neuronal thresholds for action potential (AP) generation is one of the key determinants of spike-train temporal-pattern transformations from presynaptic to postsynaptic spike trains. In this study, we model the nonlinear dynamics of the threshold variation during synaptically driven broadband intracellular activity. First, membrane potentials of single CA1 pyramidal cells were recorded under physiologically plausible broadband stimulation conditions. Second, a method was developed to measure AP thresholds from the continuous recordings of membrane potentials. It involves measuring the turning points of APs by analyzing the third-order derivatives of the membrane potentials. Four stimulation paradigms with different temporal patterns were applied to validate this method by comparing the measured AP turning points and the actual AP thresholds estimated with varying stimulation intensities. Results show that the AP turning points provide consistent measurement of the AP thresholds, except for a constant offset. It indicates that 1) the variation of AP turning points represents the nonlinearities of threshold dynamics; and 2) an optimization of the constant offset is required to achieve accurate spike prediction. Third, a nonlinear dynamical third-order Volterra model was built to describe the relations between the threshold dynamics and the AP activities. Results show that the model can predict threshold accurately based on the preceding APs. Finally, the dynamic threshold model was integrated into a previously developed single neuron model and resulted in a 33% improvement in spike prediction. PMID:22156947

Vertically aligned carbon nanofiber as nano-neuron interface for monitoring neural function.

PubMed

Yu, Zhe; McKnight, Timothy E; Ericson, M Nance; Melechko, Anatoli V; Simpson, Michael L; Morrison, Barclay

2012-05-01

Neural chips, which are capable of simultaneous multisite neural recording and stimulation, have been used to detect and modulate neural activity for almost thirty years. As neural interfaces, neural chips provide dynamic functional information for neural decoding and neural control. By improving sensitivity and spatial resolution, nano-scale electrodes may revolutionize neural detection and modulation at cellular and molecular levels as nano-neuron interfaces. We developed a carbon-nanofiber neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes and demonstrated its capability of both stimulating and monitoring electrophysiological signals from brain tissues in vitro and monitoring dynamic information of neuroplasticity. This novel nano-neuron interface may potentially serve as a precise, informative, biocompatible, and dual-mode neural interface for monitoring of both neuroelectrical and neurochemical activity at the single-cell level and even inside the cell. The authors demonstrate the utility of a neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes. The new device can be used to stimulate and/or monitor signals from brain tissue in vitro and for monitoring dynamic information of neuroplasticity both intracellularly and at the single cell level including neuroelectrical and neurochemical activities. Copyright © 2012 Elsevier Inc. All rights reserved.

Irregular Collective Behavior of Heterogeneous Neural Networks

NASA Astrophysics Data System (ADS)

Luccioli, Stefano; Politi, Antonio

2010-10-01

We investigate a network of integrate-and-fire neurons characterized by a distribution of spiking frequencies. Upon increasing the coupling strength, the model exhibits a transition from an asynchronous regime to a nontrivial collective behavior. Numerical simulations of large systems indicate that, at variance with the Kuramoto model, (i) the macroscopic dynamics stays irregular and (ii) the microscopic (single-neuron) evolution is linearly stable.

Time evolution of coherent structures in networks of Hindmarch Rose neurons

NASA Astrophysics Data System (ADS)

Mainieri, M. S.; Erichsen, R.; Brunnet, L. G.

2005-08-01

In the regime of partial synchronization, networks of diffusively coupled Hindmarch-Rose neurons show coherent structures developing in a region of the phase space which is wider than in the correspondent single neuron. Such structures are kept, without important changes, during several bursting periods. In this work, we study the time evolution of these structures and their dynamical stability under damage. This system may model the behavior of ensembles of neurons coupled through a bidirectional gap junction or, in a broader sense, it could also account for the molecular cascades present in the formation of flash and short time memory.

Action Potential Waveform Variability Limits Multi-Unit Separation in Freely Behaving Rats

PubMed Central

Stratton, Peter; Cheung, Allen; Wiles, Janet; Kiyatkin, Eugene; Sah, Pankaj; Windels, François

2012-01-01

Extracellular multi-unit recording is a widely used technique to study spontaneous and evoked neuronal activity in awake behaving animals. These recordings are done using either single-wire or mulitwire electrodes such as tetrodes. In this study we have tested the ability of single-wire electrodes to discriminate activity from multiple neurons under conditions of varying noise and neuronal cell density. Using extracellular single-unit recording, coupled with iontophoresis to drive cell activity across a wide dynamic range, we studied spike waveform variability, and explored systematic differences in single-unit spike waveform within and between brain regions as well as the influence of signal-to-noise ratio (SNR) on the similarity of spike waveforms. We also modelled spike misclassification for a range of cell densities based on neuronal recordings obtained at different SNRs. Modelling predictions were confirmed by classifying spike waveforms from multiple cells with various SNRs using a leading commercial spike-sorting system. Our results show that for single-wire recordings, multiple units can only be reliably distinguished under conditions of high recording SNR (≥4) and low neuronal density (≈20,000/ mm3). Physiological and behavioural changes, as well as technical limitations typical of awake animal preparations, reduce the accuracy of single-channel spike classification, resulting in serious classification errors. For SNR <4, the probability of misclassifying spikes approaches 100% in many cases. Our results suggest that in studies where the SNR is low or neuronal density is high, separation of distinct units needs to be evaluated with great caution. PMID:22719894

Subthreshold membrane potential oscillations in inferior olive neurons are dynamically regulated by P/Q- and T-type calcium channels: a study in mutant mice

PubMed Central

Choi, Soonwook; Yu, Eunah; Kim, Daesoo; Urbano, Francisco J; Makarenko, Vladimir; Shin, Hee-Sup; Llinás, Rodolfo R

2010-01-01

The role of P/Q- and T-type calcium channels in the rhythmic oscillatory behaviour of inferior olive (IO) neurons was investigated in mutant mice. Mice lacking either the CaV2.1 gene of the pore-forming α1A subunit for P/Q-type calcium channel, or the CaV3.1 gene of the pore-forming α1G subunit for T-type calcium channel were used. In vitro intracellular recording from IO neurons reveals that the amplitude and frequency of sinusoidal subthreshold oscillations (SSTOs) were reduced in the CaV2.1−/− mice. In the CaV3.1−/− mice, IO neurons also showed altered patterns of SSTOs and the probability of SSTO generation was significantly lower (15%, 5 of 34 neurons) than that of wild-type (78%, 31 of 40 neurons) or CaV2.1−/− mice (73%, 22 of 30 neurons). In addition, the low-threshold calcium spike and the sustained endogenous oscillation following rebound potentials were absent in IO neurons from CaV3.1−/− mice. Moreover, the phase-reset dynamics of oscillatory properties of single neurons and neuronal clusters in IO were remarkably altered in both CaV2.1−/− and CaV3.1−/− mice. These results suggest that both α1A P/Q- and α1G T-type calcium channels are required for the dynamic control of neuronal oscillations in the IO. These findings were supported by results from a mathematical IO neuronal model that incorporated T and P/Q channel kinetics. PMID:20547676

Brain-wide neuronal dynamics during motor adaptation in zebrafish.

PubMed

Ahrens, Misha B; Li, Jennifer M; Orger, Michael B; Robson, Drew N; Schier, Alexander F; Engert, Florian; Portugues, Ruben

2012-05-09

A fundamental question in neuroscience is how entire neural circuits generate behaviour and adapt it to changes in sensory feedback. Here we use two-photon calcium imaging to record the activity of large populations of neurons at the cellular level, throughout the brain of larval zebrafish expressing a genetically encoded calcium sensor, while the paralysed animals interact fictively with a virtual environment and rapidly adapt their motor output to changes in visual feedback. We decompose the network dynamics involved in adaptive locomotion into four types of neuronal response properties, and provide anatomical maps of the corresponding sites. A subset of these signals occurred during behavioural adjustments and are candidates for the functional elements that drive motor learning. Lesions to the inferior olive indicate a specific functional role for olivocerebellar circuitry in adaptive locomotion. This study enables the analysis of brain-wide dynamics at single-cell resolution during behaviour.

Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels

PubMed Central

Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J.

2014-01-01

This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively “hiding” its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research. PMID:25505378

Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

PubMed

Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J

2014-01-01

This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.

The synchronization of asymmetric-structured electric coupling neuronal system

NASA Astrophysics Data System (ADS)

Wang, Guanping; Jin, Wuyin; Liu, Hao; Sun, Wei

2018-02-01

Based on the Hindmarsh-Rose (HR) model, the synchronization dynamics of asymmetric-structured electric coupling two neuronal system is investigated in this paper. It is discovered that when the time-delay scope and coupling strength for the synchronization are correlated positively under unequal time delay, the time-delay difference does not make a clear distinction between the two individual inter-spike intervals (ISI) bifurcation diagrams of the two coupled neurons. Therefore, the superficial difference of the system synchronization dynamics is not obvious for the unequal time-delay feedback. In the asymmetrical current incentives under asymmetric electric coupled system, the two neurons can only be almost completely synchronized in specific area of the interval which end-pointed with two discharge modes for a single neuron under different stimuli currents before coupling, but the intervention of time-delay feedback, together with the change of the coupling strength, can make the coupled system not only almost completely synchronized within anywhere in the front area, but also outside of it.

Extending Integrate-and-Fire Model Neurons to Account for the Effects of Weak Electric Fields and Input Filtering Mediated by the Dendrite.

PubMed

Aspart, Florian; Ladenbauer, Josef; Obermayer, Klaus

2016-11-01

Transcranial brain stimulation and evidence of ephaptic coupling have recently sparked strong interests in understanding the effects of weak electric fields on the dynamics of brain networks and of coupled populations of neurons. The collective dynamics of large neuronal populations can be efficiently studied using single-compartment (point) model neurons of the integrate-and-fire (IF) type as their elements. These models, however, lack the dendritic morphology required to biophysically describe the effect of an extracellular electric field on the neuronal membrane voltage. Here, we extend the IF point neuron models to accurately reflect morphology dependent electric field effects extracted from a canonical spatial "ball-and-stick" (BS) neuron model. Even in the absence of an extracellular field, neuronal morphology by itself strongly affects the cellular response properties. We, therefore, derive additional components for leaky and nonlinear IF neuron models to reproduce the subthreshold voltage and spiking dynamics of the BS model exposed to both fluctuating somatic and dendritic inputs and an extracellular electric field. We show that an oscillatory electric field causes spike rate resonance, or equivalently, pronounced spike to field coherence. Its resonance frequency depends on the location of the synaptic background inputs. For somatic inputs the resonance appears in the beta and gamma frequency range, whereas for distal dendritic inputs it is shifted to even higher frequencies. Irrespective of an external electric field, the presence of a dendritic cable attenuates the subthreshold response at the soma to slowly-varying somatic inputs while implementing a low-pass filter for distal dendritic inputs. Our point neuron model extension is straightforward to implement and is computationally much more efficient compared to the original BS model. It is well suited for studying the dynamics of large populations of neurons with heterogeneous dendritic morphology with (and without) the influence of weak external electric fields.

Extending Integrate-and-Fire Model Neurons to Account for the Effects of Weak Electric Fields and Input Filtering Mediated by the Dendrite

PubMed Central

Obermayer, Klaus

2016-01-01

Transcranial brain stimulation and evidence of ephaptic coupling have recently sparked strong interests in understanding the effects of weak electric fields on the dynamics of brain networks and of coupled populations of neurons. The collective dynamics of large neuronal populations can be efficiently studied using single-compartment (point) model neurons of the integrate-and-fire (IF) type as their elements. These models, however, lack the dendritic morphology required to biophysically describe the effect of an extracellular electric field on the neuronal membrane voltage. Here, we extend the IF point neuron models to accurately reflect morphology dependent electric field effects extracted from a canonical spatial “ball-and-stick” (BS) neuron model. Even in the absence of an extracellular field, neuronal morphology by itself strongly affects the cellular response properties. We, therefore, derive additional components for leaky and nonlinear IF neuron models to reproduce the subthreshold voltage and spiking dynamics of the BS model exposed to both fluctuating somatic and dendritic inputs and an extracellular electric field. We show that an oscillatory electric field causes spike rate resonance, or equivalently, pronounced spike to field coherence. Its resonance frequency depends on the location of the synaptic background inputs. For somatic inputs the resonance appears in the beta and gamma frequency range, whereas for distal dendritic inputs it is shifted to even higher frequencies. Irrespective of an external electric field, the presence of a dendritic cable attenuates the subthreshold response at the soma to slowly-varying somatic inputs while implementing a low-pass filter for distal dendritic inputs. Our point neuron model extension is straightforward to implement and is computationally much more efficient compared to the original BS model. It is well suited for studying the dynamics of large populations of neurons with heterogeneous dendritic morphology with (and without) the influence of weak external electric fields. PMID:27893786

Estimating network parameters from combined dynamics of firing rate and irregularity of single neurons.

PubMed

Hamaguchi, Kosuke; Riehle, Alexa; Brunel, Nicolas

2011-01-01

High firing irregularity is a hallmark of cortical neurons in vivo, and modeling studies suggest a balance of excitation and inhibition is necessary to explain this high irregularity. Such a balance must be generated, at least partly, from local interconnected networks of excitatory and inhibitory neurons, but the details of the local network structure are largely unknown. The dynamics of the neural activity depends on the local network structure; this in turn suggests the possibility of estimating network structure from the dynamics of the firing statistics. Here we report a new method to estimate properties of the local cortical network from the instantaneous firing rate and irregularity (CV(2)) under the assumption that recorded neurons are a part of a randomly connected sparse network. The firing irregularity, measured in monkey motor cortex, exhibits two features; many neurons show relatively stable firing irregularity in time and across different task conditions; the time-averaged CV(2) is widely distributed from quasi-regular to irregular (CV(2) = 0.3-1.0). For each recorded neuron, we estimate the three parameters of a local network [balance of local excitation-inhibition, number of recurrent connections per neuron, and excitatory postsynaptic potential (EPSP) size] that best describe the dynamics of the measured firing rates and irregularities. Our analysis shows that optimal parameter sets form a two-dimensional manifold in the three-dimensional parameter space that is confined for most of the neurons to the inhibition-dominated region. High irregularity neurons tend to be more strongly connected to the local network, either in terms of larger EPSP and inhibitory PSP size or larger number of recurrent connections, compared with the low irregularity neurons, for a given excitatory/inhibitory balance. Incorporating either synaptic short-term depression or conductance-based synapses leads many low CV(2) neurons to move to the excitation-dominated region as well as to an increase of EPSP size.

Neuronal Assemblies Evidence Distributed Interactions within a Tactile Discrimination Task in Rats

PubMed Central

Deolindo, Camila S.; Kunicki, Ana C. B.; da Silva, Maria I.; Lima Brasil, Fabrício; Moioli, Renan C.

2018-01-01

Accumulating evidence suggests that neural interactions are distributed and relate to animal behavior, but many open questions remain. The neural assembly hypothesis, formulated by Hebb, states that synchronously active single neurons may transiently organize into functional neural circuits—neuronal assemblies (NAs)—and that would constitute the fundamental unit of information processing in the brain. However, the formation, vanishing, and temporal evolution of NAs are not fully understood. In particular, characterizing NAs in multiple brain regions over the course of behavioral tasks is relevant to assess the highly distributed nature of brain processing. In the context of NA characterization, active tactile discrimination tasks with rats are elucidative because they engage several cortical areas in the processing of information that are otherwise masked in passive or anesthetized scenarios. In this work, we investigate the dynamic formation of NAs within and among four different cortical regions in long-range fronto-parieto-occipital networks (primary somatosensory, primary visual, prefrontal, and posterior parietal cortices), simultaneously recorded from seven rats engaged in an active tactile discrimination task. Our results first confirm that task-related neuronal firing rate dynamics in all four regions is significantly modulated. Notably, a support vector machine decoder reveals that neural populations contain more information about the tactile stimulus than the majority of single neurons alone. Then, over the course of the task, we identify the emergence and vanishing of NAs whose participating neurons are shown to contain more information about animal behavior than randomly chosen neurons. Taken together, our results further support the role of multiple and distributed neurons as the functional unit of information processing in the brain (NA hypothesis) and their link to active animal behavior. PMID:29375324

Differential dynamics of spatial attention, position, and color coding within the parietofrontal network.

PubMed

Astrand, Elaine; Ibos, Guilhem; Duhamel, Jean-René; Ben Hamed, Suliann

2015-02-18

Despite an ever growing knowledge on how parietal and prefrontal neurons encode low-level spatial and color information or higher-level information, such as spatial attention, an understanding of how these cortical regions process neuronal information at the population level is still missing. A simple assumption would be that the function and temporal response profiles of these neuronal populations match that of its constituting individual cells. However, several recent studies suggest that this is not necessarily the case and that the single-cell approach overlooks dynamic changes in how information is distributed over the neuronal population. Here, we use a time-resolved population pattern analysis to explore how spatial position, spatial attention and color information are differentially encoded and maintained in the macaque monkey prefrontal (frontal eye fields) and parietal cortex (lateral intraparietal area). Overall, our work brings about three novel observations. First, we show that parietal and prefrontal populations operate in two distinct population regimens for the encoding of sensory and cognitive information: a stationary mode and a dynamic mode. Second, we show that the temporal dynamics of a heterogeneous neuronal population brings about complementary information to that of its functional subpopulations. Thus, both need to be investigated in parallel. Last, we show that identifying the neuronal configuration in which a neuronal population encodes given information can serve to reveal this same information in a different context. All together, this work challenges common views on neural coding in the parietofrontal network. Copyright © 2015 the authors 0270-6474/15/353174-16$15.00/0.

Correlated variability modifies working memory fidelity in primate prefrontal neuronal ensembles

PubMed Central

Leavitt, Matthew L.; Pieper, Florian; Sachs, Adam J.; Martinez-Trujillo, Julio C.

2017-01-01

Neurons in the primate lateral prefrontal cortex (LPFC) encode working memory (WM) representations via sustained firing, a phenomenon hypothesized to arise from recurrent dynamics within ensembles of interconnected neurons. Here, we tested this hypothesis by using microelectrode arrays to examine spike count correlations (rsc) in LPFC neuronal ensembles during a spatial WM task. We found a pattern of pairwise rsc during WM maintenance indicative of stronger coupling between similarly tuned neurons and increased inhibition between dissimilarly tuned neurons. We then used a linear decoder to quantify the effects of the high-dimensional rsc structure on information coding in the neuronal ensembles. We found that the rsc structure could facilitate or impair coding, depending on the size of the ensemble and tuning properties of its constituent neurons. A simple optimization procedure demonstrated that near-maximum decoding performance could be achieved using a relatively small number of neurons. These WM-optimized subensembles were more signal correlation (rsignal)-diverse and anatomically dispersed than predicted by the statistics of the full recorded population of neurons, and they often contained neurons that were poorly WM-selective, yet enhanced coding fidelity by shaping the ensemble’s rsc structure. We observed a pattern of rsc between LPFC neurons indicative of recurrent dynamics as a mechanism for WM-related activity and that the rsc structure can increase the fidelity of WM representations. Thus, WM coding in LPFC neuronal ensembles arises from a complex synergy between single neuron coding properties and multidimensional, ensemble-level phenomena. PMID:28275096

Two-Photon Functional Imaging of the Auditory Cortex in Behaving Mice: From Neural Networks to Single Spines.

PubMed

Li, Ruijie; Wang, Meng; Yao, Jiwei; Liang, Shanshan; Liao, Xiang; Yang, Mengke; Zhang, Jianxiong; Yan, Junan; Jia, Hongbo; Chen, Xiaowei; Li, Xingyi

2018-01-01

In vivo two-photon Ca 2+ imaging is a powerful tool for recording neuronal activities during perceptual tasks and has been increasingly applied to behaving animals for acute or chronic experiments. However, the auditory cortex is not easily accessible to imaging because of the abundant temporal muscles, arteries around the ears and their lateral locations. Here, we report a protocol for two-photon Ca 2+ imaging in the auditory cortex of head-fixed behaving mice. By using a custom-made head fixation apparatus and a head-rotated fixation procedure, we achieved two-photon imaging and in combination with targeted cell-attached recordings of auditory cortical neurons in behaving mice. Using synthetic Ca 2+ indicators, we recorded the Ca 2+ transients at multiple scales, including neuronal populations, single neurons, dendrites and single spines, in auditory cortex during behavior. Furthermore, using genetically encoded Ca 2+ indicators (GECIs), we monitored the neuronal dynamics over days throughout the process of associative learning. Therefore, we achieved two-photon functional imaging at multiple scales in auditory cortex of behaving mice, which extends the tool box for investigating the neural basis of audition-related behaviors.

Two-Photon Functional Imaging of the Auditory Cortex in Behaving Mice: From Neural Networks to Single Spines

PubMed Central

Li, Ruijie; Wang, Meng; Yao, Jiwei; Liang, Shanshan; Liao, Xiang; Yang, Mengke; Zhang, Jianxiong; Yan, Junan; Jia, Hongbo; Chen, Xiaowei; Li, Xingyi

2018-01-01

In vivo two-photon Ca2+ imaging is a powerful tool for recording neuronal activities during perceptual tasks and has been increasingly applied to behaving animals for acute or chronic experiments. However, the auditory cortex is not easily accessible to imaging because of the abundant temporal muscles, arteries around the ears and their lateral locations. Here, we report a protocol for two-photon Ca2+ imaging in the auditory cortex of head-fixed behaving mice. By using a custom-made head fixation apparatus and a head-rotated fixation procedure, we achieved two-photon imaging and in combination with targeted cell-attached recordings of auditory cortical neurons in behaving mice. Using synthetic Ca2+ indicators, we recorded the Ca2+ transients at multiple scales, including neuronal populations, single neurons, dendrites and single spines, in auditory cortex during behavior. Furthermore, using genetically encoded Ca2+ indicators (GECIs), we monitored the neuronal dynamics over days throughout the process of associative learning. Therefore, we achieved two-photon functional imaging at multiple scales in auditory cortex of behaving mice, which extends the tool box for investigating the neural basis of audition-related behaviors. PMID:29740289

On the continuous differentiability of inter-spike intervals of synaptically connected cortical spiking neurons in a neuronal network.

PubMed

Kumar, Gautam; Kothare, Mayuresh V

2013-12-01

We derive conditions for continuous differentiability of inter-spike intervals (ISIs) of spiking neurons with respect to parameters (decision variables) of an external stimulating input current that drives a recurrent network of synaptically connected neurons. The dynamical behavior of individual neurons is represented by a class of discontinuous single-neuron models. We report here that ISIs of neurons in the network are continuously differentiable with respect to decision variables if (1) a continuously differentiable trajectory of the membrane potential exists between consecutive action potentials with respect to time and decision variables and (2) the partial derivative of the membrane potential of spiking neurons with respect to time is not equal to the partial derivative of their firing threshold with respect to time at the time of action potentials. Our theoretical results are supported by showing fulfillment of these conditions for a class of known bidimensional spiking neuron models.

The dynamical analysis of modified two-compartment neuron model and FPGA implementation

NASA Astrophysics Data System (ADS)

Lin, Qianjin; Wang, Jiang; Yang, Shuangming; Yi, Guosheng; Deng, Bin; Wei, Xile; Yu, Haitao

2017-10-01

The complexity of neural models is increasing with the investigation of larger biological neural network, more various ionic channels and more detailed morphologies, and the implementation of biological neural network is a task with huge computational complexity and power consumption. This paper presents an efficient digital design using piecewise linearization on field programmable gate array (FPGA), to succinctly implement the reduced two-compartment model which retains essential features of more complicated models. The design proposes an approximate neuron model which is composed of a set of piecewise linear equations, and it can reproduce different dynamical behaviors to depict the mechanisms of a single neuron model. The consistency of hardware implementation is verified in terms of dynamical behaviors and bifurcation analysis, and the simulation results including varied ion channel characteristics coincide with the biological neuron model with a high accuracy. Hardware synthesis on FPGA demonstrates that the proposed model has reliable performance and lower hardware resource compared with the original two-compartment model. These investigations are conducive to scalability of biological neural network in reconfigurable large-scale neuromorphic system.

Multifractal analysis of information processing in hippocampal neural ensembles during working memory under Δ9-tetrahydrocannabinol administration

PubMed Central

Fetterhoff, Dustin; Opris, Ioan; Simpson, Sean L.; Deadwyler, Sam A.; Hampson, Robert E.; Kraft, Robert A.

2014-01-01

Background Multifractal analysis quantifies the time-scale-invariant properties in data by describing the structure of variability over time. By applying this analysis to hippocampal interspike interval sequences recorded during performance of a working memory task, a measure of long-range temporal correlations and multifractal dynamics can reveal single neuron correlates of information processing. New method Wavelet leaders-based multifractal analysis (WLMA) was applied to hippocampal interspike intervals recorded during a working memory task. WLMA can be used to identify neurons likely to exhibit information processing relevant to operation of brain–computer interfaces and nonlinear neuronal models. Results Neurons involved in memory processing (“Functional Cell Types” or FCTs) showed a greater degree of multifractal firing properties than neurons without task-relevant firing characteristics. In addition, previously unidentified FCTs were revealed because multifractal analysis suggested further functional classification. The cannabinoid-type 1 receptor partial agonist, tetrahydrocannabinol (THC), selectively reduced multifractal dynamics in FCT neurons compared to non-FCT neurons. Comparison with existing methods WLMA is an objective tool for quantifying the memory-correlated complexity represented by FCTs that reveals additional information compared to classification of FCTs using traditional z-scores to identify neuronal correlates of behavioral events. Conclusion z-Score-based FCT classification provides limited information about the dynamical range of neuronal activity characterized by WLMA. Increased complexity, as measured with multifractal analysis, may be a marker of functional involvement in memory processing. The level of multifractal attributes can be used to differentially emphasize neural signals to improve computational models and algorithms underlying brain–computer interfaces. PMID:25086297

Predictive Coding of Dynamical Variables in Balanced Spiking Networks

PubMed Central

Boerlin, Martin; Machens, Christian K.; Denève, Sophie

2013-01-01

Two observations about the cortex have puzzled neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks that represent information efficiently in their spikes. We illustrate this insight with spiking networks that represent dynamical variables. Our approach is based on two assumptions: We assume that information about dynamical variables can be read out linearly from neural spike trains, and we assume that neurons only fire a spike if that improves the representation of the dynamical variables. Based on these assumptions, we derive a network of leaky integrate-and-fire neurons that is able to implement arbitrary linear dynamical systems. We show that the membrane voltage of the neurons is equivalent to a prediction error about a common population-level signal. Among other things, our approach allows us to construct an integrator network of spiking neurons that is robust against many perturbations. Most importantly, neural variability in our networks cannot be equated to noise. Despite exhibiting the same single unit properties as widely used population code models (e.g. tuning curves, Poisson distributed spike trains), balanced networks are orders of magnitudes more reliable. Our approach suggests that spikes do matter when considering how the brain computes, and that the reliability of cortical representations could have been strongly underestimated. PMID:24244113

Ligand-Induced Dynamics of Neurotrophin Receptors Investigated by Single-Molecule Imaging Approaches

PubMed Central

Marchetti, Laura; Luin, Stefano; Bonsignore, Fulvio; de Nadai, Teresa; Beltram, Fabio; Cattaneo, Antonino

2015-01-01

Neurotrophins are secreted proteins that regulate neuronal development and survival, as well as maintenance and plasticity of the adult nervous system. The biological activity of neurotrophins stems from their binding to two membrane receptor types, the tropomyosin receptor kinase and the p75 neurotrophin receptors (NRs). The intracellular signalling cascades thereby activated have been extensively investigated. Nevertheless, a comprehensive description of the ligand-induced nanoscale details of NRs dynamics and interactions spanning from the initial lateral movements triggered at the plasma membrane to the internalization and transport processes is still missing. Recent advances in high spatio-temporal resolution imaging techniques have yielded new insight on the dynamics of NRs upon ligand binding. Here we discuss requirements, potential and practical implementation of these novel approaches for the study of neurotrophin trafficking and signalling, in the framework of current knowledge available also for other ligand-receptor systems. We shall especially highlight the correlation between the receptor dynamics activated by different neurotrophins and the respective signalling outcome, as recently revealed by single-molecule tracking of NRs in living neuronal cells. PMID:25603178

Simultaneous cellular-resolution optical perturbation and imaging of place cell firing fields

PubMed Central

Rickgauer, John Peter; Deisseroth, Karl; Tank, David W.

2015-01-01

Linking neural microcircuit function to emergent properties of the mammalian brain requires fine-scale manipulation and measurement of neural activity during behavior, where each neuron’s coding and dynamics can be characterized. We developed an optical method for simultaneous cellular-resolution stimulation and large-scale recording of neuronal activity in behaving mice. Dual-wavelength two-photon excitation allowed largely independent functional imaging with a green fluorescent calcium sensor (GCaMP3, λ = 920 ± 6 nm) and single-neuron photostimulation with a red-shifted optogenetic probe (C1V1, λ = 1,064 ± 6 nm) in neurons coexpressing the two proteins. We manipulated task-modulated activity in individual hippocampal CA1 place cells during spatial navigation in a virtual reality environment, mimicking natural place-field activity, or ‘biasing’, to reveal subthreshold dynamics. Notably, manipulating single place-cell activity also affected activity in small groups of other place cells that were active around the same time in the task, suggesting a functional role for local place cell interactions in shaping firing fields. PMID:25402854

Analytical Calculation of Mutual Information between Weakly Coupled Poisson-Spiking Neurons in Models of Dynamically Gated Communication.

PubMed

Cannon, Jonathan

2017-01-01

Mutual information is a commonly used measure of communication between neurons, but little theory exists describing the relationship between mutual information and the parameters of the underlying neuronal interaction. Such a theory could help us understand how specific physiological changes affect the capacity of neurons to synaptically communicate, and, in particular, they could help us characterize the mechanisms by which neuronal dynamics gate the flow of information in the brain. Here we study a pair of linear-nonlinear-Poisson neurons coupled by a weak synapse. We derive an analytical expression describing the mutual information between their spike trains in terms of synapse strength, neuronal activation function, the time course of postsynaptic currents, and the time course of the background input received by the two neurons. This expression allows mutual information calculations that would otherwise be computationally intractable. We use this expression to analytically explore the interaction of excitation, information transmission, and the convexity of the activation function. Then, using this expression to quantify mutual information in simulations, we illustrate the information-gating effects of neural oscillations and oscillatory coherence, which may either increase or decrease the mutual information across the synapse depending on parameters. Finally, we show analytically that our results can quantitatively describe the selection of one information pathway over another when multiple sending neurons project weakly to a single receiving neuron.

Investigating sub-spine actin dynamics in rat hippocampal neurons with super-resolution optical imaging.

PubMed

Tatavarty, Vedakumar; Kim, Eun-Ji; Rodionov, Vladimir; Yu, Ji

2009-11-09

Morphological changes in dendritic spines represent an important mechanism for synaptic plasticity which is postulated to underlie the vital cognitive phenomena of learning and memory. These morphological changes are driven by the dynamic actin cytoskeleton that is present in dendritic spines. The study of actin dynamics in these spines traditionally has been hindered by the small size of the spine. In this study, we utilize a photo-activation localization microscopy (PALM)-based single-molecule tracking technique to analyze F-actin movements with approximately 30-nm resolution in cultured hippocampal neurons. We were able to observe the kinematic (physical motion of actin filaments, i.e., retrograde flow) and kinetic (F-actin turn-over) dynamics of F-actin at the single-filament level in dendritic spines. We found that F-actin in dendritic spines exhibits highly heterogeneous kinematic dynamics at the individual filament level, with simultaneous actin flows in both retrograde and anterograde directions. At the ensemble level, movements of filaments integrate into a net retrograde flow of approximately 138 nm/min. These results suggest a weakly polarized F-actin network that consists of mostly short filaments in dendritic spines.

High-fidelity optical reporting of neuronal electrical activity with an ultrafast fluorescent voltage sensor

PubMed Central

St-Pierre, François; Marshall, Jesse D; Yang, Ying; Gong, Yiyang; Schnitzer, Mark J; Lin, Michael Z

2015-01-01

Accurate optical reporting of electrical activity in genetically defined neuronal populations is a long-standing goal in neuroscience. Here we describe Accelerated Sensor of Action Potentials 1 (ASAP1), a novel voltage sensor design in which a circularly permuted green fluorescent protein is inserted within an extracellular loop of a voltage-sensing domain, rendering fluorescence responsive to membrane potential. ASAP1 demonstrates on- and off- kinetics of 2.1 and 2.0 ms, reliably detects single action potentials and subthreshold potential changes, and tracks trains of action potential waveforms up to 200 Hz in single trials. With a favorable combination of brightness, dynamic range, and speed, ASAP1 enables continuous monitoring of membrane potential in neurons at KHz frame rates using standard epifluorescence microscopy. PMID:24755780

Bursting as a source of non-linear determinism in the firing patterns of nigral dopamine neurons

PubMed Central

Jeong, Jaeseung; Shi, Wei-Xing; Hoffman, Ralph; Oh, Jihoon; Gore, John C.; Bunney, Benjamin S.; Peterson, Bradley S.

2012-01-01

Nigral dopamine (DA) neurons in vivo exhibit complex firing patterns consisting of tonic single-spikes and phasic bursts that encode information for certain types of reward-related learning and behavior. Non-linear dynamical analysis has previously demonstrated the presence of a non-linear deterministic structure in complex firing patterns of DA neurons, yet the origin of this non-linear determinism remains unknown. In this study, we hypothesized that bursting activity is the primary source of non-linear determinism in the firing patterns of DA neurons. To test this hypothesis, we investigated the dimension complexity of inter-spike interval data recorded in vivo from bursting and non-bursting DA neurons in the chloral hydrate-anesthetized rat substantia nigra. We found that bursting DA neurons exhibited non-linear determinism in their firing patterns, whereas non-bursting DA neurons showed truly stochastic firing patterns. Determinism was also detected in the isolated burst and inter-burst interval data extracted from firing patterns of bursting neurons. Moreover, less bursting DA neurons in halothane-anesthetized rats exhibited higher dimensional spiking dynamics than do more bursting DA neurons in chloral hydrate-anesthetized rats. These results strongly indicate that bursting activity is the main source of low-dimensional, non-linear determinism in the firing patterns of DA neurons. This finding furthermore suggests that bursts are the likely carriers of meaningful information in the firing activities of DA neurons. PMID:22831464

Dissociation between sustained single-neuron spiking and transient β-LFP oscillations in primate motor cortex

PubMed Central

Rule, Michael E.; Vargas-Irwin, Carlos E.; Donoghue, John P.

2017-01-01

Determining the relationship between single-neuron spiking and transient (20 Hz) β-local field potential (β-LFP) oscillations is an important step for understanding the role of these oscillations in motor cortex. We show that whereas motor cortex firing rates and beta spiking rhythmicity remain sustained during steady-state movement preparation periods, β-LFP oscillations emerge, in contrast, as short transient events. Single-neuron mean firing rates within and outside transient β-LFP events showed no differences, and no consistent correlation was found between the beta oscillation amplitude and firing rates, as was the case for movement- and visual cue-related β-LFP suppression. Importantly, well-isolated single units featuring beta-rhythmic spiking (43%, 125/292) showed no apparent or only weak phase coupling with the transient β-LFP oscillations. Similar results were obtained for the population spiking. These findings were common in triple microelectrode array recordings from primary motor (M1), ventral (PMv), and dorsal premotor (PMd) cortices in nonhuman primates during movement preparation. Although beta spiking rhythmicity indicates strong membrane potential fluctuations in the beta band, it does not imply strong phase coupling with β-LFP oscillations. The observed dissociation points to two different sources of variation in motor cortex β-LFPs: one that impacts single-neuron spiking dynamics and another related to the generation of mesoscopic β-LFP signals. Furthermore, our findings indicate that rhythmic spiking and diverse neuronal firing rates, which encode planned actions during movement preparation, may naturally limit the ability of different neuronal populations to strongly phase-couple to a single dominant oscillation frequency, leading to the observed spiking and β-LFP dissociation. NEW & NOTEWORTHY We show that whereas motor cortex spiking rates and beta (~20 Hz) spiking rhythmicity remain sustained during steady-state movement preparation periods, β-local field potential (β-LFP) oscillations emerge, in contrast, as transient events. Furthermore, the β-LFP phase at which neurons spike drifts: phase coupling is typically weak or absent. This dissociation points to two sources of variation in the level of motor cortex beta: one that impacts single-neuron spiking and another related to the generation of measured mesoscopic β-LFPs. PMID:28100654

Mouse Visual Neocortex Supports Multiple Stereotyped Patterns of Microcircuit Activity

PubMed Central

Sadovsky, Alexander J.

2014-01-01

Spiking correlations between neocortical neurons provide insight into the underlying synaptic connectivity that defines cortical microcircuitry. Here, using two-photon calcium fluorescence imaging, we observed the simultaneous dynamics of hundreds of neurons in slices of mouse primary visual cortex (V1). Consistent with a balance of excitation and inhibition, V1 dynamics were characterized by a linear scaling between firing rate and circuit size. Using lagged firing correlations between neurons, we generated functional wiring diagrams to evaluate the topological features of V1 microcircuitry. We found that circuit connectivity exhibited both cyclic graph motifs, indicating recurrent wiring, and acyclic graph motifs, indicating feedforward wiring. After overlaying the functional wiring diagrams onto the imaged field of view, we found properties consistent with Rentian scaling: wiring diagrams were topologically efficient because they minimized wiring with a modular architecture. Within single imaged fields of view, V1 contained multiple discrete circuits that were overlapping and highly interdigitated but were still distinct from one another. The majority of neurons that were shared between circuits displayed peri-event spiking activity whose timing was specific to the active circuit, whereas spike times for a smaller percentage of neurons were invariant to circuit identity. These data provide evidence that V1 microcircuitry exhibits balanced dynamics, is efficiently arranged in anatomical space, and is capable of supporting a diversity of multineuron spike firing patterns from overlapping sets of neurons. PMID:24899701

The relevance of network micro-structure for neural dynamics.

PubMed

Pernice, Volker; Deger, Moritz; Cardanobile, Stefano; Rotter, Stefan

2013-01-01

The activity of cortical neurons is determined by the input they receive from presynaptic neurons. Many previous studies have investigated how specific aspects of the statistics of the input affect the spike trains of single neurons and neurons in recurrent networks. However, typically very simple random network models are considered in such studies. Here we use a recently developed algorithm to construct networks based on a quasi-fractal probability measure which are much more variable than commonly used network models, and which therefore promise to sample the space of recurrent networks in a more exhaustive fashion than previously possible. We use the generated graphs as the underlying network topology in simulations of networks of integrate-and-fire neurons in an asynchronous and irregular state. Based on an extensive dataset of networks and neuronal simulations we assess statistical relations between features of the network structure and the spiking activity. Our results highlight the strong influence that some details of the network structure have on the activity dynamics of both single neurons and populations, even if some global network parameters are kept fixed. We observe specific and consistent relations between activity characteristics like spike-train irregularity or correlations and network properties, for example the distributions of the numbers of in- and outgoing connections or clustering. Exploiting these relations, we demonstrate that it is possible to estimate structural characteristics of the network from activity data. We also assess higher order correlations of spiking activity in the various networks considered here, and find that their occurrence strongly depends on the network structure. These results provide directions for further theoretical studies on recurrent networks, as well as new ways to interpret spike train recordings from neural circuits.

Absence of NEFL in patient-specific neurons in early-onset Charcot-Marie-Tooth neuropathy.

PubMed

Sainio, Markus T; Ylikallio, Emil; Mäenpää, Laura; Lahtela, Jenni; Mattila, Pirkko; Auranen, Mari; Palmio, Johanna; Tyynismaa, Henna

2018-06-01

We used patient-specific neuronal cultures to characterize the molecular genetic mechanism of recessive nonsense mutations in neurofilament light ( NEFL ) underlying early-onset Charcot-Marie-Tooth (CMT) disease. Motor neurons were differentiated from induced pluripotent stem cells of a patient with early-onset CMT carrying a novel homozygous nonsense mutation in NEFL . Quantitative PCR, protein analytics, immunocytochemistry, electron microscopy, and single-cell transcriptomics were used to investigate patient and control neurons. We show that the recessive nonsense mutation causes a nearly total loss of NEFL messenger RNA (mRNA), leading to the complete absence of NEFL protein in patient's cultured neurons. Yet the cultured neurons were able to differentiate and form neuronal networks and neurofilaments. Single-neuron gene expression fingerprinting pinpointed NEFL as the most downregulated gene in the patient neurons and provided data of intermediate filament transcript abundancy and dynamics in cultured neurons. Blocking of nonsense-mediated decay partially rescued the loss of NEFL mRNA. The strict neuronal specificity of neurofilament has hindered the mechanistic studies of recessive NEFL nonsense mutations. Here, we show that such mutation leads to the absence of NEFL, causing childhood-onset neuropathy through a loss-of-function mechanism. We propose that the neurofilament accumulation, a common feature of many neurodegenerative diseases, mimics the absence of NEFL seen in recessive CMT if aggregation prevents the proper localization of wild-type NEFL in neurons. Our results suggest that the removal of NEFL as a proposed treatment option is harmful in humans.

A reanalysis of "Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons".

PubMed

Engelken, Rainer; Farkhooi, Farzad; Hansel, David; van Vreeswijk, Carl; Wolf, Fred

2016-01-01

Neuronal activity in the central nervous system varies strongly in time and across neuronal populations. It is a longstanding proposal that such fluctuations generically arise from chaotic network dynamics. Various theoretical studies predict that the rich dynamics of rate models operating in the chaotic regime can subserve circuit computation and learning. Neurons in the brain, however, communicate via spikes and it is a theoretical challenge to obtain similar rate fluctuations in networks of spiking neuron models. A recent study investigated spiking balanced networks of leaky integrate and fire (LIF) neurons and compared their dynamics to a matched rate network with identical topology, where single unit input-output functions were chosen from isolated LIF neurons receiving Gaussian white noise input. A mathematical analogy between the chaotic instability in networks of rate units and the spiking network dynamics was proposed. Here we revisit the behavior of the spiking LIF networks and these matched rate networks. We find expected hallmarks of a chaotic instability in the rate network: For supercritical coupling strength near the transition point, the autocorrelation time diverges. For subcritical coupling strengths, we observe critical slowing down in response to small external perturbations. In the spiking network, we found in contrast that the timescale of the autocorrelations is insensitive to the coupling strength and that rate deviations resulting from small input perturbations rapidly decay. The decay speed even accelerates for increasing coupling strength. In conclusion, our reanalysis demonstrates fundamental differences between the behavior of pulse-coupled spiking LIF networks and rate networks with matched topology and input-output function. In particular there is no indication of a corresponding chaotic instability in the spiking network.

Natural image sequences constrain dynamic receptive fields and imply a sparse code.

PubMed

Häusler, Chris; Susemihl, Alex; Nawrot, Martin P

2013-11-06

In their natural environment, animals experience a complex and dynamic visual scenery. Under such natural stimulus conditions, neurons in the visual cortex employ a spatially and temporally sparse code. For the input scenario of natural still images, previous work demonstrated that unsupervised feature learning combined with the constraint of sparse coding can predict physiologically measured receptive fields of simple cells in the primary visual cortex. This convincingly indicated that the mammalian visual system is adapted to the natural spatial input statistics. Here, we extend this approach to the time domain in order to predict dynamic receptive fields that can account for both spatial and temporal sparse activation in biological neurons. We rely on temporal restricted Boltzmann machines and suggest a novel temporal autoencoding training procedure. When tested on a dynamic multi-variate benchmark dataset this method outperformed existing models of this class. Learning features on a large dataset of natural movies allowed us to model spatio-temporal receptive fields for single neurons. They resemble temporally smooth transformations of previously obtained static receptive fields and are thus consistent with existing theories. A neuronal spike response model demonstrates how the dynamic receptive field facilitates temporal and population sparseness. We discuss the potential mechanisms and benefits of a spatially and temporally sparse representation of natural visual input. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Rotational dynamics of cargos at pauses during axonal transport.

PubMed

Gu, Yan; Sun, Wei; Wang, Gufeng; Jeftinija, Ksenija; Jeftinija, Srdija; Fang, Ning

2012-01-01

Direct visualization of axonal transport in live neurons is essential for our understanding of the neuronal functions and the working mechanisms of microtubule-based motor proteins. Here we use the high-speed single particle orientation and rotational tracking technique to directly visualize the rotational dynamics of cargos in both active directional transport and pausing stages of axonal transport, with a temporal resolution of 2 ms. Both long and short pauses are imaged, and the correlations between the pause duration, the rotational behaviour of the cargo at the pause, and the moving direction after the pause are established. Furthermore, the rotational dynamics leading to switching tracks are visualized in detail. These first-time observations of cargo's rotational dynamics provide new insights on how kinesin and dynein motors take the cargo through the alternating stages of active directional transport and pause.

A chimeric path to neuronal synchronization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Essaki Arumugam, Easwara Moorthy; Spano, Mark L.

2015-01-15

Synchronization of neuronal activity is associated with neurological disorders such as epilepsy. This process of neuronal synchronization is not fully understood. To further our understanding, we have experimentally studied the progression of this synchronization from normal neuronal firing to full synchronization. We implemented nine FitzHugh-Nagumo neurons (a simplified Hodgkin-Huxley model) via discrete electronics. For different coupling parameters (synaptic strengths), the neurons in the ring were either unsynchronized or completely synchronized when locally coupled in a ring. When a single long-range connection (nonlocal coupling) was introduced, an intermediate state known as a chimera appeared. The results indicate that (1) epilepsy ismore » likely not only a dynamical disease but also a topological disease, strongly tied to the connectivity of the underlying network of neurons, and (2) the synchronization process in epilepsy may not be an “all or none” phenomenon, but can pass through an intermediate stage (chimera)« less

A chimeric path to neuronal synchronization

NASA Astrophysics Data System (ADS)

Essaki Arumugam, Easwara Moorthy; Spano, Mark L.

2015-01-01

Synchronization of neuronal activity is associated with neurological disorders such as epilepsy. This process of neuronal synchronization is not fully understood. To further our understanding, we have experimentally studied the progression of this synchronization from normal neuronal firing to full synchronization. We implemented nine FitzHugh-Nagumo neurons (a simplified Hodgkin-Huxley model) via discrete electronics. For different coupling parameters (synaptic strengths), the neurons in the ring were either unsynchronized or completely synchronized when locally coupled in a ring. When a single long-range connection (nonlocal coupling) was introduced, an intermediate state known as a chimera appeared. The results indicate that (1) epilepsy is likely not only a dynamical disease but also a topological disease, strongly tied to the connectivity of the underlying network of neurons, and (2) the synchronization process in epilepsy may not be an "all or none" phenomenon, but can pass through an intermediate stage (chimera).

Biomechanics of Single Cortical Neurons

PubMed Central

Bernick, Kristin B.; Prevost, Thibault P.; Suresh, Subra; Socrate, Simona

2011-01-01

This study presents experimental results and computational analysis of the large strain dynamic behavior of single neurons in vitro with the objective of formulating a novel quantitative framework for the biomechanics of cortical neurons. Relying on the atomic force microscopy (AFM) technique, novel testing protocols are developed to enable the characterization of neural soma deformability over a range of indentation rates spanning three orders of magnitude – 10, 1, and 0.1 μm/s. Modified spherical AFM probes were utilized to compress the cell bodies of neonatal rat cortical neurons in load, unload, reload and relaxation conditions. The cell response showed marked hysteretic features, strong non-linearities, and substantial time/rate dependencies. The rheological data were complemented with geometrical measurements of cell body morphology, i.e. cross-diameter and height estimates. A constitutive model, validated by the present experiments, is proposed to quantify the mechanical behavior of cortical neurons. The model aimed to correlate empirical findings with measurable degrees of (hyper-) elastic resilience and viscosity at the cell level. The proposed formulation, predicated upon previous constitutive model developments undertaken at the cortical tissue level, was implemented into a three-dimensional finite element framework. The simulated cell response was calibrated to the experimental measurements under the selected test conditions, providing a novel single cell model that could form the basis for further refinements. PMID:20971217

A model of metastable dynamics during ongoing and evoked cortical activity

NASA Astrophysics Data System (ADS)

La Camera, Giancarlo

The dynamics of simultaneously recorded spike trains in alert animals often evolve through temporal sequences of metastable states. Little is known about the network mechanisms responsible for the genesis of such sequences, or their potential role in neural coding. In the gustatory cortex of alert rates, state sequences can be observed also in the absence of overt sensory stimulation, and thus form the basis of the so-called `ongoing activity'. This activity is characterized by a partial degree of coordination among neurons, sharp transitions among states, and multi-stability of single neurons' firing rates. A recurrent spiking network model with clustered topology can account for both the spontaneous generation of state sequences and the (network-generated) multi-stability. In the model, each network state results from the activation of specific neural clusters with potentiated intra-cluster connections. A mean field solution of the model shows a large number of stable states, each characterized by a subset of simultaneously active clusters. The firing rate in each cluster during ongoing activity depends on the number of active clusters, so that the same neuron can have different firing rates depending on the state of the network. Because of dense intra-cluster connectivity and recurrent inhibition, in finite networks the stable states lose stability due to finite size effects. Simulations of the dynamics show that the model ensemble activity continuously hops among the different states, reproducing the ongoing dynamics observed in the data. Moreover, when probed with external stimuli, the model correctly predicts the quenching of single neuron multi-stability into bi-stability, the reduction of dimensionality of the population activity, the reduction of trial-to-trial variability, and a potential role for metastable states in the anticipation of expected events. Altogether, these results provide a unified mechanistic model of ongoing and evoked cortical dynamics. NSF IIS-1161852, NIDCD K25-DC013557, NIDCD R01-DC010389.

Clique of Functional Hubs Orchestrates Population Bursts in Developmentally Regulated Neural Networks

PubMed Central

Luccioli, Stefano; Ben-Jacob, Eshel; Barzilai, Ari; Bonifazi, Paolo; Torcini, Alessandro

2014-01-01

It has recently been discovered that single neuron stimulation can impact network dynamics in immature and adult neuronal circuits. Here we report a novel mechanism which can explain in neuronal circuits, at an early stage of development, the peculiar role played by a few specific neurons in promoting/arresting the population activity. For this purpose, we consider a standard neuronal network model, with short-term synaptic plasticity, whose population activity is characterized by bursting behavior. The addition of developmentally inspired constraints and correlations in the distribution of the neuronal connectivities and excitabilities leads to the emergence of functional hub neurons, whose stimulation/deletion is critical for the network activity. Functional hubs form a clique, where a precise sequential activation of the neurons is essential to ignite collective events without any need for a specific topological architecture. Unsupervised time-lagged firings of supra-threshold cells, in connection with coordinated entrainments of near-threshold neurons, are the key ingredients to orchestrate population activity. PMID:25255443

Dynamics of Propofol-Induced Loss of Consciousness Across Primate Neocortex.

PubMed

Ishizawa, Yumiko; Ahmed, Omar J; Patel, Shaun R; Gale, John T; Sierra-Mercado, Demetrio; Brown, Emery N; Eskandar, Emad N

2016-07-20

The precise neural mechanisms underlying transitions between consciousness and anesthetic-induced unconsciousness remain unclear. Here, we studied intracortical neuronal dynamics leading to propofol-induced unconsciousness by recording single-neuron activity and local field potentials directly in the functionally interconnecting somatosensory (S1) and frontal ventral premotor (PMv) network during a gradual behavioral transition from full alertness to loss of consciousness (LOC) and on through a deeper anesthetic level. Macaque monkeys were trained for a behavioral task designed to determine the trial-by-trial alertness and neuronal response to tactile and auditory stimulation. We show that disruption of coherent beta oscillations between S1 and PMv preceded, but did not coincide with, the LOC. LOC appeared to correspond to pronounced but brief gamma-/high-beta-band oscillations (lasting ∼3 min) in PMv, followed by a gamma peak in S1. We also demonstrate that the slow oscillations appeared after LOC in S1 and then in PMv after a delay, together suggesting that neuronal dynamics are very different across S1 versus PMv during LOC. Finally, neurons in both S1 and PMv transition from responding to bimodal (tactile and auditory) stimulation before LOC to only tactile modality during unconsciousness, consistent with an inhibition of multisensory integration in this network. Our results show that propofol-induced LOC is accompanied by spatiotemporally distinct oscillatory neuronal dynamics across the somatosensory and premotor network and suggest that a transitional state from wakefulness to unconsciousness is not a continuous process, but rather a series of discrete neural changes. How information is processed by the brain during awake and anesthetized states and, crucially, during the transition is not clearly understood. We demonstrate that neuronal dynamics are very different within an interconnecting cortical network (primary somatosensory and frontal premotor area) during the loss of consciousness (LOC) induced by propofol in nonhuman primates. Coherent beta oscillations between these regions are disrupted before LOC. Pronounced but brief gamma-band oscillations appear to correspond to LOC. In addition, neurons in both of these cortices transition from responding to both tactile and auditory stimulation before LOC to only tactile modality during unconsciousness. We demonstrate that propofol-induced LOC is accompanied by spatiotemporally distinctive neuronal dynamics in this network with concurrent changes in multisensory processing. Copyright © 2016 the authors 0270-6474/16/367718-09$15.00/0.

Global dynamics of a stochastic neuronal oscillator

NASA Astrophysics Data System (ADS)

Yamanobe, Takanobu

2013-11-01

Nonlinear oscillators have been used to model neurons that fire periodically in the absence of input. These oscillators, which are called neuronal oscillators, share some common response structures with other biological oscillations such as cardiac cells. In this study, we analyze the dependence of the global dynamics of an impulse-driven stochastic neuronal oscillator on the relaxation rate to the limit cycle, the strength of the intrinsic noise, and the impulsive input parameters. To do this, we use a Markov operator that both reflects the density evolution of the oscillator and is an extension of the phase transition curve, which describes the phase shift due to a single isolated impulse. Previously, we derived the Markov operator for the finite relaxation rate that describes the dynamics of the entire phase plane. Here, we construct a Markov operator for the infinite relaxation rate that describes the stochastic dynamics restricted to the limit cycle. In both cases, the response of the stochastic neuronal oscillator to time-varying impulses is described by a product of Markov operators. Furthermore, we calculate the number of spikes between two consecutive impulses to relate the dynamics of the oscillator to the number of spikes per unit time and the interspike interval density. Specifically, we analyze the dynamics of the number of spikes per unit time based on the properties of the Markov operators. Each Markov operator can be decomposed into stationary and transient components based on the properties of the eigenvalues and eigenfunctions. This allows us to evaluate the difference in the number of spikes per unit time between the stationary and transient responses of the oscillator, which we show to be based on the dependence of the oscillator on past activity. Our analysis shows how the duration of the past neuronal activity depends on the relaxation rate, the noise strength, and the impulsive input parameters.

Global dynamics of a stochastic neuronal oscillator.

PubMed

Yamanobe, Takanobu

2013-11-01

Nonlinear oscillators have been used to model neurons that fire periodically in the absence of input. These oscillators, which are called neuronal oscillators, share some common response structures with other biological oscillations such as cardiac cells. In this study, we analyze the dependence of the global dynamics of an impulse-driven stochastic neuronal oscillator on the relaxation rate to the limit cycle, the strength of the intrinsic noise, and the impulsive input parameters. To do this, we use a Markov operator that both reflects the density evolution of the oscillator and is an extension of the phase transition curve, which describes the phase shift due to a single isolated impulse. Previously, we derived the Markov operator for the finite relaxation rate that describes the dynamics of the entire phase plane. Here, we construct a Markov operator for the infinite relaxation rate that describes the stochastic dynamics restricted to the limit cycle. In both cases, the response of the stochastic neuronal oscillator to time-varying impulses is described by a product of Markov operators. Furthermore, we calculate the number of spikes between two consecutive impulses to relate the dynamics of the oscillator to the number of spikes per unit time and the interspike interval density. Specifically, we analyze the dynamics of the number of spikes per unit time based on the properties of the Markov operators. Each Markov operator can be decomposed into stationary and transient components based on the properties of the eigenvalues and eigenfunctions. This allows us to evaluate the difference in the number of spikes per unit time between the stationary and transient responses of the oscillator, which we show to be based on the dependence of the oscillator on past activity. Our analysis shows how the duration of the past neuronal activity depends on the relaxation rate, the noise strength, and the impulsive input parameters.

Exploring the brain on multiple scales with correlative two-photon and light sheet microscopy

NASA Astrophysics Data System (ADS)

Silvestri, Ludovico; Allegra Mascaro, Anna Letizia; Costantini, Irene; Sacconi, Leonardo; Pavone, Francesco S.

2014-02-01

One of the unique features of the brain is that its activity cannot be framed in a single spatio-temporal scale, but rather spans many orders of magnitude both in space and time. A single imaging technique can reveal only a small part of this complex machinery. To obtain a more comprehensive view of brain functionality, complementary approaches should be combined into a correlative framework. Here, we describe a method to integrate data from in vivo two-photon fluorescence imaging and ex vivo light sheet microscopy, taking advantage of blood vessels as reference chart. We show how the apical dendritic arbor of a single cortical pyramidal neuron imaged in living thy1-GFP-M mice can be found in the large-scale brain reconstruction obtained with light sheet microscopy. Starting from the apical portion, the whole pyramidal neuron can then be segmented. The correlative approach presented here allows contextualizing within a three-dimensional anatomic framework the neurons whose dynamics have been observed with high detail in vivo.

Functional topography of single cortical cells: an intracellular approach combined with optical imaging.

PubMed

Buzás, P; Eysel, U T; Kisvárday, Z F

1998-11-01

Pyramidal cells mediating long-range corticocortical connections have been assumed to play an important role in visual perceptual mechanisms [C.D. Gilbert, Horizontal integration and cortical dynamics, Neuron 9 (1992) 1-13]. However, no information is available as yet on the specificity of individual pyramidal cells with respect to functional maps, e.g., orientation map. Here, we show a combination of techniques with which the functional topography of single pyramidal neurons can be explored in utmost detail. To this end, we used optical imaging of intrinsic signals followed by intracellular recording and staining with biocytin in vivo. The axonal and dendritic trees of the labelled neurons were reconstructed in three dimensions and aligned with corresponding functional orientation maps. The results indicate that, contrary to the sharp orientation tuning of neurons shown by the recorded spike activity, the efferent connections (axon terminal distribution) of the same pyramidal cells were found to terminate at a much broader range of orientations. Copyright 1998 Elsevier Science B.V.

Stable Sequential Activity Underlying the Maintenance of a Precisely Executed Skilled Behavior.

PubMed

Katlowitz, Kalman A; Picardo, Michel A; Long, Michael A

2018-05-21

A vast array of motor skills can be maintained throughout life. Do these behaviors require stability of individual neuron tuning or can the output of a given circuit remain constant despite fluctuations in single cells? This question is difficult to address due to the variability inherent in most motor actions studied in the laboratory. A notable exception, however, is the courtship song of the adult zebra finch, which is a learned, highly precise motor act mediated by orderly dynamics within premotor neurons of the forebrain. By longitudinally tracking the activity of excitatory projection neurons during singing using two-photon calcium imaging, we find that both the number and the precise timing of song-related spiking events remain nearly identical over the span of several weeks to months. These findings demonstrate that learned, complex behaviors can be stabilized by maintaining precise and invariant tuning at the level of single neurons. Copyright © 2018 Elsevier Inc. All rights reserved.

Bursting as a source of non-linear determinism in the firing patterns of nigral dopamine neurons.

PubMed

Jeong, Jaeseung; Shi, Wei-Xing; Hoffman, Ralph; Oh, Jihoon; Gore, John C; Bunney, Benjamin S; Peterson, Bradley S

2012-11-01

Nigral dopamine (DA) neurons in vivo exhibit complex firing patterns consisting of tonic single-spikes and phasic bursts that encode information for certain types of reward-related learning and behavior. Non-linear dynamical analysis has previously demonstrated the presence of a non-linear deterministic structure in complex firing patterns of DA neurons, yet the origin of this non-linear determinism remains unknown. In this study, we hypothesized that bursting activity is the primary source of non-linear determinism in the firing patterns of DA neurons. To test this hypothesis, we investigated the dimension complexity of inter-spike interval data recorded in vivo from bursting and non-bursting DA neurons in the chloral hydrate-anesthetized rat substantia nigra. We found that bursting DA neurons exhibited non-linear determinism in their firing patterns, whereas non-bursting DA neurons showed truly stochastic firing patterns. Determinism was also detected in the isolated burst and inter-burst interval data extracted from firing patterns of bursting neurons. Moreover, less bursting DA neurons in halothane-anesthetized rats exhibited higher dimensional spiking dynamics than do more bursting DA neurons in chloral hydrate-anesthetized rats. These results strongly indicate that bursting activity is the main source of low-dimensional, non-linear determinism in the firing patterns of DA neurons. This finding furthermore suggests that bursts are the likely carriers of meaningful information in the firing activities of DA neurons. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

State Dependency of Chemosensory Coding in the Gustatory Thalamus (VPMpc) of Alert Rats

PubMed Central

Liu, Haixin

2015-01-01

The parvicellular portion of the ventroposteromedial nucleus (VPMpc) is the part of the thalamus that processes gustatory information. Anatomical evidence shows that the VPMpc receives ascending gustatory inputs from the parabrachial nucleus (PbN) in the brainstem and sends projections to the gustatory cortex (GC). Although taste processing in PbN and GC has been the subject of intense investigation in behaving rodents, much less is known on how VPMpc neurons encode gustatory information. Here we present results from single-unit recordings in the VPMpc of alert rats receiving multiple tastants. Thalamic neurons respond to taste with time-varying modulations of firing rates, consistent with those observed in GC and PbN. These responses encode taste quality as well as palatability. Comparing responses to tastants either passively delivered, or self-administered after a cue, unveiled the effects of general expectation on taste processing in VPMpc. General expectation led to an improvement of taste coding by modulating response dynamics, and single neuron ability to encode multiple tastants. Our results demonstrate that the time course of taste coding as well as single neurons' ability to encode for multiple qualities are not fixed but rather can be altered by the state of the animal. Together, the data presented here provide the first description that taste coding in VPMpc is dynamic and state-dependent. SIGNIFICANCE STATEMENT Over the past years, a great deal of attention has been devoted to understanding taste coding in the brainstem and cortex of alert rodents. Thanks to this research, we now know that taste coding is dynamic, distributed, and context-dependent. Alas, virtually nothing is known on how the gustatory thalamus (VPMpc) processes gustatory information in behaving rats. This manuscript investigates taste processing in the VPMpc of behaving rats. Our results show that thalamic neurons encode taste and palatability with time-varying patterns of activity and that thalamic coding of taste is modulated by general expectation. Our data will appeal not only to researchers interested in taste, but also to a broader audience of sensory and systems neuroscientists interested in the thalamocortical system. PMID:26609147

On the Dynamics of the Spontaneous Activity in Neuronal Networks

PubMed Central

Bonifazi, Paolo; Ruaro, Maria Elisabetta; Torre, Vincent

2007-01-01

Most neuronal networks, even in the absence of external stimuli, produce spontaneous bursts of spikes separated by periods of reduced activity. The origin and functional role of these neuronal events are still unclear. The present work shows that the spontaneous activity of two very different networks, intact leech ganglia and dissociated cultures of rat hippocampal neurons, share several features. Indeed, in both networks: i) the inter-spike intervals distribution of the spontaneous firing of single neurons is either regular or periodic or bursting, with the fraction of bursting neurons depending on the network activity; ii) bursts of spontaneous spikes have the same broad distributions of size and duration; iii) the degree of correlated activity increases with the bin width, and the power spectrum of the network firing rate has a 1/f behavior at low frequencies, indicating the existence of long-range temporal correlations; iv) the activity of excitatory synaptic pathways mediated by NMDA receptors is necessary for the onset of the long-range correlations and for the presence of large bursts; v) blockage of inhibitory synaptic pathways mediated by GABAA receptors causes instead an increase in the correlation among neurons and leads to a burst distribution composed only of very small and very large bursts. These results suggest that the spontaneous electrical activity in neuronal networks with different architectures and functions can have very similar properties and common dynamics. PMID:17502919

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays.

PubMed

Gertz, Monica L; Baker, Zachary; Jose, Sharon; Peixoto, Nathalia

2017-05-29

Micro-electrode arrays (MEAs) can be used to investigate drug toxicity, design paradigms for next-generation personalized medicine, and study network dynamics in neuronal cultures. In contrast with more traditional methods, such as patch-clamping, which can only record activity from a single cell, MEAs can record simultaneously from multiple sites in a network, without requiring the arduous task of placing each electrode individually. Moreover, numerous control and stimulation configurations can be easily applied within the same experimental setup, allowing for a broad range of dynamics to be explored. One of the key dynamics of interest in these in vitro studies has been the extent to which cultured networks display properties indicative of learning. Mouse neuronal cells cultured on MEAs display an increase in response following training induced by electrical stimulation. This protocol demonstrates how to culture neuronal cells on MEAs; successfully record from over 95% of the plated dishes; establish a protocol to train the networks to respond to patterns of stimulation; and sort, plot, and interpret the results from such experiments. The use of a proprietary system for stimulating and recording neuronal cultures is demonstrated. Software packages are also used to sort neuronal units. A custom-designed graphical user interface is used to visualize post-stimulus time histograms, inter-burst intervals, and burst duration, as well as to compare the cellular response to stimulation before and after a training protocol. Finally, representative results and future directions of this research effort are discussed.

Sensory Cortical Population Dynamics Uniquely Track Behavior across Learning and Extinction

PubMed Central

Katz, Donald B.

2014-01-01

Neural responses in many cortical regions encode information relevant to behavior: information that necessarily changes as that behavior changes with learning. Although such responses are reasonably theorized to be related to behavior causation, the true nature of that relationship cannot be clarified by simple learning studies, which show primarily that responses change with experience. Neural activity that truly tracks behavior (as opposed to simply changing with experience) will not only change with learning but also change back when that learning is extinguished. Here, we directly probed for this pattern, recording the activity of ensembles of gustatory cortical single neurons as rats that normally consumed sucrose avidly were trained first to reject it (i.e., conditioned taste aversion learning) and then to enjoy it again (i.e., extinction), all within 49 h. Both learning and extinction altered cortical responses, consistent with the suggestion (based on indirect evidence) that extinction is a novel form of learning. But despite the fact that, as expected, postextinction single-neuron responses did not resemble “naive responses,” ensemble response dynamics changed with learning and reverted with extinction: both the speed of stimulus processing and the relationships among ensemble responses to the different stimuli tracked behavioral relevance. These data suggest that population coding is linked to behavior with a fidelity that single-neuron coding is not. PMID:24453316

A Complex-Valued Firing-Rate Model That Approximates the Dynamics of Spiking Networks

PubMed Central

Schaffer, Evan S.; Ostojic, Srdjan; Abbott, L. F.

2013-01-01

Firing-rate models provide an attractive approach for studying large neural networks because they can be simulated rapidly and are amenable to mathematical analysis. Traditional firing-rate models assume a simple form in which the dynamics are governed by a single time constant. These models fail to replicate certain dynamic features of populations of spiking neurons, especially those involving synchronization. We present a complex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation and apply it to the linear, quadratic and exponential integrate-and-fire models. Despite being almost as simple as a traditional firing-rate description, this model can reproduce firing-rate dynamics due to partial synchronization of the action potentials in a spiking model, and it successfully predicts the transition to spike synchronization in networks of coupled excitatory and inhibitory neurons. PMID:24204236

Two-photon imaging and analysis of neural network dynamics

NASA Astrophysics Data System (ADS)

Lütcke, Henry; Helmchen, Fritjof

2011-08-01

The glow of a starry night sky, the smell of a freshly brewed cup of coffee or the sound of ocean waves breaking on the beach are representations of the physical world that have been created by the dynamic interactions of thousands of neurons in our brains. How the brain mediates perceptions, creates thoughts, stores memories and initiates actions remains one of the most profound puzzles in biology, if not all of science. A key to a mechanistic understanding of how the nervous system works is the ability to measure and analyze the dynamics of neuronal networks in the living organism in the context of sensory stimulation and behavior. Dynamic brain properties have been fairly well characterized on the microscopic level of individual neurons and on the macroscopic level of whole brain areas largely with the help of various electrophysiological techniques. However, our understanding of the mesoscopic level comprising local populations of hundreds to thousands of neurons (so-called 'microcircuits') remains comparably poor. Predominantly, this has been due to the technical difficulties involved in recording from large networks of neurons with single-cell spatial resolution and near-millisecond temporal resolution in the brain of living animals. In recent years, two-photon microscopy has emerged as a technique which meets many of these requirements and thus has become the method of choice for the interrogation of local neural circuits. Here, we review the state-of-research in the field of two-photon imaging of neuronal populations, covering the topics of microscope technology, suitable fluorescent indicator dyes, staining techniques, and in particular analysis techniques for extracting relevant information from the fluorescence data. We expect that functional analysis of neural networks using two-photon imaging will help to decipher fundamental operational principles of neural microcircuits.

Vertically aligned carbon nanofiber as nano-neuron interface for monitoring neural function

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ericson, Milton Nance; McKnight, Timothy E; Melechko, Anatoli Vasilievich

2012-01-01

Neural chips, which are capable of simultaneous, multi-site neural recording and stimulation, have been used to detect and modulate neural activity for almost 30 years. As a neural interface, neural chips provide dynamic functional information for neural decoding and neural control. By improving sensitivity and spatial resolution, nano-scale electrodes may revolutionize neural detection and modulation at cellular and molecular levels as nano-neuron interfaces. We developed a carbon-nanofiber neural chip with lithographically defined arrays of vertically aligned carbon nanofiber electrodes and demonstrated its capability of both stimulating and monitoring electrophysiological signals from brain tissues in vitro and monitoring dynamic information ofmore » neuroplasticity. This novel nano-neuron interface can potentially serve as a precise, informative, biocompatible, and dual-mode neural interface for monitoring of both neuroelectrical and neurochemical activity at the single cell level and even inside the cell.« less

Magnetic skyrmion-based artificial neuron device

NASA Astrophysics Data System (ADS)

Li, Sai; Kang, Wang; Huang, Yangqi; Zhang, Xichao; Zhou, Yan; Zhao, Weisheng

2017-08-01

Neuromorphic computing, inspired by the biological nervous system, has attracted considerable attention. Intensive research has been conducted in this field for developing artificial synapses and neurons, attempting to mimic the behaviors of biological synapses and neurons, which are two basic elements of a human brain. Recently, magnetic skyrmions have been investigated as promising candidates in neuromorphic computing design owing to their topologically protected particle-like behaviors, nanoscale size and low driving current density. In one of our previous studies, a skyrmion-based artificial synapse was proposed, with which both short-term plasticity and long-term potentiation functions have been demonstrated. In this work, we further report on a skyrmion-based artificial neuron by exploiting the tunable current-driven skyrmion motion dynamics, mimicking the leaky-integrate-fire function of a biological neuron. With a simple single-device implementation, this proposed artificial neuron may enable us to build a dense and energy-efficient spiking neuromorphic computing system.

Propagating waves can explain irregular neural dynamics.

PubMed

Keane, Adam; Gong, Pulin

2015-01-28

Cortical neurons in vivo fire quite irregularly. Previous studies about the origin of such irregular neural dynamics have given rise to two major models: a balanced excitation and inhibition model, and a model of highly synchronized synaptic inputs. To elucidate the network mechanisms underlying synchronized synaptic inputs and account for irregular neural dynamics, we investigate a spatially extended, conductance-based spiking neural network model. We show that propagating wave patterns with complex dynamics emerge from the network model. These waves sweep past neurons, to which they provide highly synchronized synaptic inputs. On the other hand, these patterns only emerge from the network with balanced excitation and inhibition; our model therefore reconciles the two major models of irregular neural dynamics. We further demonstrate that the collective dynamics of propagating wave patterns provides a mechanistic explanation for a range of irregular neural dynamics, including the variability of spike timing, slow firing rate fluctuations, and correlated membrane potential fluctuations. In addition, in our model, the distributions of synaptic conductance and membrane potential are non-Gaussian, consistent with recent experimental data obtained using whole-cell recordings. Our work therefore relates the propagating waves that have been widely observed in the brain to irregular neural dynamics. These results demonstrate that neural firing activity, although appearing highly disordered at the single-neuron level, can form dynamical coherent structures, such as propagating waves at the population level. Copyright © 2015 the authors 0270-6474/15/351591-15$15.00/0.

BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies.

PubMed

Wan, Yinan; Long, Fuhui; Qu, Lei; Xiao, Hang; Hawrylycz, Michael; Myers, Eugene W; Peng, Hanchuan

2015-10-01

Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.

Effects of Morphology Constraint on Electrophysiological Properties of Cortical Neurons

NASA Astrophysics Data System (ADS)

Zhu, Geng; Du, Liping; Jin, Lei; Offenhäusser, Andreas

2016-04-01

There is growing interest in engineering nerve cells in vitro to control architecture and connectivity of cultured neuronal networks or to build neuronal networks with predictable computational function. Pattern technologies, such as micro-contact printing, have been developed to design ordered neuronal networks. However, electrophysiological characteristics of the single patterned neuron haven’t been reported. Here, micro-contact printing, using polyolefine polymer (POP) stamps with high resolution, was employed to grow cortical neurons in a designed structure. The results demonstrated that the morphology of patterned neurons was well constrained, and the number of dendrites was decreased to be about 2. Our electrophysiological results showed that alterations of dendritic morphology affected firing patterns of neurons and neural excitability. When stimulated by current, though both patterned and un-patterned neurons presented regular spiking, the dynamics and strength of the response were different. The un-patterned neurons exhibited a monotonically increasing firing frequency in response to injected current, while the patterned neurons first exhibited frequency increase and then a slow decrease. Our findings indicate that the decrease in dendritic complexity of cortical neurons will influence their electrophysiological characteristics and alter their information processing activity, which could be considered when designing neuronal circuitries.

Plasticity in single neuron and circuit computations

NASA Astrophysics Data System (ADS)

Destexhe, Alain; Marder, Eve

2004-10-01

Plasticity in neural circuits can result from alterations in synaptic strength or connectivity, as well as from changes in the excitability of the neurons themselves. To better understand the role of plasticity in the brain, we need to establish how brain circuits work and the kinds of computations that different circuit structures achieve. By linking theoretical and experimental studies, we are beginning to reveal the consequences of plasticity mechanisms for network dynamics, in both simple invertebrate circuits and the complex circuits of mammalian cerebral cortex.

Spontaneous neuronal activity as a self-organized critical phenomenon

NASA Astrophysics Data System (ADS)

de Arcangelis, L.; Herrmann, H. J.

2013-01-01

Neuronal avalanches are a novel mode of activity in neuronal networks, experimentally found in vitro and in vivo, and exhibit a robust critical behaviour. Avalanche activity can be modelled within the self-organized criticality framework, including threshold firing, refractory period and activity-dependent synaptic plasticity. The size and duration distributions confirm that the system acts in a critical state, whose scaling behaviour is very robust. Next, we discuss the temporal organization of neuronal avalanches. This is given by the alternation between states of high and low activity, named up and down states, leading to a balance between excitation and inhibition controlled by a single parameter. During these periods both the single neuron state and the network excitability level, keeping memory of past activity, are tuned by homeostatic mechanisms. Finally, we verify if a system with no characteristic response can ever learn in a controlled and reproducible way. Learning in the model occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. Learning is a truly collective process and the learning dynamics exhibits universal features. Even complex rules can be learned provided that the plastic adaptation is sufficiently slow.

GABA and Gap Junctions in the Development of Synchronized Activity in Human Pluripotent Stem Cell-Derived Neural Networks

PubMed Central

Mäkinen, Meeri Eeva-Liisa; Ylä-Outinen, Laura; Narkilahti, Susanna

2018-01-01

The electrical activity of the brain arises from single neurons communicating with each other. However, how single neurons interact during early development to give rise to neural network activity remains poorly understood. We studied the emergence of synchronous neural activity in human pluripotent stem cell (hPSC)-derived neural networks simultaneously on a single-neuron level and network level. The contribution of gamma-aminobutyric acid (GABA) and gap junctions to the development of synchronous activity in hPSC-derived neural networks was studied with GABA agonist and antagonist and by blocking gap junctional communication, respectively. We characterized the dynamics of the network-wide synchrony in hPSC-derived neural networks with high spatial resolution (calcium imaging) and temporal resolution microelectrode array (MEA). We found that the emergence of synchrony correlates with a decrease in very strong GABA excitation. However, the synchronous network was found to consist of a heterogeneous mixture of synchronously active cells with variable responses to GABA, GABA agonists and gap junction blockers. Furthermore, we show how single-cell distributions give rise to the network effect of GABA, GABA agonists and gap junction blockers. Finally, based on our observations, we suggest that the earliest form of synchronous neuronal activity depends on gap junctions and a decrease in GABA induced depolarization but not on GABAA mediated signaling. PMID:29559893

Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice

PubMed Central

Cavanagh, Sean E; Wallis, Joni D; Kennerley, Steven W; Hunt, Laurence T

2016-01-01

Correlates of value are routinely observed in the prefrontal cortex (PFC) during reward-guided decision making. In previous work (Hunt et al., 2015), we argued that PFC correlates of chosen value are a consequence of varying rates of a dynamical evidence accumulation process. Yet within PFC, there is substantial variability in chosen value correlates across individual neurons. Here we show that this variability is explained by neurons having different temporal receptive fields of integration, indexed by examining neuronal spike rate autocorrelation structure whilst at rest. We find that neurons with protracted resting temporal receptive fields exhibit stronger chosen value correlates during choice. Within orbitofrontal cortex, these neurons also sustain coding of chosen value from choice through the delivery of reward, providing a potential neural mechanism for maintaining predictions and updating stored values during learning. These findings reveal that within PFC, variability in temporal specialisation across neurons predicts involvement in specific decision-making computations. DOI: http://dx.doi.org/10.7554/eLife.18937.001 PMID:27705742

Spatial and Temporal Regulation of Receptor Endocytosis in Neuronal Dendrites Revealed by Imaging of Single Vesicle Formation.

PubMed

Rosendale, Morgane; Jullié, Damien; Choquet, Daniel; Perrais, David

2017-02-21

Endocytosis in neuronal dendrites is known to play a critical role in synaptic transmission and plasticity such as long-term depression (LTD). However, the inability to detect endocytosis directly in living neurons has hampered studies of its dynamics and regulation. Here, we visualized the formation of individual endocytic vesicles containing pHluorin-tagged receptors with high temporal resolution in the dendrites of cultured hippocampal neurons. We show that transferrin receptors (TfRs) are constitutively internalized at optically static clathrin-coated structures. These structures are slightly enriched near synapses that represent preferential sites for the endocytosis of postsynaptic AMPA-type receptors (AMPARs), but not for non-synaptic TfRs. Moreover, the frequency of AMPAR endocytosis events increases after the induction of NMDAR-dependent chemical LTD, but the activity of perisynaptic endocytic zones is not differentially regulated. We conclude that endocytosis is a highly dynamic and stereotyped process that internalizes receptors in precisely localized endocytic zones. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

A model of cerebellar computations for dynamical state estimation

NASA Technical Reports Server (NTRS)

Paulin, M. G.; Hoffman, L. F.; Assad, C.

2001-01-01

The cerebellum is a neural structure that is essential for agility in vertebrate movements. Its contribution to motor control appears to be due to a fundamental role in dynamical state estimation, which also underlies its role in various non-motor tasks. Single spikes in vestibular sensory neurons carry information about head state. We show how computations for optimal dynamical state estimation may be accomplished when signals are encoded in spikes. This provides a novel way to design dynamical state estimators, and a novel way to interpret the structure and function of the cerebellum.

1D-3D hybrid modeling-from multi-compartment models to full resolution models in space and time.

PubMed

Grein, Stephan; Stepniewski, Martin; Reiter, Sebastian; Knodel, Markus M; Queisser, Gillian

2014-01-01

Investigation of cellular and network dynamics in the brain by means of modeling and simulation has evolved into a highly interdisciplinary field, that uses sophisticated modeling and simulation approaches to understand distinct areas of brain function. Depending on the underlying complexity, these models vary in their level of detail, in order to cope with the attached computational cost. Hence for large network simulations, single neurons are typically reduced to time-dependent signal processors, dismissing the spatial aspect of each cell. For single cell or networks with relatively small numbers of neurons, general purpose simulators allow for space and time-dependent simulations of electrical signal processing, based on the cable equation theory. An emerging field in Computational Neuroscience encompasses a new level of detail by incorporating the full three-dimensional morphology of cells and organelles into three-dimensional, space and time-dependent, simulations. While every approach has its advantages and limitations, such as computational cost, integrated and methods-spanning simulation approaches, depending on the network size could establish new ways to investigate the brain. In this paper we present a hybrid simulation approach, that makes use of reduced 1D-models using e.g., the NEURON simulator-which couples to fully resolved models for simulating cellular and sub-cellular dynamics, including the detailed three-dimensional morphology of neurons and organelles. In order to couple 1D- and 3D-simulations, we present a geometry-, membrane potential- and intracellular concentration mapping framework, with which graph- based morphologies, e.g., in the swc- or hoc-format, are mapped to full surface and volume representations of the neuron and computational data from 1D-simulations can be used as boundary conditions for full 3D simulations and vice versa. Thus, established models and data, based on general purpose 1D-simulators, can be directly coupled to the emerging field of fully resolved, highly detailed 3D-modeling approaches. We present the developed general framework for 1D/3D hybrid modeling and apply it to investigate electrically active neurons and their intracellular spatio-temporal calcium dynamics.

1D-3D hybrid modeling—from multi-compartment models to full resolution models in space and time

PubMed Central

Grein, Stephan; Stepniewski, Martin; Reiter, Sebastian; Knodel, Markus M.; Queisser, Gillian

2014-01-01

Investigation of cellular and network dynamics in the brain by means of modeling and simulation has evolved into a highly interdisciplinary field, that uses sophisticated modeling and simulation approaches to understand distinct areas of brain function. Depending on the underlying complexity, these models vary in their level of detail, in order to cope with the attached computational cost. Hence for large network simulations, single neurons are typically reduced to time-dependent signal processors, dismissing the spatial aspect of each cell. For single cell or networks with relatively small numbers of neurons, general purpose simulators allow for space and time-dependent simulations of electrical signal processing, based on the cable equation theory. An emerging field in Computational Neuroscience encompasses a new level of detail by incorporating the full three-dimensional morphology of cells and organelles into three-dimensional, space and time-dependent, simulations. While every approach has its advantages and limitations, such as computational cost, integrated and methods-spanning simulation approaches, depending on the network size could establish new ways to investigate the brain. In this paper we present a hybrid simulation approach, that makes use of reduced 1D-models using e.g., the NEURON simulator—which couples to fully resolved models for simulating cellular and sub-cellular dynamics, including the detailed three-dimensional morphology of neurons and organelles. In order to couple 1D- and 3D-simulations, we present a geometry-, membrane potential- and intracellular concentration mapping framework, with which graph- based morphologies, e.g., in the swc- or hoc-format, are mapped to full surface and volume representations of the neuron and computational data from 1D-simulations can be used as boundary conditions for full 3D simulations and vice versa. Thus, established models and data, based on general purpose 1D-simulators, can be directly coupled to the emerging field of fully resolved, highly detailed 3D-modeling approaches. We present the developed general framework for 1D/3D hybrid modeling and apply it to investigate electrically active neurons and their intracellular spatio-temporal calcium dynamics. PMID:25120463

Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface

NASA Astrophysics Data System (ADS)

Sachs, Nicholas A.; Ruiz-Torres, Ricardo; Perreault, Eric J.; Miller, Lee E.

2016-02-01

Objective. It is quite remarkable that brain machine interfaces (BMIs) can be used to control complex movements with fewer than 100 neurons. Success may be due in part to the limited range of dynamical conditions under which most BMIs are tested. Achieving high-quality control that spans these conditions with a single linear mapping will be more challenging. Even for simple reaching movements, existing BMIs must reduce the stochastic noise of neurons by averaging the control signals over time, instead of over the many neurons that normally control movement. This forces a compromise between a decoder with dynamics allowing rapid movement and one that allows postures to be maintained with little jitter. Our current work presents a method for addressing this compromise, which may also generalize to more highly varied dynamical situations, including movements with more greatly varying speed. Approach. We have developed a system that uses two independent Wiener filters as individual components in a single decoder, one optimized for movement, and the other for postural control. We computed an LDA classifier using the same neural inputs. The decoder combined the outputs of the two filters in proportion to the likelihood assigned by the classifier to each state. Main results. We have performed online experiments with two monkeys using this neural-classifier, dual-state decoder, comparing it to a standard, single-state decoder as well as to a dual-state decoder that switched states automatically based on the cursor’s proximity to a target. The performance of both monkeys using the classifier decoder was markedly better than that of the single-state decoder and comparable to the proximity decoder. Significance. We have demonstrated a novel strategy for dealing with the need to make rapid movements while also maintaining precise cursor control when approaching and stabilizing within targets. Further gains can undoubtedly be realized by optimizing the performance of the individual movement and posture decoders.

Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface.

PubMed

Sachs, Nicholas A; Ruiz-Torres, Ricardo; Perreault, Eric J; Miller, Lee E

2016-02-01

It is quite remarkable that brain machine interfaces (BMIs) can be used to control complex movements with fewer than 100 neurons. Success may be due in part to the limited range of dynamical conditions under which most BMIs are tested. Achieving high-quality control that spans these conditions with a single linear mapping will be more challenging. Even for simple reaching movements, existing BMIs must reduce the stochastic noise of neurons by averaging the control signals over time, instead of over the many neurons that normally control movement. This forces a compromise between a decoder with dynamics allowing rapid movement and one that allows postures to be maintained with little jitter. Our current work presents a method for addressing this compromise, which may also generalize to more highly varied dynamical situations, including movements with more greatly varying speed. We have developed a system that uses two independent Wiener filters as individual components in a single decoder, one optimized for movement, and the other for postural control. We computed an LDA classifier using the same neural inputs. The decoder combined the outputs of the two filters in proportion to the likelihood assigned by the classifier to each state. We have performed online experiments with two monkeys using this neural-classifier, dual-state decoder, comparing it to a standard, single-state decoder as well as to a dual-state decoder that switched states automatically based on the cursor's proximity to a target. The performance of both monkeys using the classifier decoder was markedly better than that of the single-state decoder and comparable to the proximity decoder. We have demonstrated a novel strategy for dealing with the need to make rapid movements while also maintaining precise cursor control when approaching and stabilizing within targets. Further gains can undoubtedly be realized by optimizing the performance of the individual movement and posture decoders.

Modulation of Temporal Precision in Thalamic Population Responses to Natural Visual Stimuli

PubMed Central

Desbordes, Gaëlle; Jin, Jianzhong; Alonso, Jose-Manuel; Stanley, Garrett B.

2010-01-01

Natural visual stimuli have highly structured spatial and temporal properties which influence the way visual information is encoded in the visual pathway. In response to natural scene stimuli, neurons in the lateral geniculate nucleus (LGN) are temporally precise – on a time scale of 10–25 ms – both within single cells and across cells within a population. This time scale, established by non stimulus-driven elements of neuronal firing, is significantly shorter than that of natural scenes, yet is critical for the neural representation of the spatial and temporal structure of the scene. Here, a generalized linear model (GLM) that combines stimulus-driven elements with spike-history dependence associated with intrinsic cellular dynamics is shown to predict the fine timing precision of LGN responses to natural scene stimuli, the corresponding correlation structure across nearby neurons in the population, and the continuous modulation of spike timing precision and latency across neurons. A single model captured the experimentally observed neural response, across different levels of contrasts and different classes of visual stimuli, through interactions between the stimulus correlation structure and the nonlinearity in spike generation and spike history dependence. Given the sensitivity of the thalamocortical synapse to closely timed spikes and the importance of fine timing precision for the faithful representation of natural scenes, the modulation of thalamic population timing over these time scales is likely important for cortical representations of the dynamic natural visual environment. PMID:21151356

Cortical Double-Opponent Cells in Color Perception: Perceptual Scaling and Chromatic Visual Evoked Potentials.

PubMed

Nunez, Valerie; Shapley, Robert M; Gordon, James

2018-01-01

In the early visual cortex V1, there are currently only two known neural substrates for color perception: single-opponent and double-opponent cells. Our aim was to explore the relative contributions of these neurons to color perception. We measured the perceptual scaling of color saturation for equiluminant color checkerboard patterns (designed to stimulate double-opponent neurons preferentially) and uniformly colored squares (designed to stimulate only single-opponent neurons) at several cone contrasts. The spatially integrative responses of single-opponent neurons would produce the same response magnitude for checkerboards as for uniform squares of the same space-averaged cone contrast. However, perceived saturation of color checkerboards was higher than for the corresponding squares. The perceptual results therefore imply that double-opponent cells are involved in color perception of patterns. We also measured the chromatic visual evoked potential (cVEP) produced by the same stimuli; checkerboard cVEPs were much larger than those for corresponding squares, implying that double-opponent cells also contribute to the cVEP response. The total Fourier power of the cVEP grew sublinearly with cone contrast. However, the 6-Hz Fourier component's power grew linearly with contrast-like saturation perception. This may also indicate that cortical coding of color depends on response dynamics.

Cortical Double-Opponent Cells in Color Perception: Perceptual Scaling and Chromatic Visual Evoked Potentials

PubMed Central

Shapley, Robert M.; Gordon, James

2018-01-01

In the early visual cortex V1, there are currently only two known neural substrates for color perception: single-opponent and double-opponent cells. Our aim was to explore the relative contributions of these neurons to color perception. We measured the perceptual scaling of color saturation for equiluminant color checkerboard patterns (designed to stimulate double-opponent neurons preferentially) and uniformly colored squares (designed to stimulate only single-opponent neurons) at several cone contrasts. The spatially integrative responses of single-opponent neurons would produce the same response magnitude for checkerboards as for uniform squares of the same space-averaged cone contrast. However, perceived saturation of color checkerboards was higher than for the corresponding squares. The perceptual results therefore imply that double-opponent cells are involved in color perception of patterns. We also measured the chromatic visual evoked potential (cVEP) produced by the same stimuli; checkerboard cVEPs were much larger than those for corresponding squares, implying that double-opponent cells also contribute to the cVEP response. The total Fourier power of the cVEP grew sublinearly with cone contrast. However, the 6-Hz Fourier component’s power grew linearly with contrast-like saturation perception. This may also indicate that cortical coding of color depends on response dynamics. PMID:29375753

Flow-Based Network Analysis of the Caenorhabditis elegans Connectome

PubMed Central

Bacik, Karol A.; Schaub, Michael T.; Billeh, Yazan N.; Barahona, Mauricio

2016-01-01

We exploit flow propagation on the directed neuronal network of the nematode C. elegans to reveal dynamically relevant features of its connectome. We find flow-based groupings of neurons at different levels of granularity, which we relate to functional and anatomical constituents of its nervous system. A systematic in silico evaluation of the full set of single and double neuron ablations is used to identify deletions that induce the most severe disruptions of the multi-resolution flow structure. Such ablations are linked to functionally relevant neurons, and suggest potential candidates for further in vivo investigation. In addition, we use the directional patterns of incoming and outgoing network flows at all scales to identify flow profiles for the neurons in the connectome, without pre-imposing a priori categories. The four flow roles identified are linked to signal propagation motivated by biological input-response scenarios. PMID:27494178

Dynamic control of glutamatergic synaptic input in the spinal cord by muscarinic receptor subtypes defined using knockout mice.

PubMed

Chen, Shao-Rui; Chen, Hong; Yuan, Wei-Xiu; Wess, Jürgen; Pan, Hui-Lin

2010-12-24

Activation of muscarinic acetylcholine receptors (mAChRs) in the spinal cord inhibits pain transmission. At least three mAChR subtypes (M(2), M(3), and M(4)) are present in the spinal dorsal horn. However, it is not clear how each mAChR subtype contributes to the regulation of glutamatergic input to dorsal horn neurons. We recorded spontaneous excitatory postsynaptic currents (sEPSCs) from lamina II neurons in spinal cord slices from wild-type (WT) and mAChR subtype knock-out (KO) mice. The mAChR agonist oxotremorine-M increased the frequency of glutamatergic sEPSCs in 68.2% neurons from WT mice and decreased the sEPSC frequency in 21.2% neurons. Oxotremorine-M also increased the sEPSC frequency in ∼50% neurons from M(3)-single KO and M(1)/M(3) double-KO mice. In addition, the M(3) antagonist J104129 did not block the stimulatory effect of oxotremorine-M in the majority of neurons from WT mice. Strikingly, in M(5)-single KO mice, oxotremorine-M increased sEPSCs in only 26.3% neurons, and J104129 abolished this effect. In M(2)/M(4) double-KO mice, but not M(2)- or M(4)-single KO mice, oxotremorine-M inhibited sEPSCs in significantly fewer neurons compared with WT mice, and blocking group II/III metabotropic glutamate receptors abolished this effect. The M(2)/M(4) antagonist himbacine either attenuated the inhibitory effect of oxotremorine-M or potentiated the stimulatory effect of oxotremorine-M in WT mice. Our study demonstrates that activation of the M(2) and M(4) receptor subtypes inhibits synaptic glutamate release to dorsal horn neurons. M(5) is the predominant receptor subtype that potentiates glutamatergic synaptic transmission in the spinal cord.

Dynamic Control of Glutamatergic Synaptic Input in the Spinal Cord by Muscarinic Receptor Subtypes Defined Using Knockout Mice*

PubMed Central

Chen, Shao-Rui; Chen, Hong; Yuan, Wei-Xiu; Wess, Jürgen; Pan, Hui-Lin

2010-01-01

Activation of muscarinic acetylcholine receptors (mAChRs) in the spinal cord inhibits pain transmission. At least three mAChR subtypes (M2, M3, and M4) are present in the spinal dorsal horn. However, it is not clear how each mAChR subtype contributes to the regulation of glutamatergic input to dorsal horn neurons. We recorded spontaneous excitatory postsynaptic currents (sEPSCs) from lamina II neurons in spinal cord slices from wild-type (WT) and mAChR subtype knock-out (KO) mice. The mAChR agonist oxotremorine-M increased the frequency of glutamatergic sEPSCs in 68.2% neurons from WT mice and decreased the sEPSC frequency in 21.2% neurons. Oxotremorine-M also increased the sEPSC frequency in ∼50% neurons from M3-single KO and M1/M3 double-KO mice. In addition, the M3 antagonist J104129 did not block the stimulatory effect of oxotremorine-M in the majority of neurons from WT mice. Strikingly, in M5-single KO mice, oxotremorine-M increased sEPSCs in only 26.3% neurons, and J104129 abolished this effect. In M2/M4 double-KO mice, but not M2- or M4-single KO mice, oxotremorine-M inhibited sEPSCs in significantly fewer neurons compared with WT mice, and blocking group II/III metabotropic glutamate receptors abolished this effect. The M2/M4 antagonist himbacine either attenuated the inhibitory effect of oxotremorine-M or potentiated the stimulatory effect of oxotremorine-M in WT mice. Our study demonstrates that activation of the M2 and M4 receptor subtypes inhibits synaptic glutamate release to dorsal horn neurons. M5 is the predominant receptor subtype that potentiates glutamatergic synaptic transmission in the spinal cord. PMID:20940295

Single-unit labeling of medial olivocochlear neurons: the cochlear frequency map for efferent axons.

PubMed

Brown, M Christian

2014-06-01

Medial olivocochlear (MOC) neurons are efferent neurons that project axons from the brain to the cochlea. Their action on outer hair cells reduces the gain of the "cochlear amplifier," which shifts the dynamic range of hearing and reduces the effects of noise masking. The MOC effects in one ear can be elicited by sound in that ipsilateral ear or by sound in the contralateral ear. To study how MOC neurons project onto the cochlea to mediate these effects, single-unit labeling in guinea pigs was used to study the mapping of MOC neurons for neurons responsive to ipsilateral sound vs. those responsive to contralateral sound. MOC neurons were sharply tuned to sound frequency with a well-defined characteristic frequency (CF). However, their labeled termination spans in the organ of Corti ranged from narrow to broad, innervating between 14 and 69 outer hair cells per axon in a "patchy" pattern. For units responsive to ipsilateral sound, the midpoint of innervation was mapped according to CF in a relationship generally similar to, but with more variability than, that of auditory-nerve fibers. Thus, based on CF mappings, most of the MOC terminations miss outer hair cells involved in the cochlear amplifier for their CF, which are located more basally. Compared with ipsilaterally responsive neurons, contralaterally responsive neurons had an apical offset in termination and a larger span of innervation (an average of 10.41% cochlear distance), suggesting that when contralateral sound activates the MOC reflex, the actions are different than those for ipsilateral sound. Copyright © 2014 the American Physiological Society.

Single-unit labeling of medial olivocochlear neurons: the cochlear frequency map for efferent axons

PubMed Central

2014-01-01

Medial olivocochlear (MOC) neurons are efferent neurons that project axons from the brain to the cochlea. Their action on outer hair cells reduces the gain of the “cochlear amplifier,” which shifts the dynamic range of hearing and reduces the effects of noise masking. The MOC effects in one ear can be elicited by sound in that ipsilateral ear or by sound in the contralateral ear. To study how MOC neurons project onto the cochlea to mediate these effects, single-unit labeling in guinea pigs was used to study the mapping of MOC neurons for neurons responsive to ipsilateral sound vs. those responsive to contralateral sound. MOC neurons were sharply tuned to sound frequency with a well-defined characteristic frequency (CF). However, their labeled termination spans in the organ of Corti ranged from narrow to broad, innervating between 14 and 69 outer hair cells per axon in a “patchy” pattern. For units responsive to ipsilateral sound, the midpoint of innervation was mapped according to CF in a relationship generally similar to, but with more variability than, that of auditory-nerve fibers. Thus, based on CF mappings, most of the MOC terminations miss outer hair cells involved in the cochlear amplifier for their CF, which are located more basally. Compared with ipsilaterally responsive neurons, contralaterally responsive neurons had an apical offset in termination and a larger span of innervation (an average of 10.41% cochlear distance), suggesting that when contralateral sound activates the MOC reflex, the actions are different than those for ipsilateral sound. PMID:24598524

Brain-wide neuronal dynamics during motor adaptation in zebrafish

PubMed Central

Ahrens, Misha B; Li, Jennifer M; Orger, Michael B; Robson, Drew N; Schier, Alexander F; Engert, Florian; Portugues, Ruben

2013-01-01

A fundamental question in neuroscience is how entire neural circuits generate behavior and adapt it to changes in sensory feedback. Here we use two-photon calcium imaging to record activity of large populations of neurons at the cellular level throughout the brain of larval zebrafish expressing a genetically-encoded calcium sensor, while the paralyzed animals interact fictively with a virtual environment and rapidly adapt their motor output to changes in visual feedback. We decompose the network dynamics involved in adaptive locomotion into four types of neural response properties, and provide anatomical maps of the corresponding sites. A subset of these signals occurred during behavioral adjustments and are candidates for the functional elements that drive motor learning. Lesions to the inferior olive indicate a specific functional role for olivocerebellar circuitry in adaptive locomotion. This study enables the analysis of brain-wide dynamics at single-cell resolution during behavior. PMID:22622571

In vitro large-scale experimental and theoretical studies for the realization of bi-directional brain-prostheses.

PubMed

Bonifazi, Paolo; Difato, Francesco; Massobrio, Paolo; Breschi, Gian L; Pasquale, Valentina; Levi, Timothée; Goldin, Miri; Bornat, Yannick; Tedesco, Mariateresa; Bisio, Marta; Kanner, Sivan; Galron, Ronit; Tessadori, Jacopo; Taverna, Stefano; Chiappalone, Michela

2013-01-01

Brain-machine interfaces (BMI) were born to control "actions from thoughts" in order to recover motor capability of patients with impaired functional connectivity between the central and peripheral nervous system. The final goal of our studies is the development of a new proof-of-concept BMI-a neuromorphic chip for brain repair-to reproduce the functional organization of a damaged part of the central nervous system. To reach this ambitious goal, we implemented a multidisciplinary "bottom-up" approach in which in vitro networks are the paradigm for the development of an in silico model to be incorporated into a neuromorphic device. In this paper we present the overall strategy and focus on the different building blocks of our studies: (i) the experimental characterization and modeling of "finite size networks" which represent the smallest and most general self-organized circuits capable of generating spontaneous collective dynamics; (ii) the induction of lesions in neuronal networks and the whole brain preparation with special attention on the impact on the functional organization of the circuits; (iii) the first production of a neuromorphic chip able to implement a real-time model of neuronal networks. A dynamical characterization of the finite size circuits with single cell resolution is provided. A neural network model based on Izhikevich neurons was able to replicate the experimental observations. Changes in the dynamics of the neuronal circuits induced by optical and ischemic lesions are presented respectively for in vitro neuronal networks and for a whole brain preparation. Finally the implementation of a neuromorphic chip reproducing the network dynamics in quasi-real time (10 ns precision) is presented.

Theta phase precession and phase selectivity: a cognitive device description of neural coding

NASA Astrophysics Data System (ADS)

Zalay, Osbert C.; Bardakjian, Berj L.

2009-06-01

Information in neural systems is carried by way of phase and rate codes. Neuronal signals are processed through transformative biophysical mechanisms at the cellular and network levels. Neural coding transformations can be represented mathematically in a device called the cognitive rhythm generator (CRG). Incoming signals to the CRG are parsed through a bank of neuronal modes that orchestrate proportional, integrative and derivative transformations associated with neural coding. Mode outputs are then mixed through static nonlinearities to encode (spatio) temporal phase relationships. The static nonlinear outputs feed and modulate a ring device (limit cycle) encoding output dynamics. Small coupled CRG networks were created to investigate coding functionality associated with neuronal phase preference and theta precession in the hippocampus. Phase selectivity was found to be dependent on mode shape and polarity, while phase precession was a product of modal mixing (i.e. changes in the relative contribution or amplitude of mode outputs resulted in shifting phase preference). Nonlinear system identification was implemented to help validate the model and explain response characteristics associated with modal mixing; in particular, principal dynamic modes experimentally derived from a hippocampal neuron were inserted into a CRG and the neuron's dynamic response was successfully cloned. From our results, small CRG networks possessing disynaptic feedforward inhibition in combination with feedforward excitation exhibited frequency-dependent inhibitory-to-excitatory and excitatory-to-inhibitory transitions that were similar to transitions seen in a single CRG with quadratic modal mixing. This suggests nonlinear modal mixing to be a coding manifestation of the effect of network connectivity in shaping system dynamic behavior. We hypothesize that circuits containing disynaptic feedforward inhibition in the nervous system may be candidates for interpreting upstream rate codes to guide downstream processes such as phase precession, because of their demonstrated frequency-selective properties.

Real-time imaging of Huntingtin aggregates diverting target search and gene transcription

PubMed Central

Li, Li; Liu, Hui; Dong, Peng; Li, Dong; Legant, Wesley R; Grimm, Jonathan B; Lavis, Luke D; Betzig, Eric; Tjian, Robert; Liu, Zhe

2016-01-01

The presumptive altered dynamics of transient molecular interactions in vivo contributing to neurodegenerative diseases have remained elusive. Here, using single-molecule localization microscopy, we show that disease-inducing Huntingtin (mHtt) protein fragments display three distinct dynamic states in living cells – 1) fast diffusion, 2) dynamic clustering and 3) stable aggregation. Large, stable aggregates of mHtt exclude chromatin and form 'sticky' decoy traps that impede target search processes of key regulators involved in neurological disorders. Functional domain mapping based on super-resolution imaging reveals an unexpected role of aromatic amino acids in promoting protein-mHtt aggregate interactions. Genome-wide expression analysis and numerical simulation experiments suggest mHtt aggregates reduce transcription factor target site sampling frequency and impair critical gene expression programs in striatal neurons. Together, our results provide insights into how mHtt dynamically forms aggregates and disrupts the finely-balanced gene control mechanisms in neuronal cells. DOI: http://dx.doi.org/10.7554/eLife.17056.001 PMID:27484239

Learning shapes the aversion and reward responses of lateral habenula neurons

PubMed Central

Wang, Daqing; Li, Yi; Feng, Qiru; Guo, Qingchun; Zhou, Jingfeng; Luo, Minmin

2017-01-01

The lateral habenula (LHb) is believed to encode negative motivational values. It remains unknown how LHb neurons respond to various stressors and how learning shapes their responses. Here, we used fiber-photometry and electrophysiology to track LHb neuronal activity in freely-behaving mice. Bitterness, pain, and social attack by aggressors intensively excite LHb neurons. Aversive Pavlovian conditioning induced activation by the aversion-predicting cue in a few trials. The experience of social defeat also conditioned excitatory responses to previously neutral social stimuli. In contrast, fiber photometry and single-unit recordings revealed that sucrose reward inhibited LHb neurons and often produced excitatory rebound. It required prolonged conditioning and high reward probability to induce inhibition by reward-predicting cues. Therefore, LHb neurons can bidirectionally process a diverse array of aversive and reward signals. Importantly, their responses are dynamically shaped by learning, suggesting that the LHb participates in experience-dependent selection of behavioral responses to stressors and rewards. DOI: http://dx.doi.org/10.7554/eLife.23045.001 PMID:28561735

Three dimensional two-photon brain imaging in freely moving mice using a miniature fiber coupled microscope with active axial-scanning.

PubMed

Ozbay, Baris N; Futia, Gregory L; Ma, Ming; Bright, Victor M; Gopinath, Juliet T; Hughes, Ethan G; Restrepo, Diego; Gibson, Emily A

2018-05-25

We present a miniature head mounted two-photon fiber-coupled microscope (2P-FCM) for neuronal imaging with active axial focusing enabled using a miniature electrowetting lens. We show three-dimensional two-photon imaging of neuronal structure and record neuronal activity from GCaMP6s fluorescence from multiple focal planes in a freely-moving mouse. Two-color simultaneous imaging of GFP and tdTomato fluorescence is also demonstrated. Additionally, dynamic control of the axial scanning of the electrowetting lens allows tilting of the focal plane enabling neurons in multiple depths to be imaged in a single plane. Two-photon imaging allows increased penetration depth in tissue yielding a working distance of 450 μm with an additional 180 μm of active axial focusing. The objective NA is 0.45 with a lateral resolution of 1.8 μm, an axial resolution of 10 μm, and a field-of-view of 240 μm diameter. The 2P-FCM has a weight of only ~2.5 g and is capable of repeatable and stable head-attachment. The 2P-FCM with dynamic axial scanning provides a new capability to record from functionally distinct neuronal layers, opening new opportunities in neuroscience research.

Tracking single membrane targets of human autoantibodies using single nanoparticle imaging.

PubMed

Jézéquel, Julie; Dupuis, Julien P; Maingret, François; Groc, Laurent

2018-04-21

Over the past decade, an increasing number of neurological and neuropsychiatric diseases have been associated with the expression of autoantibodies directed against neuronal targets, including neurotransmitter receptors. Although cell-based assays are routinely used in clinics to detect the presence of immunoglobulins, such tests often provide heterogeneous outcomes due to their limited sensitivity, especially at low titers. Thus, there is an urging need for new methods allowing the detection of autoantibodies in seropositive patients that cannot always be clinically distinguished from seronegative ones. Here we make a case for single nanoparticle imaging approaches as a highly sensitive antibody detection assay. Through high-affinity interactions between functionalized nanoparticles and autoantibodies that recognize extracellular domains of membrane neuronal targets, single nanoparticle imaging allows a live surface staining of transmembrane proteins and gives access to their surface dynamics. We show here that this method is well-suited to detect low titers of purified immunoglobulin G (IgG) from first-episode psychotic patients and demonstrate that these IgG target glutamatergic N-Methyl-d-Aspartate receptors (NMDAR) in live hippocampal neurons. The molecular behaviors of targeted membrane receptors were indistinguishable from those of endogenous GluN1 NMDAR subunit and were virtually independent of the IgG concentration present in the sample contrary to classical cell-based assays. Single nanoparticle imaging emerges as a real-time sensitive method to detect IgG directed against neuronal surface proteins, which could be used as an additional step to rule out ambiguous seropositivity diagnoses. Copyright © 2018 Elsevier B.V. All rights reserved.

Computing by robust transience: How the fronto-parietal network performs sequential category-based decisions

PubMed Central

Chaisangmongkon, Warasinee; Swaminathan, Sruthi K.; Freedman, David J.; Wang, Xiao-Jing

2017-01-01

Summary Decision making involves dynamic interplay between internal judgements and external perception, which has been investigated in delayed match-to-category (DMC) experiments. Our analysis of neural recordings shows that, during DMC tasks, LIP and PFC neurons demonstrate mixed, time-varying, and heterogeneous selectivity, but previous theoretical work has not established the link between these neural characteristics and population-level computations. We trained a recurrent network model to perform DMC tasks and found that the model can remarkably reproduce key features of neuronal selectivity at the single-neuron and population levels. Analysis of the trained networks elucidates that robust transient trajectories of the neural population are the key driver of sequential categorical decisions. The directions of trajectories are governed by network self-organized connectivity, defining a ‘neural landscape’, consisting of a task-tailored arrangement of slow states and dynamical tunnels. With this model, we can identify functionally-relevant circuit motifs and generalize the framework to solve other categorization tasks. PMID:28334612

Encoding of reward expectation by monkey anterior insular neurons

PubMed Central

Mizuhiki, Takashi; Richmond, Barry J.

2012-01-01

The insula, a cortical brain region that is known to encode information about autonomic, visceral, and olfactory functions, has recently been shown to encode information during reward-seeking tasks in both single neuronal recording and functional magnetic resonance imaging studies. To examine the reward-related activation, we recorded from 170 single neurons in anterior insula of 2 monkeys during a multitrial reward schedule task, where the monkeys had to complete a schedule of 1, 2, 3, or 4 trials to earn a reward. In one block of trials a visual cue indicated whether a reward would or would not be delivered in the current trial after the monkey successfully detected that a red spot turned green, and in other blocks the visual cue was random with respect to reward delivery. Over one-quarter of 131 responsive neurons were activated when the current trial would (certain or uncertain) be rewarded if performed correctly. These same neurons failed to respond in trials that were certain, as indicated by the cue, to be unrewarded. Another group of neurons responded when the reward was delivered, similar to results reported previously. The dynamics of population activity in anterior insula also showed strong signals related to knowing when a reward is coming. The most parsimonious explanation is that this activity codes for a type of expected outcome, where the expectation encompasses both certain and uncertain rewards. PMID:22402653

Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation.

PubMed

Preissl, Sebastian; Fang, Rongxin; Huang, Hui; Zhao, Yuan; Raviram, Ramya; Gorkin, David U; Zhang, Yanxiao; Sos, Brandon C; Afzal, Veena; Dickel, Diane E; Kuan, Samantha; Visel, Axel; Pennacchio, Len A; Zhang, Kun; Ren, Bing

2018-03-01

Analysis of chromatin accessibility can reveal transcriptional regulatory sequences, but heterogeneity of primary tissues poses a significant challenge in mapping the precise chromatin landscape in specific cell types. Here we report single-nucleus ATAC-seq, a combinatorial barcoding-assisted single-cell assay for transposase-accessible chromatin that is optimized for use on flash-frozen primary tissue samples. We apply this technique to the mouse forebrain through eight developmental stages. Through analysis of more than 15,000 nuclei, we identify 20 distinct cell populations corresponding to major neuronal and non-neuronal cell types. We further define cell-type-specific transcriptional regulatory sequences, infer potential master transcriptional regulators and delineate developmental changes in forebrain cellular composition. Our results provide insight into the molecular and cellular dynamics that underlie forebrain development in the mouse and establish technical and analytical frameworks that are broadly applicable to other heterogeneous tissues.

Spatiotemporal dynamics of large-scale brain activity

NASA Astrophysics Data System (ADS)

Neuman, Jeremy

Understanding the dynamics of large-scale brain activity is a tough challenge. One reason for this is the presence of an incredible amount of complexity arising from having roughly 100 billion neurons connected via 100 trillion synapses. Because of the extremely high number of degrees of freedom in the nervous system, the question of how the brain manages to properly function and remain stable, yet also be adaptable, must be posed. Neuroscientists have identified many ways the nervous system makes this possible, of which synaptic plasticity is possibly the most notable one. On the other hand, it is vital to understand how the nervous system also loses stability, resulting in neuropathological diseases such as epilepsy, a disease which affects 1% of the population. In the following work, we seek to answer some of these questions from two different perspectives. The first uses mean-field theory applied to neuronal populations, where the variables of interest are the percentages of active excitatory and inhibitory neurons in a network, to consider how the nervous system responds to external stimuli, self-organizes and generates epileptiform activity. The second method uses statistical field theory, in the framework of single neurons on a lattice, to study the concept of criticality, an idea borrowed from physics which posits that in some regime the brain operates in a collectively stable or marginally stable manner. This will be examined in two different neuronal networks with self-organized criticality serving as the overarching theme for the union of both perspectives. One of the biggest problems in neuroscience is the question of to what extent certain details are significant to the functioning of the brain. These details give rise to various spatiotemporal properties that at the smallest of scales explain the interaction of single neurons and synapses and at the largest of scales describe, for example, behaviors and sensations. In what follows, we will shed some light on this issue.

Single-Cell Analysis of Experience-Dependent Transcriptomic States in Mouse Visual Cortex

PubMed Central

Hrvatin, Sinisa; Hochbaum, Daniel R.; Nagy, M. Aurel; Cicconet, Marcelo; Robertson, Keiramarie; Cheadle, Lucas; Zilionis, Rapolas; Ratner, Alex; Borges-Monroy, Rebeca; Klein, Allon M.; Sabatini, Bernardo L.; Greenberg, Michael E.

2017-01-01

Activity-dependent transcriptional responses shape cortical function. However, we lack a comprehensive understanding of the diversity of these responses across the full range of cortical cell types, and how these changes contribute to neuronal plasticity and disease. Here we applied high-throughput single-cell RNA-sequencing to investigate the breadth of transcriptional changes that occur across cell types in mouse visual cortex following exposure to light. We identified significant and divergent transcriptional responses to stimulation in each of the 30 cell types characterized, revealing 611 stimulus-responsive genes. Excitatory pyramidal neurons exhibit inter- and intra-laminar heterogeneity in the induction of stimulus responsive genes. Non-neuronal cells demonstrated clear transcriptional responses that may regulate experience-dependent changes in neurovascular coupling and myelination. Together, these results reveal the dynamic landscape of stimulus-dependent transcriptional changes that occur across cell types in visual cortex, which are likely critical for cortical function and may be sites of de-regulation in developmental brain disorders. PMID:29230054

Single-cell axotomy of cultured hippocampal neurons integrated in neuronal circuits.

PubMed

Gomis-Rüth, Susana; Stiess, Michael; Wierenga, Corette J; Meyn, Liane; Bradke, Frank

2014-05-01

An understanding of the molecular mechanisms of axon regeneration after injury is key for the development of potential therapies. Single-cell axotomy of dissociated neurons enables the study of the intrinsic regenerative capacities of injured axons. This protocol describes how to perform single-cell axotomy on dissociated hippocampal neurons containing synapses. Furthermore, to axotomize hippocampal neurons integrated in neuronal circuits, we describe how to set up coculture with a few fluorescently labeled neurons. This approach allows axotomy of single cells in a complex neuronal network and the observation of morphological and molecular changes during axon regeneration. Thus, single-cell axotomy of mature neurons is a valuable tool for gaining insights into cell intrinsic axon regeneration and the plasticity of neuronal polarity of mature neurons. Dissociation of the hippocampus and plating of hippocampal neurons takes ∼2 h. Neurons are then left to grow for 2 weeks, during which time they integrate into neuronal circuits. Subsequent axotomy takes 10 min per neuron and further imaging takes 10 min per neuron.

Onset dynamics of action potentials in rat neocortical neurons and identified snail neurons: quantification of the difference.

PubMed

Volgushev, Maxim; Malyshev, Aleksey; Balaban, Pavel; Chistiakova, Marina; Volgushev, Stanislav; Wolf, Fred

2008-04-09

The generation of action potentials (APs) is a key process in the operation of nerve cells and the communication between neurons. Action potentials in mammalian central neurons are characterized by an exceptionally fast onset dynamics, which differs from the typically slow and gradual onset dynamics seen in identified snail neurons. Here we describe a novel method of analysis which provides a quantitative measure of the onset dynamics of action potentials. This method captures the difference between the fast, step-like onset of APs in rat neocortical neurons and the gradual, exponential-like AP onset in identified snail neurons. The quantitative measure of the AP onset dynamics, provided by the method, allows us to perform quantitative analyses of factors influencing the dynamics.

Onset Dynamics of Action Potentials in Rat Neocortical Neurons and Identified Snail Neurons: Quantification of the Difference

PubMed Central

Volgushev, Maxim; Malyshev, Aleksey; Balaban, Pavel; Chistiakova, Marina; Volgushev, Stanislav; Wolf, Fred

2008-01-01

The generation of action potentials (APs) is a key process in the operation of nerve cells and the communication between neurons. Action potentials in mammalian central neurons are characterized by an exceptionally fast onset dynamics, which differs from the typically slow and gradual onset dynamics seen in identified snail neurons. Here we describe a novel method of analysis which provides a quantitative measure of the onset dynamics of action potentials. This method captures the difference between the fast, step-like onset of APs in rat neocortical neurons and the gradual, exponential-like AP onset in identified snail neurons. The quantitative measure of the AP onset dynamics, provided by the method, allows us to perform quantitative analyses of factors influencing the dynamics. PMID:18398478

Neuronal integration of dynamic sources: Bayesian learning and Bayesian inference.

PubMed

Siegelmann, Hava T; Holzman, Lars E

2010-09-01

One of the brain's most basic functions is integrating sensory data from diverse sources. This ability causes us to question whether the neural system is computationally capable of intelligently integrating data, not only when sources have known, fixed relative dependencies but also when it must determine such relative weightings based on dynamic conditions, and then use these learned weightings to accurately infer information about the world. We suggest that the brain is, in fact, fully capable of computing this parallel task in a single network and describe a neural inspired circuit with this property. Our implementation suggests the possibility that evidence learning requires a more complex organization of the network than was previously assumed, where neurons have different specialties, whose emergence brings the desired adaptivity seen in human online inference.

Dynamics of networks of excitatory and inhibitory neurons in response to time-dependent inputs.

PubMed

Ledoux, Erwan; Brunel, Nicolas

2011-01-01

We investigate the dynamics of recurrent networks of excitatory (E) and inhibitory (I) neurons in the presence of time-dependent inputs. The dynamics is characterized by the network dynamical transfer function, i.e., how the population firing rate is modulated by sinusoidal inputs at arbitrary frequencies. Two types of networks are studied and compared: (i) a Wilson-Cowan type firing rate model; and (ii) a fully connected network of leaky integrate-and-fire (LIF) neurons, in a strong noise regime. We first characterize the region of stability of the "asynchronous state" (a state in which population activity is constant in time when external inputs are constant) in the space of parameters characterizing the connectivity of the network. We then systematically characterize the qualitative behaviors of the dynamical transfer function, as a function of the connectivity. We find that the transfer function can be either low-pass, or with a single or double resonance, depending on the connection strengths and synaptic time constants. Resonances appear when the system is close to Hopf bifurcations, that can be induced by two separate mechanisms: the I-I connectivity and the E-I connectivity. Double resonances can appear when excitatory delays are larger than inhibitory delays, due to the fact that two distinct instabilities exist with a finite gap between the corresponding frequencies. In networks of LIF neurons, changes in external inputs and external noise are shown to be able to change qualitatively the network transfer function. Firing rate models are shown to exhibit the same diversity of transfer functions as the LIF network, provided delays are present. They can also exhibit input-dependent changes of the transfer function, provided a suitable static non-linearity is incorporated.

Substrates for Neuronal Cotransmission With Neuropeptides and Small Molecule Neurotransmitters in Drosophila

PubMed Central

Nässel, Dick R.

2018-01-01

It has been known for more than 40 years that individual neurons can produce more than one neurotransmitter and that neuropeptides often are colocalized with small molecule neurotransmitters (SMNs). Over the years much progress has been made in understanding the functional consequences of cotransmission in the nervous system of mammals. There are also some excellent invertebrate models that have revealed roles of coexpressed neuropeptides and SMNs in increasing complexity, flexibility, and dynamics in neuronal signaling. However, for the fly Drosophila there are surprisingly few functional studies on cotransmission, although there is ample evidence for colocalization of neuroactive compounds in neurons of the CNS, based both on traditional techniques and novel single cell transcriptome analysis. With the hope to trigger interest in initiating cotransmission studies, this review summarizes what is known about Drosophila neurons and neuronal circuits where different neuropeptides and SMNs are colocalized. Coexistence of neuroactive substances has been recorded in different neuron types such as neuroendocrine cells, interneurons, sensory cells and motor neurons. Some of the circuits highlighted here are well established in the analysis of learning and memory, circadian clock networks regulating rhythmic activity and sleep, as well as neurons and neuroendocrine cells regulating olfaction, nociception, feeding, metabolic homeostasis, diuretic functions, reproduction, and developmental processes. One emerging trait is the broad role of short neuropeptide F in cotransmission and presynaptic facilitation in a number of different neuronal circuits. This review also discusses the functional relevance of coexisting peptides in the intestine. Based on recent single cell transcriptomics data, it is likely that the neuronal systems discussed in this review are just a fraction of the total set of circuits where cotransmission occurs in Drosophila. Thus, a systematic search for colocalized neuroactive compounds in further neurons in anatomically defined circuits is of interest for the near future. PMID:29651236

Multimodal Integration After Unilateral Labyrinthine Lesion: Single Vestibular Nuclei Neuron Responses and Implications for Postural Compensation

PubMed Central

Sadeghi, Soroush G.; Minor, Lloyd B.

2011-01-01

Plasticity in neuronal responses is necessary for compensation following brain lesions and adaptation to new conditions and motor learning. In a previous study, we showed that compensatory changes in the vestibuloocular reflex (VOR) following unilateral vestibular loss were characterized by dynamic reweighting of inputs from vestibular and extravestibular modalities at the level of single neurons that constitute the first central stage of VOR signal processing. Here, we studied another class of neurons, i.e., the vestibular-only neurons, in the vestibular nuclei that mediate vestibulospinal reflexes and provide information for higher brain areas. We investigated changes in the relative contribution of vestibular, neck proprioceptive, and efference copy signals in the response of these neurons during compensation after contralateral vestibular loss in Macaca mulata monkeys. We show that the time course of recovery of vestibular sensitivity of neurons corresponds with that of lower extremity muscle and tendon reflexes reported in previous studies. More important, we found that information from neck proprioceptors, which did not influence neuronal responses before the lesion, were unmasked after lesion. Such inputs influenced the early stages of the compensation process evidenced by faster and more substantial recovery of the resting discharge in proprioceptive-sensitive neurons. Interestingly, unlike our previous study of VOR interneurons, the improvement in the sensitivity of the two groups of neurons did not show any difference in the early or late stages after lesion. Finally, neuronal responses during active head movements were not different before and after lesion and were attenuated relative to passive movements over the course of recovery, similar to that observed in control conditions. Comparison of compensatory changes observed in the vestibuloocular and vestibulospinal pathways provides evidence for similarities and differences between the two classes of neurons that mediate these pathways at the functional and cellular levels. PMID:21148096

Multimodal integration after unilateral labyrinthine lesion: single vestibular nuclei neuron responses and implications for postural compensation.

PubMed

Sadeghi, Soroush G; Minor, Lloyd B; Cullen, Kathleen E

2011-02-01

Plasticity in neuronal responses is necessary for compensation following brain lesions and adaptation to new conditions and motor learning. In a previous study, we showed that compensatory changes in the vestibuloocular reflex (VOR) following unilateral vestibular loss were characterized by dynamic reweighting of inputs from vestibular and extravestibular modalities at the level of single neurons that constitute the first central stage of VOR signal processing. Here, we studied another class of neurons, i.e., the vestibular-only neurons, in the vestibular nuclei that mediate vestibulospinal reflexes and provide information for higher brain areas. We investigated changes in the relative contribution of vestibular, neck proprioceptive, and efference copy signals in the response of these neurons during compensation after contralateral vestibular loss in Macaca mulata monkeys. We show that the time course of recovery of vestibular sensitivity of neurons corresponds with that of lower extremity muscle and tendon reflexes reported in previous studies. More important, we found that information from neck proprioceptors, which did not influence neuronal responses before the lesion, were unmasked after lesion. Such inputs influenced the early stages of the compensation process evidenced by faster and more substantial recovery of the resting discharge in proprioceptive-sensitive neurons. Interestingly, unlike our previous study of VOR interneurons, the improvement in the sensitivity of the two groups of neurons did not show any difference in the early or late stages after lesion. Finally, neuronal responses during active head movements were not different before and after lesion and were attenuated relative to passive movements over the course of recovery, similar to that observed in control conditions. Comparison of compensatory changes observed in the vestibuloocular and vestibulospinal pathways provides evidence for similarities and differences between the two classes of neurons that mediate these pathways at the functional and cellular levels.

Intrinsic Neuronal Properties Switch the Mode of Information Transmission in Networks

PubMed Central

Gjorgjieva, Julijana; Mease, Rebecca A.; Moody, William J.; Fairhall, Adrienne L.

2014-01-01

Diverse ion channels and their dynamics endow single neurons with complex biophysical properties. These properties determine the heterogeneity of cell types that make up the brain, as constituents of neural circuits tuned to perform highly specific computations. How do biophysical properties of single neurons impact network function? We study a set of biophysical properties that emerge in cortical neurons during the first week of development, eventually allowing these neurons to adaptively scale the gain of their response to the amplitude of the fluctuations they encounter. During the same time period, these same neurons participate in large-scale waves of spontaneously generated electrical activity. We investigate the potential role of experimentally observed changes in intrinsic neuronal properties in determining the ability of cortical networks to propagate waves of activity. We show that such changes can strongly affect the ability of multi-layered feedforward networks to represent and transmit information on multiple timescales. With properties modeled on those observed at early stages of development, neurons are relatively insensitive to rapid fluctuations and tend to fire synchronously in response to wave-like events of large amplitude. Following developmental changes in voltage-dependent conductances, these same neurons become efficient encoders of fast input fluctuations over few layers, but lose the ability to transmit slower, population-wide input variations across many layers. Depending on the neurons' intrinsic properties, noise plays different roles in modulating neuronal input-output curves, which can dramatically impact network transmission. The developmental change in intrinsic properties supports a transformation of a networks function from the propagation of network-wide information to one in which computations are scaled to local activity. This work underscores the significance of simple changes in conductance parameters in governing how neurons represent and propagate information, and suggests a role for background synaptic noise in switching the mode of information transmission. PMID:25474701

Estradiol selectively enhances auditory function in avian forebrain neurons

PubMed Central

Caras, Melissa L.; O’Brien, Matthew; Brenowitz, Eliot A.; Rubel, Edwin W

2012-01-01

Sex steroids modulate vertebrate sensory processing, but the impact of circulating hormone levels on forebrain function remains unclear. We tested the hypothesis that circulating sex steroids modulate single-unit responses in the avian telencephalic auditory nucleus, field L. We mimicked breeding or non-breeding conditions by manipulating plasma 17β-estradiol levels in wild-caught female Gambel’s white-crowned sparrows (Zonotrichia leucophrys gambelii). Extracellular responses of single neurons to tones and conspecific songs presented over a range of intensities revealed that estradiol selectively enhanced auditory function in cells that exhibited monotonic rate-level functions to pure tones. In these cells, estradiol treatment increased spontaneous and maximum evoked firing rates, increased pure tone response strengths and sensitivity, and expanded the range of intensities over which conspecific song stimuli elicited significant responses. Estradiol did not significantly alter the sensitivity or dynamic ranges of cells that exhibited non-monotonic rate-level functions. Notably, there was a robust correlation between plasma estradiol concentrations in individual birds and physiological response properties in monotonic, but not non-monotonic neurons. These findings demonstrate that functionally distinct classes of anatomically overlapping forebrain neurons are differentially regulated by sex steroid hormones in a dose-dependent manner. PMID:23223283

Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation.

PubMed

Augustin, Moritz; Ladenbauer, Josef; Baumann, Fabian; Obermayer, Klaus

2017-06-01

The spiking activity of single neurons can be well described by a nonlinear integrate-and-fire model that includes somatic adaptation. When exposed to fluctuating inputs sparsely coupled populations of these model neurons exhibit stochastic collective dynamics that can be effectively characterized using the Fokker-Planck equation. This approach, however, leads to a model with an infinite-dimensional state space and non-standard boundary conditions. Here we derive from that description four simple models for the spike rate dynamics in terms of low-dimensional ordinary differential equations using two different reduction techniques: one uses the spectral decomposition of the Fokker-Planck operator, the other is based on a cascade of two linear filters and a nonlinearity, which are determined from the Fokker-Planck equation and semi-analytically approximated. We evaluate the reduced models for a wide range of biologically plausible input statistics and find that both approximation approaches lead to spike rate models that accurately reproduce the spiking behavior of the underlying adaptive integrate-and-fire population. Particularly the cascade-based models are overall most accurate and robust, especially in the sensitive region of rapidly changing input. For the mean-driven regime, when input fluctuations are not too strong and fast, however, the best performing model is based on the spectral decomposition. The low-dimensional models also well reproduce stable oscillatory spike rate dynamics that are generated either by recurrent synaptic excitation and neuronal adaptation or through delayed inhibitory synaptic feedback. The computational demands of the reduced models are very low but the implementation complexity differs between the different model variants. Therefore we have made available implementations that allow to numerically integrate the low-dimensional spike rate models as well as the Fokker-Planck partial differential equation in efficient ways for arbitrary model parametrizations as open source software. The derived spike rate descriptions retain a direct link to the properties of single neurons, allow for convenient mathematical analyses of network states, and are well suited for application in neural mass/mean-field based brain network models.

Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation

PubMed Central

Baumann, Fabian; Obermayer, Klaus

2017-01-01

The spiking activity of single neurons can be well described by a nonlinear integrate-and-fire model that includes somatic adaptation. When exposed to fluctuating inputs sparsely coupled populations of these model neurons exhibit stochastic collective dynamics that can be effectively characterized using the Fokker-Planck equation. This approach, however, leads to a model with an infinite-dimensional state space and non-standard boundary conditions. Here we derive from that description four simple models for the spike rate dynamics in terms of low-dimensional ordinary differential equations using two different reduction techniques: one uses the spectral decomposition of the Fokker-Planck operator, the other is based on a cascade of two linear filters and a nonlinearity, which are determined from the Fokker-Planck equation and semi-analytically approximated. We evaluate the reduced models for a wide range of biologically plausible input statistics and find that both approximation approaches lead to spike rate models that accurately reproduce the spiking behavior of the underlying adaptive integrate-and-fire population. Particularly the cascade-based models are overall most accurate and robust, especially in the sensitive region of rapidly changing input. For the mean-driven regime, when input fluctuations are not too strong and fast, however, the best performing model is based on the spectral decomposition. The low-dimensional models also well reproduce stable oscillatory spike rate dynamics that are generated either by recurrent synaptic excitation and neuronal adaptation or through delayed inhibitory synaptic feedback. The computational demands of the reduced models are very low but the implementation complexity differs between the different model variants. Therefore we have made available implementations that allow to numerically integrate the low-dimensional spike rate models as well as the Fokker-Planck partial differential equation in efficient ways for arbitrary model parametrizations as open source software. The derived spike rate descriptions retain a direct link to the properties of single neurons, allow for convenient mathematical analyses of network states, and are well suited for application in neural mass/mean-field based brain network models. PMID:28644841

The Second Spiking Threshold: Dynamics of Laminar Network Spiking in the Visual Cortex

PubMed Central

Forsberg, Lars E.; Bonde, Lars H.; Harvey, Michael A.; Roland, Per E.

2016-01-01

Most neurons have a threshold separating the silent non-spiking state and the state of producing temporal sequences of spikes. But neurons in vivo also have a second threshold, found recently in granular layer neurons of the primary visual cortex, separating spontaneous ongoing spiking from visually evoked spiking driven by sharp transients. Here we examine whether this second threshold exists outside the granular layer and examine details of transitions between spiking states in ferrets exposed to moving objects. We found the second threshold, separating spiking states evoked by stationary and moving visual stimuli from the spontaneous ongoing spiking state, in all layers and zones of areas 17 and 18 indicating that the second threshold is a property of the network. Spontaneous and evoked spiking, thus can easily be distinguished. In addition, the trajectories of spontaneous ongoing states were slow, frequently changing direction. In single trials, sharp as well as smooth and slow transients transform the trajectories to be outward directed, fast and crossing the threshold to become evoked. Although the speeds of the evolution of the evoked states differ, the same domain of the state space is explored indicating uniformity of the evoked states. All evoked states return to the spontaneous evoked spiking state as in a typical mono-stable dynamical system. In single trials, neither the original spiking rates, nor the temporal evolution in state space could distinguish simple visual scenes. PMID:27582693

Interplay of intrinsic and synaptic conductances in the generation of high-frequency oscillations in interneuronal networks with irregular spiking.

PubMed

Baroni, Fabiano; Burkitt, Anthony N; Grayden, David B

2014-05-01

High-frequency oscillations (above 30 Hz) have been observed in sensory and higher-order brain areas, and are believed to constitute a general hallmark of functional neuronal activation. Fast inhibition in interneuronal networks has been suggested as a general mechanism for the generation of high-frequency oscillations. Certain classes of interneurons exhibit subthreshold oscillations, but the effect of this intrinsic neuronal property on the population rhythm is not completely understood. We study the influence of intrinsic damped subthreshold oscillations in the emergence of collective high-frequency oscillations, and elucidate the dynamical mechanisms that underlie this phenomenon. We simulate neuronal networks composed of either Integrate-and-Fire (IF) or Generalized Integrate-and-Fire (GIF) neurons. The IF model displays purely passive subthreshold dynamics, while the GIF model exhibits subthreshold damped oscillations. Individual neurons receive inhibitory synaptic currents mediated by spiking activity in their neighbors as well as noisy synaptic bombardment, and fire irregularly at a lower rate than population frequency. We identify three factors that affect the influence of single-neuron properties on synchronization mediated by inhibition: i) the firing rate response to the noisy background input, ii) the membrane potential distribution, and iii) the shape of Inhibitory Post-Synaptic Potentials (IPSPs). For hyperpolarizing inhibition, the GIF IPSP profile (factor iii)) exhibits post-inhibitory rebound, which induces a coherent spike-mediated depolarization across cells that greatly facilitates synchronous oscillations. This effect dominates the network dynamics, hence GIF networks display stronger oscillations than IF networks. However, the restorative current in the GIF neuron lowers firing rates and narrows the membrane potential distribution (factors i) and ii), respectively), which tend to decrease synchrony. If inhibition is shunting instead of hyperpolarizing, post-inhibitory rebound is not elicited and factors i) and ii) dominate, yielding lower synchrony in GIF networks than in IF networks.

Dynamics of human subthalamic neuron phase-locking to motor and sensory cortical oscillations during movement.

PubMed

Lipski, Witold J; Wozny, Thomas A; Alhourani, Ahmad; Kondylis, Efstathios D; Turner, Robert S; Crammond, Donald J; Richardson, Robert Mark

2017-09-01

Coupled oscillatory activity recorded between sensorimotor regions of the basal ganglia-thalamocortical loop is thought to reflect information transfer relevant to movement. A neuronal firing-rate model of basal ganglia-thalamocortical circuitry, however, has dominated thinking about basal ganglia function for the past three decades, without knowledge of the relationship between basal ganglia single neuron firing and cortical population activity during movement itself. We recorded activity from 34 subthalamic nucleus (STN) neurons, simultaneously with cortical local field potentials and motor output, in 11 subjects with Parkinson's disease (PD) undergoing awake deep brain stimulator lead placement. STN firing demonstrated phase synchronization to both low- and high-beta-frequency cortical oscillations, and to the amplitude envelope of gamma oscillations, in motor cortex. We found that during movement, the magnitude of this synchronization was dynamically modulated in a phase-frequency-specific manner. Importantly, we found that phase synchronization was not correlated with changes in neuronal firing rate. Furthermore, we found that these relationships were not exclusive to motor cortex, because STN firing also demonstrated phase synchronization to both premotor and sensory cortex. The data indicate that models of basal ganglia function ultimately will need to account for the activity of populations of STN neurons that are bound in distinct functional networks with both motor and sensory cortices and code for movement parameters independent of changes in firing rate. NEW & NOTEWORTHY Current models of basal ganglia-thalamocortical networks do not adequately explain simple motor functions, let alone dysfunction in movement disorders. Our findings provide data that inform models of human basal ganglia function by demonstrating how movement is encoded by networks of subthalamic nucleus (STN) neurons via dynamic phase synchronization with cortex. The data also demonstrate, for the first time in humans, a mechanism through which the premotor and sensory cortices are functionally connected to the STN. Copyright © 2017 the American Physiological Society.

Interplay of Intrinsic and Synaptic Conductances in the Generation of High-Frequency Oscillations in Interneuronal Networks with Irregular Spiking

PubMed Central

Baroni, Fabiano; Burkitt, Anthony N.; Grayden, David B.

2014-01-01

High-frequency oscillations (above 30 Hz) have been observed in sensory and higher-order brain areas, and are believed to constitute a general hallmark of functional neuronal activation. Fast inhibition in interneuronal networks has been suggested as a general mechanism for the generation of high-frequency oscillations. Certain classes of interneurons exhibit subthreshold oscillations, but the effect of this intrinsic neuronal property on the population rhythm is not completely understood. We study the influence of intrinsic damped subthreshold oscillations in the emergence of collective high-frequency oscillations, and elucidate the dynamical mechanisms that underlie this phenomenon. We simulate neuronal networks composed of either Integrate-and-Fire (IF) or Generalized Integrate-and-Fire (GIF) neurons. The IF model displays purely passive subthreshold dynamics, while the GIF model exhibits subthreshold damped oscillations. Individual neurons receive inhibitory synaptic currents mediated by spiking activity in their neighbors as well as noisy synaptic bombardment, and fire irregularly at a lower rate than population frequency. We identify three factors that affect the influence of single-neuron properties on synchronization mediated by inhibition: i) the firing rate response to the noisy background input, ii) the membrane potential distribution, and iii) the shape of Inhibitory Post-Synaptic Potentials (IPSPs). For hyperpolarizing inhibition, the GIF IPSP profile (factor iii)) exhibits post-inhibitory rebound, which induces a coherent spike-mediated depolarization across cells that greatly facilitates synchronous oscillations. This effect dominates the network dynamics, hence GIF networks display stronger oscillations than IF networks. However, the restorative current in the GIF neuron lowers firing rates and narrows the membrane potential distribution (factors i) and ii), respectively), which tend to decrease synchrony. If inhibition is shunting instead of hyperpolarizing, post-inhibitory rebound is not elicited and factors i) and ii) dominate, yielding lower synchrony in GIF networks than in IF networks. PMID:24784237

Development of a bench-top device for parallel climate-controlled recordings of neuronal cultures activity with microelectrode arrays.

PubMed

Regalia, Giulia; Biffi, Emilia; Achilli, Silvia; Ferrigno, Giancarlo; Menegon, Andrea; Pedrocchi, Alessandra

2016-02-01

Two binding requirements for in vitro studies on long-term neuronal networks dynamics are (i) finely controlled environmental conditions to keep neuronal cultures viable and provide reliable data for more than a few hours and (ii) parallel operation on multiple neuronal cultures to shorten experimental time scales and enhance data reproducibility. In order to fulfill these needs with a Microelectrode Arrays (MEA)-based system, we designed a stand-alone device that permits to uninterruptedly monitor neuronal cultures activity over long periods, overcoming drawbacks of existing MEA platforms. We integrated in a single device: (i) a closed chamber housing four MEAs equipped with access for chemical manipulations, (ii) environmental control systems and embedded sensors to reproduce and remotely monitor the standard in vitro culture environment on the lab bench (i.e. in terms of temperature, air CO2 and relative humidity), and (iii) a modular MEA interface analog front-end for reliable and parallel recordings. The system has been proven to assure environmental conditions stable, physiological and homogeneos across different cultures. Prolonged recordings (up to 10 days) of spontaneous and pharmacologically stimulated neuronal culture activity have not shown signs of rundown thanks to the environmental stability and have not required to withdraw the cells from the chamber for culture medium manipulations. This system represents an effective MEA-based solution to elucidate neuronal network phenomena with slow dynamics, such as long-term plasticity, effects of chronic pharmacological stimulations or late-onset pathological mechanisms. © 2015 Wiley Periodicals, Inc.

Encoding of Olfactory Information with Oscillating Neural Assemblies

NASA Astrophysics Data System (ADS)

Laurent, Gilles; Davidowitz, Hananel

1994-09-01

In the brain, fast oscillations of local field potentials, which are thought to arise from the coherent and rhythmic activity of large numbers of neurons, were observed first in the olfactory system and have since been described in many neocortical areas. The importance of these oscillations in information coding, however, is controversial. Here, local field potential and intracellular recordings were obtained from the antennal lobe and mushroom body of the locust Schistocerca americana. Different odors evoked coherent oscillations in different, but usually overlapping, ensembles of neurons. The phase of firing of individual neurons relative to the population was not dependent on the odor. The components of a coherently oscillating ensemble of neurons changed over the duration of a single exposure to an odor. It is thus proposed that odors are encoded by specific but dynamic assemblies of coherently oscillating neurons. Such distributed and temporal representation of complex sensory signals may facilitate combinatorial coding and associative learning in these, and possibly other, sensory networks.

Volumetric Two-photon Imaging of Neurons Using Stereoscopy (vTwINS)

PubMed Central

Song, Alexander; Charles, Adam S.; Koay, Sue Ann; Gauthier, Jeff L.; Thiberge, Stephan Y.; Pillow, Jonathan W.; Tank, David W.

2017-01-01

Two-photon laser scanning microscopy of calcium dynamics using fluorescent indicators is a widely used imaging method for large scale recording of neural activity in vivo. Here we introduce volumetric Two-photon Imaging of Neurons using Stereoscopy (vTwINS), a volumetric calcium imaging method that employs an elongated, V-shaped point spread function to image a 3D brain volume. Single neurons project to spatially displaced “image pairs” in the resulting 2D image, and the separation distance between images is proportional to depth in the volume. To demix the fluorescence time series of individual neurons, we introduce a novel orthogonal matching pursuit algorithm that also infers source locations within the 3D volume. We illustrate vTwINS by imaging neural population activity in mouse primary visual cortex and hippocampus. Our results demonstrate that vTwINS provides an effective method for volumetric two-photon calcium imaging that increases the number of neurons recorded while maintaining a high frame-rate. PMID:28319111

Unmasking of spiral ganglion neuron firing dynamics by membrane potential and neurotrophin-3.

PubMed

Crozier, Robert A; Davis, Robin L

2014-07-16

Type I spiral ganglion neurons have a unique role relative to other sensory afferents because, as a single population, they must convey the richness, complexity, and precision of auditory information as they shape signals transmitted to the brain. To understand better the sophistication of spiral ganglion response properties, we compared somatic whole-cell current-clamp recordings from basal and apical neurons obtained during the first 2 postnatal weeks from CBA/CaJ mice. We found that during this developmental time period neuron response properties changed from uniformly excitable to differentially plastic. Low-frequency, apical and high-frequency basal neurons at postnatal day 1 (P1)-P3 were predominantly slowly accommodating (SA), firing at low thresholds with little alteration in accommodation response mode induced by changes in resting membrane potential (RMP) or added neurotrophin-3 (NT-3). In contrast, P10-P14 apical and basal neurons were predominately rapidly accommodating (RA), had higher firing thresholds, and responded to elevation of RMP and added NT-3 by transitioning to the SA category without affecting the instantaneous firing rate. Therefore, older neurons appeared to be uniformly less excitable under baseline conditions yet displayed a previously unrecognized capacity to change response modes dynamically within a remarkably stable accommodation framework. Because the soma is interposed in the signal conduction pathway, these specializations can potentially lead to shaping and filtering of the transmitted signal. These results suggest that spiral ganglion neurons possess electrophysiological mechanisms that enable them to adapt their response properties to the characteristics of incoming stimuli and thus have the capacity to encode a wide spectrum of auditory information. Copyright © 2014 the authors 0270-6474/14/349688-15$15.00/0.

Systematic analysis of the contributions of stochastic voltage gated channels to neuronal noise

PubMed Central

O'Donnell, Cian; van Rossum, Mark C. W.

2014-01-01

Electrical signaling in neurons is mediated by the opening and closing of large numbers of individual ion channels. The ion channels' state transitions are stochastic and introduce fluctuations in the macroscopic current through ion channel populations. This creates an unavoidable source of intrinsic electrical noise for the neuron, leading to fluctuations in the membrane potential and spontaneous spikes. While this effect is well known, the impact of channel noise on single neuron dynamics remains poorly understood. Most results are based on numerical simulations. There is no agreement, even in theoretical studies, on which ion channel type is the dominant noise source, nor how inclusion of additional ion channel types affects voltage noise. Here we describe a framework to calculate voltage noise directly from an arbitrary set of ion channel models, and discuss how this can be use to estimate spontaneous spike rates. PMID:25360105

Two-photon calcium imaging during fictive navigation in virtual environments

PubMed Central

Ahrens, Misha B.; Huang, Kuo Hua; Narayan, Sujatha; Mensh, Brett D.; Engert, Florian

2013-01-01

A full understanding of nervous system function requires recording from large populations of neurons during naturalistic behaviors. Here we enable paralyzed larval zebrafish to fictively navigate two-dimensional virtual environments while we record optically from many neurons with two-photon imaging. Electrical recordings from motor nerves in the tail are decoded into intended forward swims and turns, which are used to update a virtual environment displayed underneath the fish. Several behavioral features—such as turning responses to whole-field motion and dark avoidance—are well-replicated in this virtual setting. We readily observed neuronal populations in the hindbrain with laterally selective responses that correlated with right or left optomotor behavior. We also observed neurons in the habenula, pallium, and midbrain with response properties specific to environmental features. Beyond single-cell correlations, the classification of network activity in such virtual settings promises to reveal principles of brainwide neural dynamics during behavior. PMID:23761738

Populations of striatal medium spiny neurons encode vibrotactile frequency in rats: modulation by slow wave oscillations

PubMed Central

Hawking, Thomas G.

2013-01-01

Dorsolateral striatum (DLS) is implicated in tactile perception and receives strong projections from somatosensory cortex. However, the sensory representations encoded by striatal projection neurons are not well understood. Here we characterized the contribution of DLS to the encoding of vibrotactile information in rats by assessing striatal responses to precise frequency stimuli delivered to a single vibrissa. We applied stimuli in a frequency range (45–90 Hz) that evokes discriminable percepts and carries most of the power of vibrissa vibration elicited by a range of complex fine textures. Both medium spiny neurons and evoked potentials showed tactile responses that were modulated by slow wave oscillations. Furthermore, medium spiny neuron population responses represented stimulus frequency on par with previously reported behavioral benchmarks. Our results suggest that striatum encodes frequency information of vibrotactile stimuli which is dynamically modulated by ongoing brain state. PMID:23114217

Two-photon calcium imaging during fictive navigation in virtual environments.

PubMed

Ahrens, Misha B; Huang, Kuo Hua; Narayan, Sujatha; Mensh, Brett D; Engert, Florian

2013-01-01

A full understanding of nervous system function requires recording from large populations of neurons during naturalistic behaviors. Here we enable paralyzed larval zebrafish to fictively navigate two-dimensional virtual environments while we record optically from many neurons with two-photon imaging. Electrical recordings from motor nerves in the tail are decoded into intended forward swims and turns, which are used to update a virtual environment displayed underneath the fish. Several behavioral features-such as turning responses to whole-field motion and dark avoidance-are well-replicated in this virtual setting. We readily observed neuronal populations in the hindbrain with laterally selective responses that correlated with right or left optomotor behavior. We also observed neurons in the habenula, pallium, and midbrain with response properties specific to environmental features. Beyond single-cell correlations, the classification of network activity in such virtual settings promises to reveal principles of brainwide neural dynamics during behavior.

MACF1 regulates the migration of pyramidal neurons via microtubule dynamics and GSK-3 signaling

PubMed Central

Ka, Minhan; Jung, Eui-Man; Mueller, Ulrich; Kim, Woo-Yang

2014-01-01

Neuronal migration and subsequent differentiation play critical roles for establishing functional neural circuitry in the developing brain. However, the molecular mechanisms that regulate these processes are poorly understood. Here, we show that microtubule actin crosslinking factor 1 (MACF1) determines neuronal positioning by regulating microtubule dynamics and mediating GSK-3 signaling during brain development. First, using MACF1 floxed allele mice and in utero gene manipulation, we find that MACF1 deletion suppresses migration of cortical pyramidal neurons and results in aberrant neuronal positioning in the developing brain. The cell autonomous deficit in migration is associated with abnormal dynamics of leading processes and centrosomes. Furthermore, microtubule stability is severely damaged in neurons lacking MACF1, resulting in abnormal microtubule dynamics. Finally, MACF1 interacts with and mediates GSK-3 signaling in developing neurons. Our findings establish a cellular mechanism underlying neuronal migration and provide insights into the regulation of cytoskeleton dynamics in developing neurons. PMID:25224226

MACF1 regulates the migration of pyramidal neurons via microtubule dynamics and GSK-3 signaling.

PubMed

Ka, Minhan; Jung, Eui-Man; Mueller, Ulrich; Kim, Woo-Yang

2014-11-01

Neuronal migration and subsequent differentiation play critical roles for establishing functional neural circuitry in the developing brain. However, the molecular mechanisms that regulate these processes are poorly understood. Here, we show that microtubule actin crosslinking factor 1 (MACF1) determines neuronal positioning by regulating microtubule dynamics and mediating GSK-3 signaling during brain development. First, using MACF1 floxed allele mice and in utero gene manipulation, we find that MACF1 deletion suppresses migration of cortical pyramidal neurons and results in aberrant neuronal positioning in the developing brain. The cell autonomous deficit in migration is associated with abnormal dynamics of leading processes and centrosomes. Furthermore, microtubule stability is severely damaged in neurons lacking MACF1, resulting in abnormal microtubule dynamics. Finally, MACF1 interacts with and mediates GSK-3 signaling in developing neurons. Our findings establish a cellular mechanism underlying neuronal migration and provide insights into the regulation of cytoskeleton dynamics in developing neurons. Copyright © 2014 Elsevier Inc. All rights reserved.

Existence of multiple receptors in single neurons: responses of single bullfrog olfactory neurons to many cAMP-dependent and independent odorants.

PubMed

Kashiwayanagi, M; Shimano, K; Kurihara, K

1996-11-04

The responses of single bullfrog olfactory neurons to various odorants were measured with the whole-cell patch clamp which offers direct information on cellular events and with the ciliary recording technique to obtain stable quantitative data from many neurons. A large portion of single olfactory neurons (about 64% and 79% in the whole-cell recording and in the ciliary recording, respectively) responded to many odorants with quite diverse molecular structures, including both odorants previously indicated to be cAMP-dependent (increasing) and independent odorants. One odorant elicited a response in many cells; e.g. hedione and citralva elicited the response in 100% and 92% of total neurons examined with the ciliary recording technique. To confirm that a single neuron carries different receptors or transduction pathways, the cross-adaptation technique was applied to single neurons. Application of hedione to a single neuron after desensitization of the current in response to lyral or citralva induced an inward current with a similar magnitude to that applied alone. It was suggested that most single olfactory neurons carry multiple receptors and at least dual transduction pathways.

Application of active electrode compensation to perform continuous voltage-clamp recordings with sharp microelectrodes.

PubMed

Gómez-González, J F; Destexhe, A; Bal, T

2014-10-01

Electrophysiological recordings of single neurons in brain tissues are very common in neuroscience. Glass microelectrodes filled with an electrolyte are used to impale the cell membrane in order to record the membrane potential or to inject current. Their high resistance induces a high voltage drop when passing current and it is essential to correct the voltage measurements. In particular, for voltage clamping, the traditional alternatives are two-electrode voltage-clamp technique or discontinuous single electrode voltage-clamp (dSEVC). Nevertheless, it is generally difficult to impale two electrodes in a same neuron and the switching frequency is limited to low frequencies in the case of dSEVC. We present a novel fully computer-implemented alternative to perform continuous voltage-clamp recordings with a single sharp-electrode. To reach such voltage-clamp recordings, we combine an active electrode compensation algorithm (AEC) with a digital controller (AECVC). We applied two types of control-systems: a linear controller (proportional plus integrative controller) and a model-based controller (optimal control). We compared the performance of the two methods to dSEVC using a dynamic model cell and experiments in brain slices. The AECVC method provides an entirely digital method to perform continuous recording and smooth switching between voltage-clamp, current clamp or dynamic-clamp configurations without introducing artifacts.

Human Nav1.8: enhanced persistent and ramp currents contribute to distinct firing properties of human DRG neurons

PubMed Central

Han, Chongyang; Estacion, Mark; Huang, Jianying; Vasylyev, Dymtro; Zhao, Peng; Dib-Hajj, Sulayman D.

2015-01-01

Although species-specific differences in ion channel properties are well-documented, little has been known about the properties of the human Nav1.8 channel, an important contributor to pain signaling. Here we show, using techniques that include voltage clamp, current clamp, and dynamic clamp in dorsal root ganglion (DRG) neurons, that human Nav1.8 channels display slower inactivation kinetics and produce larger persistent current and ramp current than previously reported in other species. DRG neurons expressing human Nav1.8 channels unexpectedly produce significantly longer-lasting action potentials, including action potentials with half-widths in some cells >10 ms, and increased firing frequency compared with the narrower and usually single action potentials generated by DRG neurons expressing rat Nav1.8 channels. We also show that native human DRG neurons recapitulate these properties of Nav1.8 current and the long-lasting action potentials. Together, our results demonstrate strikingly distinct properties of human Nav1.8, which contribute to the firing properties of human DRG neurons. PMID:25787950

Human Na(v)1.8: enhanced persistent and ramp currents contribute to distinct firing properties of human DRG neurons.

PubMed

Han, Chongyang; Estacion, Mark; Huang, Jianying; Vasylyev, Dymtro; Zhao, Peng; Dib-Hajj, Sulayman D; Waxman, Stephen G

2015-05-01

Although species-specific differences in ion channel properties are well-documented, little has been known about the properties of the human Nav1.8 channel, an important contributor to pain signaling. Here we show, using techniques that include voltage clamp, current clamp, and dynamic clamp in dorsal root ganglion (DRG) neurons, that human Na(v)1.8 channels display slower inactivation kinetics and produce larger persistent current and ramp current than previously reported in other species. DRG neurons expressing human Na(v)1.8 channels unexpectedly produce significantly longer-lasting action potentials, including action potentials with half-widths in some cells >10 ms, and increased firing frequency compared with the narrower and usually single action potentials generated by DRG neurons expressing rat Na(v)1.8 channels. We also show that native human DRG neurons recapitulate these properties of Na(v)1.8 current and the long-lasting action potentials. Together, our results demonstrate strikingly distinct properties of human Na(v)1.8, which contribute to the firing properties of human DRG neurons.

Two-photon imaging of neuronal activity in motor cortex of marmosets during upper-limb movement tasks.

PubMed

Ebina, Teppei; Masamizu, Yoshito; Tanaka, Yasuhiro R; Watakabe, Akiya; Hirakawa, Reiko; Hirayama, Yuka; Hira, Riichiro; Terada, Shin-Ichiro; Koketsu, Daisuke; Hikosaka, Kazuo; Mizukami, Hiroaki; Nambu, Atsushi; Sasaki, Erika; Yamamori, Tetsuo; Matsuzaki, Masanori

2018-05-14

Two-photon imaging in behaving animals has revealed neuronal activities related to behavioral and cognitive function at single-cell resolution. However, marmosets have posed a challenge due to limited success in training on motor tasks. Here we report the development of protocols to train head-fixed common marmosets to perform upper-limb movement tasks and simultaneously perform two-photon imaging. After 2-5 months of training sessions, head-fixed marmosets can control a manipulandum to move a cursor to a target on a screen. We conduct two-photon calcium imaging of layer 2/3 neurons in the motor cortex during this motor task performance, and detect task-relevant activity from multiple neurons at cellular and subcellular resolutions. In a two-target reaching task, some neurons show direction-selective activity over the training days. In a short-term force-field adaptation task, some neurons change their activity when the force field is on. Two-photon calcium imaging in behaving marmosets may become a fundamental technique for determining the spatial organization of the cortical dynamics underlying action and cognition.

Dynamic response and transfer function of social systems: A neuro-inspired model of collective human activity patterns.

PubMed

Lymperopoulos, Ilias N

2017-10-01

The interaction of social networks with the external environment gives rise to non-stationary activity patterns reflecting the temporal structure and strength of exogenous influences that drive social dynamical processes far from an equilibrium state. Following a neuro-inspired approach, based on the dynamics of a passive neuronal membrane, and the firing rate dynamics of single neurons and neuronal populations, we build a state-of-the-art model of the collective social response to exogenous interventions. In this regard, we analyze online activity patterns with a view to determining the transfer function of social systems, that is, the dynamic relationship between external influences and the resulting activity. To this end, first we estimate the impulse response (Green's function) of collective activity, and then we show that the convolution of the impulse response with a time-varying external influence field accurately reproduces empirical activity patterns. To capture the dynamics of collective activity when the generating process is in a state of statistical equilibrium, we incorporate into the model a noisy input convolved with the impulse response function, thus precisely reproducing the fluctuations of stationary collective activity around a resting value. The outstanding goodness-of-fit of the model results to empirical observations, indicates that the model explains human activity patterns generated by time-dependent external influences in various socio-economic contexts. The proposed model can be used for inferring the temporal structure and strength of external influences, as well as the inertia of collective social activity. Furthermore, it can potentially predict social activity patterns. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dynamic representation of 3D auditory space in the midbrain of the free-flying echolocating bat

PubMed Central

2018-01-01

Essential to spatial orientation in the natural environment is a dynamic representation of direction and distance to objects. Despite the importance of 3D spatial localization to parse objects in the environment and to guide movement, most neurophysiological investigations of sensory mapping have been limited to studies of restrained subjects, tested with 2D, artificial stimuli. Here, we show for the first time that sensory neurons in the midbrain superior colliculus (SC) of the free-flying echolocating bat encode 3D egocentric space, and that the bat’s inspection of objects in the physical environment sharpens tuning of single neurons, and shifts peak responses to represent closer distances. These findings emerged from wireless neural recordings in free-flying bats, in combination with an echo model that computes the animal’s instantaneous stimulus space. Our research reveals dynamic 3D space coding in a freely moving mammal engaged in a real-world navigation task. PMID:29633711

Defective Gpsm2/Gαi3 signalling disrupts stereocilia development and growth cone actin dynamics in Chudley-McCullough syndrome

NASA Astrophysics Data System (ADS)

Mauriac, Stephanie A.; Hien, Yeri E.; Bird, Jonathan E.; Carvalho, Steve Dos-Santos; Peyroutou, Ronan; Lee, Sze Chim; Moreau, Maite M.; Blanc, Jean-Michel; Geyser, Aysegul; Medina, Chantal; Thoumine, Olivier; Beer-Hammer, Sandra; Friedman, Thomas B.; Rüttiger, Lukas; Forge, Andrew; Nürnberg, Bernd; Sans, Nathalie; Montcouquiol, Mireille

2017-04-01

Mutations in GPSM2 cause Chudley-McCullough syndrome (CMCS), an autosomal recessive neurological disorder characterized by early-onset sensorineural deafness and brain anomalies. Here, we show that mutation of the mouse orthologue of GPSM2 affects actin-rich stereocilia elongation in auditory and vestibular hair cells, causing deafness and balance defects. The G-protein subunit Gαi3, a well-documented partner of Gpsm2, participates in the elongation process, and its absence also causes hearing deficits. We show that Gpsm2 defines an ~200 nm nanodomain at the tips of stereocilia and this localization requires the presence of Gαi3, myosin 15 and whirlin. Using single-molecule tracking, we report that loss of Gpsm2 leads to decreased outgrowth and a disruption of actin dynamics in neuronal growth cones. Our results elucidate the aetiology of CMCS and highlight a new molecular role for Gpsm2/Gαi3 in the regulation of actin dynamics in epithelial and neuronal tissues.

Dynamic changes in dopamine neuron function after DNSP-11 treatment: effects in vivo and increased ERK 1/2 phosphorylation in vitro.

PubMed

Fuqua, Joshua L; Littrell, Ofelia M; Lundblad, Martin; Turchan-Cholewo, Jadwiga; Abdelmoti, Lina G; Galperin, Emilia; Bradley, Luke H; Cass, Wayne A; Gash, Don M; Gerhardt, Greg A

2014-04-01

Glial cell-line derived neurotrophic factor (GDNF) has demonstrated robust effects on dopamine (DA) neuron function and survival. A post-translational processing model of the human GDNF proprotein theorizes the formation of smaller, amidated peptide(s) from the proregion that exhibit neurobiological function, including an 11-amino-acid peptide named dopamine neuron stimulating peptide-11 (DNSP-11). A single treatment of DNSP-11 was delivered to the substantia nigra in the rat to investigate effects on DA-neuron function. Four weeks after treatment, potassium (K+) and D-amphetamine evoked DA release were studied in the striatum using microdialysis. There were no significant changes in DA-release after DNSP-11 treatment determined by microdialysis. Dopamine release was further examined in discrete regions of the striatum using high-speed chronoamperometry at 1-, 2-, and 4-weeks after DNSP-11 treatment. Two weeks after DNSP-11 treatment, potassium-evoked DA release was increased in specific subregions of the striatum. However, spontaneous locomotor activity was unchanged by DNSP-11 treatment. In addition, we show that a single treatment of DNSP-11 in the MN9D dopaminergic neuronal cell line results in phosphorylation of ERK1/2, which suggests a novel cellular mechanism responsible for increases in DA function. Copyright © 2014 Elsevier Inc. All rights reserved.

Neural Computations in a Dynamical System with Multiple Time Scales.

PubMed

Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

2016-01-01

Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions.

The Emergence of Single Neurons in Clinical Neurology

PubMed Central

Cash, Sydney S.; Hochberg, Leigh R.

2015-01-01

Summary Single neuron actions and interactions are the sine qua non of brain function, and nearly all diseases and injuries of the central nervous system trace their clinical sequelae to neuronal dysfunction or failure. Remarkably, discussion of neuronal activity is largely absent in clinical neuroscience. Advances in neurotechnology and computational capabilities, accompanied by shifts in theoretical frameworks, have led to renewed interest in the information represented by single neurons. Using direct interfaces with the nervous system, millisecond-scale information will soon be extracted from single neurons in clinical environments, supporting personalized treatment of neurologic and psychiatric disease. In this review we focus on single neuronal activity in restoring communication and motor control in patients suffering from devastating neurological injuries. We also explore the single neuron's role in epilepsy and movement disorders, surgical anesthesia, and in cognitive processes disrupted in neurodegenerative and neuropsychiatric disease. Finally, we speculate on how technological advances will revolutionize neurotherapeutics. PMID:25856488

The emergence of single neurons in clinical neurology.

PubMed

Cash, Sydney S; Hochberg, Leigh R

2015-04-08

Single neuron actions and interactions are the sine qua non of brain function, and nearly all diseases and injuries of the CNS trace their clinical sequelae to neuronal dysfunction or failure. Remarkably, discussion of neuronal activity is largely absent in clinical neuroscience. Advances in neurotechnology and computational capabilities, accompanied by shifts in theoretical frameworks, have led to renewed interest in the information represented by single neurons. Using direct interfaces with the nervous system, millisecond-scale information will soon be extracted from single neurons in clinical environments, supporting personalized treatment of neurologic and psychiatric disease. In this Perspective, we focus on single-neuronal activity in restoring communication and motor control in patients suffering from devastating neurological injuries. We also explore the single neuron's role in epilepsy and movement disorders, surgical anesthesia, and in cognitive processes disrupted in neurodegenerative and neuropsychiatric disease. Finally, we speculate on how technological advances will revolutionize neurotherapeutics. Copyright © 2015 Elsevier Inc. All rights reserved.

The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code.

PubMed

Kunkel, Susanne; Schenck, Wolfram

2017-01-01

NEST is a simulator for spiking neuronal networks that commits to a general purpose approach: It allows for high flexibility in the design of network models, and its applications range from small-scale simulations on laptops to brain-scale simulations on supercomputers. Hence, developers need to test their code for various use cases and ensure that changes to code do not impair scalability. However, running a full set of benchmarks on a supercomputer takes up precious compute-time resources and can entail long queuing times. Here, we present the NEST dry-run mode, which enables comprehensive dynamic code analysis without requiring access to high-performance computing facilities. A dry-run simulation is carried out by a single process, which performs all simulation steps except communication as if it was part of a parallel environment with many processes. We show that measurements of memory usage and runtime of neuronal network simulations closely match the corresponding dry-run data. Furthermore, we demonstrate the successful application of the dry-run mode in the areas of profiling and performance modeling.

The NEST Dry-Run Mode: Efficient Dynamic Analysis of Neuronal Network Simulation Code

PubMed Central

Kunkel, Susanne; Schenck, Wolfram

2017-01-01

NEST is a simulator for spiking neuronal networks that commits to a general purpose approach: It allows for high flexibility in the design of network models, and its applications range from small-scale simulations on laptops to brain-scale simulations on supercomputers. Hence, developers need to test their code for various use cases and ensure that changes to code do not impair scalability. However, running a full set of benchmarks on a supercomputer takes up precious compute-time resources and can entail long queuing times. Here, we present the NEST dry-run mode, which enables comprehensive dynamic code analysis without requiring access to high-performance computing facilities. A dry-run simulation is carried out by a single process, which performs all simulation steps except communication as if it was part of a parallel environment with many processes. We show that measurements of memory usage and runtime of neuronal network simulations closely match the corresponding dry-run data. Furthermore, we demonstrate the successful application of the dry-run mode in the areas of profiling and performance modeling. PMID:28701946

Revealing Compartmentalized Diffusion in Living Cells with Interferometric Scattering Microscopy.

PubMed

de Wit, Gabrielle; Albrecht, David; Ewers, Helge; Kukura, Philipp

2018-06-19

The spatiotemporal organization and dynamics of the plasma membrane and its constituents are central to cellular function. Fluorescence-based single-particle tracking has emerged as a powerful approach for studying the single molecule behavior of plasma-membrane-associated events because of its excellent background suppression, at the expense of imaging speed and observation time. Here, we show that interferometric scattering microscopy combined with 40 nm gold nanoparticle labeling can be used to follow the motion of membrane proteins in the plasma membrane of live cultured mammalian cell lines and hippocampal neurons with up to 3 nm precision and 25 μs temporal resolution. The achievable spatiotemporal precision enabled us to reveal signatures of compartmentalization in neurons likely caused by the actin cytoskeleton. Copyright © 2018 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Distributed and Dynamic Neural Encoding of Multiple Motion Directions of Transparently Moving Stimuli in Cortical Area MT

PubMed Central

Xiao, Jianbo

2015-01-01

Segmenting visual scenes into distinct objects and surfaces is a fundamental visual function. To better understand the underlying neural mechanism, we investigated how neurons in the middle temporal cortex (MT) of macaque monkeys represent overlapping random-dot stimuli moving transparently in slightly different directions. It has been shown that the neuronal response elicited by two stimuli approximately follows the average of the responses elicited by the constituent stimulus components presented alone. In this scheme of response pooling, the ability to segment two simultaneously presented motion directions is limited by the width of the tuning curve to motion in a single direction. We found that, although the population-averaged neuronal tuning showed response averaging, subgroups of neurons showed distinct patterns of response tuning and were capable of representing component directions that were separated by a small angle—less than the tuning width to unidirectional stimuli. One group of neurons preferentially represented the component direction at a specific side of the bidirectional stimuli, weighting one stimulus component more strongly than the other. Another group of neurons pooled the component responses nonlinearly and showed two separate peaks in their tuning curves even when the average of the component responses was unimodal. We also show for the first time that the direction tuning of MT neurons evolved from initially representing the vector-averaged direction of slightly different stimuli to gradually representing the component directions. Our results reveal important neural processes underlying image segmentation and suggest that information about slightly different stimulus components is computed dynamically and distributed across neurons. SIGNIFICANCE STATEMENT Natural scenes often contain multiple entities. The ability to segment visual scenes into distinct objects and surfaces is fundamental to sensory processing and is crucial for generating the perception of our environment. Because cortical neurons are broadly tuned to a given visual feature, segmenting two stimuli that differ only slightly is a challenge for the visual system. In this study, we discovered that many neurons in the visual cortex are capable of representing individual components of slightly different stimuli by selectively and nonlinearly pooling the responses elicited by the stimulus components. We also show for the first time that the neural representation of individual stimulus components developed over a period of ∼70–100 ms, revealing a dynamic process of image segmentation. PMID:26658869

Neuronal synchrony: Peculiarity and generality

PubMed Central

Nowotny, Thomas; Huerta, Ramon; Rabinovich, Mikhail I.

2008-01-01

Synchronization in neuronal systems is a new and intriguing application of dynamical systems theory. Why are neuronal systems different as a subject for synchronization? (1) Neurons in themselves are multidimensional nonlinear systems that are able to exhibit a wide variety of different activity patterns. Their “dynamical repertoire” includes regular or chaotic spiking, regular or chaotic bursting, multistability, and complex transient regimes. (2) Usually, neuronal oscillations are the result of the cooperative activity of many synaptically connected neurons (a neuronal circuit). Thus, it is necessary to consider synchronization between different neuronal circuits as well. (3) The synapses that implement the coupling between neurons are also dynamical elements and their intrinsic dynamics influences the process of synchronization or entrainment significantly. In this review we will focus on four new problems: (i) the synchronization in minimal neuronal networks with plastic synapses (synchronization with activity dependent coupling), (ii) synchronization of bursts that are generated by a group of nonsymmetrically coupled inhibitory neurons (heteroclinic synchronization), (iii) the coordination of activities of two coupled neuronal networks (partial synchronization of small composite structures), and (iv) coarse grained synchronization in larger systems (synchronization on a mesoscopic scale). PMID:19045493

Near-field electromagnetic holography for high-resolution analysis of network interactions in neuronal tissue

PubMed Central

Kjeldsen, Henrik D.; Kaiser, Marcus; Whittington, Miles A.

2015-01-01

Background Brain function is dependent upon the concerted, dynamical interactions between a great many neurons distributed over many cortical subregions. Current methods of quantifying such interactions are limited by consideration only of single direct or indirect measures of a subsample of all neuronal population activity. New method Here we present a new derivation of the electromagnetic analogy to near-field acoustic holography allowing high-resolution, vectored estimates of interactions between sources of electromagnetic activity that significantly improves this situation. In vitro voltage potential recordings were used to estimate pseudo-electromagnetic energy flow vector fields, current and energy source densities and energy dissipation in reconstruction planes at depth into the neural tissue parallel to the recording plane of the microelectrode array. Results The properties of the reconstructed near-field estimate allowed both the utilization of super-resolution techniques to increase the imaging resolution beyond that of the microelectrode array, and facilitated a novel approach to estimating causal relationships between activity in neocortical subregions. Comparison with existing methods The holographic nature of the reconstruction method allowed significantly better estimation of the fine spatiotemporal detail of neuronal population activity, compared with interpolation alone, beyond the spatial resolution of the electrode arrays used. Pseudo-energy flow vector mapping was possible with high temporal precision, allowing a near-realtime estimate of causal interaction dynamics. Conclusions Basic near-field electromagnetic holography provides a powerful means to increase spatial resolution from electrode array data with careful choice of spatial filters and distance to reconstruction plane. More detailed approaches may provide the ability to volumetrically reconstruct activity patterns on neuronal tissue, but the ability to extract vectored data with the method presented already permits the study of dynamic causal interactions without bias from any prior assumptions on anatomical connectivity. PMID:26026581

Near-field electromagnetic holography for high-resolution analysis of network interactions in neuronal tissue.

PubMed

Kjeldsen, Henrik D; Kaiser, Marcus; Whittington, Miles A

2015-09-30

Brain function is dependent upon the concerted, dynamical interactions between a great many neurons distributed over many cortical subregions. Current methods of quantifying such interactions are limited by consideration only of single direct or indirect measures of a subsample of all neuronal population activity. Here we present a new derivation of the electromagnetic analogy to near-field acoustic holography allowing high-resolution, vectored estimates of interactions between sources of electromagnetic activity that significantly improves this situation. In vitro voltage potential recordings were used to estimate pseudo-electromagnetic energy flow vector fields, current and energy source densities and energy dissipation in reconstruction planes at depth into the neural tissue parallel to the recording plane of the microelectrode array. The properties of the reconstructed near-field estimate allowed both the utilization of super-resolution techniques to increase the imaging resolution beyond that of the microelectrode array, and facilitated a novel approach to estimating causal relationships between activity in neocortical subregions. The holographic nature of the reconstruction method allowed significantly better estimation of the fine spatiotemporal detail of neuronal population activity, compared with interpolation alone, beyond the spatial resolution of the electrode arrays used. Pseudo-energy flow vector mapping was possible with high temporal precision, allowing a near-realtime estimate of causal interaction dynamics. Basic near-field electromagnetic holography provides a powerful means to increase spatial resolution from electrode array data with careful choice of spatial filters and distance to reconstruction plane. More detailed approaches may provide the ability to volumetrically reconstruct activity patterns on neuronal tissue, but the ability to extract vectored data with the method presented already permits the study of dynamic causal interactions without bias from any prior assumptions on anatomical connectivity. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Frontal Cortex Activation Causes Rapid Plasticity of Auditory Cortical Processing

PubMed Central

Winkowski, Daniel E.; Bandyopadhyay, Sharba; Shamma, Shihab A.

2013-01-01

Neurons in the primary auditory cortex (A1) can show rapid changes in receptive fields when animals are engaged in sound detection and discrimination tasks. The source of a signal to A1 that triggers these changes is suspected to be in frontal cortical areas. How or whether activity in frontal areas can influence activity and sensory processing in A1 and the detailed changes occurring in A1 on the level of single neurons and in neuronal populations remain uncertain. Using electrophysiological techniques in mice, we found that pairing orbitofrontal cortex (OFC) stimulation with sound stimuli caused rapid changes in the sound-driven activity within A1 that are largely mediated by noncholinergic mechanisms. By integrating in vivo two-photon Ca2+ imaging of A1 with OFC stimulation, we found that pairing OFC activity with sounds caused dynamic and selective changes in sensory responses of neural populations in A1. Further, analysis of changes in signal and noise correlation after OFC pairing revealed improvement in neural population-based discrimination performance within A1. This improvement was frequency specific and dependent on correlation changes. These OFC-induced influences on auditory responses resemble behavior-induced influences on auditory responses and demonstrate that OFC activity could underlie the coordination of rapid, dynamic changes in A1 to dynamic sensory environments. PMID:24227723

Self-Organized Information Processing in Neuronal Networks: Replacing Layers in Deep Networks by Dynamics

NASA Astrophysics Data System (ADS)

Kirst, Christoph

It is astonishing how the sub-parts of a brain co-act to produce coherent behavior. What are mechanism that coordinate information processing and communication and how can those be changed flexibly in order to cope with variable contexts? Here we show that when information is encoded in the deviations around a collective dynamical reference state of a recurrent network the propagation of these fluctuations is strongly dependent on precisely this underlying reference. Information here 'surfs' on top of the collective dynamics and switching between states enables fast and flexible rerouting of information. This in turn affects local processing and consequently changes in the global reference dynamics that re-regulate the distribution of information. This provides a generic mechanism for self-organized information processing as we demonstrate with an oscillatory Hopfield network that performs contextual pattern recognition. Deep neural networks have proven to be very successful recently. Here we show that generating information channels via collective reference dynamics can effectively compress a deep multi-layer architecture into a single layer making this mechanism a promising candidate for the organization of information processing in biological neuronal networks.

Synchronization transition in neuronal networks composed of chaotic or non-chaotic oscillators.

PubMed

Xu, Kesheng; Maidana, Jean Paul; Castro, Samy; Orio, Patricio

2018-05-30

Chaotic dynamics has been shown in the dynamics of neurons and neural networks, in experimental data and numerical simulations. Theoretical studies have proposed an underlying role of chaos in neural systems. Nevertheless, whether chaotic neural oscillators make a significant contribution to network behaviour and whether the dynamical richness of neural networks is sensitive to the dynamics of isolated neurons, still remain open questions. We investigated synchronization transitions in heterogeneous neural networks of neurons connected by electrical coupling in a small world topology. The nodes in our model are oscillatory neurons that - when isolated - can exhibit either chaotic or non-chaotic behaviour, depending on conductance parameters. We found that the heterogeneity of firing rates and firing patterns make a greater contribution than chaos to the steepness of the synchronization transition curve. We also show that chaotic dynamics of the isolated neurons do not always make a visible difference in the transition to full synchrony. Moreover, macroscopic chaos is observed regardless of the dynamics nature of the neurons. However, performing a Functional Connectivity Dynamics analysis, we show that chaotic nodes can promote what is known as multi-stable behaviour, where the network dynamically switches between a number of different semi-synchronized, metastable states.

A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements.

PubMed

Durstewitz, Daniel

2017-06-01

The computational and cognitive properties of neural systems are often thought to be implemented in terms of their (stochastic) network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit recordings or neuroimaging data, is an important step toward understanding its computations. Ideally, one would not only seek a (lower-dimensional) state space representation of the dynamics, but would wish to have access to its statistical properties and their generative equations for in-depth analysis. Recurrent neural networks (RNNs) are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective. Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs (PLRNNs) within the statistical framework of state space models, which accounts for noise in both the underlying latent dynamics and the observation process. The Expectation-Maximization algorithm is used to infer the latent state distribution, through a global Laplace approximation, and the PLRNN parameters iteratively. After validating the procedure on toy examples, and using inference through particle filters for comparison, the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex (ACC) obtained during performance of a classical working memory task, delayed alternation. Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance, including stimulus-selective delay activity. The estimated models were rarely multi-stable, however, but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point. In summary, the present work advances a semi-analytical (thus reasonably fast) maximum-likelihood estimation framework for PLRNNs that may enable to recover relevant aspects of the nonlinear dynamics underlying observed neuronal time series, and directly link these to computational properties.

Visualizing long-term single-molecule dynamics in vivo by stochastic protein labeling.

PubMed

Liu, Hui; Dong, Peng; Ioannou, Maria S; Li, Li; Shea, Jamien; Pasolli, H Amalia; Grimm, Jonathan B; Rivlin, Patricia K; Lavis, Luke D; Koyama, Minoru; Liu, Zhe

2018-01-09

Our ability to unambiguously image and track individual molecules in live cells is limited by packing of multiple copies of labeled molecules within the resolution limit. Here we devise a universal genetic strategy to precisely control copy number of fluorescently labeled molecules in a cell. This system has a dynamic range of ∼10,000-fold, enabling sparse labeling of proteins expressed at different abundance levels. Combined with photostable labels, this system extends the duration of automated single-molecule tracking by two orders of magnitude. We demonstrate long-term imaging of synaptic vesicle dynamics in cultured neurons as well as in intact zebrafish. We found axon initial segment utilizes a "waterfall" mechanism gating synaptic vesicle transport polarity by promoting anterograde transport processivity. Long-time observation also reveals that transcription factor hops between clustered binding sites in spatially restricted subnuclear regions, suggesting that topological structures in the nucleus shape local gene activities by a sequestering mechanism. This strategy thus greatly expands the spatiotemporal length scales of live-cell single-molecule measurements, enabling new experiments to quantitatively understand complex control of molecular dynamics in vivo.

Live Imaging of Calcium Dynamics during Axon Degeneration Reveals Two Functionally Distinct Phases of Calcium Influx

PubMed Central

Yamagishi, Yuya; Tessier-Lavigne, Marc

2015-01-01

Calcium is a key regulator of axon degeneration caused by trauma and disease, but its specific spatial and temporal dynamics in injured axons remain unclear. To clarify the function of calcium in axon degeneration, we observed calcium dynamics in single injured neurons in live zebrafish larvae and tested the temporal requirement for calcium in zebrafish neurons and cultured mouse DRG neurons. Using laser axotomy to induce Wallerian degeneration (WD) in zebrafish peripheral sensory axons, we monitored calcium dynamics from injury to fragmentation, revealing two stereotyped phases of axonal calcium influx. First, axotomy triggered a transient local calcium wave originating at the injury site. This initial calcium wave only disrupted mitochondria near the injury site and was not altered by expression of the protective WD slow (WldS) protein. Inducing multiple waves with additional axotomies did not change the kinetics of degeneration. In contrast, a second phase of calcium influx occurring minutes before fragmentation spread as a wave throughout the axon, entered mitochondria, and was abolished by WldS expression. In live zebrafish, chelating calcium after the first wave, but before the second wave, delayed the progress of fragmentation. In cultured DRG neurons, chelating calcium early in the process of WD did not alter degeneration, but chelating calcium late in WD delayed fragmentation. We propose that a terminal calcium wave is a key instructive component of the axon degeneration program. SIGNIFICANCE STATEMENT Axon degeneration resulting from trauma or neurodegenerative disease can cause devastating deficits in neural function. Understanding the molecular and cellular events that execute axon degeneration is essential for developing treatments to address these conditions. Calcium is known to contribute to axon degeneration, but its temporal requirements in this process have been unclear. Live calcium imaging in severed zebrafish neurons and temporally controlled pharmacological treatments in both zebrafish and cultured mouse sensory neurons revealed that axonal calcium influx late in the degeneration process regulates axon fragmentation. These findings suggest that temporal considerations will be crucial for developing treatments for diseases associated with axon degeneration. PMID:26558774

Electrical stimulation of dorsal root entry zone attenuates wide-dynamic range neuronal activity in rats

PubMed Central

Yang, Fei; Zhang, Chen; Xu, Qian; Tiwari, Vinod; He, Shao-Qiu; Wang, Yun; Dong, Xinzhong; Vera-Portocarrero, Louis P.; Wacnik, Paul W.; Raja, Srinivasa N.; Guan, Yun

2014-01-01

Objectives Recent clinical studies suggest that neurostimulation at the dorsal root entry zone (DREZ) may alleviate neuropathic pain. However, the mechanisms of action for this therapeutic effect are unclear. Here, we examined whether DREZ stimulation inhibits spinal wide-dynamic-range (WDR) neuronal activity in nerve-injured rats. Materials and Methods We conducted in vivo extracellular single-unit recordings of WDR neurons in rats after an L5 spinal nerve ligation (SNL) or sham surgery. We set bipolar electrical stimulation (50 Hz, 0.2 ms, 5 min) of the DREZ at the intensity that activated only Aα/β-fibers by measuring the lowest current at which DREZ stimulation evoked a peak antidromic sciatic Aα/β-compound action potential without inducing an Aδ/C-compound action potential (i.e., Ab1). Results The elevated spontaneous activity rate of WDR neurons in SNL rats [n=25; data combined from day 14–16 (n = 15) and day 45–75 post-SNL groups (n=10)] was significantly decreased from the pre-stimulation level (p<0.01) at 0–15 min and 30–45 min post-stimulation. In both sham-operated (n=8) and nerve-injured rats, DREZ stimulation attenuated the C-component, but not A-component, of the WDR neuronal response to graded intracutaneous electrical stimuli (0.1–10 mA, 2 ms) applied to the skin receptive field. Further, DREZ stimulation blocked windup (a short form of neuronal sensitization) to repetitive noxious stimuli (0.5 Hz) at 0–15 min in all groups (p<0.05). Conclusions Attenuation of WDR neuronal activity may contribute to DREZ stimulation-induced analgesia. This finding supports the notion that DREZ may be a useful target for neuromodulatory control of pain. PMID:25308522

An integrated multi-electrode-optrode array for in vitro optogenetics

PubMed Central

Welkenhuysen, Marleen; Hoffman, Luis; Luo, Zhengxiang; De Proft, Anabel; Van den Haute, Chris; Baekelandt, Veerle; Debyser, Zeger; Gielen, Georges; Puers, Robert; Braeken, Dries

2016-01-01

Modulation of a group of cells or tissue needs to be very precise in order to exercise effective control over the cell population under investigation. Optogenetic tools have already demonstrated to be of great value in the study of neuronal circuits and in neuromodulation. Ideally, they should permit very accurate resolution, preferably down to the single cell level. Further, to address a spatially distributed sample, independently addressable multiple optical outputs should be present. In current techniques, at least one of these requirements is not fulfilled. In addition to this, it is interesting to directly monitor feedback of the modulation by electrical registration of the activity of the stimulated cells. Here, we present the fabrication and characterization of a fully integrated silicon-based multi-electrode-optrode array (MEOA) for in vitro optogenetics. We demonstrate that this device allows for artifact-free electrical recording. Moreover, the MEOA was used to reliably elicit spiking activity from ChR2-transduced neurons. Thanks to the single cell resolution stimulation capability, we could determine spatial and temporal activation patterns and spike latencies of the neuronal network. This integrated approach to multi-site combined optical stimulation and electrical recording significantly advances today’s tool set for neuroscientists in their search to unravel neuronal network dynamics. PMID:26832455

Dynamic SERS nanosensor for neurotransmitter sensing near neurons.

PubMed

Lussier, Félix; Brulé, Thibault; Bourque, Marie-Josée; Ducrot, Charles; Trudeau, Louis-Éric; Masson, Jean-François

2017-12-04

Current electrophysiology and electrochemistry techniques have provided unprecedented understanding of neuronal activity. However, these techniques are suited to a small, albeit important, panel of neurotransmitters such as glutamate, GABA and dopamine, and these constitute only a subset of the broader range of neurotransmitters involved in brain chemistry. Surface-enhanced Raman scattering (SERS) provides a unique opportunity to detect a broader range of neurotransmitters in close proximity to neurons. Dynamic SERS (D-SERS) nanosensors based on patch-clamp-like nanopipettes decorated with gold nanoraspberries can be located accurately under a microscope using techniques analogous to those used in current electrophysiology or electrochemistry experiments. In this manuscript, we demonstrate that D-SERS can measure in a single experiment ATP, glutamate (glu), acetylcholine (ACh), GABA and dopamine (DA), among other neurotransmitters, with the potential for detecting a greater number of neurotransmitters. The SERS spectra of these neurotransmitters were identified with a barcoding data processing method and time series of the neurotransmitter levels were constructed. The D-SERS nanosensor was then located near cultured mouse dopaminergic neurons. The detection of neurotransmitters was performed in response to a series of K + depolarisations, and allowed the detection of elevated levels of both ATP and dopamine. Control experiments were also performed near glial cells, showing only very low basal detection neurotransmitter events. This paper demonstrates the potential of D-SERS to detect neurotransmitter secretion events near living neurons, but also constitutes a strong proof-of-concept for the broad application of SERS to the detection of secretion events by neurons or other cell types in order to study normal or pathological cell functions.

Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models.

PubMed

Mazzoni, Alberto; Lindén, Henrik; Cuntz, Hermann; Lansner, Anders; Panzeri, Stefano; Einevoll, Gaute T

2015-12-01

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.

Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models

PubMed Central

Cuntz, Hermann; Lansner, Anders; Panzeri, Stefano; Einevoll, Gaute T.

2015-01-01

Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best “LFP proxy”, we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with “ground-truth” LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo. PMID:26657024

Discrimination of communication vocalizations by single neurons and groups of neurons in the auditory midbrain.

PubMed

Schneider, David M; Woolley, Sarah M N

2010-06-01

Many social animals including songbirds use communication vocalizations for individual recognition. The perception of vocalizations depends on the encoding of complex sounds by neurons in the ascending auditory system, each of which is tuned to a particular subset of acoustic features. Here, we examined how well the responses of single auditory neurons could be used to discriminate among bird songs and we compared discriminability to spectrotemporal tuning. We then used biologically realistic models of pooled neural responses to test whether the responses of groups of neurons discriminated among songs better than the responses of single neurons and whether discrimination by groups of neurons was related to spectrotemporal tuning and trial-to-trial response variability. The responses of single auditory midbrain neurons could be used to discriminate among vocalizations with a wide range of abilities, ranging from chance to 100%. The ability to discriminate among songs using single neuron responses was not correlated with spectrotemporal tuning. Pooling the responses of pairs of neurons generally led to better discrimination than the average of the two inputs and the most discriminating input. Pooling the responses of three to five single neurons continued to improve neural discrimination. The increase in discriminability was largest for groups of neurons with similar spectrotemporal tuning. Further, we found that groups of neurons with correlated spike trains achieved the largest gains in discriminability. We simulated neurons with varying levels of temporal precision and measured the discriminability of responses from single simulated neurons and groups of simulated neurons. Simulated neurons with biologically observed levels of temporal precision benefited more from pooling correlated inputs than did neurons with highly precise or imprecise spike trains. These findings suggest that pooling correlated neural responses with the levels of precision observed in the auditory midbrain increases neural discrimination of complex vocalizations.

Orientation selectivity in inhibition-dominated networks of spiking neurons: effect of single neuron properties and network dynamics.

PubMed

Sadeh, Sadra; Rotter, Stefan

2015-01-01

The neuronal mechanisms underlying the emergence of orientation selectivity in the primary visual cortex of mammals are still elusive. In rodents, visual neurons show highly selective responses to oriented stimuli, but neighboring neurons do not necessarily have similar preferences. Instead of a smooth map, one observes a salt-and-pepper organization of orientation selectivity. Modeling studies have recently confirmed that balanced random networks are indeed capable of amplifying weakly tuned inputs and generating highly selective output responses, even in absence of feature-selective recurrent connectivity. Here we seek to elucidate the neuronal mechanisms underlying this phenomenon by resorting to networks of integrate-and-fire neurons, which are amenable to analytic treatment. Specifically, in networks of perfect integrate-and-fire neurons, we observe that highly selective and contrast invariant output responses emerge, very similar to networks of leaky integrate-and-fire neurons. We then demonstrate that a theory based on mean firing rates and the detailed network topology predicts the output responses, and explains the mechanisms underlying the suppression of the common-mode, amplification of modulation, and contrast invariance. Increasing inhibition dominance in our networks makes the rectifying nonlinearity more prominent, which in turn adds some distortions to the otherwise essentially linear prediction. An extension of the linear theory can account for all the distortions, enabling us to compute the exact shape of every individual tuning curve in our networks. We show that this simple form of nonlinearity adds two important properties to orientation selectivity in the network, namely sharpening of tuning curves and extra suppression of the modulation. The theory can be further extended to account for the nonlinearity of the leaky model by replacing the rectifier by the appropriate smooth input-output transfer function. These results are robust and do not depend on the state of network dynamics, and hold equally well for mean-driven and fluctuation-driven regimes of activity.

Orientation Selectivity in Inhibition-Dominated Networks of Spiking Neurons: Effect of Single Neuron Properties and Network Dynamics

PubMed Central

Sadeh, Sadra; Rotter, Stefan

2015-01-01

The neuronal mechanisms underlying the emergence of orientation selectivity in the primary visual cortex of mammals are still elusive. In rodents, visual neurons show highly selective responses to oriented stimuli, but neighboring neurons do not necessarily have similar preferences. Instead of a smooth map, one observes a salt-and-pepper organization of orientation selectivity. Modeling studies have recently confirmed that balanced random networks are indeed capable of amplifying weakly tuned inputs and generating highly selective output responses, even in absence of feature-selective recurrent connectivity. Here we seek to elucidate the neuronal mechanisms underlying this phenomenon by resorting to networks of integrate-and-fire neurons, which are amenable to analytic treatment. Specifically, in networks of perfect integrate-and-fire neurons, we observe that highly selective and contrast invariant output responses emerge, very similar to networks of leaky integrate-and-fire neurons. We then demonstrate that a theory based on mean firing rates and the detailed network topology predicts the output responses, and explains the mechanisms underlying the suppression of the common-mode, amplification of modulation, and contrast invariance. Increasing inhibition dominance in our networks makes the rectifying nonlinearity more prominent, which in turn adds some distortions to the otherwise essentially linear prediction. An extension of the linear theory can account for all the distortions, enabling us to compute the exact shape of every individual tuning curve in our networks. We show that this simple form of nonlinearity adds two important properties to orientation selectivity in the network, namely sharpening of tuning curves and extra suppression of the modulation. The theory can be further extended to account for the nonlinearity of the leaky model by replacing the rectifier by the appropriate smooth input-output transfer function. These results are robust and do not depend on the state of network dynamics, and hold equally well for mean-driven and fluctuation-driven regimes of activity. PMID:25569445

Auditory cortical neurons are sensitive to static and continuously changing interaural phase cues.

PubMed

Reale, R A; Brugge, J F

1990-10-01

1. The interaural-phase-difference (IPD) sensitivity of single neurons in the primary auditory (AI) cortex of the anesthetized cat was studied at stimulus frequencies ranging from 120 to 2,500 Hz. Best frequencies of the 43 AI cells sensitive to IPD ranged from 190 to 2,400 Hz. 2. A static IPD was produced when a pair of low-frequency tone bursts, differing from one another only in starting phase, were presented dichotically. The resulting IPD-sensitivity curves, which plot the number of discharges evoked by the binaural signal as a function of IPD, were deeply modulated circular functions. IPD functions were analyzed for their mean vector length (r) and mean interaural phase (phi). Phase sensitivity was relatively independent of best frequency (BF) but highly dependent on stimulus frequency. Regardless of BF or stimulus frequency within the excitatory response area the majority of cells fired maximally when the ipsilateral tone lagged the contralateral signal and fired least when this interaural-phase relationship was reversed. 3. Sensitivity to continuously changing IPD was studied by delivering to the two ears 3-s tones that differed slightly in frequency, resulting in a binaural beat. Approximately 26% of the cells that showed a sensitivity to static changes in IPD also showed a sensitivity to dynamically changing IPD created by this binaural tonal combination. The discharges were highly periodic and tightly synchronized to a particular phase of the binaural beat cycle. High synchrony can be attributed to the fact that cortical neurons typically respond to an excitatory stimulus with but a single spike that is often precisely timed to stimulus onset. A period histogram, binned on the binaural beat frequency (fb), produced an equivalent IPD-sensitivity function for dynamically changing interaural phase. For neurons sensitive to both static and continuously changing interaural phase there was good correspondence between their static (phi s) and dynamic (phi d) mean interaural phases. 4. All cells responding to a dynamically changing stimulus exhibited a linear relationship between mean interaural phase and beat frequency. Most cells responded equally well to binaural beats regardless of the initial direction of phase change. For a fixed duration stimulus, and at relatively low fb, the number of spikes evoked increased with increasing fb, reflecting the increasing number of effective stimulus cycles. At higher fb, AI neurons were unable to follow the rate at which the most effective phase repeated itself during the 3 s of stimulation.(ABSTRACT TRUNCATED AT 400 WORDS)

Spiking irregularity and frequency modulate the behavioral report of single-neuron stimulation.

PubMed

Doron, Guy; von Heimendahl, Moritz; Schlattmann, Peter; Houweling, Arthur R; Brecht, Michael

2014-02-05

The action potential activity of single cortical neurons can evoke measurable sensory effects, but it is not known how spiking parameters and neuronal subtypes affect the evoked sensations. Here, we examined the effects of spike train irregularity, spike frequency, and spike number on the detectability of single-neuron stimulation in rat somatosensory cortex. For regular-spiking, putative excitatory neurons, detectability increased with spike train irregularity and decreasing spike frequencies but was not affected by spike number. Stimulation of single, fast-spiking, putative inhibitory neurons led to a larger sensory effect compared to regular-spiking neurons, and the effect size depended only on spike irregularity. An ideal-observer analysis suggests that, under our experimental conditions, rats were using integration windows of a few hundred milliseconds or more. Our data imply that the behaving animal is sensitive to single neurons' spikes and even to their temporal patterning. Copyright © 2014 Elsevier Inc. All rights reserved.

Raindrops of synaptic noise on dual excitability landscape: an approach to astrocyte network modelling

NASA Astrophysics Data System (ADS)

Verisokin, Andrey Yu.; Postnov, Dmitry E.; Verveyko, Darya V.; Brazhe, Alexey R.

2018-04-01

The most abundant non-neuronal cells in the brain, astrocytes, populate all parts of the central nervous system (CNS). Astrocytic calcium activity ranging from subcellular sparkles to intercellular waves is believed to be the key to a plethora of regulatory pathways in the central nervous system from synaptic plasticity to blood flow regulation. Modeling of the calcium wave initiation and transmission and their spatiotemporal dynamics is therefore an important step stone in understanding the crucial cogs of cognition. Astrocytes are active sensors of ongoing neuronal and synaptic activity, and neurotransmitters diffusing from the synaptic cleft make a strong impact on the astrocytic activity. Here we propose a model describing the patterns of calcium wave formation at a single cell level and discuss the interplay between astrocyte shape the calcium waves dynamics driven by local stochastic surges of glutamate simulating synaptic activity.

Dynamic nigrostriatal dopamine biases action selection

PubMed Central

Howard, Christopher D.; Li, Hao; Geddes, Claire E.; Jin, Xin

2017-01-01

Summary Dopamine is thought to play a critical role in reinforcement learning and goal-directed behavior, but its function in action selection remains largely unknown. Here, we demonstrate that nigrostriatal dopamine biases ongoing action selection. When mice were trained to dynamically switch the action selected at different time points, changes in firing rate of nigrostriatal dopamine neurons, as well as dopamine signaling in the dorsal striatum, were found to be associated with action selection. This dopamine profile is specific to behavioral choice, scalable with interval duration, and doesn’t reflect reward prediction error, timing, or value as single factors alone. Genetic deletion of NMDA receptors on dopamine or striatal neurons, or optogenetic manipulation of dopamine concentration, alters dopamine signaling and biases action selection. These results unveil a crucial role of nigrostriatal dopamine in integrating diverse information for regulating upcoming actions and have important implications for neurological disorders including Parkinson’s disease and substance dependence. PMID:28285820

Dynamic Nigrostriatal Dopamine Biases Action Selection.

PubMed

Howard, Christopher D; Li, Hao; Geddes, Claire E; Jin, Xin

2017-03-22

Dopamine is thought to play a critical role in reinforcement learning and goal-directed behavior, but its function in action selection remains largely unknown. Here we demonstrate that nigrostriatal dopamine biases ongoing action selection. When mice were trained to dynamically switch the action selected at different time points, changes in firing rate of nigrostriatal dopamine neurons, as well as dopamine signaling in the dorsal striatum, were found to be associated with action selection. This dopamine profile is specific to behavioral choice, scalable with interval duration, and doesn't reflect reward prediction error, timing, or value as single factors alone. Genetic deletion of NMDA receptors on dopamine or striatal neurons or optogenetic manipulation of dopamine concentration alters dopamine signaling and biases action selection. These results unveil a crucial role of nigrostriatal dopamine in integrating diverse information for regulating upcoming actions, and they have important implications for neurological disorders, including Parkinson's disease and substance dependence. Copyright © 2017 Elsevier Inc. All rights reserved.

Global dynamics of selective attention and its lapses in primary auditory cortex.

PubMed

Lakatos, Peter; Barczak, Annamaria; Neymotin, Samuel A; McGinnis, Tammy; Ross, Deborah; Javitt, Daniel C; O'Connell, Monica Noelle

2016-12-01

Previous research demonstrated that while selectively attending to relevant aspects of the external world, the brain extracts pertinent information by aligning its neuronal oscillations to key time points of stimuli or their sampling by sensory organs. This alignment mechanism is termed oscillatory entrainment. We investigated the global, long-timescale dynamics of this mechanism in the primary auditory cortex of nonhuman primates, and hypothesized that lapses of entrainment would correspond to lapses of attention. By examining electrophysiological and behavioral measures, we observed that besides the lack of entrainment by external stimuli, attentional lapses were also characterized by high-amplitude alpha oscillations, with alpha frequency structuring of neuronal ensemble and single-unit operations. Entrainment and alpha-oscillation-dominated periods were strongly anticorrelated and fluctuated rhythmically at an ultra-slow rate. Our results indicate that these two distinct brain states represent externally versus internally oriented computational resources engaged by large-scale task-positive and task-negative functional networks.

Control of neuronal polarity and plasticity--a renaissance for microtubules?

PubMed

Hoogenraad, Casper C; Bradke, Frank

2009-12-01

Microtubules have been regarded as essential structures for stable neuronal morphology but new studies are highlighting their role in dynamic neuronal processes. Recent work demonstrates that the microtubule cytoskeleton has an active role during different phases of neuronal polarization - microtubules and their stability determine axon formation, they maintain the identity of axons and they regulate the dynamics of dendritic spines, the major sites of excitatory synaptic input. Although microtubules fulfill distinct cellular functions at different developmental stages, the underlying molecular mechanisms are remarkably similar. Reccurring themes are that microtubules direct specific membrane traffic and affect actin dynamics to locally organize axon growth and spine dynamics. We review the novel role of microtubules during neuronal development and discuss models for microtubule-dependent signaling in neuronal plasticity.

Synchrony dynamics underlying effective connectivity reconstruction of neuronal circuits

NASA Astrophysics Data System (ADS)

Yu, Haitao; Guo, Xinmeng; Qin, Qing; Deng, Yun; Wang, Jiang; Liu, Jing; Cao, Yibin

2017-04-01

Reconstruction of effective connectivity between neurons is essential for neural systems with function-related significance, characterizing directionally causal influences among neurons. In this work, causal interactions between neurons in spinal dorsal root ganglion, activated by manual acupuncture at Zusanli acupoint of experimental rats, are estimated using Granger causality (GC) method. Different patterns of effective connectivity are obtained for different frequencies and types of acupuncture. Combined with synchrony analysis between neurons, we show a dependence of effective connection on the synchronization dynamics. Based on the experimental findings, a neuronal circuit model with synaptic connections is constructed. The variation of neuronal effective connectivity with respect to its structural connectivity and synchronization dynamics is further explored. Simulation results show that reciprocally causal interactions with statistically significant are formed between well-synchronized neurons. The effective connectivity may be not necessarily equivalent to synaptic connections, but rather depend on the synchrony relationship. Furthermore, transitions of effective interaction between neurons are observed following the synchronization transitions induced by conduction delay and synaptic conductance. These findings are helpful to further investigate the dynamical mechanisms underlying the reconstruction of effective connectivity of neuronal population.

Metastable dynamical patterns and their stabilization in arrays of bidirectionally coupled sigmoidal neurons

NASA Astrophysics Data System (ADS)

Horikawa, Yo

2013-12-01

Transient patterns in a bistable ring of bidirectionally coupled sigmoidal neurons were studied. When the system had a pair of spatially uniform steady solutions, the instability of unstable spatially nonuniform steady solutions decreased exponentially with the number of neurons because of the symmetry of the system. As a result, transient spatially nonuniform patterns showed dynamical metastability: Their duration increased exponentially with the number of neurons and the duration of randomly generated patterns obeyed a power-law distribution. However, these metastable dynamical patterns were easily stabilized in the presence of small variations in coupling strength. Metastable rotating waves and their pinning in the presence of asymmetry in the direction of coupling and the disappearance of metastable dynamical patterns due to asymmetry in the output function of a neuron were also examined. Further, in a two-dimensional array of neurons with nearest-neighbor coupling, intrinsically one-dimensional patterns were dominant in transients, and self-excitation in these neurons affected the metastable dynamical patterns.

In vivo neuronal calcium imaging in C. elegans.

PubMed

Chung, Samuel H; Sun, Lin; Gabel, Christopher V

2013-04-10

The nematode worm C. elegans is an ideal model organism for relatively simple, low cost neuronal imaging in vivo. Its small transparent body and simple, well-characterized nervous system allows identification and fluorescence imaging of any neuron within the intact animal. Simple immobilization techniques with minimal impact on the animal's physiology allow extended time-lapse imaging. The development of genetically-encoded calcium sensitive fluorophores such as cameleon and GCaMP allow in vivo imaging of neuronal calcium relating both cell physiology and neuronal activity. Numerous transgenic strains expressing these fluorophores in specific neurons are readily available or can be constructed using well-established techniques. Here, we describe detailed procedures for measuring calcium dynamics within a single neuron in vivo using both GCaMP and cameleon. We discuss advantages and disadvantages of both as well as various methods of sample preparation (animal immobilization) and image analysis. Finally, we present results from two experiments: 1) Using GCaMP to measure the sensory response of a specific neuron to an external electrical field and 2) Using cameleon to measure the physiological calcium response of a neuron to traumatic laser damage. Calcium imaging techniques such as these are used extensively in C. elegans and have been extended to measurements in freely moving animals, multiple neurons simultaneously and comparison across genetic backgrounds. C. elegans presents a robust and flexible system for in vivo neuronal imaging with advantages over other model systems in technical simplicity and cost.

Molecular determinants of magnesium-dependent synaptic plasticity at electrical synapses formed by connexin36

NASA Astrophysics Data System (ADS)

Palacios-Prado, Nicolás; Chapuis, Sandrine; Panjkovich, Alejandro; Fregeac, Julien; Nagy, James I.; Bukauskas, Feliksas F.

2014-08-01

Neuronal gap junction (GJ) channels composed of connexin36 (Cx36) play an important role in neuronal synchronization and network dynamics. Here we show that Cx36-containing electrical synapses between inhibitory neurons of the thalamic reticular nucleus are bidirectionally modulated by changes in intracellular free magnesium concentration ([Mg2+]i). Chimeragenesis demonstrates that the first extracellular loop of Cx36 contains a Mg2+-sensitive domain, and site-directed mutagenesis shows that the pore-lining residue D47 is critical in determining high Mg2+-sensitivity. Single-channel analysis of Mg2+-sensitive chimeras and mutants reveals that [Mg2+]i controls the strength of electrical coupling mostly via gating mechanisms. In addition, asymmetric transjunctional [Mg2+]i induces strong instantaneous rectification, providing a novel mechanism for electrical rectification in homotypic Cx36 GJs. We suggest that Mg2+-dependent synaptic plasticity of Cx36-containing electrical synapses could underlie neuronal circuit reconfiguration via changes in brain energy metabolism that affects neuronal levels of intracellular ATP and [Mg2+]i.

Cortical pyramidal cells as non-linear oscillators: experiment and spike-generation theory.

PubMed

Brumberg, Joshua C; Gutkin, Boris S

2007-09-26

Cortical neurons are capable of generating trains of action potentials in response to current injections. These discharges can take different forms, e.g., repetitive firing that adapts during the period of current injection or bursting behaviors. We have used a combined experimental and computational approach to characterize the dynamics leading to action potential responses in single neurons. Specifically we investigated the origin of complex firing patterns in response to sinusoidal current injections. Using a reduced model, the theta-neuron, alongside recordings from cortical pyramidal cells we show that both real and simulated neurons show phase-locking to sine wave stimuli up to a critical frequency, above which period skipping and 1-to-x phase-locking occurs. The locking behavior follows a complex "devil's staircase" phenomena, where locked modes are interleaved with irregular firing. We further show that the critical frequency depends on the time scale of spike generation and on the level of spike frequency adaptation. These results suggest that phase-locking of neuronal responses to complex input patterns can be explained by basic properties of the spike-generating machinery.

Genetically encoded proton sensors reveal activity-dependent pH changes in neurons.

PubMed

Raimondo, Joseph V; Irkle, Agnese; Wefelmeyer, Winnie; Newey, Sarah E; Akerman, Colin J

2012-01-01

The regulation of hydrogen ion concentration (pH) is fundamental to cell viability, metabolism, and enzymatic function. Within the nervous system, the control of pH is also involved in diverse and dynamic processes including development, synaptic transmission, and the control of network excitability. As pH affects neuronal activity, and can also itself be altered by neuronal activity, the existence of tools to accurately measure hydrogen ion fluctuations is important for understanding the role pH plays under physiological and pathological conditions. Outside of their use as a marker of synaptic release, genetically encoded pH sensors have not been utilized to study hydrogen ion fluxes associated with network activity. By combining whole-cell patch clamp with simultaneous two-photon or confocal imaging, we quantified the amplitude and time course of neuronal, intracellular, acidic transients evoked by epileptiform activity in two separate in vitro models of temporal lobe epilepsy. In doing so, we demonstrate the suitability of three genetically encoded pH sensors: deGFP4, E(2)GFP, and Cl-sensor for investigating activity-dependent pH changes at the level of single neurons.

Molecular Determinants of Magnesium-Dependent Synaptic Plasticity at Electrical Synapses Formed by Connexin36

PubMed Central

Palacios-Prado, Nicolás; Chapuis, Sandrine; Panjkovich, Alejandro; Fregeac, Julien; Nagy, James I.; Bukauskas, Feliksas F.

2014-01-01

Neuronal gap junction (GJ) channels composed of connexin36 (Cx36) play an important role in neuronal synchronization and network dynamics. Here we show that Cx36-containing electrical synapses between inhibitory neurons of the thalamic reticular nucleus are bi-directionally modulated by changes in intracellular free magnesium concentration ([Mg2+]i). Chimeragenesis demonstrates that the first extracellular loop of Cx36 contains a Mg2+-sensitive domain, and site-directed mutagenesis shows that the pore-lining residue D47 is critical in determining high Mg2+-sensitivity. Single channel analysis of Mg2+-sensitive chimeras and mutants reveals that [Mg2+]i controls the strength of electrical coupling mostly via gating mechanisms. In addition, asymmetric transjunctional [Mg2+]i induces strong instantaneous rectification, providing a novel mechanism for electrical rectification in homotypic Cx36 GJs. We suggest that Mg2+-dependent synaptic plasticity of Cx36-containing electrical synapses could underlie neuronal circuit reconfiguration via changes in brain energy metabolism that affects neuronal levels of intracellular ATP and [Mg2+]i. PMID:25135336

Bayesian population decoding of spiking neurons.

PubMed

Gerwinn, Sebastian; Macke, Jakob; Bethge, Matthias

2009-01-01

The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a 'spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

Neural Correlates of Object-Associated Choice Behavior in the Perirhinal Cortex of Rats

PubMed Central

Ahn, Jae-Rong

2015-01-01

The perirhinal cortex (PRC) is reportedly important for object recognition memory, with supporting physiological evidence obtained largely from primate studies. Whether neurons in the rodent PRC also exhibit similar physiological correlates of object recognition, however, remains to be determined. We recorded single units from the PRC in a PRC-dependent, object-cued spatial choice task in which, when cued by an object image, the rat chose the associated spatial target from two identical discs appearing on a touchscreen monitor. The firing rates of PRC neurons were significantly modulated by critical events in the task, such as object sampling and choice response. Neuronal firing in the PRC was correlated primarily with the conjunctive relationships between an object and its associated choice response, although some neurons also responded to the choice response alone. However, we rarely observed a PRC neuron that represented a specific object exclusively regardless of spatial response in rats, although the neurons were influenced by the perceptual ambiguity of the object at the population level. Some PRC neurons fired maximally after a choice response, and this post-choice feedback signal significantly enhanced the neuronal specificity for the choice response in the subsequent trial. Our findings suggest that neurons in the rat PRC may not participate exclusively in object recognition memory but that their activity may be more dynamically modulated in conjunction with other variables, such as choice response and its outcomes. PMID:25632144

Dynamics of Phosphoinositide-Dependent Signaling in Sympathetic Neurons

PubMed Central

Kruse, Martin; Vivas, Oscar; Traynor-Kaplan, Alexis

2016-01-01

In neurons, loss of plasma membrane phosphatidylinositol 4,5-bisphosphate [PI(4,5)P2] leads to a decrease in exocytosis and changes in electrical excitability. Restoration of PI(4,5)P2 levels after phospholipase C activation is therefore essential for a return to basal neuronal activity. However, the dynamics of phosphoinositide metabolism have not been analyzed in neurons. We measured dynamic changes of PI(4,5)P2, phosphatidylinositol 4-phosphate, diacylglycerol, inositol 1,4,5-trisphosphate, and Ca2+ upon muscarinic stimulation in sympathetic neurons from adult male Sprague-Dawley rats with electrophysiological and optical approaches. We used this kinetic information to develop a quantitative description of neuronal phosphoinositide metabolism. The measurements and analysis show and explain faster synthesis of PI(4,5)P2 in sympathetic neurons than in electrically nonexcitable tsA201 cells. They can be used to understand dynamic effects of receptor-mediated phospholipase C activation on excitability and other PI(4,5)P2-dependent processes in neurons. SIGNIFICANCE STATEMENT Phosphatidylinositol 4,5-bisphosphate [PI(4,5)P2] is a minor phospholipid in the cytoplasmic leaflet of the plasma membrane. Depletion of PI(4,5)P2 via phospholipase C-mediated hydrolysis leads to a decrease in exocytosis and alters electrical excitability in neurons. Restoration of PI(4,5)P2 is essential for a return to basal neuronal activity. However, the dynamics of phosphoinositide metabolism have not been analyzed in neurons. We studied the dynamics of phosphoinositide metabolism in sympathetic neurons upon muscarinic stimulation and used the kinetic information to develop a quantitative description of neuronal phosphoinositide metabolism. The measurements and analysis show a several-fold faster synthesis of PI(4,5)P2 in sympathetic neurons than in an electrically nonexcitable cell line, and provide a framework for future studies of PI(4,5)P2-dependent processes in neurons. PMID:26818524

A low cost, modular, and physiologically inspired electronic neuron

NASA Astrophysics Data System (ADS)

Sitt, J. D.; Campetella, F.; Aliaga, J.

2010-12-01

We describe a low cost design of an electronic neuron, which is designed to represent the dynamical properties of the membrane potential of biological neurons by modeling the states of the membrane channels. This electronic neuron can be used to study the nonlinear properties of the membrane voltage dynamics and to develop and analyze small neuronal circuits using electronic neurons as building blocks.

Transparent, Flexible, Low Noise Graphene Electrodes for Simultaneous Electrophysiology and Neuroimaging

PubMed Central

Kuzum, Duygu; Takano, Hajime; Shim, Euijae; Reed, Jason C; Juul, Halvor; Richardson, Andrew G.; de Vries, Julius; Bink, Hank; Dichter, Marc A.; Lucas, Timothy H.; Coulter, Douglas A.; Cubukcu, Ertugrul; Litt, Brian

2014-01-01

Calcium imaging is a versatile experimental approach capable of resolving single neurons with single-cell spatial resolution in the brain. Electrophysiological recordings provide high temporal, but limited spatial resolution, due to the geometrical inaccessibility of the brain. An approach that integrates the advantages of both techniques could provide new insights into functions of neural circuits. Here, we report a transparent, flexible neural electrode technology based on graphene, which enables simultaneous optical imaging and electrophysiological recording. We demonstrate that hippocampal slices can be imaged through transparent graphene electrodes by both confocal and two-photon microscopy without causing any light-induced artifacts in the electrical recordings. Graphene electrodes record high frequency bursting activity and slow synaptic potentials that are hard to resolve by multi-cellular calcium imaging. This transparent electrode technology may pave the way for high spatio-temporal resolution electrooptic mapping of the dynamic neuronal activity. PMID:25327632

Lipid Regulated Intramolecular Conformational Dynamics of SNARE-Protein Ykt6

NASA Astrophysics Data System (ADS)

Dai, Yawei; Seeger, Markus; Weng, Jingwei; Song, Song; Wang, Wenning; Tan, Yan-Wen

2016-08-01

Cellular informational and metabolic processes are propagated with specific membrane fusions governed by soluble N-ethylmaleimide sensitive factor attachment protein receptors (SNARE). SNARE protein Ykt6 is highly expressed in brain neurons and plays a critical role in the membrane-trafficking process. Studies suggested that Ykt6 undergoes a conformational change at the interface between its longin domain and the SNARE core. In this work, we study the conformational state distributions and dynamics of rat Ykt6 by means of single-molecule Förster Resonance Energy Transfer (smFRET) and Fluorescence Cross-Correlation Spectroscopy (FCCS). We observed that intramolecular conformational dynamics between longin domain and SNARE core occurred at the timescale ~200 μs. Furthermore, this dynamics can be regulated and even eliminated by the presence of lipid dodecylphoshpocholine (DPC). Our molecular dynamic (MD) simulations have shown that, the SNARE core exhibits a flexible structure while the longin domain retains relatively stable in apo state. Combining single molecule experiments and theoretical MD simulations, we are the first to provide a quantitative dynamics of Ykt6 and explain the functional conformational change from a qualitative point of view.

Investigation of vertical cavity surface emitting laser dynamics for neuromorphic photonic systems

NASA Astrophysics Data System (ADS)

Hurtado, A.; Schires, K.; Henning, I. D.; Adams, M. J.

2012-03-01

We report an approach based upon vertical cavity surface emitting lasers (VCSELs) to reproduce optically different behaviors exhibited by biological neurons but on a much faster timescale. The technique proposed is based on the polarization switching and nonlinear dynamics induced in a single VCSEL under polarized optical injection. The particular attributes of VCSELs and the simple experimental configuration used in this work offer prospects of fast, reconfigurable processing elements with excellent fan-out and scaling potentials for use in future computational paradigms and artificial neural networks.

Endogenous Sequential Cortical Activity Evoked by Visual Stimuli

PubMed Central

Miller, Jae-eun Kang; Hamm, Jordan P.; Jackson, Jesse; Yuste, Rafael

2015-01-01

Although the functional properties of individual neurons in primary visual cortex have been studied intensely, little is known about how neuronal groups could encode changing visual stimuli using temporal activity patterns. To explore this, we used in vivo two-photon calcium imaging to record the activity of neuronal populations in primary visual cortex of awake mice in the presence and absence of visual stimulation. Multidimensional analysis of the network activity allowed us to identify neuronal ensembles defined as groups of cells firing in synchrony. These synchronous groups of neurons were themselves activated in sequential temporal patterns, which repeated at much higher proportions than chance and were triggered by specific visual stimuli such as natural visual scenes. Interestingly, sequential patterns were also present in recordings of spontaneous activity without any sensory stimulation and were accompanied by precise firing sequences at the single-cell level. Moreover, intrinsic dynamics could be used to predict the occurrence of future neuronal ensembles. Our data demonstrate that visual stimuli recruit similar sequential patterns to the ones observed spontaneously, consistent with the hypothesis that already existing Hebbian cell assemblies firing in predefined temporal sequences could be the microcircuit substrate that encodes visual percepts changing in time. PMID:26063915

Ensembles of Spiking Neurons with Noise Support Optimal Probabilistic Inference in a Dynamically Changing Environment

PubMed Central

Legenstein, Robert; Maass, Wolfgang

2014-01-01

It has recently been shown that networks of spiking neurons with noise can emulate simple forms of probabilistic inference through “neural sampling”, i.e., by treating spikes as samples from a probability distribution of network states that is encoded in the network. Deficiencies of the existing model are its reliance on single neurons for sampling from each random variable, and the resulting limitation in representing quickly varying probabilistic information. We show that both deficiencies can be overcome by moving to a biologically more realistic encoding of each salient random variable through the stochastic firing activity of an ensemble of neurons. The resulting model demonstrates that networks of spiking neurons with noise can easily track and carry out basic computational operations on rapidly varying probability distributions, such as the odds of getting rewarded for a specific behavior. We demonstrate the viability of this new approach towards neural coding and computation, which makes use of the inherent parallelism of generic neural circuits, by showing that this model can explain experimentally observed firing activity of cortical neurons for a variety of tasks that require rapid temporal integration of sensory information. PMID:25340749

A theory of neural dimensionality, dynamics, and measurement

NASA Astrophysics Data System (ADS)

Ganguli, Surya

In many experiments, neuroscientists tightly control behavior, record many trials, and obtain trial-averaged firing rates from hundreds of neurons in circuits containing millions of behaviorally relevant neurons. Dimensionality reduction has often shown that such datasets are strikingly simple; they can be described using a much smaller number of dimensions than the number of recorded neurons, and the resulting projections onto these dimensions yield a remarkably insightful dynamical portrait of circuit computation. This ubiquitous simplicity raises several profound and timely conceptual questions. What is the origin of this simplicity and its implications for the complexity of brain dynamics? Would neuronal datasets become more complex if we recorded more neurons? How and when can we trust dynamical portraits obtained from only hundreds of neurons in circuits containing millions of neurons? We present a theory that answers these questions, and test it using neural data recorded from reaching monkeys. Overall, this theory yields a picture of the neural measurement process as a random projection of neural dynamics, conceptual insights into how we can reliably recover dynamical portraits in such under-sampled measurement regimes, and quantitative guidelines for the design of future experiments. Moreover, it reveals the existence of phase transition boundaries in our ability to successfully decode cognition and behavior as a function of the number of recorded neurons, the complexity of the task, and the smoothness of neural dynamics. membership pending.

Spike-train communities: finding groups of similar spike trains.

PubMed

Humphries, Mark D

2011-02-09

Identifying similar spike-train patterns is a key element in understanding neural coding and computation. For single neurons, similar spike patterns evoked by stimuli are evidence of common coding. Across multiple neurons, similar spike trains indicate potential cell assemblies. As recording technology advances, so does the urgent need for grouping methods to make sense of large-scale datasets of spike trains. Existing methods require specifying the number of groups in advance, limiting their use in exploratory analyses. I derive a new method from network theory that solves this key difficulty: it self-determines the maximum number of groups in any set of spike trains, and groups them to maximize intragroup similarity. This method brings us revealing new insights into the encoding of aversive stimuli by dopaminergic neurons, and the organization of spontaneous neural activity in cortex. I show that the characteristic pause response of a rat's dopaminergic neuron depends on the state of the superior colliculus: when it is inactive, aversive stimuli invoke a single pattern of dopaminergic neuron spiking; when active, multiple patterns occur, yet the spike timing in each is reliable. In spontaneous multineuron activity from the cortex of anesthetized cat, I show the existence of neural ensembles that evolve in membership and characteristic timescale of organization during global slow oscillations. I validate these findings by showing that the method both is remarkably reliable at detecting known groups and can detect large-scale organization of dynamics in a model of the striatum.

Temporal-pattern recognition by single neurons in a sensory pathway devoted to social communication behavior

PubMed Central

Carlson, Bruce A.

2010-01-01

Sensory systems often encode stimulus information into the temporal pattern of action potential activity. However, little is known about how the information contained within these patterns is extracted by postsynaptic neurons. Similar to temporal coding by sensory neurons, social information in mormyrid fish is encoded into the temporal patterning of an electric organ discharge (EOD). In the current study, sensitivity to temporal patterns of electrosensory stimuli was found to arise within the midbrain posterior exterolateral nucleus (ELp). Whole-cell patch recordings from ELp neurons in vivo revealed three patterns of interpulse interval (IPI) tuning: low-pass neurons tuned to long intervals, high-pass neurons tuned to short intervals and band-pass neurons tuned to intermediate intervals. Many neurons within each class also responded preferentially to either increasing or decreasing IPIs. Playback of electric signaling patterns recorded from freely behaving fish revealed that the IPI and direction tuning of ELp neurons resulted in selective responses to particular social communication displays characterized by distinct IPI patterns. The postsynaptic potential responses of many neurons indicated a combination of excitatory and inhibitory synaptic input, and the IPI tuning of ELp neurons was directly related to rate-dependent changes in the direction and amplitude of postsynaptic potentials. These results suggest that differences in the dynamics of short-term synaptic plasticity in excitatory and inhibitory pathways may tune central sensory neurons to particular temporal patterns of presynaptic activity. This may represent a general mechanism for the processing of behaviorally-relevant stimulus information encoded into temporal patterns of activity by sensory neurons. PMID:19641105

Temporal-pattern recognition by single neurons in a sensory pathway devoted to social communication behavior.

PubMed

Carlson, Bruce A

2009-07-29

Sensory systems often encode stimulus information into the temporal pattern of action potential activity. However, little is known about how the information contained within these patterns is extracted by postsynaptic neurons. Similar to temporal coding by sensory neurons, social information in mormyrid fish is encoded into the temporal patterning of an electric organ discharge. In the current study, sensitivity to temporal patterns of electrosensory stimuli was found to arise within the midbrain posterior exterolateral nucleus (ELp). Whole-cell patch recordings from ELp neurons in vivo revealed three patterns of interpulse interval (IPI) tuning: low-pass neurons tuned to long intervals, high-pass neurons tuned to short intervals, and bandpass neurons tuned to intermediate intervals. Many neurons within each class also responded preferentially to either increasing or decreasing IPIs. Playback of electric signaling patterns recorded from freely behaving fish revealed that the IPI and direction tuning of ELp neurons resulted in selective responses to particular social communication displays characterized by distinct IPI patterns. The postsynaptic potential responses of many neurons indicated a combination of excitatory and inhibitory synaptic input, and the IPI tuning of ELp neurons was directly related to rate-dependent changes in the direction and amplitude of postsynaptic potentials. These results suggest that differences in the dynamics of short-term synaptic plasticity in excitatory and inhibitory pathways may tune central sensory neurons to particular temporal patterns of presynaptic activity. This may represent a general mechanism for the processing of behaviorally relevant stimulus information encoded into temporal patterns of activity by sensory neurons.

Programmable logic construction kits for hyper-real-time neuronal modeling.

PubMed

Guerrero-Rivera, Ruben; Morrison, Abigail; Diesmann, Markus; Pearce, Tim C

2006-11-01

Programmable logic designs are presented that achieve exact integration of leaky integrate-and-fire soma and dynamical synapse neuronal models and incorporate spike-time dependent plasticity and axonal delays. Highly accurate numerical performance has been achieved by modifying simpler forward-Euler-based circuitry requiring minimal circuit allocation, which, as we show, behaves equivalently to exact integration. These designs have been implemented and simulated at the behavioral and physical device levels, demonstrating close agreement with both numerical and analytical results. By exploiting finely grained parallelism and single clock cycle numerical iteration, these designs achieve simulation speeds at least five orders of magnitude faster than the nervous system, termed here hyper-real-time operation, when deployed on commercially available field-programmable gate array (FPGA) devices. Taken together, our designs form a programmable logic construction kit of commonly used neuronal model elements that supports the building of large and complex architectures of spiking neuron networks for real-time neuromorphic implementation, neurophysiological interfacing, or efficient parameter space investigations.

Fast two-layer two-photon imaging of neuronal cell populations using an electrically tunable lens

PubMed Central

Grewe, Benjamin F.; Voigt, Fabian F.; van ’t Hoff, Marcel; Helmchen, Fritjof

2011-01-01

Functional two-photon Ca2+-imaging is a versatile tool to study the dynamics of neuronal populations in brain slices and living animals. However, population imaging is typically restricted to a single two-dimensional image plane. By introducing an electrically tunable lens into the excitation path of a two-photon microscope we were able to realize fast axial focus shifts within 15 ms. The maximum axial scan range was 0.7 mm employing a 40x NA0.8 water immersion objective, plenty for typically required ranges of 0.2–0.3 mm. By combining the axial scanning method with 2D acousto-optic frame scanning and random-access scanning, we measured neuronal population activity of about 40 neurons across two imaging planes separated by 40 μm and achieved scan rates up to 20–30 Hz. The method presented is easily applicable and allows upgrading of existing two-photon microscopes for fast 3D scanning. PMID:21750778

Lifting the veil on the dynamics of neuronal activities evoked by transcranial magnetic stimulation

PubMed Central

Li, Bingshuo; Virtanen, Juha P; Oeltermann, Axel; Schwarz, Cornelius; Giese, Martin A; Ziemann, Ulf

2017-01-01

Transcranial magnetic stimulation (TMS) is a widely used non-invasive tool to study and modulate human brain functions. However, TMS-evoked activity of individual neurons has remained largely inaccessible due to the large TMS-induced electromagnetic fields. Here, we present a general method providing direct in vivo electrophysiological access to TMS-evoked neuronal activity 0.8–1 ms after TMS onset. We translated human single-pulse TMS to rodents and unveiled time-grained evoked activities of motor cortex layer V neurons that show high-frequency spiking within the first 6 ms depending on TMS-induced current orientation and a multiphasic spike-rhythm alternating between excitation and inhibition in the 6–300 ms epoch, all of which can be linked to various human TMS responses recorded at the level of spinal cord and muscles. The advance here facilitates a new level of insight into the TMS-brain interaction that is vital for developing this non-invasive tool to purposefully explore and effectively treat the human brain. PMID:29165241

Activity-dependent switch of GABAergic inhibition into glutamatergic excitation in astrocyte-neuron networks.

PubMed

Perea, Gertrudis; Gómez, Ricardo; Mederos, Sara; Covelo, Ana; Ballesteros, Jesús J; Schlosser, Laura; Hernández-Vivanco, Alicia; Martín-Fernández, Mario; Quintana, Ruth; Rayan, Abdelrahman; Díez, Adolfo; Fuenzalida, Marco; Agarwal, Amit; Bergles, Dwight E; Bettler, Bernhard; Manahan-Vaughan, Denise; Martín, Eduardo D; Kirchhoff, Frank; Araque, Alfonso

2016-12-24

Interneurons are critical for proper neural network function and can activate Ca 2+ signaling in astrocytes. However, the impact of the interneuron-astrocyte signaling into neuronal network operation remains unknown. Using the simplest hippocampal Astrocyte-Neuron network, i.e., GABAergic interneuron, pyramidal neuron, single CA3-CA1 glutamatergic synapse, and astrocytes, we found that interneuron-astrocyte signaling dynamically affected excitatory neurotransmission in an activity- and time-dependent manner, and determined the sign (inhibition vs potentiation) of the GABA-mediated effects. While synaptic inhibition was mediated by GABA A receptors, potentiation involved astrocyte GABA B receptors, astrocytic glutamate release, and presynaptic metabotropic glutamate receptors. Using conditional astrocyte-specific GABA B receptor ( Gabbr1 ) knockout mice, we confirmed the glial source of the interneuron-induced potentiation, and demonstrated the involvement of astrocytes in hippocampal theta and gamma oscillations in vivo. Therefore, astrocytes decode interneuron activity and transform inhibitory into excitatory signals, contributing to the emergence of novel network properties resulting from the interneuron-astrocyte interplay.

Electrophysiological characterization of the rat trigeminal caudalis (Vc) neurons following intramuscular injection of capsaicin

PubMed Central

Chun, Yang H; Ro, Jin Y

2009-01-01

Extracellular single unit recording experiments were performed to examine response characteristics of wide dynamic range neurons in the Vc that receive masseter afferent input in Sprague Dawley rats. Capsaicin, or its vehicle, was directly administered into the masseter muscle and changes in resting discharge, responses to mechanical stimulation on the cutaneous receptive field and the electrical threshold for masseter nerve stimulation were assessed. Intramuscular capsaicin induced significant increase in the background discharge and mechanical hypersensitivity to the cutaneous stimulation and lowered the threshold masseter nerve stimulation evoked responses in the majority of neurons. The capsaicin-induced increase in evoked responses, but not the resting discharge, was partially attenuated when the muscle was pretreated with a mGluR antagonist. The present study suggests that injury or inflammation in the masseter muscle induce generalized hyperexcitability of central trigeminal neurons and that the blockade of peripherally localized mGluR5 can effectively attenuate muscular hypersensitivity. PMID:19818833

Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws

PubMed Central

Palva, J. Matias; Zhigalov, Alexander; Hirvonen, Jonni; Korhonen, Onerva; Linkenkaer-Hansen, Klaus; Palva, Satu

2013-01-01

Scale-free fluctuations are ubiquitous in behavioral performance and neuronal activity. In time scales from seconds to hundreds of seconds, psychophysical dynamics and the amplitude fluctuations of neuronal oscillations are governed by power-law-form long-range temporal correlations (LRTCs). In millisecond time scales, neuronal activity comprises cascade-like neuronal avalanches that exhibit power-law size and lifetime distributions. However, it remains unknown whether these neuronal scaling laws are correlated with those characterizing behavioral performance or whether neuronal LRTCs and avalanches are related. Here, we show that the neuronal scaling laws are strongly correlated both with each other and with behavioral scaling laws. We used source reconstructed magneto- and electroencephalographic recordings to characterize the dynamics of ongoing cortical activity. We found robust power-law scaling in neuronal LRTCs and avalanches in resting-state data and during the performance of audiovisual threshold stimulus detection tasks. The LRTC scaling exponents of the behavioral performance fluctuations were correlated with those of concurrent neuronal avalanches and LRTCs in anatomically identified brain systems. The behavioral exponents also were correlated with neuronal scaling laws derived from a resting-state condition and with a similar anatomical topography. Finally, despite the difference in time scales, the scaling exponents of neuronal LRTCs and avalanches were strongly correlated during both rest and task performance. Thus, long and short time-scale neuronal dynamics are related and functionally significant at the behavioral level. These data suggest that the temporal structures of human cognitive fluctuations and behavioral variability stem from the scaling laws of individual and intrinsic brain dynamics. PMID:23401536

Fast numerical methods for simulating large-scale integrate-and-fire neuronal networks.

PubMed

Rangan, Aaditya V; Cai, David

2007-02-01

We discuss numerical methods for simulating large-scale, integrate-and-fire (I&F) neuronal networks. Important elements in our numerical methods are (i) a neurophysiologically inspired integrating factor which casts the solution as a numerically tractable integral equation, and allows us to obtain stable and accurate individual neuronal trajectories (i.e., voltage and conductance time-courses) even when the I&F neuronal equations are stiff, such as in strongly fluctuating, high-conductance states; (ii) an iterated process of spike-spike corrections within groups of strongly coupled neurons to account for spike-spike interactions within a single large numerical time-step; and (iii) a clustering procedure of firing events in the network to take advantage of localized architectures, such as spatial scales of strong local interactions, which are often present in large-scale computational models-for example, those of the primary visual cortex. (We note that the spike-spike corrections in our methods are more involved than the correction of single neuron spike-time via a polynomial interpolation as in the modified Runge-Kutta methods commonly used in simulations of I&F neuronal networks.) Our methods can evolve networks with relatively strong local interactions in an asymptotically optimal way such that each neuron fires approximately once in [Formula: see text] operations, where N is the number of neurons in the system. We note that quantifications used in computational modeling are often statistical, since measurements in a real experiment to characterize physiological systems are typically statistical, such as firing rate, interspike interval distributions, and spike-triggered voltage distributions. We emphasize that it takes much less computational effort to resolve statistical properties of certain I&F neuronal networks than to fully resolve trajectories of each and every neuron within the system. For networks operating in realistic dynamical regimes, such as strongly fluctuating, high-conductance states, our methods are designed to achieve statistical accuracy when very large time-steps are used. Moreover, our methods can also achieve trajectory-wise accuracy when small time-steps are used.

Spiking network simulation code for petascale computers.

PubMed

Kunkel, Susanne; Schmidt, Maximilian; Eppler, Jochen M; Plesser, Hans E; Masumoto, Gen; Igarashi, Jun; Ishii, Shin; Fukai, Tomoki; Morrison, Abigail; Diesmann, Markus; Helias, Moritz

2014-01-01

Brain-scale networks exhibit a breathtaking heterogeneity in the dynamical properties and parameters of their constituents. At cellular resolution, the entities of theory are neurons and synapses and over the past decade researchers have learned to manage the heterogeneity of neurons and synapses with efficient data structures. Already early parallel simulation codes stored synapses in a distributed fashion such that a synapse solely consumes memory on the compute node harboring the target neuron. As petaflop computers with some 100,000 nodes become increasingly available for neuroscience, new challenges arise for neuronal network simulation software: Each neuron contacts on the order of 10,000 other neurons and thus has targets only on a fraction of all compute nodes; furthermore, for any given source neuron, at most a single synapse is typically created on any compute node. From the viewpoint of an individual compute node, the heterogeneity in the synaptic target lists thus collapses along two dimensions: the dimension of the types of synapses and the dimension of the number of synapses of a given type. Here we present a data structure taking advantage of this double collapse using metaprogramming techniques. After introducing the relevant scaling scenario for brain-scale simulations, we quantitatively discuss the performance on two supercomputers. We show that the novel architecture scales to the largest petascale supercomputers available today.

Spiking network simulation code for petascale computers

PubMed Central

Kunkel, Susanne; Schmidt, Maximilian; Eppler, Jochen M.; Plesser, Hans E.; Masumoto, Gen; Igarashi, Jun; Ishii, Shin; Fukai, Tomoki; Morrison, Abigail; Diesmann, Markus; Helias, Moritz

2014-01-01

Brain-scale networks exhibit a breathtaking heterogeneity in the dynamical properties and parameters of their constituents. At cellular resolution, the entities of theory are neurons and synapses and over the past decade researchers have learned to manage the heterogeneity of neurons and synapses with efficient data structures. Already early parallel simulation codes stored synapses in a distributed fashion such that a synapse solely consumes memory on the compute node harboring the target neuron. As petaflop computers with some 100,000 nodes become increasingly available for neuroscience, new challenges arise for neuronal network simulation software: Each neuron contacts on the order of 10,000 other neurons and thus has targets only on a fraction of all compute nodes; furthermore, for any given source neuron, at most a single synapse is typically created on any compute node. From the viewpoint of an individual compute node, the heterogeneity in the synaptic target lists thus collapses along two dimensions: the dimension of the types of synapses and the dimension of the number of synapses of a given type. Here we present a data structure taking advantage of this double collapse using metaprogramming techniques. After introducing the relevant scaling scenario for brain-scale simulations, we quantitatively discuss the performance on two supercomputers. We show that the novel architecture scales to the largest petascale supercomputers available today. PMID:25346682

Single-neuron labeling with inducible cre-mediated knockout in transgenic mice

PubMed Central

Young, Paul; Qiu, Li; Wang, Dongqing; Zhao, Shengli; Gross, James; Feng, Guoping

2011-01-01

To facilitate functional analysis of neuronal connectivity in a mammalian nervous system tightly packed with billions of cells, we developed a new technique that allows inducible genetic manipulations within fluorescently labeled single neurons in mice. We term this technique SLICK for Single-neuron Labeling with Inducible Cre-mediated Knockout. SLICK is achieved by co-expressing a drug-inducible form of cre recombinase and a fluorescent protein within the same small subsets of neurons. Thus, SLICK combines the powerful cre recombinase system for conditional genetic manipulation and the fluorescent labeling of single neurons for imaging. We demonstrate efficient inducible genetic manipulation in several types of neurons using SLICK. Furthermore, we apply SLICK to eliminate synaptic transmission in a small subset of neuromuscular junctions. Our results provide evidence for the long-term stability of inactive neuromuscular synapses in adult animals. More broadly, these studies demonstrate a cre-LoxP compatible system for dissecting gene functions in single identifiable neurons. PMID:18454144

Distributed Representation of Visual Objects by Single Neurons in the Human Brain

PubMed Central

Valdez, André B.; Papesh, Megan H.; Treiman, David M.; Smith, Kris A.; Goldinger, Stephen D.

2015-01-01

It remains unclear how single neurons in the human brain represent whole-object visual stimuli. While recordings in both human and nonhuman primates have shown distributed representations of objects (many neurons encoding multiple objects), recordings of single neurons in the human medial temporal lobe, taken as subjects' discriminated objects during multiple presentations, have shown gnostic representations (single neurons encoding one object). Because some studies suggest that repeated viewing may enhance neural selectivity for objects, we had human subjects discriminate objects in a single, more naturalistic viewing session. We found that, across 432 well isolated neurons recorded in the hippocampus and amygdala, the average fraction of objects encoded was 26%. We also found that more neurons encoded several objects versus only one object in the hippocampus (28 vs 18%, p < 0.001) and in the amygdala (30 vs 19%, p < 0.001). Thus, during realistic viewing experiences, typical neurons in the human medial temporal lobe code for a considerable range of objects, across multiple semantic categories. PMID:25834044

Amplitude Modulations of Acoustic Communication Signals

NASA Astrophysics Data System (ADS)

Turesson, Hjalmar K.

2011-12-01

In human speech, amplitude modulations at 3 -- 8 Hz are important for discrimination and detection. Two different neurophysiological theories have been proposed to explain this effect. The first theory proposes that, as a consequence of neocortical synaptic dynamics, signals that are amplitude modulated at 3 -- 8 Hz are propagated better than un-modulated signals, or signals modulated above 8 Hz. This suggests that neural activity elicited by vocalizations modulated at 3 -- 8 Hz is optimally transmitted, and the vocalizations better discriminated and detected. The second theory proposes that 3 -- 8 Hz amplitude modulations interact with spontaneous neocortical oscillations. Specifically, vocalizations modulated at 3 -- 8 Hz entrain local populations of neurons, which in turn, modulate the amplitude of high frequency gamma oscillations. This suggests that vocalizations modulated at 3 -- 8 Hz should induce stronger cross-frequency coupling. Similar to human speech, we found that macaque monkey vocalizations also are amplitude modulated between 3 and 8 Hz. Humans and macaque monkeys share similarities in vocal production, implying that the auditory systems subserving perception of acoustic communication signals also share similarities. Based on the similarities between human speech and macaque monkey vocalizations, we addressed how amplitude modulated vocalizations are processed in the auditory cortex of macaque monkeys, and what behavioral relevance modulations may have. Recording single neuron activity, as well as, the activity of local populations of neurons allowed us to test both of the neurophysiological theories presented above. We found that single neuron responses to vocalizations amplitude modulated at 3 -- 8 Hz resulted in better stimulus discrimination than vocalizations lacking 3 -- 8 Hz modulations, and that the effect most likely was mediated by synaptic dynamics. In contrast, we failed to find support for the oscillation-based model proposing a coupling between 3 -- 8 Hz oscillations and gamma band amplitude. In a behavioral experiment, we found that 3 -- 8 amplitude modulations improved auditory detection in noise. In conclusion, our results suggest that, as in human speech, 3 -- 8 Hz amplitude modulations have a behaviorally important effect, and that this effect probably is mediated by synaptic dynamics.

Transistor analogs of emergent iono-neuronal dynamics.

PubMed

Rachmuth, Guy; Poon, Chi-Sang

2008-06-01

Neuromorphic analog metal-oxide-silicon (MOS) transistor circuits promise compact, low-power, and high-speed emulations of iono-neuronal dynamics orders-of-magnitude faster than digital simulation. However, their inherently limited input voltage dynamic range vs power consumption and silicon die area tradeoffs makes them highly sensitive to transistor mismatch due to fabrication inaccuracy, device noise, and other nonidealities. This limitation precludes robust analog very-large-scale-integration (aVLSI) circuits implementation of emergent iono-neuronal dynamics computations beyond simple spiking with limited ion channel dynamics. Here we present versatile neuromorphic analog building-block circuits that afford near-maximum voltage dynamic range operating within the low-power MOS transistor weak-inversion regime which is ideal for aVLSI implementation or implantable biomimetic device applications. The fabricated microchip allowed robust realization of dynamic iono-neuronal computations such as coincidence detection of presynaptic spikes or pre- and postsynaptic activities. As a critical performance benchmark, the high-speed and highly interactive iono-neuronal simulation capability on-chip enabled our prompt discovery of a minimal model of chaotic pacemaker bursting, an emergent iono-neuronal behavior of fundamental biological significance which has hitherto defied experimental testing or computational exploration via conventional digital or analog simulations. These compact and power-efficient transistor analogs of emergent iono-neuronal dynamics open new avenues for next-generation neuromorphic, neuroprosthetic, and brain-machine interface applications.

Shaping Neuronal Network Activity by Presynaptic Mechanisms

PubMed Central

Ashery, Uri

2015-01-01

Neuronal microcircuits generate oscillatory activity, which has been linked to basic functions such as sleep, learning and sensorimotor gating. Although synaptic release processes are well known for their ability to shape the interaction between neurons in microcircuits, most computational models do not simulate the synaptic transmission process directly and hence cannot explain how changes in synaptic parameters alter neuronal network activity. In this paper, we present a novel neuronal network model that incorporates presynaptic release mechanisms, such as vesicle pool dynamics and calcium-dependent release probability, to model the spontaneous activity of neuronal networks. The model, which is based on modified leaky integrate-and-fire neurons, generates spontaneous network activity patterns, which are similar to experimental data and robust under changes in the model's primary gain parameters such as excitatory postsynaptic potential and connectivity ratio. Furthermore, it reliably recreates experimental findings and provides mechanistic explanations for data obtained from microelectrode array recordings, such as network burst termination and the effects of pharmacological and genetic manipulations. The model demonstrates how elevated asynchronous release, but not spontaneous release, synchronizes neuronal network activity and reveals that asynchronous release enhances utilization of the recycling vesicle pool to induce the network effect. The model further predicts a positive correlation between vesicle priming at the single-neuron level and burst frequency at the network level; this prediction is supported by experimental findings. Thus, the model is utilized to reveal how synaptic release processes at the neuronal level govern activity patterns and synchronization at the network level. PMID:26372048

Neural correlates of olfactory learning paradigms in an identified neuron in the honeybee brain.

PubMed

Mauelshagen, J

1993-02-01

1. Sensitization and classical odor conditioning of the proboscis extension reflex were functionally analyzed by repeated intracellular recordings from a single identified neuron (PE1-neuron) in the central bee brain. This neuron belongs to the class of "extrinsic cells" arising from the pedunculus of the mushroom bodies and has extensive arborizations in the median and lateral protocerebrum. The recordings were performed on isolated bee heads. 2. Two different series of physiological experiments were carried out with the use of a similar temporal succession of stimuli as in previous behavioral experiments. In the first series, one group of animals was used for a single conditioning trial [conditioned stimulus (CS), carnation; unconditioned stimulus (US), sucrose solution to the antennae and proboscis), a second group was used for sensitization (sensitizing stimulus, sucrose solution to the antennae and/or proboscis), and the third group served as control (no sucrose stimulation). In the second series, a differential conditioning paradigm (paired odor CS+, carnation; unpaired odor CS-, orange blossom) was applied to test the associative nature of the conditioning effect. 3. The PE1-neuron showed a characteristic burstlike odor response before the training procedures. The treatments resulted in different spike-frequency modulations of this response, which were specific for the nonassociative and associative stimulus paradigms applied. During differential conditioning, there are dynamic up and down modulations of spike frequencies and of the DC potentials underlying the responses to the CS+. Overall, only transient changes in the minute range were observed. 4. The results of the sensitization procedures suggest two qualitatively different US pathways. The comparison between sensitization and one-trial conditioning shows differential effects of nonassociative and associative stimulus paradigms on the response behavior of the PE1-neuron. The results of the differential conditioning procedure reveal that the effect observed for the one-trial conditioning paradigm is of an associative nature and that there might be modulations, which are specific for single and multiple trial conditioning procedures. It is hypothesized that the PE1-neuron is a possible element involved in the short-term acquisition, rather than in the long-term storage, of an associative olfactory memory in the honeybee.

A path integral approach to the Hodgkin-Huxley model

NASA Astrophysics Data System (ADS)

Baravalle, Roman; Rosso, Osvaldo A.; Montani, Fernando

2017-11-01

To understand how single neurons process sensory information, it is necessary to develop suitable stochastic models to describe the response variability of the recorded spike trains. Spikes in a given neuron are produced by the synergistic action of sodium and potassium of the voltage-dependent channels that open or close the gates. Hodgkin and Huxley (HH) equations describe the ionic mechanisms underlying the initiation and propagation of action potentials, through a set of nonlinear ordinary differential equations that approximate the electrical characteristics of the excitable cell. Path integral provides an adequate approach to compute quantities such as transition probabilities, and any stochastic system can be expressed in terms of this methodology. We use the technique of path integrals to determine the analytical solution driven by a non-Gaussian colored noise when considering the HH equations as a stochastic system. The different neuronal dynamics are investigated by estimating the path integral solutions driven by a non-Gaussian colored noise q. More specifically we take into account the correlational structures of the complex neuronal signals not just by estimating the transition probability associated to the Gaussian approach of the stochastic HH equations, but instead considering much more subtle processes accounting for the non-Gaussian noise that could be induced by the surrounding neural network and by feedforward correlations. This allows us to investigate the underlying dynamics of the neural system when different scenarios of noise correlations are considered.

Single cell analysis of voltage-gated potassium channels that determines neuronal types of rat hypothalamic paraventricular nucleus neurons.

PubMed

Lee, S K; Lee, S; Shin, S Y; Ryu, P D; Lee, S Y

2012-03-15

The hypothalamic paraventricular nucleus (PVN), a site for the integration of both the neuroendocrine and autonomic systems, has heterogeneous cell composition. These neurons are classified into type I and type II neurons based on their electrophysiological properties. In the present study, we investigated the molecular identification of voltage-gated K+ (Kv) channels, which determines a distinctive characteristic of type I PVN neurons, by means of single-cell reverse transcription-polymerase chain reaction (RT-PCR) along with slice patch clamp recordings. In order to determine the mRNA expression profiles, firstly, the PVN neurons of male rats were classified into type I and type II neurons, and then, single-cell RT-PCR and single-cell real-time RT-PCR analysis were performed using the identical cell. The single-cell RT-PCR analysis revealed that Kv1.2, Kv1.3, Kv1.4, Kv4.1, Kv4.2, and Kv4.3 were expressed both in type I and in type II neurons, and several Kv channels were co-expressed in a single PVN neuron. However, we found that the expression densities of Kv4.2 and Kv4.3 were significantly higher in type I neurons than in type II neurons. Taken together, several Kv channels encoding A-type K+ currents are present both in type I and in type II neurons, and among those, Kv4.2 and Kv4.3 are the major Kv subunits responsible for determining the distinct electrophysiological properties. Thus these 2 Kv subunits may play important roles in determining PVN cell types and regulating PVN neuronal excitability. This study further provides key molecular mechanisms for differentiating type I and type II PVN neurons. Copyright © 2012 IBRO. Published by Elsevier Ltd. All rights reserved.

Synaptic and nonsynaptic plasticity approximating probabilistic inference

PubMed Central

Tully, Philip J.; Hennig, Matthias H.; Lansner, Anders

2014-01-01

Learning and memory operations in neural circuits are believed to involve molecular cascades of synaptic and nonsynaptic changes that lead to a diverse repertoire of dynamical phenomena at higher levels of processing. Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability all conspire to form and maintain memories. But it is still unclear how these seemingly redundant mechanisms could jointly orchestrate learning in a more unified system. To this end, a Hebbian learning rule for spiking neurons inspired by Bayesian statistics is proposed. In this model, synaptic weights and intrinsic currents are adapted on-line upon arrival of single spikes, which initiate a cascade of temporally interacting memory traces that locally estimate probabilities associated with relative neuronal activation levels. Trace dynamics enable synaptic learning to readily demonstrate a spike-timing dependence, stably return to a set-point over long time scales, and remain competitive despite this stability. Beyond unsupervised learning, linking the traces with an external plasticity-modulating signal enables spike-based reinforcement learning. At the postsynaptic neuron, the traces are represented by an activity-dependent ion channel that is shown to regulate the input received by a postsynaptic cell and generate intrinsic graded persistent firing levels. We show how spike-based Hebbian-Bayesian learning can be performed in a simulated inference task using integrate-and-fire (IAF) neurons that are Poisson-firing and background-driven, similar to the preferred regime of cortical neurons. Our results support the view that neurons can represent information in the form of probability distributions, and that probabilistic inference could be a functional by-product of coupled synaptic and nonsynaptic mechanisms operating over several timescales. The model provides a biophysical realization of Bayesian computation by reconciling several observed neural phenomena whose functional effects are only partially understood in concert. PMID:24782758

Examination of Axonal Injury and Regeneration in Microfluidic Neuronal Culture Using Pulsed Laser Microbeam Dissection

PubMed Central

Hellman, Amy N.; Vahidi, Behrad; Kim, Hyung Joon; Mismar, Wael; Steward, Oswald; Jeon, Noo Li; Venugopalan, Vasan

2010-01-01

We describe the integrated use of pulsed laser microbeams and microfluidic cell culture to examine the dynamics of axonal injury and regeneration in vitro. Microfabrication methods are used to place high purity dissociated central nervous system neurons in specific regions that allow the axons to interact with permissive and inhibitory substrates. Acute injury to neuron bundles is produced via the delivery of single 180 ps duration, λ=532 nm laser pulses. Laser pulse energies of 400 nJ and 800 nJ produce partial and complete transection of the axons, respectively, resulting in elliptical lesions 25 μm and 50 μm in size. The dynamics of the resulting degeneration and regrowth of proximal and distal axonal segments are examined for up to 8 h using time-lapse microscopy. We find the proximal and distal dieback distances from the site of laser microbeam irradiation to be roughly equal for both partial and complete transection of the axons. In addition, distinct growth cones emerge from the proximal neurite segments within 1–2 h post-injury, followed by a uniform front of regenerating axons that originate from the proximal segment and traverse the injury site within 8 h. We also examine the use of EGTA to chelate the extracellular calcium and potentially reduce the severity of the axonal degeneration following injury. While we find the addition of EGTA to reduce the severity of the initial dieback, it also hampers neurite repair and interfere with the formation of neuronal growth cones to traverse the injury site. This integrated use of laser microbeam dissection within a microfluidic cell culture system to produce precise zones of neuronal injury shows potential for high-throughput screening of agents to promote neuronal regeneration. PMID:20532390

Visualization of local Ca2+ dynamics with genetically encoded bioluminescent reporters.

PubMed

Rogers, Kelly L; Stinnakre, Jacques; Agulhon, Cendra; Jublot, Delphine; Shorte, Spencer L; Kremer, Eric J; Brûlet, Philippe

2005-02-01

Measurements of local Ca2+ signalling at different developmental stages and/or in specific cell types is important for understanding aspects of brain functioning. The use of light excitation in fluorescence imaging can cause phototoxicity, photobleaching and auto-fluorescence. In contrast, bioluminescence does not require the input of radiative energy and can therefore be measured over long periods, with very high temporal resolution. Aequorin is a genetically encoded Ca(2+)-sensitive bioluminescent protein, however, its low quantum yield prevents dynamic measurements of Ca2+ responses in single cells. To overcome this limitation, we recently reported the bi-functional Ca2+ reporter gene, GFP-aequorin (GA), which was developed specifically to improve the light output and stability of aequorin chimeras [V. Baubet, et al., (2000) PNAS, 97, 7260-7265]. In the current study, we have genetically targeted GA to different microdomains important in synaptic transmission, including to the mitochondrial matrix, endoplasmic reticulum, synaptic vesicles and to the postsynaptic density. We demonstrate that these reporters enable 'real-time' measurements of subcellular Ca2+ changes in single mammalian neurons using bioluminescence. The high signal-to-noise ratio of these reporters is also important in that it affords the visualization of Ca2+ dynamics in cell-cell communication in neuronal cultures and tissue slices. Further, we demonstrate the utility of this approach in ex-vivo preparations of mammalian retina, a paradigm in which external light input should be controlled. This represents a novel molecular imaging approach for non-invasive monitoring of local Ca2+ dynamics and cellular communication in tissue or whole animal studies.

The Slow Dynamics of Intracellular Sodium Concentration Increase the Time Window of Neuronal Integration: A Simulation Study

PubMed Central

Zylbertal, Asaph; Yarom, Yosef; Wagner, Shlomo

2017-01-01

Changes in intracellular Na+ concentration ([Na+]i) are rarely taken into account when neuronal activity is examined. As opposed to Ca2+, [Na+]i dynamics are strongly affected by longitudinal diffusion, and therefore they are governed by the morphological structure of the neurons, in addition to the localization of influx and efflux mechanisms. Here, we examined [Na+]i dynamics and their effects on neuronal computation in three multi-compartmental neuronal models, representing three distinct cell types: accessory olfactory bulb (AOB) mitral cells, cortical layer V pyramidal cells, and cerebellar Purkinje cells. We added [Na+]i as a state variable to these models, and allowed it to modulate the Na+ Nernst potential, the Na+-K+ pump current, and the Na+-Ca2+ exchanger rate. Our results indicate that in most cases [Na+]i dynamics are significantly slower than [Ca2+]i dynamics, and thus may exert a prolonged influence on neuronal computation in a neuronal type specific manner. We show that [Na+]i dynamics affect neuronal activity via three main processes: reduction of EPSP amplitude in repeatedly active synapses due to reduction of the Na+ Nernst potential; activity-dependent hyperpolarization due to increased activity of the Na+-K+ pump; specific tagging of active synapses by extended Ca2+ elevation, intensified by concurrent back-propagating action potentials or complex spikes. Thus, we conclude that [Na+]i dynamics should be considered whenever synaptic plasticity, extensive synaptic input, or bursting activity are examined. PMID:28970791

A microprobe for parallel optical and electrical recordings from single neurons in vivo.

PubMed

LeChasseur, Yoan; Dufour, Suzie; Lavertu, Guillaume; Bories, Cyril; Deschênes, Martin; Vallée, Réal; De Koninck, Yves

2011-04-01

Recording electrical activity from identified neurons in intact tissue is key to understanding their role in information processing. Recent fluorescence labeling techniques have opened new possibilities to combine electrophysiological recording with optical detection of individual neurons deep in brain tissue. For this purpose we developed dual-core fiberoptics-based microprobes, with an optical core to locally excite and collect fluorescence, and an electrolyte-filled hollow core for extracellular single unit electrophysiology. This design provides microprobes with tips < 10 μm, enabling analyses with single-cell optical resolution. We demonstrate combined electrical and optical detection of single fluorescent neurons in rats and mice. We combined electrical recordings and optical Ca²(+) measurements from single thalamic relay neurons in rats, and achieved detection and activation of single channelrhodopsin-expressing neurons in Thy1::ChR2-YFP transgenic mice. The microprobe expands possibilities for in vivo electrophysiological recording, providing parallel access to single-cell optical monitoring and control.

Mitochondrial dynamics and bioenergetic dysfunction is associated with synaptic alterations in mutant SOD1 motor neurons

PubMed Central

Magrané, Jordi; Sahawneh, Mary Anne; Przedborski, Serge; Estévez, Álvaro G.; Manfredi, Giovanni

2012-01-01

Mutations in Cu,Zn superoxide dismutase (SOD1) cause familial amyotrophic lateral sclerosis (FALS), a rapidly fatal motor neuron disease. Mutant SOD1 has pleiotropic toxic effects on motor neurons, among which mitochondrial dysfunction has been proposed as one of the contributing factors in motor neuron demise. Mitochondria are highly dynamic in neurons; they are constantly reshaped by fusion and move along neurites to localize at sites of high-energy utilization, such as synapses. The finding of abnormal mitochondria accumulation in neuromuscular junctions, where the SOD1-FALS degenerative process is though to initiate, suggests that impaired mitochondrial dynamics in motor neurons may be involved in pathogenesis. We addressed this hypothesis by live imaging microscopy of photo-switchable fluorescent mitoDendra in transgenic rat motor neurons expressing mutant or wild type human SOD1. We demonstrate that mutant SOD1 motor neurons have impaired mitochondrial fusion in axons and cell bodies. Mitochondria also display selective impairment of retrograde axonal transport, with reduced frequency and velocity of movements. Fusion and transport defects are associated with smaller mitochondrial size, decreased mitochondrial density, and defective mitochondrial membrane potential. Furthermore, mislocalization of mitochondria at synapses among motor neurons, in vitro, correlates with abnormal synaptic number, structure, and function. Dynamics abnormalities are specific to mutant SOD1 motor neuron mitochondria, since they are absent in wild type SOD1 motor neurons, they do not involve other organelles, and they are not found in cortical neurons. Taken together, these results suggest that impaired mitochondrial dynamics may contribute to the selective degeneration of motor neurons in SOD1-FALS. PMID:22219285

Techniques for extracting single-trial activity patterns from large-scale neural recordings

PubMed Central

Churchland, Mark M; Yu, Byron M; Sahani, Maneesh; Shenoy, Krishna V

2008-01-01

Summary Large, chronically-implanted arrays of microelectrodes are an increasingly common tool for recording from primate cortex, and can provide extracellular recordings from many (order of 100) neurons. While the desire for cortically-based motor prostheses has helped drive their development, such arrays also offer great potential to advance basic neuroscience research. Here we discuss the utility of array recording for the study of neural dynamics. Neural activity often has dynamics beyond that driven directly by the stimulus. While governed by those dynamics, neural responses may nevertheless unfold differently for nominally identical trials, rendering many traditional analysis methods ineffective. We review recent studies – some employing simultaneous recording, some not – indicating that such variability is indeed present both during movement generation, and during the preceding premotor computations. In such cases, large-scale simultaneous recordings have the potential to provide an unprecedented view of neural dynamics at the level of single trials. However, this enterprise will depend not only on techniques for simultaneous recording, but also on the use and further development of analysis techniques that can appropriately reduce the dimensionality of the data, and allow visualization of single-trial neural behavior. PMID:18093826

The relationship between nernst equilibrium variability and the multifractality of interspike intervals in the hippocampus.

PubMed

Meier, Stephen R; Lancaster, Jarrett L; Fetterhoff, Dustin; Kraft, Robert A; Hampson, Robert E; Starobin, Joseph M

2017-04-01

Spatiotemporal patterns of action potentials are considered to be closely related to information processing in the brain. Auto-generating neurons contributing to these processing tasks are known to cause multifractal behavior in the inter-spike intervals of the output action potentials. In this paper we define a novel relationship between this multifractality and the adaptive Nernst equilibrium in hippocampal neurons. Using this relationship we are able to differentiate between various drugs at varying dosages. Conventional methods limit their ability to account for cellular charge depletion by not including these adaptive Nernst equilibria. Our results provide a new theoretical approach for measuring the effects which drugs have on single-cell dynamics.

Brain plasticity and functionality explored by nonlinear optical microscopy

NASA Astrophysics Data System (ADS)

Sacconi, L.; Allegra, L.; Buffelli, M.; Cesare, P.; D'Angelo, E.; Gandolfi, D.; Grasselli, G.; Lotti, J.; Mapelli, J.; Strata, P.; Pavone, F. S.

2010-02-01

In combination with fluorescent protein (XFP) expression techniques, two-photon microscopy has become an indispensable tool to image cortical plasticity in living mice. In parallel to its application in imaging, multi-photon absorption has also been used as a tool for the dissection of single neurites with submicrometric precision without causing any visible collateral damage to the surrounding neuronal structures. In this work, multi-photon nanosurgery is applied to dissect single climbing fibers expressing GFP in the cerebellar cortex. The morphological consequences are then characterized with time lapse 3-dimensional two-photon imaging over a period of minutes to days after the procedure. Preliminary investigations show that the laser induced fiber dissection recalls a regenerative process in the fiber itself over a period of days. These results show the possibility of this innovative technique to investigate regenerative processes in adult brain. In parallel with imaging and manipulation technique, non-linear microscopy offers the opportunity to optically record electrical activity in intact neuronal networks. In this work, we combined the advantages of second-harmonic generation (SHG) with a random access (RA) excitation scheme to realize a new microscope (RASH) capable of optically recording fast membrane potential events occurring in a wide-field of view. The RASH microscope, in combination with bulk loading of tissue with FM4-64 dye, was used to simultaneously record electrical activity from clusters of Purkinje cells in acute cerebellar slices. Complex spikes, both synchronous and asynchronous, were optically recorded simultaneously across a given population of neurons. Spontaneous electrical activity was also monitored simultaneously in pairs of neurons, where action potentials were recorded without averaging across trials. These results show the strength of this technique in describing the temporal dynamics of neuronal assemblies, opening promising perspectives in understanding the computations of neuronal networks.

Posterior cingulate cortex mediates outcome-contingent allocation of behavior

PubMed Central

Hayden, Benjamin Y.; Nair, Amrita C.; McCoy, Allison N.; Platt, Michael L.

2008-01-01

SUMMARY Adaptive decision making requires selecting an action and then monitoring its consequences to improve future decisions. The neuronal mechanisms supporting action evaluation and subsequent behavioral modification, however, remain poorly understood. To investigate the contribution of posterior cingulate cortex (CGp) to these processes, we recorded activity of single neurons in monkeys performing a gambling task in which the reward outcome of each choice strongly influenced subsequent choices. We found that CGp neurons signaled reward outcomes in a nonlinear fashion, and that outcome-contingent modulations in firing rate persisted into subsequent trials. Moreover, firing rate on any one trial predicted switching to the alternative option on the next trial. Finally, microstimulation in CGp following risky choices promoted a preference reversal for the safe option on the following trial. Collectively, these results demonstrate that CGp directly contributes to the evaluative processes that support dynamic changes in decision making in volatile environments. PMID:18940585

Neuronal pattern separation in the olfactory bulb improves odor discrimination learning

PubMed Central

Lagier, Samuel; Begnaud, Frédéric; Rodriguez, Ivan; Carleton, Alan

2015-01-01

Neuronal pattern separation is thought to enable the brain to disambiguate sensory stimuli with overlapping features thereby extracting valuable information. In the olfactory system, it remains unknown whether pattern separation acts as a driving force for sensory discrimination and the learning thereof. Here we show that overlapping odor-evoked input patterns to the mouse olfactory bulb (OB) are dynamically reformatted in the network at the timescale of a single breath, giving rise to separated patterns of activity in ensemble of output neurons (mitral/tufted cells; M/T). Strikingly, the extent of pattern separation in M/T assemblies predicts behavioral discrimination performance during the learning phase. Furthermore, exciting or inhibiting GABAergic OB interneurons, using optogenetics or pharmacogenetics, altered pattern separation and thereby odor discrimination learning in a bidirectional way. In conclusion, we propose that the OB network can act as a pattern separator facilitating olfactory stimuli distinction, a process that is sculpted by synaptic inhibition. PMID:26301325

In search of intelligence: evolving a developmental neuron capable of learning

NASA Astrophysics Data System (ADS)

Khan, Gul Muhammad; Miller, Julian Francis

2014-10-01

A neuro-inspired multi-chromosomal genotype for a single developmental neuron capable of learning and developing memory is proposed. This genotype is evolved so that the phenotype which changes and develops during an agent's lifetime (while problem-solving) gives the agent the capacity for learning by experience. Seven important processes of signal processing and neural structure development are identified from biology and encoded using Cartesian Genetic Programming. These chromosomes represent the electrical and developmental aspects of dendrites, axonal branches, synapses and the neuron soma. The neural morphology that occurs by running these chromosomes is highly dynamic. The dendritic/axonal branches and synaptic connections form and change in response to situations encountered in the learning task. The approach has been evaluated in the context of maze-solving and the board game of checkers (draughts) demonstrating interesting learning capabilities. The motivation underlying this research is to, ab initio, evolve genotypes that build phenotypes with an ability to learn.

Neuronal pattern separation in the olfactory bulb improves odor discrimination learning.

PubMed

Gschwend, Olivier; Abraham, Nixon M; Lagier, Samuel; Begnaud, Frédéric; Rodriguez, Ivan; Carleton, Alan

2015-10-01

Neuronal pattern separation is thought to enable the brain to disambiguate sensory stimuli with overlapping features, thereby extracting valuable information. In the olfactory system, it remains unknown whether pattern separation acts as a driving force for sensory discrimination and the learning thereof. We found that overlapping odor-evoked input patterns to the mouse olfactory bulb (OB) were dynamically reformatted in the network on the timescale of a single breath, giving rise to separated patterns of activity in an ensemble of output neurons, mitral/tufted (M/T) cells. Notably, the extent of pattern separation in M/T assemblies predicted behavioral discrimination performance during the learning phase. Furthermore, exciting or inhibiting GABAergic OB interneurons, using optogenetics or pharmacogenetics, altered pattern separation and thereby odor discrimination learning in a bidirectional way. In conclusion, we propose that the OB network can act as a pattern separator facilitating olfactory stimulus distinction, a process that is sculpted by synaptic inhibition.

Unidirectional signal propagation in primary neurons micropatterned at a single-cell resolution

NASA Astrophysics Data System (ADS)

Yamamoto, H.; Matsumura, R.; Takaoki, H.; Katsurabayashi, S.; Hirano-Iwata, A.; Niwano, M.

2016-07-01

The structure and connectivity of cultured neuronal networks can be controlled by using micropatterned surfaces. Here, we demonstrate that the direction of signal propagation can be precisely controlled at a single-cell resolution by growing primary neurons on micropatterns. To achieve this, we first examined the process by which axons develop and how synapses form in micropatterned primary neurons using immunocytochemistry. By aligning asymmetric micropatterns with a marginal gap, it was possible to pattern primary neurons with a directed polarization axis at the single-cell level. We then examined how synapses develop on micropatterned hippocampal neurons. Three types of micropatterns with different numbers of short paths for dendrite growth were compared. A normal development in synapse density was observed when micropatterns with three or more short paths were used. Finally, we performed double patch clamp recordings on micropatterned neurons to confirm that these synapses are indeed functional, and that the neuronal signal is transmitted unidirectionally in the intended orientation. This work provides a practical guideline for patterning single neurons to design functional neuronal networks in vitro with the direction of signal propagation being controlled.

Imaging Action Potential in Single Mammalian Neurons by Tracking the Accompanying Sub-Nanometer Mechanical Motion.

PubMed

Yang, Yunze; Liu, Xian-Wei; Wang, Hui; Yu, Hui; Guan, Yan; Wang, Shaopeng; Tao, Nongjian

2018-03-28

Action potentials in neurons have been studied traditionally by intracellular electrophysiological recordings and more recently by the fluorescence detection methods. Here we describe a label-free optical imaging method that can measure mechanical motion in single cells with a sub-nanometer detection limit. Using the method, we have observed sub-nanometer mechanical motion accompanying the action potential in single mammalian neurons by averaging the repeated action potential spikes. The shape and width of the transient displacement are similar to those of the electrically recorded action potential, but the amplitude varies from neuron to neuron, and from one region of a neuron to another, ranging from 0.2-0.4 nm. The work indicates that action potentials may be studied noninvasively in single mammalian neurons by label-free imaging of the accompanying sub-nanometer mechanical motion.

Distributed representation of visual objects by single neurons in the human brain.

PubMed

Valdez, André B; Papesh, Megan H; Treiman, David M; Smith, Kris A; Goldinger, Stephen D; Steinmetz, Peter N

2015-04-01

It remains unclear how single neurons in the human brain represent whole-object visual stimuli. While recordings in both human and nonhuman primates have shown distributed representations of objects (many neurons encoding multiple objects), recordings of single neurons in the human medial temporal lobe, taken as subjects' discriminated objects during multiple presentations, have shown gnostic representations (single neurons encoding one object). Because some studies suggest that repeated viewing may enhance neural selectivity for objects, we had human subjects discriminate objects in a single, more naturalistic viewing session. We found that, across 432 well isolated neurons recorded in the hippocampus and amygdala, the average fraction of objects encoded was 26%. We also found that more neurons encoded several objects versus only one object in the hippocampus (28 vs 18%, p < 0.001) and in the amygdala (30 vs 19%, p < 0.001). Thus, during realistic viewing experiences, typical neurons in the human medial temporal lobe code for a considerable range of objects, across multiple semantic categories. Copyright © 2015 the authors 0270-6474/15/355180-07$15.00/0.

KCC2-dependent Steady-state Intracellular Chloride Concentration and pH in Cortical Layer 2/3 Neurons of Anesthetized and Awake Mice.

PubMed

Boffi, Juan C; Knabbe, Johannes; Kaiser, Michaela; Kuner, Thomas

2018-01-01

Neuronal intracellular Cl - concentration ([Cl - ] i ) influences a wide range of processes such as neuronal inhibition, membrane potential dynamics, intracellular pH (pH i ) or cell volume. Up to date, neuronal [Cl - ] i has predominantly been studied in model systems of reduced complexity. Here, we implemented the genetically encoded ratiometric Cl - indicator Superclomeleon (SCLM) to estimate the steady-state [Cl - ] i in cortical neurons from anesthetized and awake mice using 2-photon microscopy. Additionally, we implemented superecliptic pHluorin (SE-pHluorin) as a ratiometric sensor to estimate the intracellular steady-state pH (pH i ) of mouse cortical neurons in vivo . We estimated an average resting [Cl - ] i of 6 ± 2 mM with no evidence of subcellular gradients in the proximal somato-dendritic domain and an average somatic pH i of 7.1 ± 0.2. Neither [Cl - ] i nor pH i were affected by isoflurane anesthesia. We deleted the cation-Cl - co-transporter KCC2 in single identified neurons of adult mice and found an increase of [Cl - ] i to approximately 26 ± 8 mM, demonstrating that under in vivo conditions KCC2 produces low [Cl - ] i in adult mouse neurons. In summary, neurons of the brain of awake adult mice exhibit a low and evenly distributed [Cl - ] i in the proximal somato-dendritic compartment that is independent of anesthesia and requires KCC2 expression for its maintenance.

Dynamic encoding of responses and outcomes by neurons in medial prefrontal cortex

PubMed Central

Luk, Chung-Hay; Wallis, Jonathan D.

2009-01-01

Medial prefrontal cortex (MPFC) and lateral prefrontal cortex (LPFC) both contribute to goal-directed behavior, but their precise role remains unclear. Several lines of evidence suggest that MPFC is more important than LPFC for outcome-guided response selection. To examine this, we trained two subjects to perform a task that required them to monitor the specific outcome associated with a specific response on a trial-by-trial basis. While the subjects performed this task, we recorded the electrical activity of single neurons simultaneously from MPFC and LPFC. There were marked differences in the neuronal properties of these two areas. Neurons encoding the response were present in both areas, but in MPFC, there were also neurons that encoded the outcome. In particular, neurons encoded the subject’s intended response and how preferable the received outcome was. Thus, only in MPFC was all the information necessary to solve the task encoded. In addition, largely separate populations of MPFC neurons encoded the response and the outcome. Neurons encoding the outcome were in the anterior parts of MPFC: posterior to the corpus callosum there was a marked drop in their incidence. Our results suggest differences in the contribution of MPFC and LPFC to action control. MPFC neurons encode the desirability of the outcome produced by a specific response on a trial-by-trial basis. This capability may contribute to several of the functions of MPFC, such as action valuation, error detection and decision-making. PMID:19515921

Assessing similarity to primary tissue and cortical layer identity in induced pluripotent stem cell-derived cortical neurons through single-cell transcriptomics

PubMed Central

Handel, Adam E.; Chintawar, Satyan; Lalic, Tatjana; Whiteley, Emma; Vowles, Jane; Giustacchini, Alice; Argoud, Karene; Sopp, Paul; Nakanishi, Mahito; Bowden, Rory; Cowley, Sally; Newey, Sarah; Akerman, Colin; Ponting, Chris P.; Cader, M. Zameel

2016-01-01

Induced pluripotent stem cell (iPSC)-derived cortical neurons potentially present a powerful new model to understand corticogenesis and neurological disease. Previous work has established that differentiation protocols can produce cortical neurons, but little has been done to characterize these at cellular resolution. In particular, it is unclear to what extent in vitro two-dimensional, relatively disordered culture conditions recapitulate the development of in vivo cortical layer identity. Single-cell multiplex reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) was used to interrogate the expression of genes previously implicated in cortical layer or phenotypic identity in individual cells. Totally, 93.6% of single cells derived from iPSCs expressed genes indicative of neuronal identity. High proportions of single neurons derived from iPSCs expressed glutamatergic receptors and synaptic genes. And, 68.4% of iPSC-derived neurons expressing at least one layer marker could be assigned to a laminar identity using canonical cortical layer marker genes. We compared single-cell RNA-seq of our iPSC-derived neurons to available single-cell RNA-seq data from human fetal and adult brain and found that iPSC-derived cortical neurons closely resembled primary fetal brain cells. Unexpectedly, a subpopulation of iPSC-derived neurons co-expressed canonical fetal deep and upper cortical layer markers. However, this appeared to be concordant with data from primary cells. Our results therefore provide reassurance that iPSC-derived cortical neurons are highly similar to primary cortical neurons at the level of single cells but suggest that current layer markers, although effective, may not be able to disambiguate cortical layer identity in all cells. PMID:26740550

Optical Probes for Neurobiological Sensing and Imaging.

PubMed

Kim, Eric H; Chin, Gregory; Rong, Guoxin; Poskanzer, Kira E; Clark, Heather A

2018-05-15

Fluorescent nanosensors and molecular probes are next-generation tools for imaging chemical signaling inside and between cells. Electrophysiology has long been considered the gold standard in elucidating neural dynamics with high temporal resolution and precision, particularly on the single-cell level. However, electrode-based techniques face challenges in illuminating the specific chemicals involved in neural cell activation with adequate spatial information. Measuring chemical dynamics is of fundamental importance to better understand synergistic interactions between neurons as well as interactions between neurons and non-neuronal cells. Over the past decade, significant technological advances in optical probes and imaging methods have enabled entirely new possibilities for studying neural cells and circuits at the chemical level. These optical imaging modalities have shown promise for combining chemical, temporal, and spatial information. This potential makes them ideal candidates to unravel the complex neural interactions at multiple scales in the brain, which could be complemented by traditional electrophysiological methods to obtain a full spatiotemporal picture of neurochemical dynamics. Despite the potential, only a handful of probe candidates have been utilized to provide detailed chemical information in the brain. To date, most live imaging and chemical mapping studies rely on fluorescent molecular indicators to report intracellular calcium (Ca 2+ ) dynamics, which correlates with neuronal activity. Methodological advances for monitoring a full array of chemicals in the brain with improved spatial, temporal, and chemical resolution will thus enable mapping of neurochemical circuits with finer precision. On the basis of numerous studies in this exciting field, we review the current efforts to develop and apply a palette of optical probes and nanosensors for chemical sensing in the brain. There is a strong impetus to further develop technologies capable of probing entire neurobiological units with high spatiotemporal resolution. Thus, we introduce selected applications for ion and neurotransmitter detection to investigate both neurons and non-neuronal brain cells. We focus on families of optical probes because of their ability to sense a wide array of molecules and convey spatial information with minimal damage to tissue. We start with a discussion of currently available molecular probes, highlight recent advances in genetically modified fluorescent probes for ions and small molecules, and end with the latest research in nanosensors for biological imaging. Customizable, nanoscale optical sensors that accurately and dynamically monitor the local environment with high spatiotemporal resolution could lead to not only new insights into the function of all cell types but also a broader understanding of how diverse neural signaling systems act in conjunction with neighboring cells in a spatially relevant manner.

Dynamic stereotypic responses of Basal Ganglia neurons to subthalamic nucleus high-frequency stimulation in the parkinsonian primate.

PubMed

Moran, Anan; Stein, Edward; Tischler, Hadass; Belelovsky, Katya; Bar-Gad, Izhar

2011-01-01

Deep brain stimulation (DBS) in the subthalamic nucleus (STN) is a well-established therapy for patients with severe Parkinson's disease (PD); however, its mechanism of action is still unclear. In this study we explored static and dynamic activation patterns in the basal ganglia (BG) during high-frequency macro-stimulation of the STN. Extracellular multi-electrode recordings were performed in primates rendered parkinsonian using 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. Recordings were preformed simultaneously in the STN and the globus pallidus externus and internus. Single units were recorded preceding and during the stimulation. During the stimulation, STN mean firing rate dropped significantly, while pallidal mean firing rates did not change significantly. The vast majority of neurons across all three nuclei displayed stimulation driven modulations, which were stereotypic within each nucleus but differed across nuclei. The predominant response pattern of STN neurons was somatic inhibition. However, most pallidal neurons demonstrated synaptic activation patterns. A minority of neurons across all nuclei displayed axonal activation. Temporal dynamics were observed in the response to stimulation over the first 10 seconds in the STN and over the first 30 seconds in the pallidum. In both pallidal segments, the synaptic activation response patterns underwent delay and decay of the magnitude of the peak response due to short term synaptic depression. We suggest that during STN macro-stimulation the STN goes through a functional ablation as its upper bound on information transmission drops significantly. This notion is further supported by the evident dissociation between the stimulation driven pre-synaptic STN somatic inhibition and the post-synaptic axonal activation of its downstream targets. Thus, BG output maintains its firing rate while losing the deleterious effect of the STN. This may be a part of the mechanism leading to the beneficial effect of DBS in PD.

Live imaging of dense-core vesicles in primary cultured hippocampal neurons.

PubMed

Kwinter, David M; Silverman, Michael A; Kwinter, David; Michael, Silverman

2009-05-29

Observing and characterizing dynamic cellular processes can yield important information about cellular activity that cannot be gained from static images. Vital fluorescent probes, particularly green fluorescent protein (GFP) have revolutionized cell biology stemming from the ability to label specific intracellular compartments and cellular structures. For example, the live imaging of GFP (and its spectral variants) chimeras have allowed for a dynamic analysis of the cytoskeleton, organelle transport, and membrane dynamics in a multitude of organisms and cell types [1-3]. Although live imaging has become prevalent, this approach still poses many technical challenges, particularly in primary cultured neurons. One challenge is the expression of GFP-tagged proteins in post-mitotic neurons; the other is the ability to capture fluorescent images while minimizing phototoxicity, photobleaching, and maintaining general cell health. Here we provide a protocol that describes a lipid-based transfection method that yields a relatively low transfection rate (~0.5%), however is ideal for the imaging of fully polarized neurons. A low transfection rate is essential so that single axons and dendrites can be characterized as to their orientation to the cell body to confirm directionality of transport, i.e., anterograde v. retrograde. Our approach to imaging GFP expressing neurons relies on a standard wide-field fluorescent microscope outfitted with a CCD camera, image capture software, and a heated imaging chamber. We have imaged a wide variety of organelles or structures, for example, dense-core vesicles, mitochondria, growth cones, and actin without any special optics or excitation requirements other than a fluorescent light source. Additionally, spectrally-distinct, fluorescently labeled proteins, e.g., GFP and dsRed-tagged proteins, can be visualized near simultaneously to characterize co-transport or other coordinated cellular events. The imaging approach described here is flexible for a variety of imaging applications and can be adopted by a laboratory for relatively little cost provided a microscope is available.

Regulation of Cortical Dynamic Range by Background Synaptic Noise and Feedforward Inhibition

PubMed Central

Khubieh, Ayah; Ratté, Stéphanie; Lankarany, Milad; Prescott, Steven A.

2016-01-01

The cortex encodes a broad range of inputs. This breadth of operation requires sensitivity to weak inputs yet non-saturating responses to strong inputs. If individual pyramidal neurons were to have a narrow dynamic range, as previously claimed, then staggered all-or-none recruitment of those neurons would be necessary for the population to achieve a broad dynamic range. Contrary to this explanation, we show here through dynamic clamp experiments in vitro and computer simulations that pyramidal neurons have a broad dynamic range under the noisy conditions that exist in the intact brain due to background synaptic input. Feedforward inhibition capitalizes on those noise effects to control neuronal gain and thereby regulates the population dynamic range. Importantly, noise allows neurons to be recruited gradually and occludes the staggered recruitment previously attributed to heterogeneous excitation. Feedforward inhibition protects spike timing against the disruptive effects of noise, meaning noise can enable the gain control required for rate coding without compromising the precise spike timing required for temporal coding. PMID:26209846

Multithreaded Stochastic PDES for Reactions and Diffusions in Neurons.

PubMed

Lin, Zhongwei; Tropper, Carl; Mcdougal, Robert A; Patoary, Mohammand Nazrul Ishlam; Lytton, William W; Yao, Yiping; Hines, Michael L

2017-07-01

Cells exhibit stochastic behavior when the number of molecules is small. Hence a stochastic reaction-diffusion simulator capable of working at scale can provide a more accurate view of molecular dynamics within the cell. This paper describes a parallel discrete event simulator, Neuron Time Warp-Multi Thread (NTW-MT), developed for the simulation of reaction diffusion models of neurons. To the best of our knowledge, this is the first parallel discrete event simulator oriented towards stochastic simulation of chemical reactions in a neuron. The simulator was developed as part of the NEURON project. NTW-MT is optimistic and thread-based, which attempts to capitalize on multi-core architectures used in high performance machines. It makes use of a multi-level queue for the pending event set and a single roll-back message in place of individual anti-messages to disperse contention and decrease the overhead of processing rollbacks. Global Virtual Time is computed asynchronously both within and among processes to get rid of the overhead for synchronizing threads. Memory usage is managed in order to avoid locking and unlocking when allocating and de-allocating memory and to maximize cache locality. We verified our simulator on a calcium buffer model. We examined its performance on a calcium wave model, comparing it to the performance of a process based optimistic simulator and a threaded simulator which uses a single priority queue for each thread. Our multi-threaded simulator is shown to achieve superior performance to these simulators. Finally, we demonstrated the scalability of our simulator on a larger CICR model and a more detailed CICR model.

L-type calcium channels refine the neural population code of sound level

PubMed Central

Grimsley, Calum Alex; Green, David Brian

2016-01-01

The coding of sound level by ensembles of neurons improves the accuracy with which listeners identify how loud a sound is. In the auditory system, the rate at which neurons fire in response to changes in sound level is shaped by local networks. Voltage-gated conductances alter local output by regulating neuronal firing, but their role in modulating responses to sound level is unclear. We tested the effects of L-type calcium channels (CaL: CaV1.1–1.4) on sound-level coding in the central nucleus of the inferior colliculus (ICC) in the auditory midbrain. We characterized the contribution of CaL to the total calcium current in brain slices and then examined its effects on rate-level functions (RLFs) in vivo using single-unit recordings in awake mice. CaL is a high-threshold current and comprises ∼50% of the total calcium current in ICC neurons. In vivo, CaL activates at sound levels that evoke high firing rates. In RLFs that increase monotonically with sound level, CaL boosts spike rates at high sound levels and increases the maximum firing rate achieved. In different populations of RLFs that change nonmonotonically with sound level, CaL either suppresses or enhances firing at sound levels that evoke maximum firing. CaL multiplies the gain of monotonic RLFs with dynamic range and divides the gain of nonmonotonic RLFs with the width of the RLF. These results suggest that a single broad class of calcium channels activates enhancing and suppressing local circuits to regulate the sensitivity of neuronal populations to sound level. PMID:27605536

Discrete model of the olivo-cerebellar system: structure and dynamics

NASA Astrophysics Data System (ADS)

Maslennikov, O. V.; Nekorkin, V. I.

2012-08-01

We propose a discrete model of the olivo-cerebellar system. The model consists of three layers of interacting elements, namely, inferior olive neurons, Purkinje cells, and deep cerebellar nuclear neurons combined into a structure by axonal connections. Each element of the structure is described by a two-dimensional map with an individual set of parameters for each type of neurons. Dynamic properties of different types of neurons are described and spontaneous and stimulusinduced dynamics of the system is explored. Unlike the previously proposed models, this study takes into account the axonal interaction of neurons of different layers, as well as the interaction of the inferior olive neurons through electrical synapses with the property of plasticity. It is shown that the inclusion of these factors plays a significant role in the formation of spatio-temporal activity of the inferior olive neurons.

Structural versus dynamical origins of mean-field behavior in a self-organized critical model of neuronal avalanches

NASA Astrophysics Data System (ADS)

Moosavi, S. Amin; Montakhab, Afshin

2015-11-01

Critical dynamics of cortical neurons have been intensively studied over the past decade. Neuronal avalanches provide the main experimental as well as theoretical tools to consider criticality in such systems. Experimental studies show that critical neuronal avalanches show mean-field behavior. There are structural as well as recently proposed [Phys. Rev. E 89, 052139 (2014), 10.1103/PhysRevE.89.052139] dynamical mechanisms that can lead to mean-field behavior. In this work we consider a simple model of neuronal dynamics based on threshold self-organized critical models with synaptic noise. We investigate the role of high-average connectivity, random long-range connections, as well as synaptic noise in achieving mean-field behavior. We employ finite-size scaling in order to extract critical exponents with good accuracy. We conclude that relevant structural mechanisms responsible for mean-field behavior cannot be justified in realistic models of the cortex. However, strong dynamical noise, which can have realistic justifications, always leads to mean-field behavior regardless of the underlying structure. Our work provides a different (dynamical) origin than the conventionally accepted (structural) mechanisms for mean-field behavior in neuronal avalanches.

Regulation of Exocytotic Fusion Pores by SNARE Protein Transmembrane Domains

PubMed Central

Wu, Zhenyong; Thiyagarajan, Sathish; O’Shaughnessy, Ben; Karatekin, Erdem

2017-01-01

Calcium-triggered exocytotic release of neurotransmitters and hormones from neurons and neuroendocrine cells underlies neuronal communication, motor activity and endocrine functions. The core of the neuronal exocytotic machinery is composed of soluble N-ethyl maleimide sensitive factor attachment protein receptors (SNAREs). Formation of complexes between vesicle-attached v- and plasma-membrane anchored t-SNAREs in a highly regulated fashion brings the membranes into close apposition. Small, soluble proteins called Complexins (Cpx) and calcium-sensing Synaptotagmins cooperate to block fusion at low resting calcium concentrations, but trigger release upon calcium increase. A growing body of evidence suggests that the transmembrane domains (TMDs) of SNARE proteins play important roles in regulating the processes of fusion and release, but the mechanisms involved are only starting to be uncovered. Here we review recent evidence that SNARE TMDs exert influence by regulating the dynamics of the fusion pore, the initial aqueous connection between the vesicular lumen and the extracellular space. Even after the fusion pore is established, hormone release by neuroendocrine cells is tightly controlled, and the same may be true of neurotransmitter release by neurons. The dynamics of the fusion pore can regulate the kinetics of cargo release and the net amount released, and can determine the mode of vesicle recycling. Manipulations of SNARE TMDs were found to affect fusion pore properties profoundly, both during exocytosis and in biochemical reconstitutions. To explain these effects, TMD flexibility, and interactions among TMDs or between TMDs and lipids have been invoked. Exocytosis has provided the best setting in which to unravel the underlying mechanisms, being unique among membrane fusion reactions in that single fusion pores can be probed using high-resolution methods. An important role will likely be played by methods that can probe single fusion pores in a biochemically defined setting which have recently become available. Finally, computer simulations are valuable mechanistic tools because they have the power to access small length scales and very short times that are experimentally inaccessible. PMID:29066949

An optimization based study of equivalent circuit models for representing recordings at the neuron-electrode interface

PubMed Central

Thakore, Vaibhav; Molnar, Peter; Hickman, James J.

2014-01-01

Extracellular neuroelectronic interfacing is an emerging field with important applications in the fields of neural prosthetics, biological computation and biosensors. Traditionally, neuron-electrode interfaces have been modeled as linear point or area contact equivalent circuits but it is now being increasingly realized that such models cannot explain the shapes and magnitudes of the observed extracellular signals. Here, results were compared and contrasted from an unprecedented optimization based study of the point contact models for an extracellular ‘on-cell’ neuron-patch electrode and a planar neuron-microelectrode interface. Concurrent electrophysiological recordings from a single neuron simultaneously interfaced to three distinct electrodes (intracellular, ‘on-cell’ patch and planar microelectrode) allowed novel insights into the mechanism of signal transduction at the neuron-electrode interface. After a systematic isolation of the nonlinear neuronal contribution to the extracellular signal, a consistent underestimation of the simulated supra-threshold extracellular signals compared to the experimentally recorded signals was observed. This conclusively demonstrated that the dynamics of the interfacial medium contribute nonlinearly to the process of signal transduction at the neuron-electrode interface. Further, an examination of the optimized model parameters for the experimental extracellular recordings from sub- and supra-threshold stimulations of the neuron-electrode junctions revealed that ionic transport at the ‘on-cell’ neuron-patch electrode is dominated by diffusion whereas at the neuron-microelectrode interface the electric double layer (EDL) effects dominate. Based on this study, the limitations of the equivalent circuit models in their failure to account for the nonlinear EDL and ionic electrodiffusion effects occurring during signal transduction at the neuron-electrode interfaces are discussed. PMID:22695342

Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons

PubMed Central

Xiao, Dongsheng; Vanni, Matthieu P; Mitelut, Catalin C; Chan, Allen W; LeDue, Jeffrey M; Xie, Yicheng; Chen, Andrew CN; Swindale, Nicholas V; Murphy, Timothy H

2017-01-01

Understanding the basis of brain function requires knowledge of cortical operations over wide-spatial scales, but also within the context of single neurons. In vivo, wide-field GCaMP imaging and sub-cortical/cortical cellular electrophysiology were used in mice to investigate relationships between spontaneous single neuron spiking and mesoscopic cortical activity. We make use of a rich set of cortical activity motifs that are present in spontaneous activity in anesthetized and awake animals. A mesoscale spike-triggered averaging procedure allowed the identification of motifs that are preferentially linked to individual spiking neurons by employing genetically targeted indicators of neuronal activity. Thalamic neurons predicted and reported specific cycles of wide-scale cortical inhibition/excitation. In contrast, spike-triggered maps derived from single cortical neurons yielded spatio-temporal maps expected for regional cortical consensus function. This approach can define network relationships between any point source of neuronal spiking and mesoscale cortical maps. DOI: http://dx.doi.org/10.7554/eLife.19976.001 PMID:28160463

Reciprocal cholinergic and GABAergic modulation of the small ventrolateral pacemaker neurons of Drosophila's circadian clock neuron network.

PubMed

Lelito, Katherine R; Shafer, Orie T

2012-04-01

The relatively simple clock neuron network of Drosophila is a valuable model system for the neuronal basis of circadian timekeeping. Unfortunately, many key neuronal classes of this network are inaccessible to electrophysiological analysis. We have therefore adopted the use of genetically encoded sensors to address the physiology of the fly's circadian clock network. Using genetically encoded Ca(2+) and cAMP sensors, we have investigated the physiological responses of two specific classes of clock neuron, the large and small ventrolateral neurons (l- and s-LN(v)s), to two neurotransmitters implicated in their modulation: acetylcholine (ACh) and γ-aminobutyric acid (GABA). Live imaging of l-LN(v) cAMP and Ca(2+) dynamics in response to cholinergic agonist and GABA application were well aligned with published electrophysiological data, indicating that our sensors were capable of faithfully reporting acute physiological responses to these transmitters within single adult clock neuron soma. We extended these live imaging methods to s-LN(v)s, critical neuronal pacemakers whose physiological properties in the adult brain are largely unknown. Our s-LN(v) experiments revealed the predicted excitatory responses to bath-applied cholinergic agonists and the predicted inhibitory effects of GABA and established that the antagonism of ACh and GABA extends to their effects on cAMP signaling. These data support recently published but physiologically untested models of s-LN(v) modulation and lead to the prediction that cholinergic and GABAergic inputs to s-LN(v)s will have opposing effects on the phase and/or period of the molecular clock within these critical pacemaker neurons.

Lipid Regulated Intramolecular Conformational Dynamics of SNARE-Protein Ykt6

PubMed Central

Dai, Yawei; Seeger, Markus; Weng, Jingwei; Song, Song; Wang, Wenning; Tan, Yan-Wen

2016-01-01

Cellular informational and metabolic processes are propagated with specific membrane fusions governed by soluble N-ethylmaleimide sensitive factor attachment protein receptors (SNARE). SNARE protein Ykt6 is highly expressed in brain neurons and plays a critical role in the membrane-trafficking process. Studies suggested that Ykt6 undergoes a conformational change at the interface between its longin domain and the SNARE core. In this work, we study the conformational state distributions and dynamics of rat Ykt6 by means of single-molecule Förster Resonance Energy Transfer (smFRET) and Fluorescence Cross-Correlation Spectroscopy (FCCS). We observed that intramolecular conformational dynamics between longin domain and SNARE core occurred at the timescale ~200 μs. Furthermore, this dynamics can be regulated and even eliminated by the presence of lipid dodecylphoshpocholine (DPC). Our molecular dynamic (MD) simulations have shown that, the SNARE core exhibits a flexible structure while the longin domain retains relatively stable in apo state. Combining single molecule experiments and theoretical MD simulations, we are the first to provide a quantitative dynamics of Ykt6 and explain the functional conformational change from a qualitative point of view. PMID:27493064

Auditory-nerve single-neuron thresholds to electrical stimulation from scala tympani electrodes.

PubMed

Parkins, C W; Colombo, J

1987-12-31

Single auditory-nerve neuron thresholds were studied in sensory-deafened squirrel monkeys to determine the effects of electrical stimulus shape and frequency on single-neuron thresholds. Frequency was separated into its components, pulse width and pulse rate, which were analyzed separately. Square and sinusoidal pulse shapes were compared. There were no or questionably significant threshold differences in charge per phase between sinusoidal and square pulses of the same pulse width. There was a small (less than 0.5 dB) but significant threshold advantage for 200 microseconds/phase pulses delivered at low pulse rates (156 pps) compared to higher pulse rates (625 pps and 2500 pps). Pulse width was demonstrated to be the prime determinant of single-neuron threshold, resulting in strength-duration curves similar to other mammalian myelinated neurons, but with longer chronaxies. The most efficient electrical stimulus pulse width to use for cochlear implant stimulation was determined to be 100 microseconds/phase. This pulse width delivers the lowest charge/phase at threshold. The single-neuron strength-duration curves were compared to strength-duration curves of a computer model based on the specific anatomy of auditory-nerve neurons. The membrane capacitance and resulting chronaxie of the model can be varied by altering the length of the unmyelinated termination of the neuron, representing the unmyelinated portion of the neuron between the habenula perforata and the hair cell. This unmyelinated segment of the auditory-nerve neuron may be subject to aminoglycoside damage. Simulating a 10 micron unmyelinated termination for this model neuron produces a strength-duration curve that closely fits the single-neuron data obtained from aminoglycoside deafened animals. Both the model and the single-neuron strength-duration curves differ significantly from behavioral threshold data obtained from monkeys and humans with cochlear implants. This discrepancy can best be explained by the involvement of higher level neurologic processes in the behavioral responses. These findings suggest that the basic principles of neural membrane function must be considered in developing or analyzing electrical stimulation strategies for cochlear prostheses if the appropriate stimulation of frequency specific populations of auditory-nerve neurons is the objective.

Early history of subplate and interstitial neurons: from Theodor Meynert (1867) to the discovery of the subplate zone (1974)

PubMed Central

Judaš, Miloš; Sedmak, Goran; Pletikos, Mihovil

2010-01-01

In this historical review, we trace the early history of research on the fetal subplate zone, subplate neurons and interstitial neurons in the white matter of the adult nervous system. We arrive at several general conclusions. First, a century of research clearly testifies that interstitial neurons, subplate neurons and the subplate zone were first observed and variously described in the human brain – or, in more general terms, in large brains of gyrencephalic mammals, characterized by an abundant white matter and slow and protracted prenatal and postnatal development. Secondly, the subplate zone cannot be meaningfully defined using a single criterion – be it a specific population of cells, fibres or a specific molecular or genetic marker. The subplate zone is a highly dynamic architectonic compartment and its size and cellular composition do not remain constant during development. Thirdly, it is important to make a clear distinction between the subplate zone and the subplate (and interstitial) neurons. The transient existence of the subplate zone (as a specific architectonic compartment of the fetal telencephalic wall) should not be equated with the putative transient existence of subplate neurons. It is clear that in rodents, and to an even greater extent in humans and monkeys, a significant number of subplate cells survive and remain functional throughout life. PMID:20979585

Representation of Dynamic Interaural Phase Difference in Auditory Cortex of Awake Rhesus Macaques

PubMed Central

Scott, Brian H.; Malone, Brian J.; Semple, Malcolm N.

2009-01-01

Neurons in auditory cortex of awake primates are selective for the spatial location of a sound source, yet the neural representation of the binaural cues that underlie this tuning remains undefined. We examined this representation in 283 single neurons across the low-frequency auditory core in alert macaques, trained to discriminate binaural cues for sound azimuth. In response to binaural beat stimuli, which mimic acoustic motion by modulating the relative phase of a tone at the two ears, these neurons robustly modulate their discharge rate in response to this directional cue. In accordance with prior studies, the preferred interaural phase difference (IPD) of these neurons typically corresponds to azimuthal locations contralateral to the recorded hemisphere. Whereas binaural beats evoke only transient discharges in anesthetized cortex, neurons in awake cortex respond throughout the IPD cycle. In this regard, responses are consistent with observations at earlier stations of the auditory pathway. Discharge rate is a band-pass function of the frequency of IPD modulation in most neurons (73%), but both discharge rate and temporal synchrony are independent of the direction of phase modulation. When subjected to a receiver operator characteristic analysis, the responses of individual neurons are insufficient to account for the perceptual acuity of these macaques in an IPD discrimination task, suggesting the need for neural pooling at the cortical level. PMID:19164111

Representation of dynamic interaural phase difference in auditory cortex of awake rhesus macaques.

PubMed

Scott, Brian H; Malone, Brian J; Semple, Malcolm N

2009-04-01

Neurons in auditory cortex of awake primates are selective for the spatial location of a sound source, yet the neural representation of the binaural cues that underlie this tuning remains undefined. We examined this representation in 283 single neurons across the low-frequency auditory core in alert macaques, trained to discriminate binaural cues for sound azimuth. In response to binaural beat stimuli, which mimic acoustic motion by modulating the relative phase of a tone at the two ears, these neurons robustly modulate their discharge rate in response to this directional cue. In accordance with prior studies, the preferred interaural phase difference (IPD) of these neurons typically corresponds to azimuthal locations contralateral to the recorded hemisphere. Whereas binaural beats evoke only transient discharges in anesthetized cortex, neurons in awake cortex respond throughout the IPD cycle. In this regard, responses are consistent with observations at earlier stations of the auditory pathway. Discharge rate is a band-pass function of the frequency of IPD modulation in most neurons (73%), but both discharge rate and temporal synchrony are independent of the direction of phase modulation. When subjected to a receiver operator characteristic analysis, the responses of individual neurons are insufficient to account for the perceptual acuity of these macaques in an IPD discrimination task, suggesting the need for neural pooling at the cortical level.

Cortical preparatory activity: representation of movement or first cog in a dynamical machine?

PubMed Central

Churchland, MM; Cunningham, JP; Kaufman, MT; Ryu, SI; Shenoy, KV

2010-01-01

Summary The motor cortices are active during both movement and movement preparation. A common assumption is that preparatory activity constitutes a sub-threshold form of movement activity: a neuron active during rightwards movements becomes modestly active during preparation of a rightwards movement. We asked whether this pattern of activity is in fact observed. We found that it was not: at the level of a single neuron, preparatory tuning was weakly correlated with movement-period tuning. Yet somewhat paradoxically, preparatory tuning could be captured by a preferred direction in an abstract ‘space’ that described the population-level pattern of movement activity. In fact, this relationship accounted for preparatory responses better than did traditional tuning models. These results are expected if preparatory activity provides the initial state of a dynamical system whose evolution produces movement activity. Our results thus suggest that preparatory activity may not represent specific factors, and may instead play a more mechanistic role. PMID:21040842

Rhythmic coordination of hippocampal neurons during associative memory processing

PubMed Central

Rangel, Lara M; Rueckemann, Jon W; Riviere, Pamela D; Keefe, Katherine R; Porter, Blake S; Heimbuch, Ian S; Budlong, Carl H; Eichenbaum, Howard

2016-01-01

Hippocampal oscillations are dynamic, with unique oscillatory frequencies present during different behavioral states. To examine the extent to which these oscillations reflect neuron engagement in distinct local circuit processes that are important for memory, we recorded single cell and local field potential activity from the CA1 region of the hippocampus as rats performed a context-guided odor-reward association task. We found that theta (4–12 Hz), beta (15–35 Hz), low gamma (35–55 Hz), and high gamma (65–90 Hz) frequencies exhibited dynamic amplitude profiles as rats sampled odor cues. Interneurons and principal cells exhibited unique engagement in each of the four rhythmic circuits in a manner that related to successful performance of the task. Moreover, principal cells coherent to each rhythm differentially represented task dimensions. These results demonstrate that distinct processing states arise from the engagement of rhythmically identifiable circuits, which have unique roles in organizing task-relevant processing in the hippocampus. DOI: http://dx.doi.org/10.7554/eLife.09849.001 PMID:26751780

Profiling neuronal ion channelopathies with non-invasive brain imaging and dynamic causal models: Case studies of single gene mutations

PubMed Central

Gilbert, Jessica R.; Symmonds, Mkael; Hanna, Michael G.; Dolan, Raymond J.; Friston, Karl J.; Moran, Rosalyn J.

2016-01-01

Clinical assessments of brain function rely upon visual inspection of electroencephalographic waveform abnormalities in tandem with functional magnetic resonance imaging. However, no current technology proffers in vivo assessments of activity at synapses, receptors and ion-channels, the basis of neuronal communication. Using dynamic causal modeling we compared electrophysiological responses from two patients with distinct monogenic ion channelopathies and a large cohort of healthy controls to demonstrate the feasibility of assaying synaptic-level channel communication non-invasively. Synaptic channel abnormality was identified in both patients (100% sensitivity) with assay specificity above 89%, furnishing estimates of neurotransmitter and voltage-gated ion throughput of sodium, calcium, chloride and potassium. This performance indicates a potential novel application as an adjunct for clinical assessments in neurological and psychiatric settings. More broadly, these findings indicate that biophysical models of synaptic channels can be estimated non-invasively, having important implications for advancing human neuroimaging to the level of non-invasive ion channel assays. PMID:26342528

Effects of self-coupling and asymmetric output on metastable dynamical transient firing patterns in arrays of neurons with bidirectional inhibitory coupling.

PubMed

Horikawa, Yo

2016-04-01

Metastable dynamical transient patterns in arrays of bidirectionally coupled neurons with self-coupling and asymmetric output were studied. First, an array of asymmetric sigmoidal neurons with symmetric inhibitory bidirectional coupling and self-coupling was considered and the bifurcations of its steady solutions were shown. Metastable dynamical transient spatially nonuniform states existed in the presence of a pair of spatially symmetric stable solutions as well as unstable spatially nonuniform solutions in a restricted range of the output gain of a neuron. The duration of the transients increased exponentially with the number of neurons up to the maximum number at which the spatially nonuniform steady solutions were stabilized. The range of the output gain for which they existed reduced as asymmetry in a sigmoidal output function of a neuron increased, while the existence range expanded as the strength of inhibitory self-coupling increased. Next, arrays of spiking neuron models with slow synaptic inhibitory bidirectional coupling and self-coupling were considered with computer simulation. In an array of Class 1 Hindmarsh-Rose type models, in which each neuron showed a graded firing rate, metastable dynamical transient firing patterns were observed in the presence of inhibitory self-coupling. This agreed with the condition for the existence of metastable dynamical transients in an array of sigmoidal neurons. In an array of Class 2 Bonhoeffer-van der Pol models, in which each neuron had a clear threshold between firing and resting, long-lasting transient firing patterns with bursting and irregular motion were observed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Computational implications of activity-dependent neuronal processes

NASA Astrophysics Data System (ADS)

Goldman, Mark Steven

Synapses, the connections between neurons, often fail to transmit a large percentage of the action potentials that they receive. I describe several models of synaptic transmission at a single stochastic synapse with an activity-dependent probability of transmission and demonstrate how synaptic transmission failures may increase the efficiency with which a synapse transmits information. Spike trains in the visual cortex of freely viewing monkeys have positive auto correlations that are indicative of a redundant representation of the information they contain. I show how a synapse with activity-dependent transmission failures modeled after those occurring in visual cortical synapses can remove this redundancy by transmitting a decorrelated subset of the spike trains it receives. I suggest that redundancy reduction at individual synapses saves synaptic resources while increasing the sensitivity of the postsynaptic neuron to information arriving along many inputs. For a neuron receiving input from many decorrelating synapses, my analysis leads to a prediction of the number of visual inputs to a neuron and the cross-correlations between these inputs and suggests that the time scale of synaptic dynamics observed in sensory areas corresponds to a fundamental time scale for processing sensory information. Systems with activity-dependent changes in their parameters, or plasticity, often display a wide variability in their individual components that belies the stability of their function, Motivated by experiments demonstrating that identified neurons with stereotyped function can have a large variability in the densities of their ion channels, or ionic conductances, I build a conductance-based model of a single neuron. The neuron's firing activity is relatively insensitive to changes in certain combinations of conductances, but markedly sensitive to changes in other combinations. Using a combined modeling and experimental approach, I show that neuromodulators and regulatory processes target sensitive combinations of conductances. I suggest that the variability observed in conductance measurements occurs along insensitive combinations of conductances and could result from homeostatic processes that allow the neuron's conductances to drift without triggering activity- dependent feedback mechanisms. These results together suggest that plastic systems may have a high degree of flexibility and variability in their components without a loss of robustness in their response properties.

Deep learning and shapes similarity for joint segmentation and tracing single neurons in SEM images

NASA Astrophysics Data System (ADS)

Rao, Qiang; Xiao, Chi; Han, Hua; Chen, Xi; Shen, Lijun; Xie, Qiwei

2017-02-01

Extracting the structure of single neurons is critical for understanding how they function within the neural circuits. Recent developments in microscopy techniques, and the widely recognized need for openness and standardization provide a community resource for automated reconstruction of dendritic and axonal morphology of single neurons. In order to look into the fine structure of neurons, we use the Automated Tape-collecting Ultra Microtome Scanning Electron Microscopy (ATUM-SEM) to get images sequence of serial sections of animal brain tissue that densely packed with neurons. Different from other neuron reconstruction method, we propose a method that enhances the SEM images by detecting the neuronal membranes with deep convolutional neural network (DCNN) and segments single neurons by active contour with group shape similarity. We joint the segmentation and tracing together and they interact with each other by alternate iteration that tracing aids the selection of candidate region patch for active contour segmentation while the segmentation provides the neuron geometrical features which improve the robustness of tracing. The tracing model mainly relies on the neuron geometrical features and is updated after neuron being segmented on the every next section. Our method enables the reconstruction of neurons of the drosophila mushroom body which is cut to serial sections and imaged under SEM. Our method provides an elementary step for the whole reconstruction of neuronal networks.

The iso-response method: measuring neuronal stimulus integration with closed-loop experiments

PubMed Central

Gollisch, Tim; Herz, Andreas V. M.

2012-01-01

Throughout the nervous system, neurons integrate high-dimensional input streams and transform them into an output of their own. This integration of incoming signals involves filtering processes and complex non-linear operations. The shapes of these filters and non-linearities determine the computational features of single neurons and their functional roles within larger networks. A detailed characterization of signal integration is thus a central ingredient to understanding information processing in neural circuits. Conventional methods for measuring single-neuron response properties, such as reverse correlation, however, are often limited by the implicit assumption that stimulus integration occurs in a linear fashion. Here, we review a conceptual and experimental alternative that is based on exploring the space of those sensory stimuli that result in the same neural output. As demonstrated by recent results in the auditory and visual system, such iso-response stimuli can be used to identify the non-linearities relevant for stimulus integration, disentangle consecutive neural processing steps, and determine their characteristics with unprecedented precision. Automated closed-loop experiments are crucial for this advance, allowing rapid search strategies for identifying iso-response stimuli during experiments. Prime targets for the method are feed-forward neural signaling chains in sensory systems, but the method has also been successfully applied to feedback systems. Depending on the specific question, “iso-response” may refer to a predefined firing rate, single-spike probability, first-spike latency, or other output measures. Examples from different studies show that substantial progress in understanding neural dynamics and coding can be achieved once rapid online data analysis and stimulus generation, adaptive sampling, and computational modeling are tightly integrated into experiments. PMID:23267315

Identification of spinal circuits involved in touch-evoked dynamic mechanical pain

PubMed Central

Cheng, Longzhen; Duan, Bo; Huang, Tianwen; Zhang, Yan; Chen, Yangyang; Britz, Olivier; Garcia-Campmany, Lidia; Ren, Xiangyu; Vong, Linh; Lowell, Bradford B.; Goulding, Martyn; Wang, Yun; Ma, Qiufu

2017-01-01

Mechanical hypersensitivity is a debilitating symptom associated with millions of chronic pain patients. It exists in distinct forms, including brush-evoked dynamic and filament-evoked punctate. Here we report that dynamic mechanical hypersensitivity induced by nerve injury or inflammation was compromised in mice with ablation of spinal VT3Lbx1 neurons defined by coexpression of VGLUT3Cre and Lbx1Flpo, as indicated by the loss of brush-evoked nocifensive responses and conditional place aversion. Electrophysiological recordings show that VT3Lbx1 neurons form morphine-resistant polysynaptic pathways relaying inputs from low-threshold Aβ mechanoreceptors to lamina I output neurons. Meanwhile, the subset of somatostatin (SOM) lineage neurons preserved in VT3Lbx1 neuron-ablated mice is largely sufficient to mediate von Frey filament-evoked punctate mechanical hypersensitivity, including both morphine-sensitive and morphine-resistant forms. Furthermore, acute silencing of VT3Lbx1 neurons attenuated pre-established dynamic mechanical hypersensitivity induced by nerve injury, suggesting these neurons as a potential cellular target for treating this form of neuropathic pain. PMID:28436981

Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex

PubMed Central

Nir, Yuval; Mukamel, Roy; Dinstein, Ilan; Privman, Eran; Harel, Michal; Fisch, Lior; Gelbard-Sagiv, Hagar; Kipervasser, Svetlana; Andelman, Fani; Neufeld, Miri Y; Kramer, Uri; Arieli, Amos; Fried, Itzhak; Malach, Rafael

2009-01-01

Animal studies have shown robust electrophysiological activity in the sensory cortex in the absence of stimuli or tasks. Similarly, recent human functional magnetic resonance imaging (fMRI) revealed widespread, spontaneously emerging cortical fluctuations. However, it is unknown what neuronal dynamics underlie this spontaneous activity in the human brain. Here we studied this issue by combining bilateral single-unit, local field potentials (LFPs) and intracranial electrocorticography (ECoG) recordings in individuals undergoing clinical monitoring. We found slow (<0.1 Hz, following 1/f-like profiles) spontaneous fluctuations of neuronal activity with significant interhemispheric correlations. These fluctuations were evident mainly in neuronal firing rates and in gamma (40–100 Hz) LFP power modulations. Notably, the interhemispheric correlations were enhanced during rapid eye movement and stage 2 sleep. Multiple intracranial ECoG recordings revealed clear selectivity for functional networks in the spontaneous gamma LFP power modulations. Our results point to slow spontaneous modulations in firing rate and gamma LFP as the likely correlates of spontaneous fMRI fluctuations in the human sensory cortex. PMID:19160509

Activity-dependent switch of GABAergic inhibition into glutamatergic excitation in astrocyte-neuron networks

PubMed Central

Perea, Gertrudis; Gómez, Ricardo; Mederos, Sara; Covelo, Ana; Ballesteros, Jesús J; Schlosser, Laura; Hernández-Vivanco, Alicia; Martín-Fernández, Mario; Quintana, Ruth; Rayan, Abdelrahman; Díez, Adolfo; Fuenzalida, Marco; Agarwal, Amit; Bergles, Dwight E; Bettler, Bernhard; Manahan-Vaughan, Denise; Martín, Eduardo D; Kirchhoff, Frank; Araque, Alfonso

2016-01-01

Interneurons are critical for proper neural network function and can activate Ca2+ signaling in astrocytes. However, the impact of the interneuron-astrocyte signaling into neuronal network operation remains unknown. Using the simplest hippocampal Astrocyte-Neuron network, i.e., GABAergic interneuron, pyramidal neuron, single CA3-CA1 glutamatergic synapse, and astrocytes, we found that interneuron-astrocyte signaling dynamically affected excitatory neurotransmission in an activity- and time-dependent manner, and determined the sign (inhibition vs potentiation) of the GABA-mediated effects. While synaptic inhibition was mediated by GABAA receptors, potentiation involved astrocyte GABAB receptors, astrocytic glutamate release, and presynaptic metabotropic glutamate receptors. Using conditional astrocyte-specific GABAB receptor (Gabbr1) knockout mice, we confirmed the glial source of the interneuron-induced potentiation, and demonstrated the involvement of astrocytes in hippocampal theta and gamma oscillations in vivo. Therefore, astrocytes decode interneuron activity and transform inhibitory into excitatory signals, contributing to the emergence of novel network properties resulting from the interneuron-astrocyte interplay. DOI: http://dx.doi.org/10.7554/eLife.20362.001 PMID:28012274

Impact of Partial Time Delay on Temporal Dynamics of Watts-Strogatz Small-World Neuronal Networks

NASA Astrophysics Data System (ADS)

Yan, Hao; Sun, Xiaojuan

2017-06-01

In this paper, we mainly discuss effects of partial time delay on temporal dynamics of Watts-Strogatz (WS) small-world neuronal networks by controlling two parameters. One is the time delay τ and the other is the probability of partial time delay pdelay. Temporal dynamics of WS small-world neuronal networks are discussed with the aid of temporal coherence and mean firing rate. With the obtained simulation results, it is revealed that for small time delay τ, the probability pdelay could weaken temporal coherence and increase mean firing rate of neuronal networks, which indicates that it could improve neuronal firings of the neuronal networks while destroying firing regularity. For large time delay τ, temporal coherence and mean firing rate do not have great changes with respect to pdelay. Time delay τ always has great influence on both temporal coherence and mean firing rate no matter what is the value of pdelay. Moreover, with the analysis of spike trains and histograms of interspike intervals of neurons inside neuronal networks, it is found that the effects of partial time delays on temporal coherence and mean firing rate could be the result of locking between the period of neuronal firing activities and the value of time delay τ. In brief, partial time delay could have great influence on temporal dynamics of the neuronal networks.

Input-output relation and energy efficiency in the neuron with different spike threshold dynamics.

PubMed

Yi, Guo-Sheng; Wang, Jiang; Tsang, Kai-Ming; Wei, Xi-Le; Deng, Bin

2015-01-01

Neuron encodes and transmits information through generating sequences of output spikes, which is a high energy-consuming process. The spike is initiated when membrane depolarization reaches a threshold voltage. In many neurons, threshold is dynamic and depends on the rate of membrane depolarization (dV/dt) preceding a spike. Identifying the metabolic energy involved in neural coding and their relationship to threshold dynamic is critical to understanding neuronal function and evolution. Here, we use a modified Morris-Lecar model to investigate neuronal input-output property and energy efficiency associated with different spike threshold dynamics. We find that the neurons with dynamic threshold sensitive to dV/dt generate discontinuous frequency-current curve and type II phase response curve (PRC) through Hopf bifurcation, and weak noise could prohibit spiking when bifurcation just occurs. The threshold that is insensitive to dV/dt, instead, results in a continuous frequency-current curve, a type I PRC and a saddle-node on invariant circle bifurcation, and simultaneously weak noise cannot inhibit spiking. It is also shown that the bifurcation, frequency-current curve and PRC type associated with different threshold dynamics arise from the distinct subthreshold interactions of membrane currents. Further, we observe that the energy consumption of the neuron is related to its firing characteristics. The depolarization of spike threshold improves neuronal energy efficiency by reducing the overlap of Na(+) and K(+) currents during an action potential. The high energy efficiency is achieved at more depolarized spike threshold and high stimulus current. These results provide a fundamental biophysical connection that links spike threshold dynamics, input-output relation, energetics and spike initiation, which could contribute to uncover neural encoding mechanism.

Input-output relation and energy efficiency in the neuron with different spike threshold dynamics

PubMed Central

Yi, Guo-Sheng; Wang, Jiang; Tsang, Kai-Ming; Wei, Xi-Le; Deng, Bin

2015-01-01

Neuron encodes and transmits information through generating sequences of output spikes, which is a high energy-consuming process. The spike is initiated when membrane depolarization reaches a threshold voltage. In many neurons, threshold is dynamic and depends on the rate of membrane depolarization (dV/dt) preceding a spike. Identifying the metabolic energy involved in neural coding and their relationship to threshold dynamic is critical to understanding neuronal function and evolution. Here, we use a modified Morris-Lecar model to investigate neuronal input-output property and energy efficiency associated with different spike threshold dynamics. We find that the neurons with dynamic threshold sensitive to dV/dt generate discontinuous frequency-current curve and type II phase response curve (PRC) through Hopf bifurcation, and weak noise could prohibit spiking when bifurcation just occurs. The threshold that is insensitive to dV/dt, instead, results in a continuous frequency-current curve, a type I PRC and a saddle-node on invariant circle bifurcation, and simultaneously weak noise cannot inhibit spiking. It is also shown that the bifurcation, frequency-current curve and PRC type associated with different threshold dynamics arise from the distinct subthreshold interactions of membrane currents. Further, we observe that the energy consumption of the neuron is related to its firing characteristics. The depolarization of spike threshold improves neuronal energy efficiency by reducing the overlap of Na+ and K+ currents during an action potential. The high energy efficiency is achieved at more depolarized spike threshold and high stimulus current. These results provide a fundamental biophysical connection that links spike threshold dynamics, input-output relation, energetics and spike initiation, which could contribute to uncover neural encoding mechanism. PMID:26074810

Predicting the synaptic information efficacy in cortical layer 5 pyramidal neurons using a minimal integrate-and-fire model.

PubMed

London, Michael; Larkum, Matthew E; Häusser, Michael

2008-11-01

Synaptic information efficacy (SIE) is a statistical measure to quantify the efficacy of a synapse. It measures how much information is gained, on the average, about the output spike train of a postsynaptic neuron if the input spike train is known. It is a particularly appropriate measure for assessing the input-output relationship of neurons receiving dynamic stimuli. Here, we compare the SIE of simulated synaptic inputs measured experimentally in layer 5 cortical pyramidal neurons in vitro with the SIE computed from a minimal model constructed to fit the recorded data. We show that even with a simple model that is far from perfect in predicting the precise timing of the output spikes of the real neuron, the SIE can still be accurately predicted. This arises from the ability of the model to predict output spikes influenced by the input more accurately than those driven by the background current. This indicates that in this context, some spikes may be more important than others. Lastly we demonstrate another aspect where using mutual information could be beneficial in evaluating the quality of a model, by measuring the mutual information between the model's output and the neuron's output. The SIE, thus, could be a useful tool for assessing the quality of models of single neurons in preserving input-output relationship, a property that becomes crucial when we start connecting these reduced models to construct complex realistic neuronal networks.

Single-Molecule Tracking Photoactivated Localization Microscopy to Map Nano-Scale Structure and Dynamics in Living Spines

PubMed Central

MacGillavry, Harold D.; Blanpied, Thomas A.

2013-01-01

Super-resolution microscopy has rapidly become an indispensable tool in cell biology and neuroscience by enabling measurement in live cells of structures smaller than the classical limit imposed by diffraction. The most widely applied super-resolution method currently is localization microscopy, which takes advantage of the ability to determine the position of individual fluorescent molecules with nanometer accuracy even in cells. By iteratively measuring sparse subsets of photoactivatable fluorescent proteins, protein distribution in macromolecular structures can be accurately reconstructed. Moreover, the motion trajectories of individual molecules within cells can be measured, providing unique ability to measure transport kinetics, exchange rates, and binding affinities of even small subsets of molecules with high temporal resolution and great spatial specificity. This unit describes protocols to measure and quantify the distribution of scaffold proteins within single synapses of cultured hippocampal neurons, and to track and measure the diffusion of intracellular constituents of the neuronal plasma membrane. PMID:25429311

Firing-rate response of linear and nonlinear integrate-and-fire neurons to modulated current-based and conductance-based synaptic drive.

PubMed

Richardson, Magnus J E

2007-08-01

Integrate-and-fire models are mainstays of the study of single-neuron response properties and emergent states of recurrent networks of spiking neurons. They also provide an analytical base for perturbative approaches that treat important biological details, such as synaptic filtering, synaptic conductance increase, and voltage-activated currents. Steady-state firing rates of both linear and nonlinear integrate-and-fire models, receiving fluctuating synaptic drive, can be calculated from the time-independent Fokker-Planck equation. The dynamic firing-rate response is less easy to extract, even at the first-order level of a weak modulation of the model parameters, but is an important determinant of neuronal response and network stability. For the linear integrate-and-fire model the response to modulations of current-based synaptic drive can be written in terms of hypergeometric functions. For the nonlinear exponential and quadratic models no such analytical forms for the response are available. Here it is demonstrated that a rather simple numerical method can be used to obtain the steady-state and dynamic response for both linear and nonlinear models to parameter modulation in the presence of current-based or conductance-based synaptic fluctuations. To complement the full numerical solution, generalized analytical forms for the high-frequency response are provided. A special case is also identified--time-constant modulation--for which the response to an arbitrarily strong modulation can be calculated exactly.

Somatosensory neuron types identified by high-coverage single-cell RNA-sequencing and functional heterogeneity

PubMed Central

Li, Chang-Lin; Li, Kai-Cheng; Wu, Dan; Chen, Yan; Luo, Hao; Zhao, Jing-Rong; Wang, Sa-Shuang; Sun, Ming-Ming; Lu, Ying-Jin; Zhong, Yan-Qing; Hu, Xu-Ye; Hou, Rui; Zhou, Bei-Bei; Bao, Lan; Xiao, Hua-Sheng; Zhang, Xu

2016-01-01

Sensory neurons are distinguished by distinct signaling networks and receptive characteristics. Thus, sensory neuron types can be defined by linking transcriptome-based neuron typing with the sensory phenotypes. Here we classify somatosensory neurons of the mouse dorsal root ganglion (DRG) by high-coverage single-cell RNA-sequencing (10 950 ± 1 218 genes per neuron) and neuron size-based hierarchical clustering. Moreover, single DRG neurons responding to cutaneous stimuli are recorded using an in vivo whole-cell patch clamp technique and classified by neuron-type genetic markers. Small diameter DRG neurons are classified into one type of low-threshold mechanoreceptor and five types of mechanoheat nociceptors (MHNs). Each of the MHN types is further categorized into two subtypes. Large DRG neurons are categorized into four types, including neurexophilin 1-expressing MHNs and mechanical nociceptors (MNs) expressing BAI1-associated protein 2-like 1 (Baiap2l1). Mechanoreceptors expressing trafficking protein particle complex 3-like and Baiap2l1-marked MNs are subdivided into two subtypes each. These results provide a new system for cataloging somatosensory neurons and their transcriptome databases. PMID:26691752

Inhibitory neurons promote robust critical firing dynamics in networks of integrate-and-fire neurons.

PubMed

Lu, Zhixin; Squires, Shane; Ott, Edward; Girvan, Michelle

2016-12-01

We study the firing dynamics of a discrete-state and discrete-time version of an integrate-and-fire neuronal network model with both excitatory and inhibitory neurons. When the integer-valued state of a neuron exceeds a threshold value, the neuron fires, sends out state-changing signals to its connected neurons, and returns to the resting state. In this model, a continuous phase transition from non-ceaseless firing to ceaseless firing is observed. At criticality, power-law distributions of avalanche size and duration with the previously derived exponents, -3/2 and -2, respectively, are observed. Using a mean-field approach, we show analytically how the critical point depends on model parameters. Our main result is that the combined presence of both inhibitory neurons and integrate-and-fire dynamics greatly enhances the robustness of critical power-law behavior (i.e., there is an increased range of parameters, including both sub- and supercritical values, for which several decades of power-law behavior occurs).

Large scale in vivo recordings to study neuronal biophysics.

PubMed

Giocomo, Lisa M

2015-06-01

Over the last several years, technological advances have enabled researchers to more readily observe single-cell membrane biophysics in awake, behaving animals. Studies utilizing these technologies have provided important insights into the mechanisms generating functional neural codes in both sensory and non-sensory cortical circuits. Crucial for a deeper understanding of how membrane biophysics control circuit dynamics however, is a continued effort to move toward large scale studies of membrane biophysics, in terms of the numbers of neurons and ion channels examined. Future work faces a number of theoretical and technical challenges on this front but recent technological developments hold great promise for a larger scale understanding of how membrane biophysics contribute to circuit coding and computation. Copyright © 2014 Elsevier Ltd. All rights reserved.

Regulation of Cortical Dynamic Range by Background Synaptic Noise and Feedforward Inhibition.

PubMed

Khubieh, Ayah; Ratté, Stéphanie; Lankarany, Milad; Prescott, Steven A

2016-08-01

The cortex encodes a broad range of inputs. This breadth of operation requires sensitivity to weak inputs yet non-saturating responses to strong inputs. If individual pyramidal neurons were to have a narrow dynamic range, as previously claimed, then staggered all-or-none recruitment of those neurons would be necessary for the population to achieve a broad dynamic range. Contrary to this explanation, we show here through dynamic clamp experiments in vitro and computer simulations that pyramidal neurons have a broad dynamic range under the noisy conditions that exist in the intact brain due to background synaptic input. Feedforward inhibition capitalizes on those noise effects to control neuronal gain and thereby regulates the population dynamic range. Importantly, noise allows neurons to be recruited gradually and occludes the staggered recruitment previously attributed to heterogeneous excitation. Feedforward inhibition protects spike timing against the disruptive effects of noise, meaning noise can enable the gain control required for rate coding without compromising the precise spike timing required for temporal coding. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Dynamic Information Encoding With Dynamic Synapses in Neural Adaptation

PubMed Central

Li, Luozheng; Mi, Yuanyuan; Zhang, Wenhao; Wang, Da-Hui; Wu, Si

2018-01-01

Adaptation refers to the general phenomenon that the neural system dynamically adjusts its response property according to the statistics of external inputs. In response to an invariant stimulation, neuronal firing rates first increase dramatically and then decrease gradually to a low level close to the background activity. This prompts a question: during the adaptation, how does the neural system encode the repeated stimulation with attenuated firing rates? It has been suggested that the neural system may employ a dynamical encoding strategy during the adaptation, the information of stimulus is mainly encoded by the strong independent spiking of neurons at the early stage of the adaptation; while the weak but synchronized activity of neurons encodes the stimulus information at the later stage of the adaptation. The previous study demonstrated that short-term facilitation (STF) of electrical synapses, which increases the synchronization between neurons, can provide a mechanism to realize dynamical encoding. In the present study, we further explore whether short-term plasticity (STP) of chemical synapses, an interaction form more common than electrical synapse in the cortex, can support dynamical encoding. We build a large-size network with chemical synapses between neurons. Notably, facilitation of chemical synapses only enhances pair-wise correlations between neurons mildly, but its effect on increasing synchronization of the network can be significant, and hence it can serve as a mechanism to convey the stimulus information. To read-out the stimulus information, we consider that a downstream neuron receives balanced excitatory and inhibitory inputs from the network, so that the downstream neuron only responds to synchronized firings of the network. Therefore, the response of the downstream neuron indicates the presence of the repeated stimulation. Overall, our study demonstrates that STP of chemical synapse can serve as a mechanism to realize dynamical neural encoding. We believe that our study shed lights on the mechanism underlying the efficient neural information processing via adaptation. PMID:29636675

Active listening: task-dependent plasticity of spectrotemporal receptive fields in primary auditory cortex.

PubMed

Fritz, Jonathan; Elhilali, Mounya; Shamma, Shihab

2005-08-01

Listening is an active process in which attentive focus on salient acoustic features in auditory tasks can influence receptive field properties of cortical neurons. Recent studies showing rapid task-related changes in neuronal spectrotemporal receptive fields (STRFs) in primary auditory cortex of the behaving ferret are reviewed in the context of current research on cortical plasticity. Ferrets were trained on spectral tasks, including tone detection and two-tone discrimination, and on temporal tasks, including gap detection and click-rate discrimination. STRF changes could be measured on-line during task performance and occurred within minutes of task onset. During spectral tasks, there were specific spectral changes (enhanced response to tonal target frequency in tone detection and discrimination, suppressed response to tonal reference frequency in tone discrimination). However, only in the temporal tasks, the STRF was changed along the temporal dimension by sharpening temporal dynamics. In ferrets trained on multiple tasks, distinctive and task-specific STRF changes could be observed in the same cortical neurons in successive behavioral sessions. These results suggest that rapid task-related plasticity is an ongoing process that occurs at a network and single unit level as the animal switches between different tasks and dynamically adapts cortical STRFs in response to changing acoustic demands.

Role of inhibitory feedback for information processing in thalamocortical circuits

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayer, Joerg; Schuster, Heinz Georg; Claussen, Jens Christian

2006-03-15

The information transfer in the thalamus is blocked dynamically during sleep, in conjunction with the occurrence of spindle waves. In order to describe the dynamic mechanisms which control the sensory transfer of information, it is necessary to have a qualitative model for the response properties of thalamic neurons. As the theoretical understanding of the mechanism remains incomplete, we analyze two modeling approaches for a recent experiment by Le Masson et al. [Nature (London) 417, 854 (2002)] on the thalamocortical loop. We use a conductance based model in order to motivate an extension of the Hindmarsh-Rose model, which mimics experimental observationsmore » of Le Masson et al. Typically, thalamic neurons posses two different firing modes, depending on their membrane potential. At depolarized potentials, the cells fire in a single spike mode and relay synaptic inputs in a one-to-one manner to the cortex. If the cell gets hyperpolarized, T-type calcium currents generate burst-mode firing which leads to a decrease in the spike transfer. In thalamocortical circuits, the cell membrane gets hyperpolarized by recurrent inhibitory feedback loops. In the case of reciprocally coupled excitatory and inhibitory neurons, inhibitory feedback leads to metastable self-sustained oscillations, which mask the incoming input, and thereby reduce the information transfer significantly.« less

Solving the two-dimensional Fokker-Planck equation for strongly correlated neurons

NASA Astrophysics Data System (ADS)

Deniz, Taşkın; Rotter, Stefan

2017-01-01

Pairs of neurons in brain networks often share much of the input they receive from other neurons. Due to essential nonlinearities of the neuronal dynamics, the consequences for the correlation of the output spike trains are generally not well understood. Here we analyze the case of two leaky integrate-and-fire neurons using an approach which is nonperturbative with respect to the degree of input correlation. Our treatment covers both weakly and strongly correlated dynamics, generalizing previous results based on linear response theory.

hamlet, a binary genetic switch between single- and multiple- dendrite neuron morphology.

PubMed

Moore, Adrian W; Jan, Lily Yeh; Jan, Yuh Nung

2002-08-23

The dendritic morphology of neurons determines the number and type of inputs they receive. In the Drosophila peripheral nervous system (PNS), the external sensory (ES) neurons have a single nonbranched dendrite, whereas the lineally related multidendritic (MD) neurons have extensively branched dendritic arbors. We report that hamlet is a binary genetic switch between these contrasting morphological types. In hamlet mutants, ES neurons are converted to an MD fate, whereas ectopic hamlet expression in MD precursors results in transformation of MD neurons into ES neurons. Moreover, hamlet expression induced in MD neurons undergoing dendrite outgrowth drastically reduces arbor branching.

Mitochondrial Dynamics Mediated by Mitofusin 1 Is Required for POMC Neuron Glucose-Sensing and Insulin Release Control.

PubMed

Ramírez, Sara; Gómez-Valadés, Alicia G; Schneeberger, Marc; Varela, Luis; Haddad-Tóvolli, Roberta; Altirriba, Jordi; Noguera, Eduard; Drougard, Anne; Flores-Martínez, Álvaro; Imbernón, Mónica; Chivite, Iñigo; Pozo, Macarena; Vidal-Itriago, Andrés; Garcia, Ainhoa; Cervantes, Sara; Gasa, Rosa; Nogueiras, Ruben; Gama-Pérez, Pau; Garcia-Roves, Pablo M; Cano, David A; Knauf, Claude; Servitja, Joan-Marc; Horvath, Tamas L; Gomis, Ramon; Zorzano, Antonio; Claret, Marc

2017-06-06

Proopiomelanocortin (POMC) neurons are critical sensors of nutrient availability implicated in energy balance and glucose metabolism control. However, the precise mechanisms underlying nutrient sensing in POMC neurons remain incompletely understood. We show that mitochondrial dynamics mediated by Mitofusin 1 (MFN1) in POMC neurons couple nutrient sensing with systemic glucose metabolism. Mice lacking MFN1 in POMC neurons exhibited defective mitochondrial architecture remodeling and attenuated hypothalamic gene expression programs during the fast-to-fed transition. This loss of mitochondrial flexibility in POMC neurons bidirectionally altered glucose sensing, causing abnormal glucose homeostasis due to defective insulin secretion by pancreatic β cells. Fed mice lacking MFN1 in POMC neurons displayed enhanced hypothalamic mitochondrial oxygen flux and reactive oxygen species generation. Central delivery of antioxidants was able to normalize the phenotype. Collectively, our data posit MFN1-mediated mitochondrial dynamics in POMC neurons as an intrinsic nutrient-sensing mechanism and unveil an unrecognized link between this subset of neurons and insulin release. Copyright © 2017 Elsevier Inc. All rights reserved.

Local subcutaneous injection of chlorogenic acid inhibits the nociceptive trigeminal spinal nucleus caudalis neurons in rats.

PubMed

Kakita, Kaede; Tsubouchi, Hirona; Adachi, Mayu; Takehana, Shiori; Shimazu, Yoshihito; Takeda, Mamoru

2017-11-29

Acute administration of chlorogenic acid (CGA) in vitro was recently shown to modulate potassium channel conductance and acid-sensing ion channels (ASICs) in the primary sensory neurons; however, in vivo peripheral effects of CGA on the nociceptive mechanical stimulation of trigeminal neuronal activity remains to be determined. The present study investigated whether local administration of CGA in vivo attenuates mechanical stimulation-induced excitability of trigeminal spinal nucleus caudalis neuronal (SpVc) activity in rats. Extracellular single-unit recordings were made of SpVc wide-dynamic range (WDR) neuronal activity elicited by non-noxious and noxious orofacial mechanical stimulation in pentobarbital anesthetized rats. The mean number of SpVc WDR neuronal firings responding to both non-noxious and noxious mechanical stimuli were significantly and dose-dependently inhibited by local subcutaneous administration of CGA (0.1-10mM), with the maximal inhibition of discharge frequency revealed within 10min and reversed after approximately 30min. The mean frequency of SpVc neuronal discharge inhibition by CGA was comparable to that by a local anesthetic, the sodium channel blocker, 1% lidocaine. These results suggest that local CGA injection into the peripheral receptive field suppresses the excitability of SpVc neurons, possibly via the activation of voltage-gated potassium channels and modulation of ASICs in the nociceptive nerve terminal of trigeminal ganglion neurons. Therefore, local injection of CGA could contribute to local anesthetic agents for the treatment of trigeminal nociceptive pain. Copyright © 2017 Elsevier Ireland Ltd and Japan Neuroscience Society. All rights reserved.

A systematic approach to selecting task relevant neurons.

PubMed

Kahn, Kevin; Saxena, Shreya; Eskandar, Emad; Thakor, Nitish; Schieber, Marc; Gale, John T; Averbeck, Bruno; Eden, Uri; Sarma, Sridevi V

2015-04-30

Since task related neurons cannot be specifically targeted during surgery, a critical decision to make is to select which neurons are task-related when performing data analysis. Including neurons unrelated to the task degrade decoding accuracy and confound neurophysiological results. Traditionally, task-related neurons are selected as those with significant changes in firing rate when a stimulus is applied. However, this assumes that neurons' encoding of stimuli are dominated by their firing rate with little regard to temporal dynamics. This paper proposes a systematic approach for neuron selection, which uses a likelihood ratio test to capture the contribution of stimulus to spiking activity while taking into account task-irrelevant intrinsic dynamics that affect firing rates. This approach is denoted as the model deterioration excluding stimulus (MDES) test. MDES is compared to firing rate selection in four case studies: a simulation, a decoding example, and two neurophysiology examples. The MDES rankings in the simulation match closely with ideal rankings, while firing rate rankings are skewed by task-irrelevant parameters. For decoding, 95% accuracy is achieved using the top 8 MDES-ranked neurons, while the top 12 firing-rate ranked neurons are needed. In the neurophysiological examples, MDES matches published results when firing rates do encode salient stimulus information, and uncovers oscillatory modulations in task-related neurons that are not captured when neurons are selected using firing rates. These case studies illustrate the importance of accounting for intrinsic dynamics when selecting task-related neurons and following the MDES approach accomplishes that. MDES selects neurons that encode task-related information irrespective of these intrinsic dynamics which can bias firing rate based selection. Copyright © 2015 Elsevier B.V. All rights reserved.

Dynamical analysis of Parkinsonian state emulated by hybrid Izhikevich neuron models

NASA Astrophysics Data System (ADS)

Liu, Chen; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Li, Huiyan; Loparo, Kenneth A.; Fietkiewicz, Chris

2015-11-01

Computational models play a significant role in exploring novel theories to complement the findings of physiological experiments. Various computational models have been developed to reveal the mechanisms underlying brain functions. Particularly, in the development of therapies to modulate behavioral and pathological abnormalities, computational models provide the basic foundations to exhibit transitions between physiological and pathological conditions. Considering the significant roles of the intrinsic properties of the globus pallidus and the coupling connections between neurons in determining the firing patterns and the dynamical activities of the basal ganglia neuronal network, we propose a hypothesis that pathological behaviors under the Parkinsonian state may originate from combined effects of intrinsic properties of globus pallidus neurons and synaptic conductances in the whole neuronal network. In order to establish a computational efficient network model, hybrid Izhikevich neuron model is used due to its capacity of capturing the dynamical characteristics of the biological neuronal activities. Detailed analysis of the individual Izhikevich neuron model can assist in understanding the roles of model parameters, which then facilitates the establishment of the basal ganglia-thalamic network model, and contributes to a further exploration of the underlying mechanisms of the Parkinsonian state. Simulation results show that the hybrid Izhikevich neuron model is capable of capturing many of the dynamical properties of the basal ganglia-thalamic neuronal network, such as variations of the firing rates and emergence of synchronous oscillations under the Parkinsonian condition, despite the simplicity of the two-dimensional neuronal model. It may suggest that the computational efficient hybrid Izhikevich neuron model can be used to explore basal ganglia normal and abnormal functions. Especially it provides an efficient way of emulating the large-scale neuron network and potentially contributes to development of improved therapy for neurological disorders such as Parkinson's disease.

A single-neuron tracing study of arkypallidal and prototypic neurons in healthy rats.

PubMed

Fujiyama, Fumino; Nakano, Takashi; Matsuda, Wakoto; Furuta, Takahiro; Udagawa, Jun; Kaneko, Takeshi

2016-12-01

The external globus pallidus (GP) is known as a relay nucleus of the indirect pathway of the basal ganglia. Recent studies in dopamine-depleted and healthy rats indicate that the GP comprises two main types of pallidofugal neurons: the so-called "prototypic" and "arkypallidal" neurons. However, the reconstruction of complete arkypallidal neurons in healthy rats has not been reported. Here we visualized the entire axonal arborization of four single arkypallidal neurons and six single prototypic neurons in rat brain using labeling with a viral vector expressing membrane-targeted green fluorescent protein and examined the distribution of axon boutons in the target nuclei. Results revealed that not only the arkypallidal neurons but nearly all of the prototypic neurons projected to the striatum with numerous axon varicosities. Thus, the striatum is a major target nucleus for pallidal neurons. Arkypallidal and prototypic GP neurons located in the calbindin-positive and calbindin-negative regions mainly projected to the corresponding positive and negative regions in the striatum. Because the GP and striatum calbindin staining patterns reflect the topographic organization of the striatopallidal projection, the striatal neurons in the sensorimotor and associative regions constitute the reciprocal connection with the GP neurons in the corresponding regions.

Network activity influences the subthreshold and spiking visual responses of pyramidal neurons in the three-layer turtle cortex.

PubMed

Wright, Nathaniel C; Wessel, Ralf

2017-10-01

A primary goal of systems neuroscience is to understand cortical function, typically by studying spontaneous and stimulus-modulated cortical activity. Mounting evidence suggests a strong and complex relationship exists between the ongoing and stimulus-modulated cortical state. To date, most work in this area has been based on spiking in populations of neurons. While advantageous in many respects, this approach is limited in scope: it records the activity of a minority of neurons and gives no direct indication of the underlying subthreshold dynamics. Membrane potential recordings can fill these gaps in our understanding, but stable recordings are difficult to obtain in vivo. Here, we recorded subthreshold cortical visual responses in the ex vivo turtle eye-attached whole brain preparation, which is ideally suited for such a study. We found that, in the absence of visual stimulation, the network was "synchronous"; neurons displayed network-mediated transitions between hyperpolarized (Down) and depolarized (Up) membrane potential states. The prevalence of these slow-wave transitions varied across turtles and recording sessions. Visual stimulation evoked similar Up states, which were on average larger and less reliable when the ongoing state was more synchronous. Responses were muted when immediately preceded by large, spontaneous Up states. Evoked spiking was sparse, highly variable across trials, and mediated by concerted synaptic inputs that were, in general, only very weakly correlated with inputs to nearby neurons. Together, these results highlight the multiplexed influence of the cortical network on the spontaneous and sensory-evoked activity of individual cortical neurons. NEW & NOTEWORTHY Most studies of cortical activity focus on spikes. Subthreshold membrane potential recordings can provide complementary insight, but stable recordings are difficult to obtain in vivo. Here, we recorded the membrane potentials of cortical neurons during ongoing and visually evoked activity. We observed a strong relationship between network and single-neuron evoked activity spanning multiple temporal scales. The membrane potential perspective of cortical dynamics thus highlights the influence of intrinsic network properties on visual processing. Copyright © 2017 the American Physiological Society.

Dynamic neuroanatomy at subcellular resolution in the zebrafish.

PubMed

Faucherre, Adèle; López-Schier, Hernán

2014-01-01

Genetic means to visualize and manipulate neuronal circuits in the intact animal have revolutionized neurobiology. "Dynamic neuroanatomy" defines a range of approaches aimed at quantifying the architecture or subcellular organization of neurons over time during their development, regeneration, or degeneration. A general feature of these approaches is their reliance on the optical isolation of defined neurons in toto by genetically expressing markers in one or few cells. Here we use the afferent neurons of the lateral line as an example to describe a simple method for the dynamic neuroanatomical study of axon terminals in the zebrafish by laser-scanning confocal microscopy.

Bidirectional Coupling between Astrocytes and Neurons Mediates Learning and Dynamic Coordination in the Brain: A Multiple Modeling Approach

PubMed Central

Wade, John J.; McDaid, Liam J.; Harkin, Jim; Crunelli, Vincenzo; Kelso, J. A. Scott

2011-01-01

In recent years research suggests that astrocyte networks, in addition to nutrient and waste processing functions, regulate both structural and synaptic plasticity. To understand the biological mechanisms that underpin such plasticity requires the development of cell level models that capture the mutual interaction between astrocytes and neurons. This paper presents a detailed model of bidirectional signaling between astrocytes and neurons (the astrocyte-neuron model or AN model) which yields new insights into the computational role of astrocyte-neuronal coupling. From a set of modeling studies we demonstrate two significant findings. Firstly, that spatial signaling via astrocytes can relay a “learning signal” to remote synaptic sites. Results show that slow inward currents cause synchronized postsynaptic activity in remote neurons and subsequently allow Spike-Timing-Dependent Plasticity based learning to occur at the associated synapses. Secondly, that bidirectional communication between neurons and astrocytes underpins dynamic coordination between neuron clusters. Although our composite AN model is presently applied to simplified neural structures and limited to coordination between localized neurons, the principle (which embodies structural, functional and dynamic complexity), and the modeling strategy may be extended to coordination among remote neuron clusters. PMID:22242121

A transgenic mouse for imaging activity-dependent dynamics of endogenous Arc mRNA in live neurons.

PubMed

Das, Sulagna; Moon, Hyungseok C; Singer, Robert H; Park, Hye Yoon

2018-06-01

Localized translation plays a crucial role in synaptic plasticity and memory consolidation. However, it has not been possible to follow the dynamics of memory-associated mRNAs in living neurons in response to neuronal activity in real time. We have generated a novel mouse model where the endogenous Arc/Arg3.1 gene is tagged in its 3' untranslated region with stem-loops that bind a bacteriophage PP7 coat protein (PCP), allowing visualization of individual mRNAs in real time. The physiological response of the tagged gene to neuronal activity is identical to endogenous Arc and reports the true dynamics of Arc mRNA from transcription to degradation. The transcription dynamics of Arc in cultured hippocampal neurons revealed two novel results: (i) A robust transcriptional burst with prolonged ON state occurs after stimulation, and (ii) transcription cycles continue even after initial stimulation is removed. The correlation of stimulation with Arc transcription and mRNA transport in individual neurons revealed that stimulus-induced Ca 2+ activity was necessary but not sufficient for triggering Arc transcription and that blocking neuronal activity did not affect the dendritic transport of newly synthesized Arc mRNAs. This mouse will provide an important reagent to investigate how individual neurons transduce activity into spatiotemporal regulation of gene expression at the synapse.

Synchronization properties of heterogeneous neuronal networks with mixed excitability type

NASA Astrophysics Data System (ADS)

Leone, Michael J.; Schurter, Brandon N.; Letson, Benjamin; Booth, Victoria; Zochowski, Michal; Fink, Christian G.

2015-03-01

We study the synchronization of neuronal networks with dynamical heterogeneity, showing that network structures with the same propensity for synchronization (as quantified by master stability function analysis) may develop dramatically different synchronization properties when heterogeneity is introduced with respect to neuronal excitability type. Specifically, we investigate networks composed of neurons with different types of phase response curves (PRCs), which characterize how oscillating neurons respond to excitatory perturbations. Neurons exhibiting type 1 PRC respond exclusively with phase advances, while neurons exhibiting type 2 PRC respond with either phase delays or phase advances, depending on when the perturbation occurs. We find that Watts-Strogatz small world networks transition to synchronization gradually as the proportion of type 2 neurons increases, whereas scale-free networks may transition gradually or rapidly, depending upon local correlations between node degree and excitability type. Random placement of type 2 neurons results in gradual transition to synchronization, whereas placement of type 2 neurons as hubs leads to a much more rapid transition, showing that type 2 hub cells easily "hijack" neuronal networks to synchronization. These results underscore the fact that the degree of synchronization observed in neuronal networks is determined by a complex interplay between network structure and the dynamical properties of individual neurons, indicating that efforts to recover structural connectivity from dynamical correlations must in general take both factors into account.

Phenotype-dependent Ca(2+) dynamics in single boutons of various anatomically identified GABAergic interneurons in the rat hippocampus.

PubMed

Lőrincz, Tibor; Kisfali, Máté; Lendvai, Balázs; Sylvester Vizi, Elek

2016-02-01

Interneurons (INs) of the hippocampus exert versatile inhibition on pyramidal cells by silencing the network at different oscillation frequencies. Although IN discharge can phase-lock to various rhythms in the hippocampus, under high-frequency axon firing, the boutons may not be able to follow the fast activity. Here, we studied Ca(2+) responses to action potentials (APs) in single boutons using combined two-photon microscopy and patch clamp electrophysiology in three types of INs: non-fast-spiking (NFS) neurons showing cannabinoid 1 receptor labelling and dendrite targeting, fast-spiking partially parvalbumin-positive cells synapsing with dendrites (DFS), and parvalbumin-positive cells with perisomatic innervation (PFS). The increase in [Ca(2+) ]i from AP trains was substantially higher in NFS boutons than in DFS or PFS boutons. The decay of bouton Ca(2+) responses was markedly faster in DFS and PFS cells compared with NFS neurons. The bouton-to-bouton variability of AP-evoked Ca(2+) transients in the same axon was surprisingly low in each cell type. Importantly, local responses were saturated after shorter trains of APs in NFS cells than in PFS cells. This feature of fast-spiking neurons might allow them to follow higher-frequency gamma oscillations for a longer time than NFS cells. The function of NFS boutons may better support asynchronous GABA release. In conclusion, we demonstrate several neuron-specific Ca(2+) transients in boutons of NFS, PFS and DFS neurons, which may serve differential functions in hippocampal networks. © 2015 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

L-type calcium channels refine the neural population code of sound level.

PubMed

Grimsley, Calum Alex; Green, David Brian; Sivaramakrishnan, Shobhana

2016-12-01

The coding of sound level by ensembles of neurons improves the accuracy with which listeners identify how loud a sound is. In the auditory system, the rate at which neurons fire in response to changes in sound level is shaped by local networks. Voltage-gated conductances alter local output by regulating neuronal firing, but their role in modulating responses to sound level is unclear. We tested the effects of L-type calcium channels (Ca L : Ca V 1.1-1.4) on sound-level coding in the central nucleus of the inferior colliculus (ICC) in the auditory midbrain. We characterized the contribution of Ca L to the total calcium current in brain slices and then examined its effects on rate-level functions (RLFs) in vivo using single-unit recordings in awake mice. Ca L is a high-threshold current and comprises ∼50% of the total calcium current in ICC neurons. In vivo, Ca L activates at sound levels that evoke high firing rates. In RLFs that increase monotonically with sound level, Ca L boosts spike rates at high sound levels and increases the maximum firing rate achieved. In different populations of RLFs that change nonmonotonically with sound level, Ca L either suppresses or enhances firing at sound levels that evoke maximum firing. Ca L multiplies the gain of monotonic RLFs with dynamic range and divides the gain of nonmonotonic RLFs with the width of the RLF. These results suggest that a single broad class of calcium channels activates enhancing and suppressing local circuits to regulate the sensitivity of neuronal populations to sound level. Copyright © 2016 the American Physiological Society.

From elements to perception: local and global processing in visual neurons.

PubMed

Spillmann, L

1999-01-01

Gestalt psychologists in the early part of the century challenged psychophysical notions that perceptual phenomena can be understood from a punctate (atomistic) analysis of the elements present in the stimulus. Their ideas slowed later attempts to explain vision in terms of single-cell recordings from individual neurons. A rapprochement between Gestalt phenomenology and neurophysiology seemed unlikely when the first ECVP was held in Marburg, Germany, in 1978. Since that time, response properties of neurons have been discovered that invite an interpretation of visual phenomena (including illusions) in terms of neuronal processing by long-range interactions, as first proposed by Mach and Hering in the last century. This article traces a personal journey into the early days of neurophysiological vision research to illustrate the progress that has taken place from the first attempts to correlate single-cell responses with visual perceptions. Whereas initially the receptive-field properties of individual classes of cells--e.g., contrast, wavelength, orientation, motion, disparity, and spatial-frequency detectors--were used to account for relatively simple visual phenomena, nowadays complex perceptions are interpreted in terms of long-range interactions, involving many neurons. This change in paradigm from local to global processing was made possible by recent findings, in the cortex, on horizontal interactions and backward propagation (feedback loops) in addition to classical feedforward processing. These mechanisms are exemplified by studies of the tilt effect and tilt aftereffect, direction-specific motion adaptation, illusory contours, filling-in and fading, figure--ground segregation by orientation and motion contrast, and pop-out in dynamic visual-noise patterns. Major questions for future research and a discussion of their epistemological implications conclude the article.

Biological conservation law as an emerging functionality in dynamical neuronal networks.

PubMed

Podobnik, Boris; Jusup, Marko; Tiganj, Zoran; Wang, Wen-Xu; Buldú, Javier M; Stanley, H Eugene

2017-11-07

Scientists strive to understand how functionalities, such as conservation laws, emerge in complex systems. Living complex systems in particular create high-ordered functionalities by pairing up low-ordered complementary processes, e.g., one process to build and the other to correct. We propose a network mechanism that demonstrates how collective statistical laws can emerge at a macro (i.e., whole-network) level even when they do not exist at a unit (i.e., network-node) level. Drawing inspiration from neuroscience, we model a highly stylized dynamical neuronal network in which neurons fire either randomly or in response to the firing of neighboring neurons. A synapse connecting two neighboring neurons strengthens when both of these neurons are excited and weakens otherwise. We demonstrate that during this interplay between the synaptic and neuronal dynamics, when the network is near a critical point, both recurrent spontaneous and stimulated phase transitions enable the phase-dependent processes to replace each other and spontaneously generate a statistical conservation law-the conservation of synaptic strength. This conservation law is an emerging functionality selected by evolution and is thus a form of biological self-organized criticality in which the key dynamical modes are collective.

Biological conservation law as an emerging functionality in dynamical neuronal networks

PubMed Central

Podobnik, Boris; Tiganj, Zoran; Wang, Wen-Xu; Buldú, Javier M.

2017-01-01

Scientists strive to understand how functionalities, such as conservation laws, emerge in complex systems. Living complex systems in particular create high-ordered functionalities by pairing up low-ordered complementary processes, e.g., one process to build and the other to correct. We propose a network mechanism that demonstrates how collective statistical laws can emerge at a macro (i.e., whole-network) level even when they do not exist at a unit (i.e., network-node) level. Drawing inspiration from neuroscience, we model a highly stylized dynamical neuronal network in which neurons fire either randomly or in response to the firing of neighboring neurons. A synapse connecting two neighboring neurons strengthens when both of these neurons are excited and weakens otherwise. We demonstrate that during this interplay between the synaptic and neuronal dynamics, when the network is near a critical point, both recurrent spontaneous and stimulated phase transitions enable the phase-dependent processes to replace each other and spontaneously generate a statistical conservation law—the conservation of synaptic strength. This conservation law is an emerging functionality selected by evolution and is thus a form of biological self-organized criticality in which the key dynamical modes are collective. PMID:29078286

Imaging Voltage in Genetically Defined Neuronal Subpopulations with a Cre Recombinase-Targeted Hybrid Voltage Sensor.

PubMed

Bayguinov, Peter O; Ma, Yihe; Gao, Yu; Zhao, Xinyu; Jackson, Meyer B

2017-09-20

Genetically encoded voltage indicators create an opportunity to monitor electrical activity in defined sets of neurons as they participate in the complex patterns of coordinated electrical activity that underlie nervous system function. Taking full advantage of genetically encoded voltage indicators requires a generalized strategy for targeting the probe to genetically defined populations of cells. To this end, we have generated a mouse line with an optimized hybrid voltage sensor (hVOS) probe within a locus designed for efficient Cre recombinase-dependent expression. Crossing this mouse with Cre drivers generated double transgenics expressing hVOS probe in GABAergic, parvalbumin, and calretinin interneurons, as well as hilar mossy cells, new adult-born neurons, and recently active neurons. In each case, imaging in brain slices from male or female animals revealed electrically evoked optical signals from multiple individual neurons in single trials. These imaging experiments revealed action potentials, dynamic aspects of dendritic integration, and trial-to-trial fluctuations in response latency. The rapid time response of hVOS imaging revealed action potentials with high temporal fidelity, and enabled accurate measurements of spike half-widths characteristic of each cell type. Simultaneous recording of rapid voltage changes in multiple neurons with a common genetic signature offers a powerful approach to the study of neural circuit function and the investigation of how neural networks encode, process, and store information. SIGNIFICANCE STATEMENT Genetically encoded voltage indicators hold great promise in the study of neural circuitry, but realizing their full potential depends on targeting the sensor to distinct cell types. Here we present a new mouse line that expresses a hybrid optical voltage sensor under the control of Cre recombinase. Crossing this line with Cre drivers generated double-transgenic mice, which express this sensor in targeted cell types. In brain slices from these animals, single-trial hybrid optical voltage sensor recordings revealed voltage changes with submillisecond resolution in multiple neurons simultaneously. This imaging tool will allow for the study of the emergent properties of neural circuits and permit experimental tests of the roles of specific types of neurons in complex circuit activity. Copyright © 2017 the authors 0270-6474/17/379305-15$15.00/0.

Does attention play a role in dynamic receptive field adaptation to changing acoustic salience in A1?

PubMed

Fritz, Jonathan B; Elhilali, Mounya; David, Stephen V; Shamma, Shihab A

2007-07-01

Acoustic filter properties of A1 neurons can dynamically adapt to stimulus statistics, classical conditioning, instrumental learning and the changing auditory attentional focus. We have recently developed an experimental paradigm that allows us to view cortical receptive field plasticity on-line as the animal meets different behavioral challenges by attending to salient acoustic cues and changing its cortical filters to enhance performance. We propose that attention is the key trigger that initiates a cascade of events leading to the dynamic receptive field changes that we observe. In our paradigm, ferrets were initially trained, using conditioned avoidance training techniques, to discriminate between background noise stimuli (temporally orthogonal ripple combinations) and foreground tonal target stimuli. They learned to generalize the task for a wide variety of distinct background and foreground target stimuli. We recorded cortical activity in the awake behaving animal and computed on-line spectrotemporal receptive fields (STRFs) of single neurons in A1. We observed clear, predictable task-related changes in STRF shape while the animal performed spectral tasks (including single tone and multi-tone detection, and two-tone discrimination) with different tonal targets. A different set of task-related changes occurred when the animal performed temporal tasks (including gap detection and click-rate discrimination). Distinctive cortical STRF changes may constitute a "task-specific signature". These spectral and temporal changes in cortical filters occur quite rapidly, within 2min of task onset, and fade just as quickly after task completion, or in some cases, persisted for hours. The same cell could multiplex by differentially changing its receptive field in different task conditions. On-line dynamic task-related changes, as well as persistent plastic changes, were observed at a single-unit, multi-unit and population level. Auditory attention is likely to be pivotal in mediating these task-related changes since the magnitude of STRF changes correlated with behavioral performance on tasks with novel targets. Overall, these results suggest the presence of an attention-triggered plasticity algorithm in A1 that can swiftly change STRF shape by transforming receptive fields to enhance figure/ground separation, by using a contrast matched filter to filter out the background, while simultaneously enhancing the salient acoustic target in the foreground. These results favor the view of a nimble, dynamic, attentive and adaptive brain that can quickly reshape its sensory filter properties and sensori-motor links on a moment-to-moment basis, depending upon the current challenges the animal faces. In this review, we summarize our results in the context of a broader survey of the field of auditory attention, and then consider neuronal networks that could give rise to this phenomenon of attention-driven receptive field plasticity in A1.

Metabolic cytometry: capillary electrophoresis with two-color fluorescence detection for the simultaneous study of two glycosphingolipid metabolic pathways in single primary neurons.

PubMed

Essaka, David C; Prendergast, Jillian; Keithley, Richard B; Palcic, Monica M; Hindsgaul, Ole; Schnaar, Ronald L; Dovichi, Norman J

2012-03-20

Metabolic cytometry is a form of chemical cytometry wherein metabolic cascades are monitored in single cells. We report the first example of metabolic cytometry where two different metabolic pathways are simultaneously monitored. Glycolipid catabolism in primary rat cerebella neurons was probed by incubation with tetramethylrhodamine-labeled GM1 (GM1-TMR). Simultaneously, both catabolism and anabolism were probed by coincubation with BODIPY-FL labeled LacCer (LacCer-BODIPY-FL). In a metabolic cytometry experiment, single cells were incubated with substrate, washed, aspirated into a capillary, and lysed. The components were separated by capillary electrophoresis equipped with a two-spectral channel laser-induced fluorescence detector. One channel monitored fluorescence generated by the metabolic products produced from GM1-TMR and the other monitored the metabolic products produced from LacCer-BODIPY-FL. The metabolic products were identified by comparison with the mobility of a set of standards. The detection system produced at least 6 orders of magnitude dynamic range in each spectral channel with negligible spectral crosstalk. Detection limits were 1 zmol for BODIPY-FL and 500 ymol for tetramethylrhodamine standard solutions.

Computational model of electrically coupled, intrinsically distinct pacemaker neurons.

PubMed

Soto-Treviño, Cristina; Rabbah, Pascale; Marder, Eve; Nadim, Farzan

2005-07-01

Electrical coupling between neurons with similar properties is often studied. Nonetheless, the role of electrical coupling between neurons with widely different intrinsic properties also occurs, but is less well understood. Inspired by the pacemaker group of the crustacean pyloric network, we developed a multicompartment, conductance-based model of a small network of intrinsically distinct, electrically coupled neurons. In the pyloric network, a small intrinsically bursting neuron, through gap junctions, drives 2 larger, tonically spiking neurons to reliably burst in-phase with it. Each model neuron has 2 compartments, one responsible for spike generation and the other for producing a slow, large-amplitude oscillation. We illustrate how these compartments interact and determine the dynamics of the model neurons. Our model captures the dynamic oscillation range measured from the isolated and coupled biological neurons. At the network level, we explore the range of coupling strengths for which synchronous bursting oscillations are possible. The spatial segregation of ionic currents significantly enhances the ability of the 2 neurons to burst synchronously, and the oscillation range of the model pacemaker network depends not only on the strength of the electrical synapse but also on the identity of the neuron receiving inputs. We also compare the activity of the electrically coupled, distinct neurons with that of a network of coupled identical bursting neurons. For small to moderate coupling strengths, the network of identical elements, when receiving asymmetrical inputs, can have a smaller dynamic range of oscillation than that of its constituent neurons in isolation.

Neural activity in cortical area V4 underlies fine disparity discrimination.

PubMed

Shiozaki, Hiroshi M; Tanabe, Seiji; Doi, Takahiro; Fujita, Ichiro

2012-03-14

Primates are capable of discriminating depth with remarkable precision using binocular disparity. Neurons in area V4 are selective for relative disparity, which is the crucial visual cue for discrimination of fine disparity. Here, we investigated the contribution of V4 neurons to fine disparity discrimination. Monkeys discriminated whether the center disk of a dynamic random-dot stereogram was in front of or behind its surrounding annulus. We first behaviorally tested the reference frame of the disparity representation used for performing this task. After learning the task with a set of surround disparities, the monkey generalized its responses to untrained surround disparities, indicating that the perceptual decisions were generated from a disparity representation in a relative frame of reference. We then recorded single-unit responses from V4 while the monkeys performed the task. On average, neuronal thresholds were higher than the behavioral thresholds. The most sensitive neurons reached thresholds as low as the psychophysical thresholds. For subthreshold disparities, the monkeys made frequent errors. The variable decisions were predictable from the fluctuation in the neuronal responses. The predictions were based on a decision model in which each V4 neuron transmits the evidence for the disparity it prefers. We finally altered the disparity representation artificially by means of microstimulation to V4. The decisions were systematically biased when microstimulation boosted the V4 responses. The bias was toward the direction predicted from the decision model. We suggest that disparity signals carried by V4 neurons underlie precise discrimination of fine stereoscopic depth.

Candidate ionotropic taste receptors in the Drosophila larva.

PubMed

Stewart, Shannon; Koh, Tong-Wey; Ghosh, Arpan C; Carlson, John R

2015-04-07

We examine in Drosophila a group of ∼35 ionotropic receptors (IRs), the IR20a clade, about which remarkably little is known. Of 28 genes analyzed, GAL4 drivers representing 11 showed expression in the larva. Eight drivers labeled neurons of the pharynx, a taste organ, and three labeled neurons of the body wall that may be chemosensory. Expression was not observed in neurons of one taste organ, the terminal organ, although these neurons express many drivers of the Gr (Gustatory receptor) family. For most drivers of the IR20a clade, we observed expression in a single pair of cells in the animal, with limited coexpression, and only a fraction of pharyngeal neurons are labeled. The organization of IR20a clade expression thus appears different from the organization of the Gr family or the Odor receptor (Or) family in the larva. A remarkable feature of the larval pharynx is that some of its organs are incorporated into the adult pharynx, and several drivers of this clade are expressed in the pharynx of both larvae and adults. Different IR drivers show different developmental dynamics across the larval stages, either increasing or decreasing. Among neurons expressing drivers in the pharynx, two projection patterns can be distinguished in the CNS. Neurons exhibiting these two kinds of projection patterns may activate different circuits, possibly signaling the presence of cues with different valence. Taken together, the simplest interpretation of our results is that the IR20a clade encodes a class of larval taste receptors.

Role of temporal processing stages by inferior temporal neurons in facial recognition.

PubMed

Sugase-Miyamoto, Yasuko; Matsumoto, Narihisa; Kawano, Kenji

2011-01-01

In this review, we focus on the role of temporal stages of encoded facial information in the visual system, which might enable the efficient determination of species, identity, and expression. Facial recognition is an important function of our brain and is known to be processed in the ventral visual pathway, where visual signals are processed through areas V1, V2, V4, and the inferior temporal (IT) cortex. In the IT cortex, neurons show selective responses to complex visual images such as faces, and at each stage along the pathway the stimulus selectivity of the neural responses becomes sharper, particularly in the later portion of the responses. In the IT cortex of the monkey, facial information is represented by different temporal stages of neural responses, as shown in our previous study: the initial transient response of face-responsive neurons represents information about global categories, i.e., human vs. monkey vs. simple shapes, whilst the later portion of these responses represents information about detailed facial categories, i.e., expression and/or identity. This suggests that the temporal stages of the neuronal firing pattern play an important role in the coding of visual stimuli, including faces. This type of coding may be a plausible mechanism underlying the temporal dynamics of recognition, including the process of detection/categorization followed by the identification of objects. Recent single-unit studies in monkeys have also provided evidence consistent with the important role of the temporal stages of encoded facial information. For example, view-invariant facial identity information is represented in the response at a later period within a region of face-selective neurons. Consistent with these findings, temporally modulated neural activity has also been observed in human studies. These results suggest a close correlation between the temporal processing stages of facial information by IT neurons and the temporal dynamics of face recognition.

Role of Temporal Processing Stages by Inferior Temporal Neurons in Facial Recognition

PubMed Central

Sugase-Miyamoto, Yasuko; Matsumoto, Narihisa; Kawano, Kenji

2011-01-01

In this review, we focus on the role of temporal stages of encoded facial information in the visual system, which might enable the efficient determination of species, identity, and expression. Facial recognition is an important function of our brain and is known to be processed in the ventral visual pathway, where visual signals are processed through areas V1, V2, V4, and the inferior temporal (IT) cortex. In the IT cortex, neurons show selective responses to complex visual images such as faces, and at each stage along the pathway the stimulus selectivity of the neural responses becomes sharper, particularly in the later portion of the responses. In the IT cortex of the monkey, facial information is represented by different temporal stages of neural responses, as shown in our previous study: the initial transient response of face-responsive neurons represents information about global categories, i.e., human vs. monkey vs. simple shapes, whilst the later portion of these responses represents information about detailed facial categories, i.e., expression and/or identity. This suggests that the temporal stages of the neuronal firing pattern play an important role in the coding of visual stimuli, including faces. This type of coding may be a plausible mechanism underlying the temporal dynamics of recognition, including the process of detection/categorization followed by the identification of objects. Recent single-unit studies in monkeys have also provided evidence consistent with the important role of the temporal stages of encoded facial information. For example, view-invariant facial identity information is represented in the response at a later period within a region of face-selective neurons. Consistent with these findings, temporally modulated neural activity has also been observed in human studies. These results suggest a close correlation between the temporal processing stages of facial information by IT neurons and the temporal dynamics of face recognition. PMID:21734904

Single-neuron identification of chemical constituents, physiological changes, and metabolism using mass spectrometry.

PubMed

Zhu, Hongying; Zou, Guichang; Wang, Ning; Zhuang, Meihui; Xiong, Wei; Huang, Guangming

2017-03-07

The use of single-cell assays has emerged as a cutting-edge technique during the past decade. Although single-cell mass spectrometry (MS) has recently achieved remarkable results, deep biological insights have not yet been obtained, probably because of various technical issues, including the unavoidable use of matrices, the inability to maintain cell viability, low throughput because of sample pretreatment, and the lack of recordings of cell physiological activities from the same cell. In this study, we describe a patch clamp/MS-based platform that enables the sensitive, rapid, and in situ chemical profiling of single living neurons. This approach integrates modified patch clamp technique and modified MS measurements to directly collect and detect nanoliter-scale samples from the cytoplasm of single neurons in mice brain slices. Abundant possible cytoplasmic constituents were detected in a single neuron at a relatively fast rate, and over 50 metabolites were identified in this study. The advantages of direct, rapid, and in situ sampling and analysis enabled us to measure the biological activities of the cytoplasmic constituents in a single neuron, including comparing neuron types by cytoplasmic chemical constituents; observing changes in constituent concentrations as the physiological conditions, such as age, vary; and identifying the metabolic pathways of small molecules.

Single-neuron identification of chemical constituents, physiological changes, and metabolism using mass spectrometry

PubMed Central

Zhu, Hongying; Zou, Guichang; Wang, Ning; Zhuang, Meihui; Xiong, Wei; Huang, Guangming

2017-01-01

The use of single-cell assays has emerged as a cutting-edge technique during the past decade. Although single-cell mass spectrometry (MS) has recently achieved remarkable results, deep biological insights have not yet been obtained, probably because of various technical issues, including the unavoidable use of matrices, the inability to maintain cell viability, low throughput because of sample pretreatment, and the lack of recordings of cell physiological activities from the same cell. In this study, we describe a patch clamp/MS-based platform that enables the sensitive, rapid, and in situ chemical profiling of single living neurons. This approach integrates modified patch clamp technique and modified MS measurements to directly collect and detect nanoliter-scale samples from the cytoplasm of single neurons in mice brain slices. Abundant possible cytoplasmic constituents were detected in a single neuron at a relatively fast rate, and over 50 metabolites were identified in this study. The advantages of direct, rapid, and in situ sampling and analysis enabled us to measure the biological activities of the cytoplasmic constituents in a single neuron, including comparing neuron types by cytoplasmic chemical constituents; observing changes in constituent concentrations as the physiological conditions, such as age, vary; and identifying the metabolic pathways of small molecules. PMID:28223513

Effect of Noopept on Dynamics of Intracellular Calcium in Neurons of Cultured Rat Hippocampal Slices.

PubMed

Kolbaev, S N; Aleksandrova, O P; Sharonova, I N; Skrebitsky, V G

2018-01-01

A neuroprotective and nootropic drug Noopept increased the frequency of spontaneous calcium transients in neurons of CA1 radial layer in cultured rat hippocampal slices. In contrast, the drug exerted no significant effect on intracellular calcium concentration and its dynamics in neurons of hippocampal CA1 pyramidal layer.

A tweaking principle for executive control: neuronal circuit mechanism for rule-based task switching and conflict resolution.

PubMed

Ardid, Salva; Wang, Xiao-Jing

2013-12-11

A hallmark of executive control is the brain's agility to shift between different tasks depending on the behavioral rule currently in play. In this work, we propose a "tweaking hypothesis" for task switching: a weak rule signal provides a small bias that is dramatically amplified by reverberating attractor dynamics in neural circuits for stimulus categorization and action selection, leading to an all-or-none reconfiguration of sensory-motor mapping. Based on this principle, we developed a biologically realistic model with multiple modules for task switching. We found that the model quantitatively accounts for complex task switching behavior: switch cost, congruency effect, and task-response interaction; as well as monkey's single-neuron activity associated with task switching. The model yields several testable predictions, in particular, that category-selective neurons play a key role in resolving sensory-motor conflict. This work represents a neural circuit model for task switching and sheds insights in the brain mechanism of a fundamental cognitive capability.

Distance-dependent gradient in NMDAR-driven spine calcium signals along tapering dendrites

PubMed Central

Walker, Alison S.; Grillo, Federico; Jackson, Rachel E.; Rigby, Mark; Lowe, Andrew S.; Vizcay-Barrena, Gema; Fleck, Roland A.; Burrone, Juan

2017-01-01

Neurons receive a multitude of synaptic inputs along their dendritic arbor, but how this highly heterogeneous population of synaptic compartments is spatially organized remains unclear. By measuring N-methyl-d-aspartic acid receptor (NMDAR)-driven calcium responses in single spines, we provide a spatial map of synaptic calcium signals along dendritic arbors of hippocampal neurons and relate this to measures of synapse structure. We find that quantal NMDAR calcium signals increase in amplitude as they approach a thinning dendritic tip end. Based on a compartmental model of spine calcium dynamics, we propose that this biased distribution in calcium signals is governed by a gradual, distance-dependent decline in spine size, which we visualized using serial block-face scanning electron microscopy. Our data describe a cell-autonomous feature of principal neurons, where tapering dendrites show an inverse distribution of spine size and NMDAR-driven calcium signals along dendritic trees, with important implications for synaptic plasticity rules and spine function. PMID:28209776

The Role of Inhibition in a Computational Model of an Auditory Cortical Neuron during the Encoding of Temporal Information

PubMed Central

Bendor, Daniel

2015-01-01

In auditory cortex, temporal information within a sound is represented by two complementary neural codes: a temporal representation based on stimulus-locked firing and a rate representation, where discharge rate co-varies with the timing between acoustic events but lacks a stimulus-synchronized response. Using a computational neuronal model, we find that stimulus-locked responses are generated when sound-evoked excitation is combined with strong, delayed inhibition. In contrast to this, a non-synchronized rate representation is generated when the net excitation evoked by the sound is weak, which occurs when excitation is coincident and balanced with inhibition. Using single-unit recordings from awake marmosets (Callithrix jacchus), we validate several model predictions, including differences in the temporal fidelity, discharge rates and temporal dynamics of stimulus-evoked responses between neurons with rate and temporal representations. Together these data suggest that feedforward inhibition provides a parsimonious explanation of the neural coding dichotomy observed in auditory cortex. PMID:25879843

A new simple /spl infin/OH neuron model as a biologically plausible principal component analyzer.

PubMed

Jankovic, M V

2003-01-01

A new approach to unsupervised learning in a single-layer neural network is discussed. An algorithm for unsupervised learning based upon the Hebbian learning rule is presented. A simple neuron model is analyzed. A dynamic neural model, which contains both feed-forward and feedback connections between the input and the output, has been adopted. The, proposed learning algorithm could be more correctly named self-supervised rather than unsupervised. The solution proposed here is a modified Hebbian rule, in which the modification of the synaptic strength is proportional not to pre- and postsynaptic activity, but instead to the presynaptic and averaged value of postsynaptic activity. It is shown that the model neuron tends to extract the principal component from a stationary input vector sequence. Usually accepted additional decaying terms for the stabilization of the original Hebbian rule are avoided. Implementation of the basic Hebbian scheme would not lead to unrealistic growth of the synaptic strengths, thanks to the adopted network structure.

Dynamical responses to external stimuli for both cases of excitatory and inhibitory synchronization in a complex neuronal network.

PubMed

Kim, Sang-Yoon; Lim, Woochang

2017-10-01

For studying how dynamical responses to external stimuli depend on the synaptic-coupling type, we consider two types of excitatory and inhibitory synchronization (i.e., synchronization via synaptic excitation and inhibition) in complex small-world networks of excitatory regular spiking (RS) pyramidal neurons and inhibitory fast spiking (FS) interneurons. For both cases of excitatory and inhibitory synchronization, effects of synaptic couplings on dynamical responses to external time-periodic stimuli S ( t ) (applied to a fraction of neurons) are investigated by varying the driving amplitude A of S ( t ). Stimulated neurons are phase-locked to external stimuli for both cases of excitatory and inhibitory couplings. On the other hand, the stimulation effect on non-stimulated neurons depends on the type of synaptic coupling. The external stimulus S ( t ) makes a constructive effect on excitatory non-stimulated RS neurons (i.e., it causes external phase lockings in the non-stimulated sub-population), while S ( t ) makes a destructive effect on inhibitory non-stimulated FS interneurons (i.e., it breaks up original inhibitory synchronization in the non-stimulated sub-population). As results of these different effects of S ( t ), the type and degree of dynamical response (e.g., synchronization enhancement or suppression), characterized by the dynamical response factor [Formula: see text] (given by the ratio of synchronization degree in the presence and absence of stimulus), are found to vary in a distinctly different way, depending on the synaptic-coupling type. Furthermore, we also measure the matching degree between the dynamics of the two sub-populations of stimulated and non-stimulated neurons in terms of a "cross-correlation" measure [Formula: see text]. With increasing A , based on [Formula: see text], we discuss the cross-correlations between the two sub-populations, affecting the dynamical responses to S ( t ).

Dynamic Clamp in Cardiac and Neuronal Systems Using RTXI

PubMed Central

Ortega, Francis A.; Butera, Robert J.; Christini, David J.; White, John A.; Dorval, Alan D.

2016-01-01

The injection of computer-simulated conductances through the dynamic clamp technique has allowed researchers to probe the intercellular and intracellular dynamics of cardiac and neuronal systems with great precision. By coupling computational models to biological systems, dynamic clamp has become a proven tool in electrophysiology with many applications, such as generating hybrid networks in neurons or simulating channelopathies in cardiomyocytes. While its applications are broad, the approach is straightforward: synthesizing traditional patch clamp, computational modeling, and closed-loop feedback control to simulate a cellular conductance. Here, we present two example applications: artificial blocking of the inward rectifier potassium current in a cardiomyocyte and coupling of a biological neuron to a virtual neuron through a virtual synapse. The design and implementation of the necessary software to administer these dynamic clamp experiments can be difficult. In this chapter, we provide an overview of designing and implementing a dynamic clamp experiment using the Real-Time eXperiment Interface (RTXI), an open- source software system tailored for real-time biological experiments. We present two ways to achieve this using RTXI’s modular format, through the creation of a custom user-made module and through existing modules found in RTXI’s online library. PMID:25023319

Mitochondrial dynamics in Parkinson's disease

PubMed Central

Van Laar, Victor S.; Berman, Sarah B.

2009-01-01

The unique energy demands of neurons require well-orchestrated distribution and maintenance of mitochondria. Thus, dynamic properties of mitochondria, including fission, fusion, trafficking, biogenesis, and degradation, are critical to all cells, but may be particularly important in neurons. Dysfunction in mitochondrial dynamics has been linked to neuropathies and is increasingly being linked to several neurodegenerative diseases, but the evidence is particularly strong, and continuously accumulating, in Parkinson's disease (PD). The unique characteristics of neurons that degenerate in PD may predispose those neuronal populations to susceptibility to alterations in mitochondrial dynamics. In addition, evidence from PD-related toxins supports that mitochondrial fission, fusion, and transport may be involved in pathogenesis. Furthermore, rapidly increasing evidence suggests that two proteins linked to familial forms of the disease, parkin and PINK1, interact in a common pathway to regulate mitochondrial fission/fusion. Parkin may also play a role in maintaining mitochondrial homeostasis through targeting damaged mitochondria for mitophagy. Taken together, the current data suggests that mitochondrial dynamics may play a role in PD pathogenesis, and a better understanding of mitochondrial dynamics within the neuron may lead to future therapeutic treatments for PD, potentially aimed at some of the earliest pathogenic events. PMID:19332061

Estimating the biophysical properties of neurons with intracellular calcium dynamics.

PubMed

Ye, Jingxin; Rozdeba, Paul J; Morone, Uriel I; Daou, Arij; Abarbanel, Henry D I

2014-06-01

We investigate the dynamics of a conductance-based neuron model coupled to a model of intracellular calcium uptake and release by the endoplasmic reticulum. The intracellular calcium dynamics occur on a time scale that is orders of magnitude slower than voltage spiking behavior. Coupling these mechanisms sets the stage for the appearance of chaotic dynamics, which we observe within certain ranges of model parameter values. We then explore the question of whether one can, using observed voltage data alone, estimate the states and parameters of the voltage plus calcium (V+Ca) dynamics model. We find the answer is negative. Indeed, we show that voltage plus another observed quantity must be known to allow the estimation to be accurate. We show that observing both the voltage time course V(t) and the intracellular Ca time course will permit accurate estimation, and from the estimated model state, accurate prediction after observations are completed. This sets the stage for how one will be able to use a more detailed model of V+Ca dynamics in neuron activity in the analysis of experimental data on individual neurons as well as functional networks in which the nodes (neurons) have these biophysical properties.

Estimating the biophysical properties of neurons with intracellular calcium dynamics

NASA Astrophysics Data System (ADS)

Ye, Jingxin; Rozdeba, Paul J.; Morone, Uriel I.; Daou, Arij; Abarbanel, Henry D. I.

2014-06-01

We investigate the dynamics of a conductance-based neuron model coupled to a model of intracellular calcium uptake and release by the endoplasmic reticulum. The intracellular calcium dynamics occur on a time scale that is orders of magnitude slower than voltage spiking behavior. Coupling these mechanisms sets the stage for the appearance of chaotic dynamics, which we observe within certain ranges of model parameter values. We then explore the question of whether one can, using observed voltage data alone, estimate the states and parameters of the voltage plus calcium (V+Ca) dynamics model. We find the answer is negative. Indeed, we show that voltage plus another observed quantity must be known to allow the estimation to be accurate. We show that observing both the voltage time course V (t) and the intracellular Ca time course will permit accurate estimation, and from the estimated model state, accurate prediction after observations are completed. This sets the stage for how one will be able to use a more detailed model of V+Ca dynamics in neuron activity in the analysis of experimental data on individual neurons as well as functional networks in which the nodes (neurons) have these biophysical properties.

Photodynamic therapy-induced nitric oxide production in neuronal and glial cells

NASA Astrophysics Data System (ADS)

Kovaleva, Vera D.; Uzdensky, Anatoly B.

2016-10-01

Nitric oxide (NO) has been recently demonstrated to enhance apoptosis of glial cells induced by photodynamic therapy (PDT), but to protect glial cells from PDT-induced necrosis in the crayfish stretch receptor, a simple neuroglial preparation that consists of a single mechanosensory neuron enveloped by satellite glial cells. We used the NO-sensitive fluorescent probe 4,5-diaminofluorescein diacetate to study the distribution and dynamics of PDT-induced NO production in the mechanosensory neuron and surrounding glial cells. The NO production in the glial envelope was higher than in the neuronal soma axon and dendrites both in control and in experimental conditions. In dark NO generator, DEA NONOate or NO synthase substrate L-arginine hydrochloride significantly increased the NO level in glial cells, whereas NO scavenger 2-Phenyl-4,4,5,5-tetramethylimidazoline-1-oxyl 3-oxide (PTIO) or inhibitors of NO synthase L-NG-nitro arginine methyl ester and Nω-nitro-L-arginine decreased it. PDT induced the transient increase in NO production with a maximum at 4 to 7 min after the irradiation start followed by its inhibition at 10 to 40 min. We suggested that PDT stimulated neuronal rather than inducible NO synthase isoform in glial cells, and the produced NO could mediate PDT-induced apoptosis.

Reconstruction of neuronal input through modeling single-neuron dynamics and computations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qin, Qing; Wang, Jiang; Yu, Haitao

Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-spacemore » method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is.« less

Noise reduction of coincidence detector output by the inferior colliculus of the barn owl.

PubMed

Christianson, G Björn; Peña, José Luis

2006-05-31

A recurring theme in theoretical work is that integration over populations of similarly tuned neurons can reduce neural noise. However, there are relatively few demonstrations of an explicit noise reduction mechanism in a neural network. Here we demonstrate that the brainstem of the barn owl includes a stage of processing apparently devoted to increasing the signal-to-noise ratio in the encoding of the interaural time difference (ITD), one of two primary binaural cues used to compute the position of a sound source in space. In the barn owl, the ITD is processed in a dedicated neural pathway that terminates at the core of the inferior colliculus (ICcc). The actual locus of the computation of the ITD is before ICcc in the nucleus laminaris (NL), and ICcc receives no inputs carrying information that did not originate in NL. Unlike in NL, the rate-ITD functions of ICcc neurons require as little as a single stimulus presentation per ITD to show coherent ITD tuning. ICcc neurons also displayed a greater dynamic range with a maximal difference in ITD response rates approximately double that seen in NL. These results indicate that ICcc neurons perform a computation functionally analogous to averaging across a population of similarly tuned NL neurons.

Reconstruction of neuronal input through modeling single-neuron dynamics and computations

NASA Astrophysics Data System (ADS)

Qin, Qing; Wang, Jiang; Yu, Haitao; Deng, Bin; Chan, Wai-lok

2016-06-01

Mathematical models provide a mathematical description of neuron activity, which can better understand and quantify neural computations and corresponding biophysical mechanisms evoked by stimulus. In this paper, based on the output spike train evoked by the acupuncture mechanical stimulus, we present two different levels of models to describe the input-output system to achieve the reconstruction of neuronal input. The reconstruction process is divided into two steps: First, considering the neuronal spiking event as a Gamma stochastic process. The scale parameter and the shape parameter of Gamma process are, respectively, defined as two spiking characteristics, which are estimated by a state-space method. Then, leaky integrate-and-fire (LIF) model is used to mimic the response system and the estimated spiking characteristics are transformed into two temporal input parameters of LIF model, through two conversion formulas. We test this reconstruction method by three different groups of simulation data. All three groups of estimates reconstruct input parameters with fairly high accuracy. We then use this reconstruction method to estimate the non-measurable acupuncture input parameters. Results show that under three different frequencies of acupuncture stimulus conditions, estimated input parameters have an obvious difference. The higher the frequency of the acupuncture stimulus is, the higher the accuracy of reconstruction is.

Rhythmogenic neuronal networks, emergent leaders, and k-cores.

PubMed

Schwab, David J; Bruinsma, Robijn F; Feldman, Jack L; Levine, Alex J

2010-11-01

Neuronal network behavior results from a combination of the dynamics of individual neurons and the connectivity of the network that links them together. We study a simplified model, based on the proposal of Feldman and Del Negro (FDN) [Nat. Rev. Neurosci. 7, 232 (2006)], of the preBötzinger Complex, a small neuronal network that participates in the control of the mammalian breathing rhythm through periodic firing bursts. The dynamics of this randomly connected network of identical excitatory neurons differ from those of a uniformly connected one. Specifically, network connectivity determines the identity of emergent leader neurons that trigger the firing bursts. When neuronal desensitization is controlled by the number of input signals to the neurons (as proposed by FDN), the network's collective desensitization--required for successful burst termination--is mediated by k-core clusters of neurons.

Encoding of Spatial Attention by Primate Prefrontal Cortex Neuronal Ensembles

PubMed Central

Treue, Stefan

2018-01-01

Abstract Single neurons in the primate lateral prefrontal cortex (LPFC) encode information about the allocation of visual attention and the features of visual stimuli. However, how this compares to the performance of neuronal ensembles at encoding the same information is poorly understood. Here, we recorded the responses of neuronal ensembles in the LPFC of two macaque monkeys while they performed a task that required attending to one of two moving random dot patterns positioned in different hemifields and ignoring the other pattern. We found single units selective for the location of the attended stimulus as well as for its motion direction. To determine the coding of both variables in the population of recorded units, we used a linear classifier and progressively built neuronal ensembles by iteratively adding units according to their individual performance (best single units), or by iteratively adding units based on their contribution to the ensemble performance (best ensemble). For both methods, ensembles of relatively small sizes (n < 60) yielded substantially higher decoding performance relative to individual single units. However, the decoder reached similar performance using fewer neurons with the best ensemble building method compared with the best single units method. Our results indicate that neuronal ensembles within the LPFC encode more information about the attended spatial and nonspatial features of visual stimuli than individual neurons. They further suggest that efficient coding of attention can be achieved by relatively small neuronal ensembles characterized by a certain relationship between signal and noise correlation structures. PMID:29568798

Is realistic neuronal modeling realistic?

PubMed Central

Almog, Mara

2016-01-01

Scientific models are abstractions that aim to explain natural phenomena. A successful model shows how a complex phenomenon arises from relatively simple principles while preserving major physical or biological rules and predicting novel experiments. A model should not be a facsimile of reality; it is an aid for understanding it. Contrary to this basic premise, with the 21st century has come a surge in computational efforts to model biological processes in great detail. Here we discuss the oxymoronic, realistic modeling of single neurons. This rapidly advancing field is driven by the discovery that some neurons don't merely sum their inputs and fire if the sum exceeds some threshold. Thus researchers have asked what are the computational abilities of single neurons and attempted to give answers using realistic models. We briefly review the state of the art of compartmental modeling highlighting recent progress and intrinsic flaws. We then attempt to address two fundamental questions. Practically, can we realistically model single neurons? Philosophically, should we realistically model single neurons? We use layer 5 neocortical pyramidal neurons as a test case to examine these issues. We subject three publically available models of layer 5 pyramidal neurons to three simple computational challenges. Based on their performance and a partial survey of published models, we conclude that current compartmental models are ad hoc, unrealistic models functioning poorly once they are stretched beyond the specific problems for which they were designed. We then attempt to plot possible paths for generating realistic single neuron models. PMID:27535372

Predominance of Movement Speed Over Direction in Neuronal Population Signals of Motor Cortex: Intracranial EEG Data and A Simple Explanatory Model

PubMed Central

Hammer, Jiří; Pistohl, Tobias; Fischer, Jörg; Kršek, Pavel; Tomášek, Martin; Marusič, Petr; Schulze-Bonhage, Andreas; Aertsen, Ad; Ball, Tonio

2016-01-01

How neuronal activity of motor cortex is related to movement is a central topic in motor neuroscience. Motor-cortical single neurons are more closely related to hand movement velocity than speed, that is, the magnitude of the (directional) velocity vector. Recently, there is also increasing interest in the representation of movement parameters in neuronal population activity, such as reflected in the intracranial EEG (iEEG). We show that in iEEG, contrasting to what has been previously found on the single neuron level, speed predominates over velocity. The predominant speed representation was present in nearly all iEEG signal features, up to the 600–1000 Hz range. Using a model of motor-cortical signals arising from neuronal populations with realistic single neuron tuning properties, we show how this reversal can be understood as a consequence of increasing population size. Our findings demonstrate that the information profile in large population signals may systematically differ from the single neuron level, a principle that may be helpful in the interpretation of neuronal population signals in general, including, for example, EEG and functional magnetic resonance imaging. Taking advantage of the robust speed population signal may help in developing brain–machine interfaces exploiting population signals. PMID:26984895

Small Modifications to Network Topology Can Induce Stochastic Bistable Spiking Dynamics in a Balanced Cortical Model

PubMed Central

McDonnell, Mark D.; Ward, Lawrence M.

2014-01-01

Abstract Directed random graph models frequently are used successfully in modeling the population dynamics of networks of cortical neurons connected by chemical synapses. Experimental results consistently reveal that neuronal network topology is complex, however, in the sense that it differs statistically from a random network, and differs for classes of neurons that are physiologically different. This suggests that complex network models whose subnetworks have distinct topological structure may be a useful, and more biologically realistic, alternative to random networks. Here we demonstrate that the balanced excitation and inhibition frequently observed in small cortical regions can transiently disappear in otherwise standard neuronal-scale models of fluctuation-driven dynamics, solely because the random network topology was replaced by a complex clustered one, whilst not changing the in-degree of any neurons. In this network, a small subset of cells whose inhibition comes only from outside their local cluster are the cause of bistable population dynamics, where different clusters of these cells irregularly switch back and forth from a sparsely firing state to a highly active state. Transitions to the highly active state occur when a cluster of these cells spikes sufficiently often to cause strong unbalanced positive feedback to each other. Transitions back to the sparsely firing state rely on occasional large fluctuations in the amount of non-local inhibition received. Neurons in the model are homogeneous in their intrinsic dynamics and in-degrees, but differ in the abundance of various directed feedback motifs in which they participate. Our findings suggest that (i) models and simulations should take into account complex structure that varies for neuron and synapse classes; (ii) differences in the dynamics of neurons with similar intrinsic properties may be caused by their membership in distinctive local networks; (iii) it is important to identify neurons that share physiological properties and location, but differ in their connectivity. PMID:24743633

Low- and high-threshold primary afferent inputs to spinal lamina III antenna-type neurons.

PubMed

Fernandes, Elisabete C; Santos, Ines C; Kokai, Eva; Luz, Liliana L; Szucs, Peter; Safronov, Boris V

2018-06-21

and non-nociceptive sensory information. Antenna-type neurons with cell bodies located in lamina III and large dendritic trees extending from the superficial lamina I to deep lamina IV are best shaped for the integration of a wide variety of inputs arising from primary afferent fibers and intrinsic spinal circuitries. While the somatodendritic morphology, the hallmark of antenna neurons, has been well studied, little is still known about the axon structure and basic physiological properties of these cells. Here we did whole-cell recordings in a rat (P9-P12) spinal cord preparation with attached dorsal roots to examine the axon course, intrinsic firing properties and primary afferent inputs of antenna cells. Nine antenna cells were identified from a large sample of biocytin-filled lamina III neurons (n = 46). Axon of antenna cells showed intensive branching in laminae III-IV and, in half of the cases, issued dorsally directed collaterals reaching lamina I. Antenna cells exhibited tonic and rhythmic firing patterns; single spikes were followed by hyper- or depolarization. The neurons received monosynaptic inputs from the low-threshold Aβ afferents, Aδ afferents as well as from the high-threshold Aδ and C afferents. When selectively activated, C-fiber-driven mono- and polysynaptic EPSPs were sufficiently strong to evoke firing in the neurons. Thus, lamina III antenna neurons integrate low-threshold and nociceptive high-threshold primary afferent inputs, and can function as wide-dynamic-range neurons able to directly connect deep dorsal horn with the major nociceptive projection area lamina I.

Neuronal avalanches and learning

NASA Astrophysics Data System (ADS)

de Arcangelis, Lucilla

2011-05-01

Networks of living neurons represent one of the most fascinating systems of biology. If the physical and chemical mechanisms at the basis of the functioning of a single neuron are quite well understood, the collective behaviour of a system of many neurons is an extremely intriguing subject. Crucial ingredient of this complex behaviour is the plasticity property of the network, namely the capacity to adapt and evolve depending on the level of activity. This plastic ability is believed, nowadays, to be at the basis of learning and memory in real brains. Spontaneous neuronal activity has recently shown features in common to other complex systems. Experimental data have, in fact, shown that electrical information propagates in a cortex slice via an avalanche mode. These avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems and successful models have been developed to describe their behaviour. In this contribution we discuss a statistical mechanical model for the complex activity in a neuronal network. The model implements the main physiological properties of living neurons and is able to reproduce recent experimental results. Then, we discuss the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules, in particular the exclusive OR (XOR) and a random rule with three inputs. The learning dynamics exhibits universal features as function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.

Experimental study of firing death in a network of chaotic FitzHugh-Nagumo neurons

NASA Astrophysics Data System (ADS)

Ciszak, Marzena; Euzzor, Stefano; Arecchi, F. Tito; Meucci, Riccardo

2013-02-01

The FitzHugh-Nagumo neurons driven by a periodic forcing undergo a period-doubling route to chaos and a transition to mixed-mode oscillations. When coupled, their dynamics tend to be synchronized. We show that the chaotically spiking neurons change their internal dynamics to subthreshold oscillations, the phenomenon referred to as firing death. These dynamical changes are observed below the critical coupling strength at which the transition to full chaotic synchronization occurs. Moreover, we find various dynamical regimes in the subthreshold oscillations, namely, regular, quasiperiodic, and chaotic states. We show numerically that these dynamical states may coexist with large-amplitude spiking regimes and that this coexistence is characterized by riddled basins of attraction. The reported results are obtained for neurons implemented in the electronic circuits as well as for the model equations. Finally, we comment on the possible scenarios where the coupling-induced firing death could play an important role in biological systems.

Pure state consciousness and its local reduction to neuronal space

NASA Astrophysics Data System (ADS)

Duggins, A. J.

2013-01-01

The single neuronal state can be represented as a vector in a complex space, spanned by an orthonormal basis of integer spike counts. In this model a scalar element of experience is associated with the instantaneous firing rate of a single sensory neuron over repeated stimulus presentations. Here the model is extended to composite neural systems that are tensor products of single neuronal vector spaces. Depiction of the mental state as a vector on this tensor product space is intended to capture the unity of consciousness. The density operator is introduced as its local reduction to the single neuron level, from which the firing rate can again be derived as the objective correlate of a subjective element. However, the relational structure of perceptual experience only emerges when the non-local mental state is considered. A metric of phenomenal proximity between neuronal elements of experience is proposed, based on the cross-correlation function of neurophysiology, but constrained by the association of theoretical extremes of correlation/anticorrelation in inseparable 2-neuron states with identical and opponent elements respectively.

Neuronal avalanches and coherence potentials

NASA Astrophysics Data System (ADS)

Plenz, D.

2012-05-01

The mammalian cortex consists of a vast network of weakly interacting excitable cells called neurons. Neurons must synchronize their activities in order to trigger activity in neighboring neurons. Moreover, interactions must be carefully regulated to remain weak (but not too weak) such that cascades of active neuronal groups avoid explosive growth yet allow for activity propagation over long-distances. Such a balance is robustly realized for neuronal avalanches, which are defined as cortical activity cascades that follow precise power laws. In experiments, scale-invariant neuronal avalanche dynamics have been observed during spontaneous cortical activity in isolated preparations in vitro as well as in the ongoing cortical activity of awake animals and in humans. Theory, models, and experiments suggest that neuronal avalanches are the signature of brain function near criticality at which the cortex optimally responds to inputs and maximizes its information capacity. Importantly, avalanche dynamics allow for the emergence of a subset of avalanches, the coherence potentials. They emerge when the synchronization of a local neuronal group exceeds a local threshold, at which the system spawns replicas of the local group activity at distant network sites. The functional importance of coherence potentials will be discussed in the context of propagating structures, such as gliders in balanced cellular automata. Gliders constitute local population dynamics that replicate in space after a finite number of generations and are thought to provide cellular automata with universal computation. Avalanches and coherence potentials are proposed to constitute a modern framework of cortical synchronization dynamics that underlies brain function.

A transgenic mouse for imaging activity-dependent dynamics of endogenous Arc mRNA in live neurons

PubMed Central

2018-01-01

Localized translation plays a crucial role in synaptic plasticity and memory consolidation. However, it has not been possible to follow the dynamics of memory-associated mRNAs in living neurons in response to neuronal activity in real time. We have generated a novel mouse model where the endogenous Arc/Arg3.1 gene is tagged in its 3′ untranslated region with stem-loops that bind a bacteriophage PP7 coat protein (PCP), allowing visualization of individual mRNAs in real time. The physiological response of the tagged gene to neuronal activity is identical to endogenous Arc and reports the true dynamics of Arc mRNA from transcription to degradation. The transcription dynamics of Arc in cultured hippocampal neurons revealed two novel results: (i) A robust transcriptional burst with prolonged ON state occurs after stimulation, and (ii) transcription cycles continue even after initial stimulation is removed. The correlation of stimulation with Arc transcription and mRNA transport in individual neurons revealed that stimulus-induced Ca2+ activity was necessary but not sufficient for triggering Arc transcription and that blocking neuronal activity did not affect the dendritic transport of newly synthesized Arc mRNAs. This mouse will provide an important reagent to investigate how individual neurons transduce activity into spatiotemporal regulation of gene expression at the synapse.

Perfect Detection of Spikes in the Linear Sub-threshold Dynamics of Point Neurons

PubMed Central

Krishnan, Jeyashree; Porta Mana, PierGianLuca; Helias, Moritz; Diesmann, Markus; Di Napoli, Edoardo

2018-01-01

Spiking neuronal networks are usually simulated with one of three main schemes: the classical time-driven and event-driven schemes, and the more recent hybrid scheme. All three schemes evolve the state of a neuron through a series of checkpoints: equally spaced in the first scheme and determined neuron-wise by spike events in the latter two. The time-driven and the hybrid scheme determine whether the membrane potential of a neuron crosses a threshold at the end of the time interval between consecutive checkpoints. Threshold crossing can, however, occur within the interval even if this test is negative. Spikes can therefore be missed. The present work offers an alternative geometric point of view on neuronal dynamics, and derives, implements, and benchmarks a method for perfect retrospective spike detection. This method can be applied to neuron models with affine or linear subthreshold dynamics. The idea behind the method is to propagate the threshold with a time-inverted dynamics, testing whether the threshold crosses the neuron state to be evolved, rather than vice versa. Algebraically this translates into a set of inequalities necessary and sufficient for threshold crossing. This test is slower than the imperfect one, but can be optimized in several ways. Comparison confirms earlier results that the imperfect tests rarely miss spikes (less than a fraction 1/108 of missed spikes) in biologically relevant settings. PMID:29379430

Rapid acquisition of novel interface control by small ensembles of arbitrarily selected primary motor cortex neurons

PubMed Central

Law, Andrew J.; Rivlis, Gil

2014-01-01

Pioneering studies demonstrated that novel degrees of freedom could be controlled individually by directly encoding the firing rate of single motor cortex neurons, without regard to each neuron's role in controlling movement of the native limb. In contrast, recent brain-computer interface work has emphasized decoding outputs from large ensembles that include substantially more neurons than the number of degrees of freedom being controlled. To bridge the gap between direct encoding by single neurons and decoding output from large ensembles, we studied monkeys controlling one degree of freedom by comodulating up to four arbitrarily selected motor cortex neurons. Performance typically exceeded random quite early in single sessions and then continued to improve to different degrees in different sessions. We therefore examined factors that might affect performance. Performance improved with larger ensembles. In contrast, other factors that might have reflected preexisting synaptic architecture—such as the similarity of preferred directions—had little if any effect on performance. Patterns of comodulation among ensemble neurons became more consistent across trials as performance improved over single sessions. Compared with the ensemble neurons, other simultaneously recorded neurons showed less modulation. Patterns of voluntarily comodulated firing among small numbers of arbitrarily selected primary motor cortex (M1) neurons thus can be found and improved rapidly, with little constraint based on the normal relationships of the individual neurons to native limb movement. This rapid flexibility in relationships among M1 neurons may in part underlie our ability to learn new movements and improve motor skill. PMID:24920030

Dehydration-induced modulation of κ-opioid inhibition of vasopressin neurone activity

PubMed Central

Scott, Victoria; Bishop, Valerie R; Leng, Gareth; Brown, Colin H

2009-01-01

Dehydration increases vasopressin (antidiuretic hormone) secretion from the posterior pituitary gland to reduce water loss in the urine. Vasopressin secretion is determined by action potential firing in vasopressin neurones, which can exhibit continuous, phasic (alternating periods of activity and silence), or irregular activity. Autocrine κ-opioid inhibition contributes to the generation of activity patterning of vasopressin neurones under basal conditions and so we used in vivo extracellular single unit recording to test the hypothesis that changes in autocrine κ-opioid inhibition drive changes in activity patterning of vasopressin neurones during dehydration. Dehydration increased the firing rate of rat vasopressin neurones displaying continuous activity (from 7.1 ± 0.5 to 9.0 ± 0.6 spikes s−1) and phasic activity (from 4.2 ± 0.7 to 7.8 ± 0.9 spikes s−1), but not those displaying irregular activity. The dehydration-induced increase in phasic activity was via an increase in intraburst firing rate. The selective κ-opioid receptor antagonist nor-binaltorphimine increased the firing rate of phasic neurones in non-dehydrated rats (from 3.4 ± 0.8 to 5.3 ± 0.6 spikes s−1) and dehydrated rats (from 6.4 ± 0.5 to 9.1 ± 1.2 spikes s−1), indicating that κ-opioid feedback inhibition of phasic bursts is maintained during dehydration. In a separate series of experiments, prodynorphin mRNA expression was increased in vasopressin neurones of hyperosmotic rats, compared to hypo-osmotic rats. Hence, it appears that dynorphin expression in vasopressin neurones undergoes dynamic changes in proportion to the required secretion of vasopressin so that, even under stimulated conditions, autocrine feedback inhibition of vasopressin neurones prevents over-excitation. PMID:19822541

Gene dosage-dependent rescue of HSP neurite defects in SPG4 patients’ neurons

PubMed Central

Havlicek, Steven; Kohl, Zacharias; Mishra, Himanshu K.; Prots, Iryna; Eberhardt, Esther; Denguir, Naime; Wend, Holger; Plötz, Sonja; Boyer, Leah; Marchetto, Maria C.N.; Aigner, Stefan; Sticht, Heinrich; Groemer, Teja W.; Hehr, Ute; Lampert, Angelika; Schlötzer-Schrehardt, Ursula; Winkler, Jürgen; Gage, Fred H.; Winner, Beate

2014-01-01

The hereditary spastic paraplegias (HSPs) are a heterogeneous group of motorneuron diseases characterized by progressive spasticity and paresis of the lower limbs. Mutations in Spastic Gait 4 (SPG4), encoding spastin, are the most frequent cause of HSP. To understand how mutations in SPG4 affect human neurons, we generated human induced pluripotent stem cells (hiPSCs) from fibroblasts of two patients carrying a c.1684C>T nonsense mutation and from two controls. These SPG4 and control hiPSCs were able to differentiate into neurons and glia at comparable efficiency. All known spastin isoforms were reduced in SPG4 neuronal cells. The complexity of SPG4 neurites was decreased, which was paralleled by an imbalance of axonal transport with less retrograde movement. Prominent neurite swellings with disrupted microtubules were present in SPG4 neurons at an ultrastructural level. While some of these swellings contain acetylated and detyrosinated tubulin, these tubulin modifications were unchanged in total cell lysates of SPG4 neurons. Upregulation of another microtubule-severing protein, p60 katanin, may partially compensate for microtubuli dynamics in SPG4 neurons. Overexpression of the M1 or M87 spastin isoforms restored neurite length, branching, numbers of primary neurites and reduced swellings in SPG4 neuronal cells. We conclude that neurite complexity and maintenance in HSP patient-derived neurons are critically sensitive to spastin gene dosage. Our data show that elevation of single spastin isoform levels is sufficient to restore neurite complexity and reduce neurite swellings in patient cells. Furthermore, our human model offers an ideal platform for pharmacological screenings with the goal to restore physiological spastin levels in SPG4 patients. PMID:24381312

Intradermal endothelin-1 excites bombesin-responsive superficial dorsal horn neurons in the mouse

PubMed Central

Akiyama, T.; Nagamine, M.; Davoodi, A.; Iodi Carstens, M.; Cevikbas, F.; Steinhoff, M.

2015-01-01

Endothelin-1 (ET-1) has been implicated in nonhistaminergic itch. Here we used electrophysiological methods to investigate whether mouse superficial dorsal horn neurons respond to intradermal (id) injection of ET-1 and whether ET-1-sensitive neurons additionally respond to other pruritic and algesic stimuli or spinal superfusion of bombesin, a homolog of gastrin-releasing peptide (GRP) that excites spinal itch-signaling neurons. Single-unit recordings were made from lumbar dorsal horn neurons in pentobarbital-anesthetized C57BL/6 mice. We searched for units that exhibited elevated firing after id injection of ET-1 (1 μg/μl). Responsive units were further tested with mechanical stimuli, bombesin (spinal superfusion, 200 μg·ml−1·min−1), heating, cooling, and additional chemicals [histamine, chloroquine, allyl isothiocyanate (AITC), capsaicin]. Of 40 ET-1-responsive units, 48% responded to brush and pinch [wide dynamic range (WDR)] and 52% to pinch only [high threshold (HT)]. Ninety-three percent responded to noxious heat, 50% to cooling, and >70% to histamine, chloroquine, AITC, and capsaicin. Fifty-seven percent responded to bombesin, suggesting that they participate in spinal itch transmission. That most ET-1-sensitive spinal neurons also responded to pruritic and algesic stimuli is consistent with previous studies of pruritogen-responsive dorsal horn neurons. We previously hypothesized that pruritogen-sensitive neurons signal itch. The observation that ET-1 activates nociceptive neurons suggests that both itch and pain signals may be generated by ET-1 to result in simultaneous sensations of itch and pain, consistent with observations that ET-1 elicits both itch- and pain-related behaviors in animals and burning itch sensations in humans. PMID:26311187

Neuronal Spike Timing Adaptation Described with a Fractional Leaky Integrate-and-Fire Model

PubMed Central

Teka, Wondimu; Marinov, Toma M.; Santamaria, Fidel

2014-01-01

The voltage trace of neuronal activities can follow multiple timescale dynamics that arise from correlated membrane conductances. Such processes can result in power-law behavior in which the membrane voltage cannot be characterized with a single time constant. The emergent effect of these membrane correlations is a non-Markovian process that can be modeled with a fractional derivative. A fractional derivative is a non-local process in which the value of the variable is determined by integrating a temporal weighted voltage trace, also called the memory trace. Here we developed and analyzed a fractional leaky integrate-and-fire model in which the exponent of the fractional derivative can vary from 0 to 1, with 1 representing the normal derivative. As the exponent of the fractional derivative decreases, the weights of the voltage trace increase. Thus, the value of the voltage is increasingly correlated with the trajectory of the voltage in the past. By varying only the fractional exponent, our model can reproduce upward and downward spike adaptations found experimentally in neocortical pyramidal cells and tectal neurons in vitro. The model also produces spikes with longer first-spike latency and high inter-spike variability with power-law distribution. We further analyze spike adaptation and the responses to noisy and oscillatory input. The fractional model generates reliable spike patterns in response to noisy input. Overall, the spiking activity of the fractional leaky integrate-and-fire model deviates from the spiking activity of the Markovian model and reflects the temporal accumulated intrinsic membrane dynamics that affect the response of the neuron to external stimulation. PMID:24675903

Properties of cerebellar fastigial neurons during translation, rotation, and eye movements

NASA Technical Reports Server (NTRS)

Shaikh, Aasef G.; Ghasia, Fatema F.; Dickman, J. David; Angelaki, Dora E.

2005-01-01

The most medial of the deep cerebellar nuclei, the fastigial nucleus (FN), receives sensory vestibular information and direct inhibition from the cerebellar vermis. We investigated the signal processing in the primate FN by recording single-unit activities during translational motion, rotational motion, and eye movements. Firing rate modulation during horizontal plane translation in the absence of eye movements was observed in all non-eye-movement-sensitive cells and 26% of the pursuit eye-movement-sensitive neurons in the caudal FN. Many non-eye-movement-sensitive cells recorded in the rostral FN of three fascicularis monkeys exhibited convergence of signals from both the otolith organs and the semicircular canals. At low frequencies of translation, the majority of these rostral FN cells changed their firing rates in phase with head velocity rather than linear acceleration. As frequency increased, FN vestibular neurons exhibited a wide range of response dynamics with most cells being characterized by increasing phase leads as a function of frequency. Unlike cells in the vestibular nuclei, none of the rostral FN cells responded to rotational motion alone, without simultaneously exhibiting sensitivity to translational motion. Modulation during earth-horizontal axis rotation was observed in more than half (77%) of the neurons, although with smaller gains than during translation. In contrast, only 47% of the cells changed their firing rates during earth-vertical axis rotations in the absence of a dynamic linear acceleration stimulus. These response properties suggest that the rostral FN represents a main processing center of otolith-driven information for inertial motion detection and spatial orientation.

Spatiotemporal processing of linear acceleration: primary afferent and central vestibular neuron responses

NASA Technical Reports Server (NTRS)

Angelaki, D. E.; Dickman, J. D.

2000-01-01

Spatiotemporal convergence and two-dimensional (2-D) neural tuning have been proposed as a major neural mechanism in the signal processing of linear acceleration. To examine this hypothesis, we studied the firing properties of primary otolith afferents and central otolith neurons that respond exclusively to horizontal linear accelerations of the head (0.16-10 Hz) in alert rhesus monkeys. Unlike primary afferents, the majority of central otolith neurons exhibited 2-D spatial tuning to linear acceleration. As a result, central otolith dynamics vary as a function of movement direction. During movement along the maximum sensitivity direction, the dynamics of all central otolith neurons differed significantly from those observed for the primary afferent population. Specifically at low frequencies (

Membrane potential dynamics of axons in cultured hippocampal neurons probed by second-harmonic-generation imaging

NASA Astrophysics Data System (ADS)

Nuriya, Mutsuo; Yasui, Masato

2010-03-01

The electrical properties of axons critically influence the nature of communication between neurons. However, due to their small size, direct measurement of membrane potential dynamics in intact and complex mammalian axons has been a challenge. Furthermore, quantitative optical measurements of axonal membrane potential dynamics have not been available. To characterize the basic principles of somatic voltage signal propagation in intact axonal arbors, second-harmonic-generation (SHG) imaging is applied to cultured mouse hippocampal neurons. When FM4-64 is applied extracellularly to dissociated neurons, whole axonal arbors are visualized by SHG imaging. Upon action potential generation by somatic current injection, nonattenuating action potentials are recorded in intact axonal arbors. Interestingly, however, both current- and voltage-clamp recordings suggest that nonregenerative subthreshold somatic voltage changes at the soma are poorly conveyed to these axonal sites. These results reveal the nature of membrane potential dynamics of cultured hippocampal neurons, and further show the possibility of SHG imaging in physiological investigations of axons.

Single-Cell Detection of Secreted Aβ and sAPPα from Human IPSC-Derived Neurons and Astrocytes.

PubMed

Liao, Mei-Chen; Muratore, Christina R; Gierahn, Todd M; Sullivan, Sarah E; Srikanth, Priya; De Jager, Philip L; Love, J Christopher; Young-Pearse, Tracy L

2016-02-03

Secreted factors play a central role in normal and pathological processes in every tissue in the body. The brain is composed of a highly complex milieu of different cell types and few methods exist that can identify which individual cells in a complex mixture are secreting specific analytes. By identifying which cells are responsible, we can better understand neural physiology and pathophysiology, more readily identify the underlying pathways responsible for analyte production, and ultimately use this information to guide the development of novel therapeutic strategies that target the cell types of relevance. We present here a method for detecting analytes secreted from single human induced pluripotent stem cell (iPSC)-derived neural cells and have applied the method to measure amyloid β (Aβ) and soluble amyloid precursor protein-alpha (sAPPα), analytes central to Alzheimer's disease pathogenesis. Through these studies, we have uncovered the dynamic range of secretion profiles of these analytes from single iPSC-derived neuronal and glial cells and have molecularly characterized subpopulations of these cells through immunostaining and gene expression analyses. In examining Aβ and sAPPα secretion from single cells, we were able to identify previously unappreciated complexities in the biology of APP cleavage that could not otherwise have been found by studying averaged responses over pools of cells. This technique can be readily adapted to the detection of other analytes secreted by neural cells, which would have the potential to open new perspectives into human CNS development and dysfunction. We have established a technology that, for the first time, detects secreted analytes from single human neurons and astrocytes. We examine secretion of the Alzheimer's disease-relevant factors amyloid β (Aβ) and soluble amyloid precursor protein-alpha (sAPPα) and present novel findings that could not have been observed without a single-cell analytical platform. First, we identify a previously unappreciated subpopulation that secretes high levels of Aβ in the absence of detectable sAPPα. Further, we show that multiple cell types secrete high levels of Aβ and sAPPα, but cells expressing GABAergic neuronal markers are overrepresented. Finally, we show that astrocytes are competent to secrete high levels of Aβ and therefore may be a significant contributor to Aβ accumulation in the brain. Copyright © 2016 the authors 0270-6474/16/361730-17$15.00/0.

Pathology Dynamics Predict Spinal Cord Injury Therapeutic Success

PubMed Central

Mitchell, Cassie S.

2008-01-01

Abstract Secondary injury, the complex cascade of cellular events following spinal cord injury (SCI), is a major source of post-insult neuron death. Experimental work has focused on the details of individual factors or mechanisms that contribute to secondary injury, but little is known about the interactions among factors leading to the overall pathology dynamics that underlie its propagation. Prior hypotheses suggest that the pathology is dominated by interactions, with therapeutic success lying in combinations of neuroprotective treatments. In this study, we provide the first comprehensive, system-level characterization of the entire secondary injury process using a novel relational model methodology that aggregates the findings of ~250 experimental studies. Our quantitative examination of the overall pathology dynamics suggests that, while the pathology is initially dominated by “fire-like,” rate-dependent interactions, it quickly switches to a “flood-like,” accumulation-dependent process with contributing factors being largely independent. Our evaluation of ~20,000 potential single and combinatorial treatments indicates this flood-like pathology results in few highly influential factors at clinically realistic treatment time frames, with multi-factor treatments being merely additive rather than synergistic in reducing neuron death. Our findings give new fundamental insight into the understanding of the secondary injury pathology as a whole, provide direction for alternative therapeutic strategies, and suggest that ultimate success in treating SCI lies in the pursuit of pathology dynamics in addition to individually involved factors. PMID:19125684

High-Throughput Phenotyping of Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes and Neurons Using Electric Field Stimulation and High-Speed Fluorescence Imaging

PubMed Central

Daily, Neil J.; Du, Zhong-Wei

2017-01-01

Abstract Electrophysiology of excitable cells, including muscle cells and neurons, has been measured by making direct contact with a single cell using a micropipette electrode. To increase the assay throughput, optical devices such as microscopes and microplate readers have been used to analyze electrophysiology of multiple cells. We have established a high-throughput (HTP) analysis of action potentials (APs) in highly enriched motor neurons and cardiomyocytes (CMs) that are differentiated from human induced pluripotent stem cells (iPSCs). A multichannel electric field stimulation (EFS) device enabled the ability to electrically stimulate cells and measure dynamic changes in APs of excitable cells ultra-rapidly (>100 data points per second) by imaging entire 96-well plates. We found that the activities of both neurons and CMs and their response to EFS and chemicals are readily discerned by our fluorescence imaging-based HTP phenotyping assay. The latest generation of calcium (Ca2+) indicator dyes, FLIPR Calcium 6 and Cal-520, with the HTP device enables physiological analysis of human iPSC-derived samples highlighting its potential application for understanding disease mechanisms and discovering new therapeutic treatments. PMID:28525289

Obtaining Arbitrary Prescribed Mean Field Dynamics for Recurrently Coupled Networks of Type-I Spiking Neurons with Analytically Determined Weights

PubMed Central

Nicola, Wilten; Tripp, Bryan; Scott, Matthew

2016-01-01

A fundamental question in computational neuroscience is how to connect a network of spiking neurons to produce desired macroscopic or mean field dynamics. One possible approach is through the Neural Engineering Framework (NEF). The NEF approach requires quantities called decoders which are solved through an optimization problem requiring large matrix inversion. Here, we show how a decoder can be obtained analytically for type I and certain type II firing rates as a function of the heterogeneity of its associated neuron. These decoders generate approximants for functions that converge to the desired function in mean-squared error like 1/N, where N is the number of neurons in the network. We refer to these decoders as scale-invariant decoders due to their structure. These decoders generate weights for a network of neurons through the NEF formula for weights. These weights force the spiking network to have arbitrary and prescribed mean field dynamics. The weights generated with scale-invariant decoders all lie on low dimensional hypersurfaces asymptotically. We demonstrate the applicability of these scale-invariant decoders and weight surfaces by constructing networks of spiking theta neurons that replicate the dynamics of various well known dynamical systems such as the neural integrator, Van der Pol system and the Lorenz system. As these decoders are analytically determined and non-unique, the weights are also analytically determined and non-unique. We discuss the implications for measured weights of neuronal networks. PMID:26973503

Obtaining Arbitrary Prescribed Mean Field Dynamics for Recurrently Coupled Networks of Type-I Spiking Neurons with Analytically Determined Weights.

PubMed

Nicola, Wilten; Tripp, Bryan; Scott, Matthew

2016-01-01

A fundamental question in computational neuroscience is how to connect a network of spiking neurons to produce desired macroscopic or mean field dynamics. One possible approach is through the Neural Engineering Framework (NEF). The NEF approach requires quantities called decoders which are solved through an optimization problem requiring large matrix inversion. Here, we show how a decoder can be obtained analytically for type I and certain type II firing rates as a function of the heterogeneity of its associated neuron. These decoders generate approximants for functions that converge to the desired function in mean-squared error like 1/N, where N is the number of neurons in the network. We refer to these decoders as scale-invariant decoders due to their structure. These decoders generate weights for a network of neurons through the NEF formula for weights. These weights force the spiking network to have arbitrary and prescribed mean field dynamics. The weights generated with scale-invariant decoders all lie on low dimensional hypersurfaces asymptotically. We demonstrate the applicability of these scale-invariant decoders and weight surfaces by constructing networks of spiking theta neurons that replicate the dynamics of various well known dynamical systems such as the neural integrator, Van der Pol system and the Lorenz system. As these decoders are analytically determined and non-unique, the weights are also analytically determined and non-unique. We discuss the implications for measured weights of neuronal networks.

New Electrochemical Methods for Studying Nanoparticle Electrocatalysis and Neuronal Exocytosis

NASA Astrophysics Data System (ADS)

Cox, Jonathan T.

This dissertation presents the construction and application of micro and nanoscale electrodes for electroanalytical analysis. The studies presented herein encompass two main areas: electrochemical catalysis, and studies of the dynamics of single cell exocytosis. The first portion of this dissertation engages the use of Pt nanoelectrodes to study the stability and electrocatalytic properties of materials. A single nanoparticle electrode (SNPE) was fabricated by immobilizing a single Au nanoparticle on a Pt disk nanoelectrode via an amine-terminated silane cross linker. In this manner we were able to effectively study the electrochemistry and electrocatalytic activity of single Au nanoparticles and found that the electrocatalytic activity is dependent on nanoparticle size. This study can further the understanding of the structure-function relationship in nanoparticle based electrocatalysis. Further work was conducted to probe the stability of Pt nanoelectrodes under conditions of potential cycling. Pt based catalysts are known to deteriorate under such conditions due to losses in electrochemical surface area and Pt dissolution. By using Pt disk nanoelectrodes we were able to study Pt dissolution via steady-state voltammetry. We observed an enhanced dissolution rate and higher charge density on nanoelectrodes than that previously found on macro scale electrodes. The goal of the second portion of this dissertation is to develop new analytical methods to study the dynamics of exocytosis from single cells. The secretion of neurotransmitters plays a key role in neuronal communication, and our studies highlight how bipolar electrochemistry can be employed to enhance detection of neurotransmitters from single cells. First, we developed a theory to quantitatively characterize the voltammetric behavior of bipolar carbon fiber microelectrodes and secondly applied those principles to single cell detection. We showed that by simply adding an additional redox mediator to the back-fill solution of a carbon fiber microelectrode, there is a significant enhancement in detection. Additionally we used solid state nanopores to detect individual phospholipid vesicles in solution. Vesicles are key cellular components that play essential biological roles especially in neurotransmission. This work represents preliminary studies in detection and size determination from vesicles isolated from individual cells.

A fast image registration approach of neural activities in light-sheet fluorescence microscopy images

NASA Astrophysics Data System (ADS)

Meng, Hui; Hui, Hui; Hu, Chaoen; Yang, Xin; Tian, Jie

2017-03-01

The ability of fast and single-neuron resolution imaging of neural activities enables light-sheet fluorescence microscopy (LSFM) as a powerful imaging technique in functional neural connection applications. The state-of-art LSFM imaging system can record the neuronal activities of entire brain for small animal, such as zebrafish or C. elegans at single-neuron resolution. However, the stimulated and spontaneous movements in animal brain result in inconsistent neuron positions during recording process. It is time consuming to register the acquired large-scale images with conventional method. In this work, we address the problem of fast registration of neural positions in stacks of LSFM images. This is necessary to register brain structures and activities. To achieve fast registration of neural activities, we present a rigid registration architecture by implementation of Graphics Processing Unit (GPU). In this approach, the image stacks were preprocessed on GPU by mean stretching to reduce the computation effort. The present image was registered to the previous image stack that considered as reference. A fast Fourier transform (FFT) algorithm was used for calculating the shift of the image stack. The calculations for image registration were performed in different threads while the preparation functionality was refactored and called only once by the master thread. We implemented our registration algorithm on NVIDIA Quadro K4200 GPU under Compute Unified Device Architecture (CUDA) programming environment. The experimental results showed that the registration computation can speed-up to 550ms for a full high-resolution brain image. Our approach also has potential to be used for other dynamic image registrations in biomedical applications.

Dynamics of feature categorization.

PubMed

Martí, Daniel; Rinzel, John

2013-01-01

In visual and auditory scenes, we are able to identify shared features among sensory objects and group them according to their similarity. This grouping is preattentive and fast and is thought of as an elementary form of categorization by which objects sharing similar features are clustered in some abstract perceptual space. It is unclear what neuronal mechanisms underlie this fast categorization. Here we propose a neuromechanistic model of fast feature categorization based on the framework of continuous attractor networks. The mechanism for category formation does not rely on learning and is based on biologically plausible assumptions, for example, the existence of populations of neurons tuned to feature values, feature-specific interactions, and subthreshold-evoked responses upon the presentation of single objects. When the network is presented with a sequence of stimuli characterized by some feature, the network sums the evoked responses and provides a running estimate of the distribution of features in the input stream. If the distribution of features is structured into different components or peaks (i.e., is multimodal), recurrent excitation amplifies the response of activated neurons, and categories are singled out as emerging localized patterns of elevated neuronal activity (bumps), centered at the centroid of each cluster. The emergence of bump states through sequential, subthreshold activation and the dependence on input statistics is a novel application of attractor networks. We show that the extraction and representation of multiple categories are facilitated by the rich attractor structure of the network, which can sustain multiple stable activity patterns for a robust range of connectivity parameters compatible with cortical physiology.

Macroscopic neural mass model constructed from a current-based network model of spiking neurons.

PubMed

Umehara, Hiroaki; Okada, Masato; Teramae, Jun-Nosuke; Naruse, Yasushi

2017-02-01

Neural mass models (NMMs) are efficient frameworks for describing macroscopic cortical dynamics including electroencephalogram and magnetoencephalogram signals. Originally, these models were formulated on an empirical basis of synaptic dynamics with relatively long time constants. By clarifying the relations between NMMs and the dynamics of microscopic structures such as neurons and synapses, we can better understand cortical and neural mechanisms from a multi-scale perspective. In a previous study, the NMMs were analytically derived by averaging the equations of synaptic dynamics over the neurons in the population and further averaging the equations of the membrane-potential dynamics. However, the averaging of synaptic current assumes that the neuron membrane potentials are nearly time invariant and that they remain at sub-threshold levels to retain the conductance-based model. This approximation limits the NMM to the non-firing state. In the present study, we newly propose a derivation of a NMM by alternatively approximating the synaptic current which is assumed to be independent of the membrane potential, thus adopting a current-based model. Our proposed model releases the constraint of the nearly constant membrane potential. We confirm that the obtained model is reducible to the previous model in the non-firing situation and that it reproduces the temporal mean values and relative power spectrum densities of the average membrane potentials for the spiking neurons. It is further ensured that the existing NMM properly models the averaged dynamics over individual neurons even if they are spiking in the populations.

FoxO regulates microtubule dynamics and polarity to promote dendrite branching in Drosophila sensory neurons

PubMed Central

Sears, James C.; Broihier, Heather T.

2016-01-01

The size and shape of dendrite arbors are defining features of neurons and critical determinants of neuronal function. The molecular mechanisms establishing arborization patterns during development are not well understood, though properly regulated microtubule (MT) dynamics and polarity are essential. We previously found that FoxO regulates axonal MTs, raising the question of whether it also regulates dendritic MTs and morphology. Here we demonstrate that FoxO promotes dendrite branching in all classes of Drosophila dendritic arborization (da) neurons. FoxO is required both for initiating growth of new branches and for maintaining existing branches. To elucidate FoxO function, we characterized MT organization in both foxO null and overexpressing neurons. We find that FoxO directs MT organization and dynamics in dendrites. Moreover, it is both necessary and sufficient for anterograde MT polymerization, which is known to promote dendrite branching. Lastly, FoxO promotes proper larval nociception, indicating a functional consequence of impaired da neuron morphology in foxO mutants. Together, our results indicate that FoxO regulates dendrite structure and function and suggest that FoxO-mediated pathways control MT dynamics and polarity. PMID:27546375

Stable long-term chronic brain mapping at the single-neuron level.

PubMed

Fu, Tian-Ming; Hong, Guosong; Zhou, Tao; Schuhmann, Thomas G; Viveros, Robert D; Lieber, Charles M

2016-10-01

Stable in vivo mapping and modulation of the same neurons and brain circuits over extended periods is critical to both neuroscience and medicine. Current electrical implants offer single-neuron spatiotemporal resolution but are limited by such factors as relative shear motion and chronic immune responses during long-term recording. To overcome these limitations, we developed a chronic in vivo recording and stimulation platform based on flexible mesh electronics, and we demonstrated stable multiplexed local field potentials and single-unit recordings in mouse brains for at least 8 months without probe repositioning. Properties of acquired signals suggest robust tracking of the same neurons over this period. This recording and stimulation platform allowed us to evoke stable single-neuron responses to chronic electrical stimulation and to carry out longitudinal studies of brain aging in freely behaving mice. Such advantages could open up future studies in mapping and modulating changes associated with learning, aging and neurodegenerative diseases.

Spiking, Bursting, and Population Dynamics in a Network of Growth Transform Neurons.

PubMed

Gangopadhyay, Ahana; Chakrabartty, Shantanu

2018-06-01

This paper investigates the dynamical properties of a network of neurons, each of which implements an asynchronous mapping based on polynomial growth transforms. In the first part of this paper, we present a geometric approach for visualizing the dynamics of the network where each of the neurons traverses a trajectory in a dual optimization space, whereas the network itself traverses a trajectory in an equivalent primal optimization space. We show that as the network learns to solve basic classification tasks, different choices of primal-dual mapping produce unique but interpretable neural dynamics like noise shaping, spiking, and bursting. While the proposed framework is general enough, in this paper, we demonstrate its use for designing support vector machines (SVMs) that exhibit noise-shaping properties similar to those of modulators, and for designing SVMs that learn to encode information using spikes and bursts. It is demonstrated that the emergent switching, spiking, and burst dynamics produced by each neuron encodes its respective margin of separation from a classification hyperplane whose parameters are encoded by the network population dynamics. We believe that the proposed growth transform neuron model and the underlying geometric framework could serve as an important tool to connect well-established machine learning algorithms like SVMs to neuromorphic principles like spiking, bursting, population encoding, and noise shaping.

Extracting neuronal functional network dynamics via adaptive Granger causality analysis.

PubMed

Sheikhattar, Alireza; Miran, Sina; Liu, Ji; Fritz, Jonathan B; Shamma, Shihab A; Kanold, Patrick O; Babadi, Behtash

2018-04-24

Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying adaptive behavior. We develop a dynamic estimation and inference paradigm for extracting functional neuronal network dynamics in the sense of Granger, by integrating techniques from adaptive filtering, compressed sensing, point process theory, and high-dimensional statistics. We demonstrate the utility of our proposed paradigm through theoretical analysis, algorithm development, and application to synthetic and real data. Application of our techniques to two-photon Ca 2+ imaging experiments from the mouse auditory cortex reveals unique features of the functional neuronal network structures underlying spontaneous activity at unprecedented spatiotemporal resolution. Our analysis of simultaneous recordings from the ferret auditory and prefrontal cortical areas suggests evidence for the role of rapid top-down and bottom-up functional dynamics across these areas involved in robust attentive behavior.

Single photon emission computed tomography in motor neuron disease with dementia.

PubMed

Sawada, H; Udaka, F; Kishi, Y; Seriu, N; Mezaki, T; Kameyama, M; Honda, M; Tomonobu, M

1988-01-01

Single photon emission computed tomography with [123 I] isopropylamphetamine was carried out on a patient with motor neuron disease with dementia. [123 I] uptake was decreased in the frontal lobes. This would reflect the histopathological findings such as neuronal loss and gliosis in the frontal lobes.

Neurons as sensors: individual and cascaded chemical sensing.

PubMed

Prasad, Shalini; Zhang, Xuan; Yang, Mo; Ozkan, Cengiz S; Ozkan, Mihrimah

2004-07-15

A single neuron sensor has been developed based on the interaction of gradient electric fields and the cell membrane. Single neurons are rapidly positioned over individual microelectrodes using positive dielectrophoretic traps. This enables the continuous extracellular electrophysiological measurements from individual neurons. The sensor developed using this technique provides the first experimental method for determining single cell sensitivity; the speed of response and the associated physiological changes to a broad spectrum of chemical agents. Binding of specific chemical agents to a specific combination of receptors induces changes to the extracellular membrane potential of a single neuron, which can be translated into unique "signature patterns" (SP), which function as identification tags. Signature patterns are derived using Fast Fourier Transformation (FFT) analysis and Wavelet Transformation (WT) analysis of the modified extracellular action potential. The validity and the sensitivity of the system are demonstrated for a variety of chemical agents ranging from behavior altering chemicals (ethanol), environmentally hazardous agents (hydrogen peroxide, EDTA) to physiologically harmful agents (pyrethroids) at pico- and femto-molar concentrations. The ability of a single neuron to selectively identify specific chemical agents when injected in a serial manner is demonstrated in "cascaded sensing".

Local protein dynamics during microvesicle exocytosis in neuroendocrine cells.

PubMed

Somasundaram, Agila; Taraska, Justin

2018-06-06

Calcium triggered exocytosis is key to many physiological processes, including neurotransmitter and hormone release by neurons and endocrine cells. Dozens of proteins regulate exocytosis, yet the temporal and spatial dynamics of these factors during vesicle fusion remain unclear. Here we use total internal reflection fluorescence microscopy to visualize local protein dynamics at single sites of exocytosis of small synaptic-like microvesicles in live cultured neuroendocrine PC12 cells. We employ two-color imaging to simultaneously observe membrane fusion (using vesicular acetylcholine transporter (VAChT) tagged to pHluorin) and the dynamics of associated proteins at the moments surrounding exocytosis. Our experiments show that many proteins, including the SNAREs syntaxin1 and VAMP2, the SNARE modulator tomosyn, and Rab proteins, are pre-clustered at fusion sites and rapidly lost at fusion. The ATPase NSF is locally recruited at fusion. Interestingly, the endocytic BAR domain-containing proteins amphiphysin1, syndapin2, and endophilins are dynamically recruited to fusion sites, and slow the loss of vesicle membrane-bound cargo from fusion sites. A similar effect on vesicle membrane protein dynamics was seen with the over-expression of the GTPases dynamin1 and dynamin2. These results suggest that proteins involved in classical clathrin-mediated endocytosis can regulate exocytosis of synaptic-like microvesicles. Our findings provide insights into the dynamics, assembly, and mechanistic roles of many key factors of exocytosis and endocytosis at single sites of microvesicle fusion in live cells.

Self-Organized Supercriticality and Oscillations in Networks of Stochastic Spiking Neurons

NASA Astrophysics Data System (ADS)

Costa, Ariadne; Brochini, Ludmila; Kinouchi, Osame

2017-08-01

Networks of stochastic spiking neurons are interesting models in the area of Theoretical Neuroscience, presenting both continuous and discontinuous phase transitions. Here we study fully connected networks analytically, numerically and by computational simulations. The neurons have dynamic gains that enable the network to converge to a stationary slightly supercritical state (self-organized supercriticality or SOSC) in the presence of the continuous transition. We show that SOSC, which presents power laws for neuronal avalanches plus some large events, is robust as a function of the main parameter of the neuronal gain dynamics. We discuss the possible applications of the idea of SOSC to biological phenomena like epilepsy and dragon king avalanches. We also find that neuronal gains can produce collective oscillations that coexists with neuronal avalanches, with frequencies compatible with characteristic brain rhythms.

Multi-timescale Modeling of Activity-Dependent Metabolic Coupling in the Neuron-Glia-Vasculature Ensemble

PubMed Central

Jolivet, Renaud; Coggan, Jay S.; Allaman, Igor; Magistretti, Pierre J.

2015-01-01

Glucose is the main energy substrate in the adult brain under normal conditions. Accumulating evidence, however, indicates that lactate produced in astrocytes (a type of glial cell) can also fuel neuronal activity. The quantitative aspects of this so-called astrocyte-neuron lactate shuttle (ANLS) are still debated. To address this question, we developed a detailed biophysical model of the brain’s metabolic interactions. Our model integrates three modeling approaches, the Buxton-Wang model of vascular dynamics, the Hodgkin-Huxley formulation of neuronal membrane excitability and a biophysical model of metabolic pathways. This approach provides a template for large-scale simulations of the neuron-glia-vasculature (NGV) ensemble, and for the first time integrates the respective timescales at which energy metabolism and neuronal excitability occur. The model is constrained by relative neuronal and astrocytic oxygen and glucose utilization, by the concentration of metabolites at rest and by the temporal dynamics of NADH upon activation. These constraints produced four observations. First, a transfer of lactate from astrocytes to neurons emerged in response to activity. Second, constrained by activity-dependent NADH transients, neuronal oxidative metabolism increased first upon activation with a subsequent delayed astrocytic glycolysis increase. Third, the model correctly predicted the dynamics of extracellular lactate and oxygen as observed in vivo in rats. Fourth, the model correctly predicted the temporal dynamics of tissue lactate, of tissue glucose and oxygen consumption, and of the BOLD signal as reported in human studies. These findings not only support the ANLS hypothesis but also provide a quantitative mathematical description of the metabolic activation in neurons and glial cells, as well as of the macroscopic measurements obtained during brain imaging. PMID:25719367

Multi-timescale modeling of activity-dependent metabolic coupling in the neuron-glia-vasculature ensemble.

PubMed

Jolivet, Renaud; Coggan, Jay S; Allaman, Igor; Magistretti, Pierre J

2015-02-01

Glucose is the main energy substrate in the adult brain under normal conditions. Accumulating evidence, however, indicates that lactate produced in astrocytes (a type of glial cell) can also fuel neuronal activity. The quantitative aspects of this so-called astrocyte-neuron lactate shuttle (ANLS) are still debated. To address this question, we developed a detailed biophysical model of the brain's metabolic interactions. Our model integrates three modeling approaches, the Buxton-Wang model of vascular dynamics, the Hodgkin-Huxley formulation of neuronal membrane excitability and a biophysical model of metabolic pathways. This approach provides a template for large-scale simulations of the neuron-glia-vasculature (NGV) ensemble, and for the first time integrates the respective timescales at which energy metabolism and neuronal excitability occur. The model is constrained by relative neuronal and astrocytic oxygen and glucose utilization, by the concentration of metabolites at rest and by the temporal dynamics of NADH upon activation. These constraints produced four observations. First, a transfer of lactate from astrocytes to neurons emerged in response to activity. Second, constrained by activity-dependent NADH transients, neuronal oxidative metabolism increased first upon activation with a subsequent delayed astrocytic glycolysis increase. Third, the model correctly predicted the dynamics of extracellular lactate and oxygen as observed in vivo in rats. Fourth, the model correctly predicted the temporal dynamics of tissue lactate, of tissue glucose and oxygen consumption, and of the BOLD signal as reported in human studies. These findings not only support the ANLS hypothesis but also provide a quantitative mathematical description of the metabolic activation in neurons and glial cells, as well as of the macroscopic measurements obtained during brain imaging.

Dynamic Divisive Normalization Predicts Time-Varying Value Coding in Decision-Related Circuits

PubMed Central

LoFaro, Thomas; Webb, Ryan; Glimcher, Paul W.

2014-01-01

Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding. PMID:25429145

Drp1 levels constitutively regulate mitochondrial dynamics and cell survival in cortical neurons.

PubMed

Uo, Takuma; Dworzak, Jenny; Kinoshita, Chizuru; Inman, Denise M; Kinoshita, Yoshito; Horner, Philip J; Morrison, Richard S

2009-08-01

Mitochondria exist as dynamic networks that are constantly remodeled through the opposing actions of fusion and fission proteins. Changes in the expression of these proteins alter mitochondrial shape and size, and may promote or inhibit the propagation of apoptotic signals. Using mitochondrially targeted EGFP or DsRed2 to identify mitochondria, we observed a short, distinctly tubular mitochondrial morphology in postnatal cortical neurons in culture and in retinal ganglion cells in vivo, whereas longer, highly interconnected mitochondrial networks were detected in cortical astrocytes in vitro and non-neuronal cells in the retina in vivo. Differential expression patterns of fusion and fission proteins, in part, appear to determine these morphological differences as neurons expressed markedly high levels of Drp1 and OPA1 proteins compared to non-neuronal cells. This finding was corroborated using optic tissue samples. Moreover, cortical neurons expressed several splice variants of Drp1 including a neuron-specific isoform which incorporates exon 3. Knockdown or dominant-negative interference of endogenous Drp1 significantly increased mitochondrial length in both neurons and non-neuronal cells, but caused cell death only in cortical neurons. Conversely, depletion of the fusion protein, Mfn2, but not Mfn1, caused extensive mitochondrial fission and cell death. Thus, Drp1 and Mfn2 in normal cortical neurons not only regulate mitochondrial morphology, but are also required for cell survival. The present findings point to unique patterns of Drp1 expression and selective vulnerability to reduced levels of Drp1 expression/activity in neurons, and demonstrate that the regulation of mitochondrial dynamics must be tightly regulated in neurons.

Drp1 levels constitutively regulate mitochondrial dynamics and cell survival in cortical neurons

PubMed Central

Uo, Takuma; Dworzak, Jenny; Kinoshita, Chizuru; Inman, Denise M.; Kinoshita, Yoshito; Horner, Philip J.; Morrison, Richard S.

2009-01-01

Mitochondria exist as dynamic networks that are constantly remodeled through the opposing actions of fusion and fission proteins. Changes in the expression of these proteins alter mitochondrial shape and size, and may promote or inhibit the propagation of apoptotic signals. Using mitochondrially targeted EGFP or DsRed2 to identify mitochondria, we observed a short, distinctly tubular mitochondrial morphology in postnatal cortical neurons in culture and in retinal ganglion cells in vivo, whereas longer, highly interconnected mitochondrial networks were detected in cortical astrocytes in vitro and non-neuronal cells in the retina in vivo. Differential expression patterns of fusion and fission proteins, in part, appear to determine these morphological differences as neurons expressed markedly high levels of Drp1 and OPA1 proteins compared to non-neuronal cells. This finding was corroborated using optic tissue samples. Moreover, cortical neurons expressed several splice variants of Drp1 including a neuron-specific isoform which incorporates exon 3. Knockdown or dominant negative interference of endogenous Drp1 significantly increased mitochondrial length in both neurons and non-neuronal cells, but caused cell death only in cortical neurons. Conversely, depletion of the fusion protein, Mfn2, but not Mfn1, caused extensive mitochondrial fission and cell death. Thus, Drp1 and Mfn2 in normal cortical neurons not only regulate mitochondrial morphology, but are also required for cell survival. The present findings point to unique patterns of Drp1 expression and selective vulnerability to reduced levels of Drp1 expression/activity in neurons, and demonstrate that the regulation of mitochondrial dynamics must be tightly regulated in neurons. PMID:19445933

Gender differences in human single neuron responses to male emotional faces.

PubMed

Newhoff, Morgan; Treiman, David M; Smith, Kris A; Steinmetz, Peter N

2015-01-01

Well-documented differences in the psychology and behavior of men and women have spurred extensive exploration of gender's role within the brain, particularly regarding emotional processing. While neuroanatomical studies clearly show differences between the sexes, the functional effects of these differences are less understood. Neuroimaging studies have shown inconsistent locations and magnitudes of gender differences in brain hemodynamic responses to emotion. To better understand the neurophysiology of these gender differences, we analyzed recordings of single neuron activity in the human brain as subjects of both genders viewed emotional expressions. This study included recordings of single-neuron activity of 14 (6 male) epileptic patients in four brain areas: amygdala (236 neurons), hippocampus (n = 270), anterior cingulate cortex (n = 256), and ventromedial prefrontal cortex (n = 174). Neural activity was recorded while participants viewed a series of avatar male faces portraying positive, negative or neutral expressions. Significant gender differences were found in the left amygdala, where 23% (n = 15∕66) of neurons in men were significantly affected by facial emotion, vs. 8% (n = 6∕76) of neurons in women. A Fisher's exact test comparing the two ratios found a highly significant difference between the two (p < 0.01). These results show specific differences between genders at the single-neuron level in the human amygdala. These differences may reflect gender-based distinctions in evolved capacities for emotional processing and also demonstrate the importance of including subject gender as an independent factor in future studies of emotional processing by single neurons in the human amygdala.

Controlling the Flow of Visual Information through the Lateral Geniculate Nucleus: From Single Cells to Neural Networks.

DTIC Science & Technology

1991-10-31

in my laboratory, Drs. Dan Kammen, Ernst Niebur and Florentin Worg6tter, as well as with three outside collaborators, Prof. John Kulli from the...also for experimentally observed cortical column structures ( Niebur and Worg6tter, 1990a,b). Temporal Dynamics of Interacting Neuronal Populations We...Connection Machine to simulate a 128 by 128 grid of 16,384 cells under a variety of stimulation patterns ( Niebur , Kammen & Koch, 1991). To explore

Potassium conductance dynamics confer robust spike-time precision in a neuromorphic model of the auditory brain stem

PubMed Central

Boahen, Kwabena

2013-01-01

A fundamental question in neuroscience is how neurons perform precise operations despite inherent variability. This question also applies to neuromorphic engineering, where low-power microchips emulate the brain using large populations of diverse silicon neurons. Biological neurons in the auditory pathway display precise spike timing, critical for sound localization and interpretation of complex waveforms such as speech, even though they are a heterogeneous population. Silicon neurons are also heterogeneous, due to a key design constraint in neuromorphic engineering: smaller transistors offer lower power consumption and more neurons per unit area of silicon, but also more variability between transistors and thus between silicon neurons. Utilizing this variability in a neuromorphic model of the auditory brain stem with 1,080 silicon neurons, we found that a low-voltage-activated potassium conductance (gKL) enables precise spike timing via two mechanisms: statically reducing the resting membrane time constant and dynamically suppressing late synaptic inputs. The relative contribution of these two mechanisms is unknown because blocking gKL in vitro eliminates dynamic adaptation but also lengthens the membrane time constant. We replaced gKL with a static leak in silico to recover the short membrane time constant and found that silicon neurons could mimic the spike-time precision of their biological counterparts, but only over a narrow range of stimulus intensities and biophysical parameters. The dynamics of gKL were required for precise spike timing robust to stimulus variation across a heterogeneous population of silicon neurons, thus explaining how neural and neuromorphic systems may perform precise operations despite inherent variability. PMID:23554436

A simple method for characterizing passive and active neuronal properties: application to striatal neurons.

PubMed

Lepora, Nathan F; Blomeley, Craig P; Hoyland, Darren; Bracci, Enrico; Overton, Paul G; Gurney, Kevin

2011-11-01

The study of active and passive neuronal dynamics usually relies on a sophisticated array of electrophysiological, staining and pharmacological techniques. We describe here a simple complementary method that recovers many findings of these more complex methods but relies only on a basic patch-clamp recording approach. Somatic short and long current pulses were applied in vitro to striatal medium spiny (MS) and fast spiking (FS) neurons from juvenile rats. The passive dynamics were quantified by fitting two-compartment models to the short current pulse data. Lumped conductances for the active dynamics were then found by compensating this fitted passive dynamics within the current-voltage relationship from the long current pulse data. These estimated passive and active properties were consistent with previous more complex estimations of the neuron properties, supporting the approach. Relationships within the MS and FS neuron types were also evident, including a graduation of MS neuron properties consistent with recent findings about D1 and D2 dopamine receptor expression. Application of the method to simulated neuron data supported the hypothesis that it gives reasonable estimates of membrane properties and gross morphology. Therefore detailed information about the biophysics can be gained from this simple approach, which is useful for both classification of neuron type and biophysical modelling. Furthermore, because these methods rely upon no manipulations to the cell other than patch clamping, they are ideally suited to in vivo electrophysiology. © 2011 The Authors. European Journal of Neuroscience © 2011 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Multistability, local pattern formation, and global collective firing in a small-world network of nonleaky integrate-and-fire neurons.

PubMed

Rothkegel, Alexander; Lehnertz, Klaus

2009-03-01

We investigate numerically the collective dynamical behavior of pulse-coupled nonleaky integrate-and-fire neurons that are arranged on a two-dimensional small-world network. To ensure ongoing activity, we impose a probability for spontaneous firing for each neuron. We study network dynamics evolving from different sets of initial conditions in dependence on coupling strength and rewiring probability. Besides a homogeneous equilibrium state for low coupling strength, we observe different local patterns including cyclic waves, spiral waves, and turbulentlike patterns, which-depending on network parameters-interfere with the global collective firing of the neurons. We attribute the various network dynamics to distinct regimes in the parameter space. For the same network parameters different network dynamics can be observed depending on the set of initial conditions only. Such a multistable behavior and the interplay between local pattern formation and global collective firing may be attributable to the spatiotemporal dynamics of biological networks.

Granger causality network reconstruction of conductance-based integrate-and-fire neuronal systems.

PubMed

Zhou, Douglas; Xiao, Yanyang; Zhang, Yaoyu; Xu, Zhiqin; Cai, David

2014-01-01

Reconstruction of anatomical connectivity from measured dynamical activities of coupled neurons is one of the fundamental issues in the understanding of structure-function relationship of neuronal circuitry. Many approaches have been developed to address this issue based on either electrical or metabolic data observed in experiment. The Granger causality (GC) analysis remains one of the major approaches to explore the dynamical causal connectivity among individual neurons or neuronal populations. However, it is yet to be clarified how such causal connectivity, i.e., the GC connectivity, can be mapped to the underlying anatomical connectivity in neuronal networks. We perform the GC analysis on the conductance-based integrate-and-fire (I&F) neuronal networks to obtain their causal connectivity. Through numerical experiments, we find that the underlying synaptic connectivity amongst individual neurons or subnetworks, can be successfully reconstructed by the GC connectivity constructed from voltage time series. Furthermore, this reconstruction is insensitive to dynamical regimes and can be achieved without perturbing systems and prior knowledge of neuronal model parameters. Surprisingly, the synaptic connectivity can even be reconstructed by merely knowing the raster of systems, i.e., spike timing of neurons. Using spike-triggered correlation techniques, we establish a direct mapping between the causal connectivity and the synaptic connectivity for the conductance-based I&F neuronal networks, and show the GC is quadratically related to the coupling strength. The theoretical approach we develop here may provide a framework for examining the validity of the GC analysis in other settings.

Phase synchronization motion and neural coding in dynamic transmission of neural information.

PubMed

Wang, Rubin; Zhang, Zhikang; Qu, Jingyi; Cao, Jianting

2011-07-01

In order to explore the dynamic characteristics of neural coding in the transmission of neural information in the brain, a model of neural network consisting of three neuronal populations is proposed in this paper using the theory of stochastic phase dynamics. Based on the model established, the neural phase synchronization motion and neural coding under spontaneous activity and stimulation are examined, for the case of varying network structure. Our analysis shows that, under the condition of spontaneous activity, the characteristics of phase neural coding are unrelated to the number of neurons participated in neural firing within the neuronal populations. The result of numerical simulation supports the existence of sparse coding within the brain, and verifies the crucial importance of the magnitudes of the coupling coefficients in neural information processing as well as the completely different information processing capability of neural information transmission in both serial and parallel couplings. The result also testifies that under external stimulation, the bigger the number of neurons in a neuronal population, the more the stimulation influences the phase synchronization motion and neural coding evolution in other neuronal populations. We verify numerically the experimental result in neurobiology that the reduction of the coupling coefficient between neuronal populations implies the enhancement of lateral inhibition function in neural networks, with the enhancement equivalent to depressing neuronal excitability threshold. Thus, the neuronal populations tend to have a stronger reaction under the same stimulation, and more neurons get excited, leading to more neurons participating in neural coding and phase synchronization motion.

Granger Causality Network Reconstruction of Conductance-Based Integrate-and-Fire Neuronal Systems

PubMed Central

Zhou, Douglas; Xiao, Yanyang; Zhang, Yaoyu; Xu, Zhiqin; Cai, David

2014-01-01

Reconstruction of anatomical connectivity from measured dynamical activities of coupled neurons is one of the fundamental issues in the understanding of structure-function relationship of neuronal circuitry. Many approaches have been developed to address this issue based on either electrical or metabolic data observed in experiment. The Granger causality (GC) analysis remains one of the major approaches to explore the dynamical causal connectivity among individual neurons or neuronal populations. However, it is yet to be clarified how such causal connectivity, i.e., the GC connectivity, can be mapped to the underlying anatomical connectivity in neuronal networks. We perform the GC analysis on the conductance-based integrate-and-fire (IF) neuronal networks to obtain their causal connectivity. Through numerical experiments, we find that the underlying synaptic connectivity amongst individual neurons or subnetworks, can be successfully reconstructed by the GC connectivity constructed from voltage time series. Furthermore, this reconstruction is insensitive to dynamical regimes and can be achieved without perturbing systems and prior knowledge of neuronal model parameters. Surprisingly, the synaptic connectivity can even be reconstructed by merely knowing the raster of systems, i.e., spike timing of neurons. Using spike-triggered correlation techniques, we establish a direct mapping between the causal connectivity and the synaptic connectivity for the conductance-based IF neuronal networks, and show the GC is quadratically related to the coupling strength. The theoretical approach we develop here may provide a framework for examining the validity of the GC analysis in other settings. PMID:24586285

Uniting functional network topology and oscillations in the fronto-parietal single unit network of behaving primates.

PubMed

Dann, Benjamin; Michaels, Jonathan A; Schaffelhofer, Stefan; Scherberger, Hansjörg

2016-08-15

The functional communication of neurons in cortical networks underlies higher cognitive processes. Yet, little is known about the organization of the single neuron network or its relationship to the synchronization processes that are essential for its formation. Here, we show that the functional single neuron network of three fronto-parietal areas during active behavior of macaque monkeys is highly complex. The network was closely connected (small-world) and consisted of functional modules spanning these areas. Surprisingly, the importance of different neurons to the network was highly heterogeneous with a small number of neurons contributing strongly to the network function (hubs), which were in turn strongly inter-connected (rich-club). Examination of the network synchronization revealed that the identified rich-club consisted of neurons that were synchronized in the beta or low frequency range, whereas other neurons were mostly non-oscillatory synchronized. Therefore, oscillatory synchrony may be a central communication mechanism for highly organized functional spiking networks.

Single mechanically-gated cation channel currents can trigger action potentials in neocortical and hippocampal pyramidal neurons.

PubMed

Nikolaev, Yury A; Dosen, Peter J; Laver, Derek R; van Helden, Dirk F; Hamill, Owen P

2015-05-22

The mammalian brain is a mechanosensitive organ that responds to different mechanical forces ranging from intrinsic forces implicated in brain morphogenesis to extrinsic forces that can cause concussion and traumatic brain injury. However, little is known of the mechanosensors that transduce these forces. In this study we use cell-attached patch recording to measure single mechanically-gated (MG) channel currents and their affects on spike activity in identified neurons in neonatal mouse brain slices. We demonstrate that both neocortical and hippocampal pyramidal neurons express stretch-activated MG cation channels that are activated by suctions of ~25mm Hg, have a single channel conductance for inward current of 50-70pS and show weak selectivity for alkali metal cations (i.e., Na(+)

Coding stimulus amplitude by correlated neural activity

NASA Astrophysics Data System (ADS)

Metzen, Michael G.; Ávila-Åkerberg, Oscar; Chacron, Maurice J.

2015-04-01

While correlated activity is observed ubiquitously in the brain, its role in neural coding has remained controversial. Recent experimental results have demonstrated that correlated but not single-neuron activity can encode the detailed time course of the instantaneous amplitude (i.e., envelope) of a stimulus. These have furthermore demonstrated that such coding required and was optimal for a nonzero level of neural variability. However, a theoretical understanding of these results is still lacking. Here we provide a comprehensive theoretical framework explaining these experimental findings. Specifically, we use linear response theory to derive an expression relating the correlation coefficient to the instantaneous stimulus amplitude, which takes into account key single-neuron properties such as firing rate and variability as quantified by the coefficient of variation. The theoretical prediction was in excellent agreement with numerical simulations of various integrate-and-fire type neuron models for various parameter values. Further, we demonstrate a form of stochastic resonance as optimal coding of stimulus variance by correlated activity occurs for a nonzero value of noise intensity. Thus, our results provide a theoretical explanation of the phenomenon by which correlated but not single-neuron activity can code for stimulus amplitude and how key single-neuron properties such as firing rate and variability influence such coding. Correlation coding by correlated but not single-neuron activity is thus predicted to be a ubiquitous feature of sensory processing for neurons responding to weak input.

Adaptive optimal input design and parametric estimation of nonlinear dynamical systems: application to neuronal modeling.

PubMed

Madi, Mahmoud K; Karameh, Fadi N

2018-05-11

Many physical models of biological processes including neural systems are characterized by parametric nonlinear dynamical relations between driving inputs, internal states, and measured outputs of the process. Fitting such models using experimental data (data assimilation) is a challenging task since the physical process often operates in a noisy, possibly non-stationary environment; moreover, conducting multiple experiments under controlled and repeatable conditions can be impractical, time consuming or costly. The accuracy of model identification, therefore, is dictated principally by the quality and dynamic richness of collected data over single or few experimental sessions. Accordingly, it is highly desirable to design efficient experiments that, by exciting the physical process with smart inputs, yields fast convergence and increased accuracy of the model. We herein introduce an adaptive framework in which optimal input design is integrated with Square root Cubature Kalman Filters (OID-SCKF) to develop an online estimation procedure that first, converges significantly quicker, thereby permitting model fitting over shorter time windows, and second, enhances model accuracy when only few process outputs are accessible. The methodology is demonstrated on common nonlinear models and on a four-area neural mass model with noisy and limited measurements. Estimation quality (speed and accuracy) is benchmarked against high-performance SCKF-based methods that commonly employ dynamically rich informed inputs for accurate model identification. For all the tested models, simulated single-trial and ensemble averages showed that OID-SCKF exhibited (i) faster convergence of parameter estimates and (ii) lower dependence on inter-trial noise variability with gains up to around 1000 msec in speed and 81% increase in variability for the neural mass models. In terms of accuracy, OID-SCKF estimation was superior, and exhibited considerably less variability across experiments, in identifying model parameters of (a) systems with challenging model inversion dynamics and (b) systems with fewer measurable outputs that directly relate to the underlying processes. Fast and accurate identification therefore carries particular promise for modeling of transient (short-lived) neuronal network dynamics using a spatially under-sampled set of noisy measurements, as is commonly encountered in neural engineering applications. © 2018 IOP Publishing Ltd.

A principled dimension-reduction method for the population density approach to modeling networks of neurons with synaptic dynamics.

PubMed

Ly, Cheng

2013-10-01

The population density approach to neural network modeling has been utilized in a variety of contexts. The idea is to group many similar noisy neurons into populations and track the probability density function for each population that encompasses the proportion of neurons with a particular state rather than simulating individual neurons (i.e., Monte Carlo). It is commonly used for both analytic insight and as a time-saving computational tool. The main shortcoming of this method is that when realistic attributes are incorporated in the underlying neuron model, the dimension of the probability density function increases, leading to intractable equations or, at best, computationally intensive simulations. Thus, developing principled dimension-reduction methods is essential for the robustness of these powerful methods. As a more pragmatic tool, it would be of great value for the larger theoretical neuroscience community. For exposition of this method, we consider a single uncoupled population of leaky integrate-and-fire neurons receiving external excitatory synaptic input only. We present a dimension-reduction method that reduces a two-dimensional partial differential-integral equation to a computationally efficient one-dimensional system and gives qualitatively accurate results in both the steady-state and nonequilibrium regimes. The method, termed modified mean-field method, is based entirely on the governing equations and not on any auxiliary variables or parameters, and it does not require fine-tuning. The principles of the modified mean-field method have potential applicability to more realistic (i.e., higher-dimensional) neural networks.

Responses of Rostral Fastigial Nucleus Neurons of Conscious Cats to Rotations in Vertical Planes

PubMed Central

Miller, D. M.; Cotter, L.A.; Gandhi, N. J.; Schor, R. H.; Huff, N. O.; Raj, S. G.; Shulman, J. A.; Yates, B. J.

2008-01-01

The rostral fastigial nucleus (RFN) of the cerebellum is thought to play an important role in postural control, and recent studies in conscious nonhuman primates suggest that this region also participates in the sensory processing required to compute body motion in space. The goal of the present study was to examine the dynamic and spatial responses to sinusoidal rotations in vertical planes of RFN neurons in conscious cats, and determine if they are similar to responses reported for monkeys. Approximately half of the RFN neurons examined were classified as graviceptive, since their firing was synchronized with stimulus position and the gain of their responses was relatively unaffected by the frequency of the tilts. The large majority (80%) of graviceptive RFN neurons were activated by pitch rotations. Most of the remaining RFN units exhibited responses to vertical oscillations that encoded stimulus velocity, and approximately 50% of these velocity units had a response vector orientation aligned near the plane of a single vertical semicircular canal. Unlike in primates, few feline RFN neurons had responses to vertical rotations that suggested integration of graviceptive (otolith) and velocity (vertical semicircular canal) signals. These data indicate that the physiological role of the RFN may differ between primates and lower mammals. The RFN in rats and cats in known to be involved in adjusting blood pressure and breathing during postural alterations in the transverse (pitch) plane. The relatively simple responses of many RFN neurons in cats are appropriate for triggering such compensatory autonomic responses. PMID:18571332

High-Throughput Mapping of Single-Neuron Projections by Sequencing of Barcoded RNA.

PubMed

Kebschull, Justus M; Garcia da Silva, Pedro; Reid, Ashlan P; Peikon, Ian D; Albeanu, Dinu F; Zador, Anthony M

2016-09-07

Neurons transmit information to distant brain regions via long-range axonal projections. In the mouse, area-to-area connections have only been systematically mapped using bulk labeling techniques, which obscure the diverse projections of intermingled single neurons. Here we describe MAPseq (Multiplexed Analysis of Projections by Sequencing), a technique that can map the projections of thousands or even millions of single neurons by labeling large sets of neurons with random RNA sequences ("barcodes"). Axons are filled with barcode mRNA, each putative projection area is dissected, and the barcode mRNA is extracted and sequenced. Applying MAPseq to the locus coeruleus (LC), we find that individual LC neurons have preferred cortical targets. By recasting neuroanatomy, which is traditionally viewed as a problem of microscopy, as a problem of sequencing, MAPseq harnesses advances in sequencing technology to permit high-throughput interrogation of brain circuits. Copyright © 2016 Elsevier Inc. All rights reserved.

Integrating multiple aspects of mitochondrial dynamics in neurons: Age-related differences and dynamic changes in a chronic rotenone model

PubMed Central

Arnold, Beth; Cassady, Steven J.; Van Laar, Victor S.; Berman, Sarah B.

2010-01-01

Changes in dynamic properties of mitochondria are increasingly implicated in neurodegenerative diseases, particularly Parkinson’s disease (PD). Static changes in mitochondrial morphology, often under acutely toxic conditions, are commonly utilized as indicators of changes in mitochondrial fission and fusion. However, in neurons, mitochondrial fission and fusion occur in a dynamic system of axonal/dendritic transport, biogenesis and degradation, and thus, likely interact and change over time. We sought to explore this using a chronic neuronal model (nonlethal low-concentration rotenone over several weeks), examining distal neurites, which may give insight into the earliest changes occurring in PD. Using this model, in live primary neurons, we directly quantified mitochondrial fission, fusion, and transport over time and integrated multiple aspects of mitochondrial dynamics, including morphology and growth/mitophagy. We found that rates of mitochondrial fission and fusion change as neurons age. In addition, we found that chronic rotenone exposure initially increased the ratio of fusion to fission, but later, this was reversed. Surprisingly, despite changes in rates of fission and fusion, mitochondrial morphology was minimally affected, demonstrating that morphology can be an inaccurate indicator of fission/fusion changes. In addition, we found evidence of subcellular compartmentalization of compensatory changes, as mitochondrial density increased in distal neurites first, which may be important in PD, where pathology may begin distally. We propose that rotenone-induced early changes such as in mitochondrial fusion are compensatory, accompanied later by detrimental fission. As evidence, in a dopaminergic neuronal model, in which chronic rotenone caused loss of neurites before cell death (like PD pathology), inhibiting fission protected against the neurite loss. This suggests that aberrant mitochondrial dynamics may contribute to the earliest neuropathologic mechanisms in PD. These data also emphasize that mitochondrial fission and fusion do not occur in isolation, and highlight the importance of analysis and integration of multiple mitochondrial dynamic functions in neurons. PMID:20850532

Neuronal boost to evolutionary dynamics.

PubMed

de Vladar, Harold P; Szathmáry, Eörs

2015-12-06

Standard evolutionary dynamics is limited by the constraints of the genetic system. A central message of evolutionary neurodynamics is that evolutionary dynamics in the brain can happen in a neuronal niche in real time, despite the fact that neurons do not reproduce. We show that Hebbian learning and structural synaptic plasticity broaden the capacity for informational replication and guided variability provided a neuronally plausible mechanism of replication is in place. The synergy between learning and selection is more efficient than the equivalent search by mutation selection. We also consider asymmetric landscapes and show that the learning weights become correlated with the fitness gradient. That is, the neuronal complexes learn the local properties of the fitness landscape, resulting in the generation of variability directed towards the direction of fitness increase, as if mutations in a genetic pool were drawn such that they would increase reproductive success. Evolution might thus be more efficient within evolved brains than among organisms out in the wild.

When Long-Range Zero-Lag Synchronization is Feasible in Cortical Networks

PubMed Central

Viriyopase, Atthaphon; Bojak, Ingo; Zeitler, Magteld; Gielen, Stan

2012-01-01

Many studies have reported long-range synchronization of neuronal activity between brain areas, in particular in the beta and gamma bands with frequencies in the range of 14–30 and 40–80 Hz, respectively. Several studies have reported synchrony with zero phase lag, which is remarkable considering the synaptic and conduction delays inherent in the connections between distant brain areas. This result has led to many speculations about the possible functional role of zero-lag synchrony, such as for neuronal communication, attention, memory, and feature binding. However, recent studies using recordings of single-unit activity and local field potentials report that neuronal synchronization may occur with non-zero phase lags. This raises the questions whether zero-lag synchrony can occur in the brain and, if so, under which conditions. We used analytical methods and computer simulations to investigate which connectivity between neuronal populations allows or prohibits zero-lag synchrony. We did so for a model where two oscillators interact via a relay oscillator. Analytical results and computer simulations were obtained for both type I Mirollo–Strogatz neurons and type II Hodgkin–Huxley neurons. We have investigated the dynamics of the model for various types of synaptic coupling and importantly considered the potential impact of Spike-Timing Dependent Plasticity (STDP) and its learning window. We confirm previous results that zero-lag synchrony can be achieved in this configuration. This is much easier to achieve with Hodgkin–Huxley neurons, which have a biphasic phase response curve, than for type I neurons. STDP facilitates zero-lag synchrony as it adjusts the synaptic strengths such that zero-lag synchrony is feasible for a much larger range of parameters than without STDP. PMID:22866034

Nonlinear Modeling of Causal Interrelationships in Neuronal Ensembles

PubMed Central

Zanos, Theodoros P.; Courellis, Spiros H.; Berger, Theodore W.; Hampson, Robert E.; Deadwyler, Sam A.; Marmarelis, Vasilis Z.

2009-01-01

The increasing availability of multiunit recordings gives new urgency to the need for effective analysis of “multidimensional” time-series data that are derived from the recorded activity of neuronal ensembles in the form of multiple sequences of action potentials—treated mathematically as point-processes and computationally as spike-trains. Whether in conditions of spontaneous activity or under conditions of external stimulation, the objective is the identification and quantification of possible causal links among the neurons generating the observed binary signals. A multiple-input/multiple-output (MIMO) modeling methodology is presented that can be used to quantify the neuronal dynamics of causal interrelationships in neuronal ensembles using spike-train data recorded from individual neurons. These causal interrelationships are modeled as transformations of spike-trains recorded from a set of neurons designated as the “inputs” into spike-trains recorded from another set of neurons designated as the “outputs.” The MIMO model is composed of a set of multiinput/single-output (MISO) modules, one for each output. Each module is the cascade of a MISO Volterra model and a threshold operator generating the output spikes. The Laguerre expansion approach is used to estimate the Volterra kernels of each MISO module from the respective input–output data using the least-squares method. The predictive performance of the model is evaluated with the use of the receiver operating characteristic (ROC) curve, from which the optimum threshold is also selected. The Mann–Whitney statistic is used to select the significant inputs for each output by examining the statistical significance of improvements in the predictive accuracy of the model when the respective inputs is included. Illustrative examples are presented for a simulated system and for an actual application using multiunit data recordings from the hippocampus of a behaving rat. PMID:18701382

Periodic activation function and a modified learning algorithm for the multivalued neuron.

PubMed

Aizenberg, Igor

2010-12-01

In this paper, we consider a new periodic activation function for the multivalued neuron (MVN). The MVN is a neuron with complex-valued weights and inputs/output, which are located on the unit circle. Although the MVN outperforms many other neurons and MVN-based neural networks have shown their high potential, the MVN still has a limited capability of learning highly nonlinear functions. A periodic activation function, which is introduced in this paper, makes it possible to learn nonlinearly separable problems and non-threshold multiple-valued functions using a single multivalued neuron. We call this neuron a multivalued neuron with a periodic activation function (MVN-P). The MVN-Ps functionality is much higher than that of the regular MVN. The MVN-P is more efficient in solving various classification problems. A learning algorithm based on the error-correction rule for the MVN-P is also presented. It is shown that a single MVN-P can easily learn and solve those benchmark classification problems that were considered unsolvable using a single neuron. It is also shown that a universal binary neuron, which can learn nonlinearly separable Boolean functions, and a regular MVN are particular cases of the MVN-P.

Axonal transport studied in a single vertebrate neuron: the giant electromotor neuron of the electric catfish, Malapterurus electricus.

PubMed

Zimmermann, H; Tashiro, T; Komiya, Y; Kurokawa, M

1989-02-01

Axonal transport was studied using a single vertebrate neuron, the giant electromotor neuron of the electric catfish, Malapterurus electricus. The electric organs of this strongly electric fish are innervated by two neurons whose axons form one electric nerve each. After injection of [35S]methionine into the spinal cord at the level of the two perikarya radioactively labelled material is exported by fast flow as a small wave with a velocity of 5.8 mm/h and a somal release time of 91 min (29 degrees C). Slow flow investigated between 15 and 39 days had a velocity of 1.36 mm/d at 29 degrees C. Analysis of radiolabelled proteins by polyacrylamide gel electrophoresis revealed different patterns of labelling between slow and fast flow. The relative molecular mass of the two major proteins labelled on slow flow correspond to actin and tubulin. Labelled proteins of higher relative molecular mass may correspond to neurofilament proteins. Our results suggest that this vertebrate single-neuron and single-axon system can be used successfully for axonal transport studies.

Neurobiologically realistic determinants of self-organized criticality in networks of spiking neurons.

PubMed

Rubinov, Mikail; Sporns, Olaf; Thivierge, Jean-Philippe; Breakspear, Michael

2011-06-01

Self-organized criticality refers to the spontaneous emergence of self-similar dynamics in complex systems poised between order and randomness. The presence of self-organized critical dynamics in the brain is theoretically appealing and is supported by recent neurophysiological studies. Despite this, the neurobiological determinants of these dynamics have not been previously sought. Here, we systematically examined the influence of such determinants in hierarchically modular networks of leaky integrate-and-fire neurons with spike-timing-dependent synaptic plasticity and axonal conduction delays. We characterized emergent dynamics in our networks by distributions of active neuronal ensemble modules (neuronal avalanches) and rigorously assessed these distributions for power-law scaling. We found that spike-timing-dependent synaptic plasticity enabled a rapid phase transition from random subcritical dynamics to ordered supercritical dynamics. Importantly, modular connectivity and low wiring cost broadened this transition, and enabled a regime indicative of self-organized criticality. The regime only occurred when modular connectivity, low wiring cost and synaptic plasticity were simultaneously present, and the regime was most evident when between-module connection density scaled as a power-law. The regime was robust to variations in other neurobiologically relevant parameters and favored systems with low external drive and strong internal interactions. Increases in system size and connectivity facilitated internal interactions, permitting reductions in external drive and facilitating convergence of postsynaptic-response magnitude and synaptic-plasticity learning rate parameter values towards neurobiologically realistic levels. We hence infer a novel association between self-organized critical neuronal dynamics and several neurobiologically realistic features of structural connectivity. The central role of these features in our model may reflect their importance for neuronal information processing.

Biophysical Insights into How Spike Threshold Depends on the Rate of Membrane Potential Depolarization in Type I and Type II Neurons

PubMed Central

Yi, Guo-Sheng; Wang, Jiang; Tsang, Kai-Ming; Wei, Xi-Le; Deng, Bin

2015-01-01

Dynamic spike threshold plays a critical role in neuronal input-output relations. In many neurons, the threshold potential depends on the rate of membrane potential depolarization (dV/dt) preceding a spike. There are two basic classes of neural excitability, i.e., Type I and Type II, according to input-output properties. Although the dynamical and biophysical basis of their spike initiation has been established, the spike threshold dynamic for each cell type has not been well described. Here, we use a biophysical model to investigate how spike threshold depends on dV/dt in two types of neuron. It is observed that Type II spike threshold is more depolarized and more sensitive to dV/dt than Type I. With phase plane analysis, we show that each threshold dynamic arises from the different separatrix and K+ current kinetics. By analyzing subthreshold properties of membrane currents, we find the activation of hyperpolarizing current prior to spike initiation is a major factor that regulates the threshold dynamics. The outward K+ current in Type I neuron does not activate at the perithresholds, which makes its spike threshold insensitive to dV/dt. The Type II K+ current activates prior to spike initiation and there is a large net hyperpolarizing current at the perithresholds, which results in a depolarized threshold as well as a pronounced threshold dynamic. These predictions are further attested in several other functionally equivalent cases of neural excitability. Our study provides a fundamental description about how intrinsic biophysical properties contribute to the threshold dynamics in Type I and Type II neurons, which could decipher their significant functions in neural coding. PMID:26083350

A neuron-astrocyte transistor-like model for neuromorphic dressed neurons.

PubMed

Valenza, G; Pioggia, G; Armato, A; Ferro, M; Scilingo, E P; De Rossi, D

2011-09-01

Experimental evidences on the role of the synaptic glia as an active partner together with the bold synapse in neuronal signaling and dynamics of neural tissue strongly suggest to investigate on a more realistic neuron-glia model for better understanding human brain processing. Among the glial cells, the astrocytes play a crucial role in the tripartite synapsis, i.e. the dressed neuron. A well-known two-way astrocyte-neuron interaction can be found in the literature, completely revising the purely supportive role for the glia. The aim of this study is to provide a computationally efficient model for neuron-glia interaction. The neuron-glia interactions were simulated by implementing the Li-Rinzel model for an astrocyte and the Izhikevich model for a neuron. Assuming the dressed neuron dynamics similar to the nonlinear input-output characteristics of a bipolar junction transistor, we derived our computationally efficient model. This model may represent the fundamental computational unit for the development of real-time artificial neuron-glia networks opening new perspectives in pattern recognition systems and in brain neurophysiology. Copyright © 2011 Elsevier Ltd. All rights reserved.

Distribution of correlated spiking events in a population-based approach for Integrate-and-Fire networks.

PubMed

Zhang, Jiwei; Newhall, Katherine; Zhou, Douglas; Rangan, Aaditya

2014-04-01

Randomly connected populations of spiking neurons display a rich variety of dynamics. However, much of the current modeling and theoretical work has focused on two dynamical extremes: on one hand homogeneous dynamics characterized by weak correlations between neurons, and on the other hand total synchrony characterized by large populations firing in unison. In this paper we address the conceptual issue of how to mathematically characterize the partially synchronous "multiple firing events" (MFEs) which manifest in between these two dynamical extremes. We further develop a geometric method for obtaining the distribution of magnitudes of these MFEs by recasting the cascading firing event process as a first-passage time problem, and deriving an analytical approximation of the first passage time density valid for large neuron populations. Thus, we establish a direct link between the voltage distributions of excitatory and inhibitory neurons and the number of neurons firing in an MFE that can be easily integrated into population-based computational methods, thereby bridging the gap between homogeneous firing regimes and total synchrony.

A Component-Based FPGA Design Framework for Neuronal Ion Channel Dynamics Simulations

PubMed Central

Mak, Terrence S. T.; Rachmuth, Guy; Lam, Kai-Pui; Poon, Chi-Sang

2008-01-01

Neuron-machine interfaces such as dynamic clamp and brain-implantable neuroprosthetic devices require real-time simulations of neuronal ion channel dynamics. Field Programmable Gate Array (FPGA) has emerged as a high-speed digital platform ideal for such application-specific computations. We propose an efficient and flexible component-based FPGA design framework for neuronal ion channel dynamics simulations, which overcomes certain limitations of the recently proposed memory-based approach. A parallel processing strategy is used to minimize computational delay, and a hardware-efficient factoring approach for calculating exponential and division functions in neuronal ion channel models is used to conserve resource consumption. Performances of the various FPGA design approaches are compared theoretically and experimentally in corresponding implementations of the AMPA and NMDA synaptic ion channel models. Our results suggest that the component-based design framework provides a more memory economic solution as well as more efficient logic utilization for large word lengths, whereas the memory-based approach may be suitable for time-critical applications where a higher throughput rate is desired. PMID:17190033

Qualitative validation of the reduction from two reciprocally coupled neurons to one self-coupled neuron in a respiratory network model.

PubMed

Dunmyre, Justin R

2011-06-01

The pre-Bötzinger complex of the mammalian brainstem is a heterogeneous neuronal network, and individual neurons within the network have varying strengths of the persistent sodium and calcium-activated nonspecific cationic currents. Individually, these currents have been the focus of modeling efforts. Previously, Dunmyre et al. (J Comput Neurosci 1-24, 2011) proposed a model and studied the interactions of these currents within one self-coupled neuron. In this work, I consider two identical, reciprocally coupled model neurons and validate the reduction to the self-coupled case. I find that all of the dynamics of the two model neuron network and the regions of parameter space where these distinct dynamics are found are qualitatively preserved in the reduction to the self-coupled case.

Studies of pathological dynamics after microvascular injury using nonlinear optical methods

NASA Astrophysics Data System (ADS)

Rosidi, Nathanael L.

Microvascular lesions are a common feature in the aging brain and clinical evidence has correlated microvascular pathology with the development of neurodegenerative diseases such as Alzheimer's disease and dementia. Traditional animal models that replicate hemorrhagic and ischemic lesions in the brain typically affect large regions in the cortex and do not reproduce the small-scale lesions linked to neurodegeneration that likely stem from injuries to single microvessels. Due in part to this lack of small-scale injury animal models, there remains an incomplete understanding of the cellular and pathophysiological dynamics following small-scale vascular lesions, making progress on therapeutic strategies difficult. We used tightly focused femtosecond laser pulses to injure single penetrating arterioles (PA) (i.e., arterioles that plunge into the brain) in the cortex of live anesthetized rodents and used two-photon excited fluorescence (2PEF) imaging to quantify blood flow changes and to determine the time course of pathological consequences in the brain after injury. We find that after ischemic occlusion of a PA, nearby pial and penetrating arterioles do not actively compensate for the reduction of blood flow observed near the occluded blood vessel. We find that capillaries connected downstream to the clotted vessel dilate but other capillaries in the vicinity do not, suggesting that any compensatory signal that results in a physiological response travels vascularly. We ruptured individual PAs to induce microhemorrhages that resulted in extravasation of blood into the parenchyma. We find that tissue compression due to the hematoma does not collapse capillaries and cause acute ischemia. 2PEF imaging of mice expressing yellow fluorescent protein (YFP) in a subset of cortical neurons revealed no dendrite degeneration out to seven days after microhemorrhage. However, we did observe an inflammatory response by microglia/macrophages as quickly as 1.5-hrs after microhemorrhage which persisted past seven days. Lastly, we looked at spine (i.e., post-synaptic terminals on dendrites) dynamics on GFP fluorescent cortical dendrites and found a higher rate of spine loss and gain after a nearby microhemorrhage out to 14 days. This higher rate of spine turnover may help provide an understanding of the development of symptomatic dysfunction due to consequences in neuronal rewiring after a microhemorrhage. The work presented in this dissertation provides quantification of pathological consequences after both ischemic and hemorrhagic injury to a single blood vessel in the brain. We see that after a small-scale ischemic lesion, surrounding blood vessels do not elicit an active response to compensate for a lack of blood flow in the targeted blood vessel and surrounding tissue. After a hemorrhage to a single blood vessel, we do not observe any neuronal degeneration or death. These hemorrhagic lesions, however, do result in an inflammatory reaction that may lead to subtle changes in neuronal rewiring or seed the development of neurodegenerative diseases. The work presented in this dissertation can help provide new insights for the development of novel stroke therapeutics as well as provide cell specific observations about the development of pathological consequences in both ischemic and hemorrhagic lesions in the brain.

Neurotrophin Signaling via Long-Distance Axonal Transport

NASA Astrophysics Data System (ADS)

Chowdary, Praveen D.; Che, Dung L.; Cui, Bianxiao

2012-05-01

Neurotrophins are a family of target-derived growth factors that support survival, development, and maintenance of innervating neurons. Owing to the unique architecture of neurons, neurotrophins that act locally on the axonal terminals must convey their signals across the entire axon for subsequent regulation of gene transcription in the cell nucleus. This long-distance retrograde signaling, a motor-driven process that can take hours or days, has been a subject of intense interest. In the last decade, live-cell imaging with high sensitivity has significantly increased our capability to track the transport of neurotrophins, their receptors, and subsequent signals in real time. This review summarizes recent research progress in understanding neurotrophin-receptor interactions at the axonal terminal and their transport dynamics along the axon. We emphasize high-resolution studies at the single-molecule level and also discuss recent technical advances in the field.

A high-throughput method for generating uniform microislands for autaptic neuronal cultures

PubMed Central

Sgro, Allyson E.; Nowak, Amy L.; Austin, Naola S.; Custer, Kenneth L.; Allen, Peter B.; Chiu, Daniel T.; Bajjalieh, Sandra M.

2013-01-01

Generating microislands of culture substrate on coverslips by spray application of poly-D lysine is a commonly used method for culturing isolated neurons that form self (autaptic) synapses. This preparation has multiple advantages for studying synaptic transmission in isolation; however, generating microislands by spraying produces islands of non-uniform size and thus cultures vary widely in the number of islands containing single neurons. To address these problems, we developed a high-throughput method for reliably generating uniformly-shaped microislands of culture substrate. Stamp molds formed of poly(dimethylsiloxane) (PDMS) were fabricated with arrays of circles and used to generate stamps made of 9.2% agarose. The agarose stamps were capable of loading sufficient poly D-lysine and collagen dissolved in acetic acid to rapidly generate coverslips containing at least 64 microislands per coverslip. When hippocampal neurons were cultured on these coverslips, there were significantly more single-neuron islands per coverslip. We noted that single neurons tended to form one of three distinct neurite-arbor morphologies, which varied with island size and the location of the cell body on the island. To our surprise, the number of synapses per autaptic neuron did not correlate with arbor shape or island size, suggesting that other factors regulate the number of synapses formed by isolated neurons. The stamping method we report can be used to increase the number of single-neuron islands per culture and aid in the rapid visualization of microislands. PMID:21515305

Inhibition of propofol on single neuron and neuronal ensemble activity in prefrontal cortex of rats during working memory task.

PubMed

Xu, Xinyu; Tian, Yu; Wang, Guolin; Tian, Xin

2014-08-15

Working memory (WM) refers to the temporary storage and manipulation of information necessary for performance of complex cognitive tasks. There is a growing interest in whether and how propofol anesthesia inhibits WM function. The aim of this study is to investigate the possible inhibition mechanism of propofol anesthesia from the view of single neuron and neuronal ensemble activities. Adult SD rats were randomly divided into two groups: propofol group (0.9 mg kg(-1)min(-1), 2h via a tail vein catheter) and control group. All the rats were tested for working memory performances in a Y-maze-rewarded alternation task (a task of delayed non-matched-to-sample) at 24, 48, 72 h after propofol anesthesia, and the behavior results of WM tasks were recorded at the same time. Spatio-temporal trains of action potentials were obtained from the original signals. Single neuron activity was characterized by peri-event time histograms analysis and neuron ensemble activities were characterized by Granger causality to describe the interactions within the neuron ensemble. The results show that: comparing with the control group, the percentage of neurons excited and related to WM was significantly decreased (p<0.01 in 24h, p<0.05 in 48 h); the interactions within neuron ensemble were significantly weakened (p<0.01 in 24h, p<0.05 in 48 h), whereas no significant difference in 72 h (p>0.05), which were consistent with the behavior results. These findings could lead to improved understanding of the mechanism of anesthesia inhibition on WM functions from the view of single neuron activity and neuron ensemble interactions. Copyright © 2014 Elsevier B.V. All rights reserved.

Individual mediodorsal thalamic neurons project to multiple areas of the rat prefrontal cortex: A single neuron-tracing study using virus vectors.

PubMed

Kuramoto, Eriko; Pan, Shixiu; Furuta, Takahiro; Tanaka, Yasuhiro R; Iwai, Haruki; Yamanaka, Atsushi; Ohno, Sachi; Kaneko, Takeshi; Goto, Tetsuya; Hioki, Hiroyuki

2017-01-01

The prefrontal cortex has an important role in a variety of cognitive and executive processes, and is generally defined by its reciprocal connections with the mediodorsal thalamic nucleus (MD). The rat MD is mainly subdivided into three segments, the medial (MDm), central (MDc), and lateral (MDl) divisions, on the basis of the cytoarchitecture and chemoarchitecture. The MD segments are known to topographically project to multiple prefrontal areas at the population level: the MDm mainly to the prelimbic, infralimbic, and agranular insular areas; the MDc to the orbital and agranular insular areas; and the MDl to the prelimbic and anterior cingulate areas. However, it is unknown whether individual MD neurons project to single or multiple prefrontal cortical areas. In the present study, we visualized individual MD neurons with Sindbis virus vectors, and reconstructed whole structures of MD neurons. While the main cortical projection targets of MDm, MDc, and MDl neurons were generally consistent with those of previous results, it was found that individual MD neurons sent their axon fibers to multiple prefrontal areas, and displayed various projection patterns in the target areas. Furthermore, the axons of single MD neurons were not homogeneously spread, but were rather distributed to form patchy axon arbors approximately 1 mm in diameter. The multiple-area projections and patchy axon arbors of single MD neurons might be able to coactivate cortical neuron groups in distant prefrontal areas simultaneously. Furthermore, considerable heterogeneity of the projection patterns is likely, to recruit the different sets of cortical neurons, and thus contributes to a variety of prefrontal functions. J. Comp. Neurol. 525:166-185, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Gender differences in human single neuron responses to male emotional faces

PubMed Central

Newhoff, Morgan; Treiman, David M.; Smith, Kris A.; Steinmetz, Peter N.

2015-01-01

Well-documented differences in the psychology and behavior of men and women have spurred extensive exploration of gender's role within the brain, particularly regarding emotional processing. While neuroanatomical studies clearly show differences between the sexes, the functional effects of these differences are less understood. Neuroimaging studies have shown inconsistent locations and magnitudes of gender differences in brain hemodynamic responses to emotion. To better understand the neurophysiology of these gender differences, we analyzed recordings of single neuron activity in the human brain as subjects of both genders viewed emotional expressions. This study included recordings of single-neuron activity of 14 (6 male) epileptic patients in four brain areas: amygdala (236 neurons), hippocampus (n = 270), anterior cingulate cortex (n = 256), and ventromedial prefrontal cortex (n = 174). Neural activity was recorded while participants viewed a series of avatar male faces portraying positive, negative or neutral expressions. Significant gender differences were found in the left amygdala, where 23% (n = 15∕66) of neurons in men were significantly affected by facial emotion, vs. 8% (n = 6∕76) of neurons in women. A Fisher's exact test comparing the two ratios found a highly significant difference between the two (p < 0.01). These results show specific differences between genders at the single-neuron level in the human amygdala. These differences may reflect gender-based distinctions in evolved capacities for emotional processing and also demonstrate the importance of including subject gender as an independent factor in future studies of emotional processing by single neurons in the human amygdala. PMID:26441597

Shape Selectivity of Middle Superior Temporal Sulcus Body Patch Neurons

PubMed Central

2017-01-01

Abstract Functional MRI studies in primates have demonstrated cortical regions that are strongly activated by visual images of bodies. The presence of such body patches in macaques allows characterization of the stimulus selectivity of their single neurons. Middle superior temporal sulcus body (MSB) patch neurons showed similar stimulus selectivity for natural, shaded, and textured images compared with their silhouettes, suggesting that shape is an important determinant of MSB responses. Here, we examined and modeled the shape selectivity of single MSB neurons. We measured the responses of single MSB neurons to a variety of shapes producing a wide range of responses. We used an adaptive stimulus sampling procedure, selecting and modifying shapes based on the responses of the neuron. Forty percent of shapes that produced the maximal response were rated by humans as animal-like, but the top shape of many MSB neurons was not judged as resembling a body. We fitted the shape selectivity of MSB neurons with a model that parameterizes shapes in terms of curvature and orientation of contour segments, with a pixel-based model, and with layers of units of convolutional neural networks (CNNs). The deep convolutional layers of CNNs provided the best goodness-of-fit, with a median explained explainable variance of the neurons’ responses of 77%. The goodness-of-fit increased along the convolutional layers’ hierarchy but was lower for the fully connected layers. Together with demonstrating the successful modeling of single unit shape selectivity with deep CNNs, the data suggest that semantic or category knowledge determines only slightly the single MSB neuron’s shape selectivity. PMID:28660250

Fast calcium and voltage-sensitive dye imaging in enteric neurones reveal calcium peaks associated with single action potential discharge.

PubMed

Michel, K; Michaelis, M; Mazzuoli, G; Mueller, K; Vanden Berghe, P; Schemann, M

2011-12-15

Slow changes in [Ca(2+)](i) reflect increased neuronal activity. Our study demonstrates that single-trial fast [Ca(2+)](i) imaging (≥200 Hz sampling rate) revealed peaks each of which are associated with single spike discharge recorded by consecutive voltage-sensitive dye (VSD) imaging in enteric neurones and nerve fibres. Fast [Ca(2+)](i) imaging also revealed subthreshold fast excitatory postsynaptic potentials. Nicotine-evoked [Ca(2+)](i) peaks were reduced by -conotoxin and blocked by ruthenium red or tetrodotoxin. Fast [Ca(2+)](i) imaging can be used to directly record single action potentials in enteric neurones. [Ca(2+)](i) peaks required opening of voltage-gated sodium and calcium channels as well as Ca(2+) release from intracellular stores.

Human single-neuron responses at the threshold of conscious recognition

PubMed Central

Quiroga, R. Quian; Mukamel, R.; Isham, E. A.; Malach, R.; Fried, I.

2008-01-01

We studied the responses of single neurons in the human medial temporal lobe while subjects viewed familiar faces, animals, and landmarks. By progressively shortening the duration of stimulus presentation, coupled with backward masking, we show two striking properties of these neurons. (i) Their responses are not statistically different for the 33-ms, 66-ms, and 132-ms stimulus durations, and only for the 264-ms presentations there is a significantly higher firing. (ii) These responses follow conscious perception, as indicated by the subjects' recognition report. Remarkably, when recognized, a single snapshot as brief as 33 ms was sufficient to trigger strong single-unit responses far outlasting stimulus presentation. These results suggest that neurons in the medial temporal lobe can reflect conscious recognition by “all-or-none” responses. PMID:18299568

Single-cell analysis of peptide expression and electrophysiology of right parietal neurons involved in male copulation behavior of a simultaneous hermaphrodite.

PubMed

El Filali, Z; de Boer, P A C M; Pieneman, A W; de Lange, R P J; Jansen, R F; Ter Maat, A; van der Schors, R C; Li, K W; van Straalen, N M; Koene, J M

2015-12-01

Male copulation is a complex behavior that requires coordinated communication between the nervous system and the peripheral reproductive organs involved in mating. In hermaphroditic animals, such as the freshwater snail Lymnaea stagnalis, this complexity increases since the animal can behave both as male and female. The performance of the sexual role as a male is coordinated via a neuronal communication regulated by many peptidergic neurons, clustered in the cerebral and pedal ganglia and dispersed in the pleural and parietal ganglia. By combining single-cell matrix-assisted laser mass spectrometry with retrograde staining and electrophysiology, we analyzed neuropeptide expression of single neurons of the right parietal ganglion and their axonal projections into the penial nerve. Based on the neuropeptide profile of these neurons, we were able to reconstruct a chemical map of the right parietal ganglion revealing a striking correlation with the earlier electrophysiological and neuroanatomical studies. Neurons can be divided into two main groups: (i) neurons that express heptapeptides and (ii) neurons that do not. The neuronal projection of the different neurons into the penial nerve reveals a pattern where (spontaneous) activity is related to branching pattern. This heterogeneity in both neurochemical anatomy and branching pattern of the parietal neurons reflects the complexity of the peptidergic neurotransmission involved in the regulation of male mating behavior in this simultaneous hermaphrodite.

Carbon Dioxide and Fruit Odor Transduction in Drosophila Olfactory Neurons. What Controls their Dynamic Properties?

PubMed Central

French, Andrew S.; Meisner, Shannon; Su, Chih-Ying; Torkkeli, Päivi H.

2014-01-01

We measured frequency response functions between odorants and action potentials in two types of neurons in Drosophila antennal basiconic sensilla. CO2 was used to stimulate ab1C neurons, and the fruit odor ethyl butyrate was used to stimulate ab3A neurons. We also measured frequency response functions for light-induced action potential responses from transgenic flies expressing H134R-channelrhodopsin-2 (ChR2) in the ab1C and ab3A neurons. Frequency response functions for all stimulation methods were well-fitted by a band-pass filter function with two time constants that determined the lower and upper frequency limits of the response. Low frequency time constants were the same in each type of neuron, independent of stimulus method, but varied between neuron types. High frequency time constants were significantly slower with ethyl butyrate stimulation than light or CO2 stimulation. In spite of these quantitative differences, there were strong similarities in the form and frequency ranges of all responses. Since light-activated ChR2 depolarizes neurons directly, rather than through a chemoreceptor mechanism, these data suggest that low frequency dynamic properties of Drosophila olfactory sensilla are dominated by neuron-specific ionic processes during action potential production. In contrast, high frequency dynamics are limited by processes associated with earlier steps in odor transduction, and CO2 is detected more rapidly than fruit odor. PMID:24466044

Firing rate of noisy integrate-and-fire neurons with synaptic current dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andrieux, David; Monnai, Takaaki; Department of Applied Physics, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555

2009-08-15

We derive analytical formulas for the firing rate of integrate-and-fire neurons endowed with realistic synaptic dynamics. In particular, we include the possibility of multiple synaptic inputs as well as the effect of an absolute refractory period into the description. The latter affects the firing rate through its interaction with the synaptic dynamics.

Label-free optical detection of action potential in mammalian neurons (Conference Presentation)

NASA Astrophysics Data System (ADS)

Batabyal, Subrata; Satpathy, Sarmishtha; Bui, Loan; Kim, Young-Tae; Mohanty, Samarendra K.; Davé, Digant P.

2017-02-01

Electrophysiology techniques are the gold standard in neuroscience for studying functionality of a single neuron to a complex neuronal network. However, electrophysiology techniques are not flawless, they are invasive nature, procedures are cumbersome to implement with limited capability of being used as a high-throughput recording system. Also, long term studies of neuronal functionality with aid of electrophysiology is not feasible. Non-invasive stimulation and detection of neuronal electrical activity has been a long standing goal in neuroscience. Introduction of optogenetics has ushered in the era of non-invasive optical stimulation of neurons, which is revolutionizing neuroscience research. Optical detection of neuronal activity that is comparable to electro-physiology is still elusive. A number of optical techniques have been reported recording of neuronal electrical activity but none is capable of reliably measuring action potential spikes that is comparable to electro-physiology. Optical detection of action potential with voltage sensitive fluorescent reporters are potential alternatives to electrophysiology techniques. The heavily rely on secondary reporters, which are often toxic in nature with background fluorescence, with slow response and low SNR making them far from ideal. The detection of one shot (without averaging)-single action potential in a true label-free way has been elusive so far. In this report, we demonstrate the optical detection of single neuronal spike in a cultured mammalian neuronal network without using any exogenous labels. To the best of our knowledge, this is the first demonstration of label free optical detection of single action potentials in a mammalian neuronal network, which was achieved using a high-speed phase sensitive interferometer. We have carried out stimulation and inhibition of neuronal firing using Glutamate and Tetrodotoxin respectively to demonstrate the different outcome (stimulation and inhibition) revealed in optical signal. We hypothesize that the interrogating optical beam is modulated during neuronal firing by electro-motility driven membrane fluctuation in conjunction with electrical wave propagation in cellular system.

Synaptic dynamics regulation in response to high frequency stimulation in neuronal networks

NASA Astrophysics Data System (ADS)

Su, Fei; Wang, Jiang; Li, Huiyan; Wei, Xile; Yu, Haitao; Deng, Bin

2018-02-01

High frequency stimulation (HFS) has confirmed its ability in modulating the pathological neural activities. However its detailed mechanism is unclear. This study aims to explore the effects of HFS on neuronal networks dynamics. First, the two-neuron FitzHugh-Nagumo (FHN) networks with static coupling strength and the small-world FHN networks with spike-time-dependent plasticity (STDP) modulated synaptic coupling strength are constructed. Then, the multi-scale method is used to transform the network models into equivalent averaged models, where the HFS intensity is modeled as the ratio between stimulation amplitude and frequency. Results show that in static two-neuron networks, there is still synaptic current projected to the postsynaptic neuron even if the presynaptic neuron is blocked by the HFS. In the small-world networks, the effects of the STDP adjusting rate parameter on the inactivation ratio and synchrony degree increase with the increase of HFS intensity. However, only when the HFS intensity becomes very large can the STDP time window parameter affect the inactivation ratio and synchrony index. Both simulation and numerical analysis demonstrate that the effects of HFS on neuronal network dynamics are realized through the adjustment of synaptic variable and conductance.

Single-Cell Memory Regulates a Neural Circuit for Sensory Behavior.

PubMed

Kobayashi, Kyogo; Nakano, Shunji; Amano, Mutsuki; Tsuboi, Daisuke; Nishioka, Tomoki; Ikeda, Shingo; Yokoyama, Genta; Kaibuchi, Kozo; Mori, Ikue

2016-01-05

Unveiling the molecular and cellular mechanisms underlying memory has been a challenge for the past few decades. Although synaptic plasticity is proven to be essential for memory formation, the significance of "single-cell memory" still remains elusive. Here, we exploited a primary culture system for the analysis of C. elegans neurons and show that a single thermosensory neuron has an ability to form, retain, and reset a temperature memory. Genetic and proteomic analyses found that the expression of the single-cell memory exhibits inter-individual variability, which is controlled by the evolutionarily conserved CaMKI/IV and Raf pathway. The variable responses of a sensory neuron influenced the neural activity of downstream interneurons, suggesting that modulation of the sensory neurons ultimately determines the behavioral output in C. elegans. Our results provide proof of single-cell memory and suggest that the individual differences in neural responses at the single-cell level can confer individuality. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Correlates of a single cortical action potential in the epidural EEG

PubMed Central

Teleńczuk, Bartosz; Baker, Stuart N; Kempter, Richard; Curio, Gabriel

2015-01-01

To identify the correlates of a single cortical action potential in surface EEG, we recorded simultaneously epidural EEG and single-unit activity in the primary somatosensory cortex of awake macaque monkeys. By averaging over EEG segments coincident with more than hundred thousand single spikes, we found short-lived (≈ 0.5 ms) triphasic EEG deflections dominated by high-frequency components > 800 Hz. The peak-to-peak amplitude of the grand-averaged spike correlate was 80 nV, which matched theoretical predictions, while single-neuron amplitudes ranged from 12 to 966 nV. Combining these estimates with post-stimulus-time histograms of single-unit responses to median-nerve stimulation allowed us to predict the shape of the evoked epidural EEG response and to estimate the number of contributing neurons. These findings establish spiking activity of cortical neurons as a primary building block of high-frequency epidural EEG, which thus can serve as a quantitative macroscopic marker of neuronal spikes. PMID:25554430

Understanding metal homeostasis in primary cultured neurons. Studies using single neuron subcellular and quantitative metallomics.

PubMed

Colvin, Robert A; Lai, Barry; Holmes, William R; Lee, Daewoo

2015-07-01

The purpose of this study was to demonstrate how single cell quantitative and subcellular metallomics inform us about both the spatial distribution and cellular mechanisms of metal buffering and homeostasis in primary cultured neurons from embryonic rat brain, which are often used as models of human disease involving metal dyshomeostasis. The present studies utilized synchrotron radiation X-ray fluorescence (SRXRF) and focused primarily on zinc and iron, two abundant metals in neurons that have been implicated in the pathogenesis of neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease. Total single cell contents for calcium, iron, zinc, copper, manganese, and nickel were determined. Resting steady state zinc showed a diffuse distribution in both soma and processes, best defined by the mass profile of the neuron with an enrichment in the nucleus compared with the cytoplasm. Zinc buffering and homeostasis was studied using two modes of cellular zinc loading - transporter and ionophore (pyrithione) mediated. Single neuron zinc contents were shown to statistically significantly increase by either loading method - ionophore: 160 million to 7 billion; transporter 160 million to 280 million atoms per neuronal soma. The newly acquired and buffered zinc still showed a diffuse distribution. Soma and processes have about equal abilities to take up zinc via transporter mediated pathways. Copper levels are distributed diffusely as well, but are relatively higher in the processes relative to zinc levels. Prior studies have observed iron puncta in certain cell types, but others have not. In the present study, iron puncta were characterized in several primary neuronal types. The results show that iron puncta could be found in all neuronal types studied and can account for up to 50% of the total steady state content of iron in neuronal soma. Although other metals can be present in iron puncta, they are predominantly iron containing and do not appear to be associated with ferritin cages or transferrin receptor endosomes. The iron content and its distribution in puncta were similar in all neuron types studied including primary dopaminergic neurons. In summary, quantitative measurements of steady state metal levels in single primary cultured neurons made possible by SRXRF analyses provide unique information on the relative levels of each metal in neuronal soma and processes, subcellular location of zinc loads, and have confirmed and extended the characterization of heretofore poorly understood cytoplasmic iron puncta.

Live Imaging of Cell Motility and Actin Cytoskeleton of Individual Neurons and Neural Crest Cells in Zebrafish Embryos

PubMed Central

Andersen, Erica; Asuri, Namrata; Clay, Matthew; Halloran, Mary

2010-01-01

The zebrafish is an ideal model for imaging cell behaviors during development in vivo. Zebrafish embryos are externally fertilized and thus easily accessible at all stages of development. Moreover, their optical clarity allows high resolution imaging of cell and molecular dynamics in the natural environment of the intact embryo. We are using a live imaging approach to analyze cell behaviors during neural crest cell migration and the outgrowth and guidance of neuronal axons. Live imaging is particularly useful for understanding mechanisms that regulate cell motility processes. To visualize details of cell motility, such as protrusive activity and molecular dynamics, it is advantageous to label individual cells. In zebrafish, plasmid DNA injection yields a transient mosaic expression pattern and offers distinct benefits over other cell labeling methods. For example, transgenic lines often label entire cell populations and thus may obscure visualization of the fine protrusions (or changes in molecular distribution) in a single cell. In addition, injection of DNA at the one-cell stage is less invasive and more precise than dye injections at later stages. Here we describe a method for labeling individual developing neurons or neural crest cells and imaging their behavior in vivo. We inject plasmid DNA into 1-cell stage embryos, which results in mosaic transgene expression. The vectors contain cell-specific promoters that drive expression of a gene of interest in a subset of sensory neurons or neural crest cells. We provide examples of cells labeled with membrane targeted GFP or with a biosensor probe that allows visualization of F-actin in living cells1. Erica Andersen, Namrata Asuri, and Matthew Clay contributed equally to this work. PMID:20130524

Exploring the limits of learning: Segregation of information integration and response selection is required for learning a serial reversal task

PubMed Central

Zanutto, B. Silvano

2017-01-01

Animals are proposed to learn the latent rules governing their environment in order to maximize their chances of survival. However, rules may change without notice, forcing animals to keep a memory of which one is currently at work. Rule switching can lead to situations in which the same stimulus/response pairing is positively and negatively rewarded in the long run, depending on variables that are not accessible to the animal. This fact raises questions on how neural systems are capable of reinforcement learning in environments where the reinforcement is inconsistent. Here we address this issue by asking about which aspects of connectivity, neural excitability and synaptic plasticity are key for a very general, stochastic spiking neural network model to solve a task in which rules change without being cued, taking the serial reversal task (SRT) as paradigm. Contrary to what could be expected, we found strong limitations for biologically plausible networks to solve the SRT. Especially, we proved that no network of neurons can learn a SRT if it is a single neural population that integrates stimuli information and at the same time is responsible of choosing the behavioural response. This limitation is independent of the number of neurons, neuronal dynamics or plasticity rules, and arises from the fact that plasticity is locally computed at each synapse, and that synaptic changes and neuronal activity are mutually dependent processes. We propose and characterize a spiking neural network model that solves the SRT, which relies on separating the functions of stimuli integration and response selection. The model suggests that experimental efforts to understand neural function should focus on the characterization of neural circuits according to their connectivity, neural dynamics, and the degree of modulation of synaptic plasticity with reward. PMID:29077735

Particle Tracking Facilitates Real Time Capable Motion Correction in 2D or 3D Two-Photon Imaging of Neuronal Activity.

PubMed

Aghayee, Samira; Winkowski, Daniel E; Bowen, Zachary; Marshall, Erin E; Harrington, Matt J; Kanold, Patrick O; Losert, Wolfgang

2017-01-01

The application of 2-photon laser scanning microscopy (TPLSM) techniques to measure the dynamics of cellular calcium signals in populations of neurons is an extremely powerful technique for characterizing neural activity within the central nervous system. The use of TPLSM on awake and behaving subjects promises new insights into how neural circuit elements cooperatively interact to form sensory perceptions and generate behavior. A major challenge in imaging such preparations is unavoidable animal and tissue movement, which leads to shifts in the imaging location (jitter). The presence of image motion can lead to artifacts, especially since quantification of TPLSM images involves analysis of fluctuations in fluorescence intensities for each neuron, determined from small regions of interest (ROIs). Here, we validate a new motion correction approach to compensate for motion of TPLSM images in the superficial layers of auditory cortex of awake mice. We use a nominally uniform fluorescent signal as a secondary signal to complement the dynamic signals from genetically encoded calcium indicators. We tested motion correction for single plane time lapse imaging as well as multiplane (i.e., volume) time lapse imaging of cortical tissue. Our procedure of motion correction relies on locating the brightest neurons and tracking their positions over time using established techniques of particle finding and tracking. We show that our tracking based approach provides subpixel resolution without compromising speed. Unlike most established methods, our algorithm also captures deformations of the field of view and thus can compensate e.g., for rotations. Object tracking based motion correction thus offers an alternative approach for motion correction, one that is well suited for real time spike inference analysis and feedback control, and for correcting for tissue distortions.

Particle Tracking Facilitates Real Time Capable Motion Correction in 2D or 3D Two-Photon Imaging of Neuronal Activity

PubMed Central

Aghayee, Samira; Winkowski, Daniel E.; Bowen, Zachary; Marshall, Erin E.; Harrington, Matt J.; Kanold, Patrick O.; Losert, Wolfgang

2017-01-01

The application of 2-photon laser scanning microscopy (TPLSM) techniques to measure the dynamics of cellular calcium signals in populations of neurons is an extremely powerful technique for characterizing neural activity within the central nervous system. The use of TPLSM on awake and behaving subjects promises new insights into how neural circuit elements cooperatively interact to form sensory perceptions and generate behavior. A major challenge in imaging such preparations is unavoidable animal and tissue movement, which leads to shifts in the imaging location (jitter). The presence of image motion can lead to artifacts, especially since quantification of TPLSM images involves analysis of fluctuations in fluorescence intensities for each neuron, determined from small regions of interest (ROIs). Here, we validate a new motion correction approach to compensate for motion of TPLSM images in the superficial layers of auditory cortex of awake mice. We use a nominally uniform fluorescent signal as a secondary signal to complement the dynamic signals from genetically encoded calcium indicators. We tested motion correction for single plane time lapse imaging as well as multiplane (i.e., volume) time lapse imaging of cortical tissue. Our procedure of motion correction relies on locating the brightest neurons and tracking their positions over time using established techniques of particle finding and tracking. We show that our tracking based approach provides subpixel resolution without compromising speed. Unlike most established methods, our algorithm also captures deformations of the field of view and thus can compensate e.g., for rotations. Object tracking based motion correction thus offers an alternative approach for motion correction, one that is well suited for real time spike inference analysis and feedback control, and for correcting for tissue distortions. PMID:28860973

Mechanisms of Gain Control by Voltage-Gated Channels in Intrinsically-Firing Neurons

PubMed Central

Patel, Ameera X.; Burdakov, Denis

2015-01-01

Gain modulation is a key feature of neural information processing, but underlying mechanisms remain unclear. In single neurons, gain can be measured as the slope of the current-frequency (input-output) relationship over any given range of inputs. While much work has focused on the control of basal firing rates and spike rate adaptation, gain control has been relatively unstudied. Of the limited studies on gain control, some have examined the roles of synaptic noise and passive somatic currents, but the roles of voltage-gated channels present ubiquitously in neurons have been less explored. Here, we systematically examined the relationship between gain and voltage-gated ion channels in a conductance-based, tonically-active, model neuron. Changes in expression (conductance density) of voltage-gated channels increased (Ca2+ channel), reduced (K+ channels), or produced little effect (h-type channel) on gain. We found that the gain-controlling ability of channels increased exponentially with the steepness of their activation within the dynamic voltage window (voltage range associated with firing). For depolarization-activated channels, this produced a greater channel current per action potential at higher firing rates. This allowed these channels to modulate gain by contributing to firing preferentially at states of higher excitation. A finer analysis of the current-voltage relationship during tonic firing identified narrow voltage windows at which the gain-modulating channels exerted their effects. As a proof of concept, we show that h-type channels can be tuned to modulate gain by changing the steepness of their activation within the dynamic voltage window. These results show how the impact of an ion channel on gain can be predicted from the relationship between channel kinetics and the membrane potential during firing. This is potentially relevant to understanding input-output scaling in a wide class of neurons found throughout the brain and other nervous systems. PMID:25816008

In Vivo Visualization of Active Polysynaptic Circuits With Longitudinal Manganese-Enhanced MRI (MEMRI).

PubMed

Almeida-Corrêa, Suellen; Czisch, Michael; Wotjak, Carsten T

2018-01-01

Manganese-enhanced magnetic resonance imaging (MEMRI) is a powerful tool for in vivo non-invasive whole-brain mapping of neuronal activity. Mn 2+ enters active neurons via voltage-gated calcium channels and increases local contrast in T 1 -weighted images. Given the property of Mn 2+ of axonal transport, this technique can also be used for tract tracing after local administration of the contrast agent. However, MEMRI is still not widely employed in basic research due to the lack of a complete description of the Mn 2+ dynamics in the brain. Here, we sought to investigate how the activity state of neurons modulates interneuronal Mn 2+ transport. To this end, we injected mice with low dose MnCl 2 2. (i.p., 20 mg/kg; repeatedly for 8 days) followed by two MEMRI scans at an interval of 1 week without further MnCl 2 injections. We assessed changes in T 1 contrast intensity before (scan 1) and after (scan 2) partial sensory deprivation (unilateral whisker trimming), while keeping the animals in a sensory enriched environment. After correcting for the general decay in Mn 2+ content, whole brain analysis revealed a single cluster with higher signal in scan 1 compared to scan 2: the left barrel cortex corresponding to the right untrimmed whiskers. In the inverse contrast (scan 2 > scan 1), a number of brain structures, including many efferents of the left barrel cortex were observed. These results suggest that continuous neuronal activity elicited by ongoing sensory stimulation accelerates Mn 2+ transport from the uptake site to its projection terminals, while the blockage of sensory-input and the resulting decrease in neuronal activity attenuates Mn 2+ transport. The description of this critical property of Mn 2+ dynamics in the brain allows a better understanding of MEMRI functional mechanisms, which will lead to more carefully designed experiments and clearer interpretation of the results.

Parallel emergence of stable and dynamic memory engrams in the hippocampus.

PubMed

Hainmueller, Thomas; Bartos, Marlene

2018-06-06

During our daily life, we depend on memories of past experiences to plan future behaviour. These memories are represented by the activity of specific neuronal groups or 'engrams' 1,2 . Neuronal engrams are assembled during learning by synaptic modification, and engram reactivation represents the memorized experience 1 . Engrams of conscious memories are initially stored in the hippocampus for several days and then transferred to cortical areas 2 . In the dentate gyrus of the hippocampus, granule cells transform rich inputs from the entorhinal cortex into a sparse output, which is forwarded to the highly interconnected pyramidal cell network in hippocampal area CA3 3 . This process is thought to support pattern separation 4 (but see refs. 5,6 ). CA3 pyramidal neurons project to CA1, the hippocampal output region. Consistent with the idea of transient memory storage in the hippocampus, engrams in CA1 and CA2 do not stabilize over time 7-10 . Nevertheless, reactivation of engrams in the dentate gyrus can induce recall of artificial memories even after weeks 2 . Reconciliation of this apparent paradox will require recordings from dentate gyrus granule cells throughout learning, which has so far not been performed for more than a single day 6,11,12 . Here, we use chronic two-photon calcium imaging in head-fixed mice performing a multiple-day spatial memory task in a virtual environment to record neuronal activity in all major hippocampal subfields. Whereas pyramidal neurons in CA1-CA3 show precise and highly context-specific, but continuously changing, representations of the learned spatial sceneries in our behavioural paradigm, granule cells in the dentate gyrus have a spatial code that is stable over many days, with low place- or context-specificity. Our results suggest that synaptic weights along the hippocampal trisynaptic loop are constantly reassigned to support the formation of dynamic representations in downstream hippocampal areas based on a stable code provided by the dentate gyrus.

Subsampling effects in neuronal avalanche distributions recorded in vivo

PubMed Central

Priesemann, Viola; Munk, Matthias HJ; Wibral, Michael

2009-01-01

Background Many systems in nature are characterized by complex behaviour where large cascades of events, or avalanches, unpredictably alternate with periods of little activity. Snow avalanches are an example. Often the size distribution f(s) of a system's avalanches follows a power law, and the branching parameter sigma, the average number of events triggered by a single preceding event, is unity. A power law for f(s), and sigma = 1, are hallmark features of self-organized critical (SOC) systems, and both have been found for neuronal activity in vitro. Therefore, and since SOC systems and neuronal activity both show large variability, long-term stability and memory capabilities, SOC has been proposed to govern neuronal dynamics in vivo. Testing this hypothesis is difficult because neuronal activity is spatially or temporally subsampled, while theories of SOC systems assume full sampling. To close this gap, we investigated how subsampling affects f(s) and sigma by imposing subsampling on three different SOC models. We then compared f(s) and sigma of the subsampled models with those of multielectrode local field potential (LFP) activity recorded in three macaque monkeys performing a short term memory task. Results Neither the LFP nor the subsampled SOC models showed a power law for f(s). Both, f(s) and sigma, depended sensitively on the subsampling geometry and the dynamics of the model. Only one of the SOC models, the Abelian Sandpile Model, exhibited f(s) and sigma similar to those calculated from LFP activity. Conclusion Since subsampling can prevent the observation of the characteristic power law and sigma in SOC systems, misclassifications of critical systems as sub- or supercritical are possible. Nevertheless, the system specific scaling of f(s) and sigma under subsampling conditions may prove useful to select physiologically motivated models of brain function. Models that better reproduce f(s) and sigma calculated from the physiological recordings may be selected over alternatives. PMID:19400967

Correlates of decisional dynamics in the dorsal anterior cingulate cortex

PubMed Central

Hayden, Benjamin Y.

2017-01-01

We hypothesized that during binary economic choice, decision makers use the first option they attend as a default to which they compare the second. To test this idea, we recorded activity of neurons in the dorsal anterior cingulate cortex (dACC) of macaques choosing between gambles presented asynchronously. We find that ensemble encoding of the value of the first offer includes both choice-dependent and choice-independent aspects, as if reflecting a partial decision. That is, its responses are neither entirely pre- nor post-decisional. In contrast, coding of the value of the second offer is entirely decision dependent (i.e., post-decisional). This result holds even when offer-value encodings are compared within the same time period. Additionally, we see no evidence for 2 pools of neurons linked to the 2 offers; instead, all comparison appears to occur within a single functionally homogenous pool of task-selective neurons. These observations suggest that economic choices reflect a context-dependent evaluation of attended options. Moreover, they raise the possibility that value representations reflect, to some extent, a tentative commitment to a choice. PMID:29141002

Synchronization and Inter-Layer Interactions of Noise-Driven Neural Networks

PubMed Central

Yuniati, Anis; Mai, Te-Lun; Chen, Chi-Ming

2017-01-01

In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a non-synchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy. In particular, we studied the interaction between a SFS layer and a BAS layer, and investigated how synchronous firing dynamics was induced in the BAS layer. We further investigated the effect of the inter-layer interaction on a BAS to SFS repair mechanism by considering three types of neuron positioning (random, grid, and lognormal distributions) and two types of inter-layer connections (random and preferential connections). Among these scenarios, we concluded that the repair mechanism has the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections. PMID:28197088

Synchronization and Inter-Layer Interactions of Noise-Driven Neural Networks.

PubMed

Yuniati, Anis; Mai, Te-Lun; Chen, Chi-Ming

2017-01-01

In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a non-synchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy. In particular, we studied the interaction between a SFS layer and a BAS layer, and investigated how synchronous firing dynamics was induced in the BAS layer. We further investigated the effect of the inter-layer interaction on a BAS to SFS repair mechanism by considering three types of neuron positioning (random, grid, and lognormal distributions) and two types of inter-layer connections (random and preferential connections). Among these scenarios, we concluded that the repair mechanism has the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections.

Linking structure and activity in nonlinear spiking networks

PubMed Central

Josić, Krešimir; Shea-Brown, Eric

2017-01-01

Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks’ spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities—including those of different cell types—combine with connectivity to shape population activity and function. PMID:28644840

Neuronal Cell Cultures from Aplysia for High-Resolution Imaging of Growth Cones

PubMed Central

Lee, Aih Cheun; Decourt, Boris; Suter, Daniel

2008-01-01

Neuronal growth cones are the highly motile structures at the tip of axons that can detect guidance cues in the environment and transduce this information into directional movement towards the appropriate target cell. To fully understand how guidance information is transmitted from the cell surface to the underlying dynamic cytoskeletal networks, one needs a model system suitable for live cell imaging of protein dynamics at high temporal and spatial resolution. Typical vertebrate growth cones are too small to quantitatively analyze F-actin and microtubule dynamics. Neurons from the sea hare Aplysia californica are 5-10 times larger than vertebrate neurons, can easily be kept at room temperature and are very robust cells for micromanipulation and biophysical measurements. Their growth cones have very defined cytoplasmic regions and a well-described cytoskeletal system. The neuronal cell bodies can be microinjected with a variety of probes for studying growth cone motility and guidance. In the present protocol we demonstrate a procedure for dissection of the abdominal ganglion, culture of bag cell neurons and setting up an imaging chamber for live cell imaging of growth cones. PMID:19066568

Ergodic properties of spiking neuronal networks with delayed interactions

NASA Astrophysics Data System (ADS)

Palmigiano, Agostina; Wolf, Fred

The dynamical stability of neuronal networks, and the possibility of chaotic dynamics in the brain pose profound questions to the mechanisms underlying perception. Here we advance on the tractability of large neuronal networks of exactly solvable neuronal models with delayed pulse-coupled interactions. Pulse coupled delayed systems with an infinite dimensional phase space can be studied in equivalent systems of fixed and finite degrees of freedom by introducing a delayer variable for each neuron. A Jacobian of the equivalent system can be analytically obtained, and numerically evaluated. We find that depending on the action potential onset rapidness and the level of heterogeneities, the asynchronous irregular regime characteristic of balanced state networks loses stability with increasing delays to either a slow synchronous irregular or a fast synchronous irregular state. In networks of neurons with slow action potential onset, the transition to collective oscillations leads to an increase of the exponential rate of divergence of nearby trajectories and of the entropy production rate of the chaotic dynamics. The attractor dimension, instead of increasing linearly with increasing delay as reported in many other studies, decreases until eventually the network reaches full synchrony

Loss of mitochondrial fission depletes axonal mitochondria in midbrain dopamine neurons.

PubMed

Berthet, Amandine; Margolis, Elyssa B; Zhang, Jue; Hsieh, Ivy; Zhang, Jiasheng; Hnasko, Thomas S; Ahmad, Jawad; Edwards, Robert H; Sesaki, Hiromi; Huang, Eric J; Nakamura, Ken

2014-10-22

Disruptions in mitochondrial dynamics may contribute to the selective degeneration of dopamine (DA) neurons in Parkinson's disease (PD). However, little is known about the normal functions of mitochondrial dynamics in these neurons, especially in axons where degeneration begins, and this makes it difficult to understand the disease process. To study one aspect of mitochondrial dynamics-mitochondrial fission-in mouse DA neurons, we deleted the central fission protein dynamin-related protein 1 (Drp1). Drp1 loss rapidly eliminates the DA terminals in the caudate-putamen and causes cell bodies in the midbrain to degenerate and lose α-synuclein. Without Drp1, mitochondrial mass dramatically decreases, especially in axons, where the mitochondrial movement becomes uncoordinated. However, in the ventral tegmental area (VTA), a subset of midbrain DA neurons characterized by small hyperpolarization-activated cation currents (Ih) is spared, despite near complete loss of their axonal mitochondria. Drp1 is thus critical for targeting mitochondria to the nerve terminal, and a disruption in mitochondrial fission can contribute to the preferential death of nigrostriatal DA neurons. Copyright © 2014 the authors 0270-6474/14/3414304-14$15.00/0.

Functional identification and reconstitution of an odorant receptor in single olfactory neurons

PubMed Central

Touhara, Kazushige; Sengoku, Shintaro; Inaki, Koichiro; Tsuboi, Akio; Hirono, Junzo; Sato, Takaaki; Sakano, Hitoshi; Haga, Tatsuya

1999-01-01

The olfactory system is remarkable in its capacity to discriminate a wide range of odorants through a series of transduction events initiated in olfactory receptor neurons. Each olfactory neuron is expected to express only a single odorant receptor gene that belongs to the G protein coupled receptor family. The ligand–receptor interaction, however, has not been clearly characterized. This study demonstrates the functional identification of olfactory receptor(s) for specific odorant(s) from single olfactory neurons by a combination of Ca2+-imaging and reverse transcription–coupled PCR analysis. First, a candidate odorant receptor was cloned from a single tissue-printed olfactory neuron that displayed odorant-induced Ca2+ increase. Next, recombinant adenovirus-mediated expression of the isolated receptor gene was established in the olfactory epithelium by using green fluorescent protein as a marker. The infected neurons elicited external Ca2+ entry when exposed to the odorant that originally was used to identify the receptor gene. Experiments performed to determine ligand specificity revealed that the odorant receptor recognized specific structural motifs within odorant molecules. The odorant receptor-mediated signal transduction appears to be reconstituted by this two-step approach: the receptor screening for given odorant(s) from single neurons and the functional expression of the receptor via recombinant adenovirus. The present approach should enable us to examine not only ligand specificity of an odorant receptor but also receptor specificity and diversity for a particular odorant of interest. PMID:10097159

Emergent dynamics of spiking neurons with fluctuating threshold

NASA Astrophysics Data System (ADS)

Bhattacharjee, Anindita; Das, M. K.

2017-05-01

Role of fluctuating threshold on neuronal dynamics is investigated. The threshold function is assumed to follow a normal probability distribution. Standard deviation of inter-spike interval of the response is computed as an indicator of irregularity in spike emission. It has been observed that, the irregularity in spiking is more if the threshold variation is more. A significant change in modal characteristics of Inter Spike Intervals (ISI) is seen to occur as a function of fluctuation parameter. Investigation is further carried out for coupled system of neurons. Cooperative dynamics of coupled neurons are discussed in view of synchronization. Total and partial synchronization regimes are depicted with the help of contour plots of synchrony measure under various conditions. Results of this investigation may provide a basis for exploring the complexities of neural communication and brain functioning.

Synaptic vesicle dynamic changes in a model of fragile X.

PubMed

Broek, Jantine A C; Lin, Zhanmin; de Gruiter, H Martijn; van 't Spijker, Heleen; Haasdijk, Elize D; Cox, David; Ozcan, Sureyya; van Cappellen, Gert W A; Houtsmuller, Adriaan B; Willemsen, Rob; de Zeeuw, Chris I; Bahn, Sabine

2016-01-01

Fragile X syndrome (FXS) is a single-gene disorder that is the most common heritable cause of intellectual disability and the most frequent monogenic cause of autism spectrum disorders (ASD). FXS is caused by an expansion of trinucleotide repeats in the promoter region of the fragile X mental retardation gene (Fmr1). This leads to a lack of fragile X mental retardation protein (FMRP), which regulates translation of a wide range of messenger RNAs (mRNAs). The extent of expression level alterations of synaptic proteins affected by FMRP loss and their consequences on synaptic dynamics in FXS has not been fully investigated. Here, we used an Fmr1 knockout (KO) mouse model to investigate the molecular mechanisms underlying FXS by monitoring protein expression changes using shotgun label-free liquid-chromatography mass spectrometry (LC-MS(E)) in brain tissue and synaptosome fractions. FXS-associated candidate proteins were validated using selected reaction monitoring (SRM) in synaptosome fractions for targeted protein quantification. Furthermore, functional alterations in synaptic release and dynamics were evaluated using live-cell imaging, and interpretation of synaptic dynamics differences was investigated using electron microscopy. Key findings relate to altered levels of proteins involved in GABA-signalling, especially in the cerebellum. Further exploration using microscopy studies found reduced synaptic vesicle unloading of hippocampal neurons and increased vesicle unloading in cerebellar neurons, which suggests a general decrease of synaptic transmission. Our findings suggest that FMRP is a regulator of synaptic vesicle dynamics, which supports the role of FMRP in presynaptic functions. Taken together, these studies provide novel insights into the molecular changes associated with FXS.

Information processing in the CNS: a supramolecular chemistry?

PubMed

Tozzi, Arturo

2015-10-01

How does central nervous system process information? Current theories are based on two tenets: (a) information is transmitted by action potentials, the language by which neurons communicate with each other-and (b) homogeneous neuronal assemblies of cortical circuits operate on these neuronal messages where the operations are characterized by the intrinsic connectivity among neuronal populations. In this view, the size and time course of any spike is stereotypic and the information is restricted to the temporal sequence of the spikes; namely, the "neural code". However, an increasing amount of novel data point towards an alternative hypothesis: (a) the role of neural code in information processing is overemphasized. Instead of simply passing messages, action potentials play a role in dynamic coordination at multiple spatial and temporal scales, establishing network interactions across several levels of a hierarchical modular architecture, modulating and regulating the propagation of neuronal messages. (b) Information is processed at all levels of neuronal infrastructure from macromolecules to population dynamics. For example, intra-neuronal (changes in protein conformation, concentration and synthesis) and extra-neuronal factors (extracellular proteolysis, substrate patterning, myelin plasticity, microbes, metabolic status) can have a profound effect on neuronal computations. This means molecular message passing may have cognitive connotations. This essay introduces the concept of "supramolecular chemistry", involving the storage of information at the molecular level and its retrieval, transfer and processing at the supramolecular level, through transitory non-covalent molecular processes that are self-organized, self-assembled and dynamic. Finally, we note that the cortex comprises extremely heterogeneous cells, with distinct regional variations, macromolecular assembly, receptor repertoire and intrinsic microcircuitry. This suggests that every neuron (or group of neurons) embodies different molecular information that hands an operational effect on neuronal computation.

Neural dynamics and information representation in microcircuits of motor cortex.

PubMed

Tsubo, Yasuhiro; Isomura, Yoshikazu; Fukai, Tomoki

2013-01-01

The brain has to analyze and respond to external events that can change rapidly from time to time, suggesting that information processing by the brain may be essentially dynamic rather than static. The dynamical features of neural computation are of significant importance in motor cortex that governs the process of movement generation and learning. In this paper, we discuss these features based primarily on our recent findings on neural dynamics and information coding in the microcircuit of rat motor cortex. In fact, cortical neurons show a variety of dynamical behavior from rhythmic activity in various frequency bands to highly irregular spike firing. Of particular interest are the similarity and dissimilarity of the neuronal response properties in different layers of motor cortex. By conducting electrophysiological recordings in slice preparation, we report the phase response curves (PRCs) of neurons in different cortical layers to demonstrate their layer-dependent synchronization properties. We then study how motor cortex recruits task-related neurons in different layers for voluntary arm movements by simultaneous juxtacellular and multiunit recordings from behaving rats. The results suggest an interesting difference in the spectrum of functional activity between the superficial and deep layers. Furthermore, the task-related activities recorded from various layers exhibited power law distributions of inter-spike intervals (ISIs), in contrast to a general belief that ISIs obey Poisson or Gamma distributions in cortical neurons. We present a theoretical argument that this power law of in vivo neurons may represent the maximization of the entropy of firing rate with limited energy consumption of spike generation. Though further studies are required to fully clarify the functional implications of this coding principle, it may shed new light on information representations by neurons and circuits in motor cortex.

Control and Synchronization of Heteroclinic Chaos: Implications for Neurodynamics

NASA Astrophysics Data System (ADS)

Arecchi, F. Tito

2004-12-01

Heteroclinic chaos (HC) implies the recurrent return of the dynamical trajectory to a saddle focus (SF) in whose neighborhood the system response to an external perturbation is very high and hence it is very easy to lock to an external stimulus. Thus HC appears as the easiest way to encode information in time by a train of equal spikes occurring at erratic times. Implementing such a dynamics with a single mode CO2 laser with feedback, we have a heteroclinic connection between the SF and a saddle node (SN) whose role it to regularize the phase space orbit away from SF. Due to these two different fixed points, the laser intensity displays identical spikes separated by erratic ISIs (interspike intervals). Such a dynamics is highly prone to spike-synchronization, either by an external signal or by mutual interaction in a network of identical systems. Applications to communication and noise induced synchronization will be reported. In experimental neuroscience a recent finding is that feature binding ,that is, combination of external stimuli with internal memories into new coherent patterns of meaning, implies the mutual synchronization of axonal spike trains in neurons which can be far away and yet share the same sequence. Several dynamical systems have been proposed to model such a behavior. We introduce a measurable parameter, namely, the synchronization "propensity". Propensity is the amount of synchronization achieved in a chaotic system by a small sinusoidal perturbation of a control parameter. It is very low for coupled Lorenz or FitzHugh-Nagumo chains. It displays isolated peaks for the Hindmarsh-Rose model, showing that this is a convenient description of the bursting behavior typical of neurons in the CPG (central pattern generator) system. Instead, HC shows a high propensity over a wide input frequency range, demonstrating that it is the most convenient model for semantic neurons.

Uniting functional network topology and oscillations in the fronto-parietal single unit network of behaving primates

PubMed Central

Dann, Benjamin; Michaels, Jonathan A; Schaffelhofer, Stefan; Scherberger, Hansjörg

2016-01-01

The functional communication of neurons in cortical networks underlies higher cognitive processes. Yet, little is known about the organization of the single neuron network or its relationship to the synchronization processes that are essential for its formation. Here, we show that the functional single neuron network of three fronto-parietal areas during active behavior of macaque monkeys is highly complex. The network was closely connected (small-world) and consisted of functional modules spanning these areas. Surprisingly, the importance of different neurons to the network was highly heterogeneous with a small number of neurons contributing strongly to the network function (hubs), which were in turn strongly inter-connected (rich-club). Examination of the network synchronization revealed that the identified rich-club consisted of neurons that were synchronized in the beta or low frequency range, whereas other neurons were mostly non-oscillatory synchronized. Therefore, oscillatory synchrony may be a central communication mechanism for highly organized functional spiking networks. DOI: http://dx.doi.org/10.7554/eLife.15719.001 PMID:27525488

Variability in common synaptic input to motor neurons modulates both force steadiness and pegboard time in young and older adults.

PubMed

Feeney, Daniel F; Mani, Diba; Enoka, Roger M

2018-06-07

We investigated the associations between grooved pegboard times, force steadiness (coefficient of variation for force), and variability in an estimate of the common synaptic input to motor neurons innervating the wrist extensor muscles during steady contractions performed by young and older adults. The discharge times of motor units were derived from recordings obtained with high-density surface electrodes while participants performed steady isometric contractions at 10% and 20% of maximal voluntary contraction (MVC) force. The steady contractions were performed with a pinch grip and wrist extension, both independently (single action) and concurrently (double action). The variance in common synaptic input to motor neurons was estimated with a state-space model of the latent common input dynamics. There was a statistically significant association between the coefficient of variation for force during the steady contractions and the estimated variance in common synaptic input in young (r 2 = 0.31) and older (r 2 = 0.39) adults, but not between either the mean or the coefficient of variation for interspike interval of single motor units with the coefficient of variation for force. Moreover, the estimated variance in common synaptic input during the double-action task with the wrist extensors at the 20% target was significantly associated with grooved pegboard time (r 2 = 0.47) for older adults, but not young adults. These findings indicate that longer pegboard times of older adults were associated with worse force steadiness and greater fluctuations in the estimated common synaptic input to motor neurons during steady contractions. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Activation of superficial dorsal horn neurons in the mouse by a PAR-2 agonist and 5-HT: potential role in itch.

PubMed

Akiyama, Tasuku; Merrill, Austin W; Carstens, Mirela Iodi; Carstens, E

2009-05-20

Itch, an unpleasant sensation associated with the desire to scratch, is symptomatic of dermatologic and systemic disorders that often resist antihistamine treatment. Histamine-independent itch mediators include serotonin (5-HT) and agonists of the protease-activated receptor-2 (PAR-2). We used behavior, Fos immunohistochemistry, and electrophysiology to investigate if these mediators activate spinal dorsal horn neurons in a manner consistent with itch. Intradermal (i.d.) injection of the PAR-2 agonist SLIGRL-NH(2) in the rostral back evoked bouts of directed hindlimb scratches over 20-30 min. Hindpaw injection of SLIGRL-NH(2) produced Fos staining in superficial dorsal horn which was then targeted for single-unit recording. Small id microinjections of SLIGRL-NH(2) or 5-HT identified responsive single units in the superficial dorsal horn of mice anesthetized with pentobarbital. Thirty-eight units characterized as wide dynamic range, nociceptive specific, or mechanically insensitive exhibited significantly increased firing after i.d. SLIGRL-NH(2) for 9 min, to partial (25%) tachyphylaxis with repeated injection. A majority additionally responded to 5-HT (70%), mustard oil (79%), and capsaicin (71%). Seven units isolated with the 5-HT search stimulus exhibited significant and prolonged responses to 5-HT with tachyphylaxis to repeated injections. The majority also responded to SLIGRL-NH(2), mustard oil, and capsaicin. The prolonged responses of superficial dorsal horn neurons to SLIGRL-NH(2) and 5-HT suggest a role in signaling itch. However, their responsiveness to algogens is inconsistent with itch specificity. Alternatively, such neurons may signal itch, whereas noxious stimulus levels recruit these and a larger population of pruritogen-insensitive cells to signal pain which masks or occludes the itch signal.

Dynamic Circuitry for Updating Spatial Representations: III. From Neurons to Behavior

PubMed Central

Berman, Rebecca A.; Heiser, Laura M.; Dunn, Catherine A.; Saunders, Richard C.; Colby, Carol L.

2008-01-01

Each time the eyes move, the visual system must adjust internal representations to account for the accompanying shift in the retinal image. In the lateral intraparietal cortex (LIP), neurons update the spatial representations of salient stimuli when the eyes move. In previous experiments, we found that split-brain monkeys were impaired on double-step saccade sequences that required updating across visual hemifields, as compared to within hemifield (Berman et al. 2005; Heiser et al. 2005). Here we describe a subsequent experiment to characterize the relationship between behavioral performance and neural activity in LIP in the split-brain monkey. We recorded from single LIP neurons while split-brain and intact monkeys performed two conditions of the double-step saccade task: one required across-hemifield updating and the other within-hemifield updating. We found that, despite extensive experience with the task, the split-brain monkeys were significantly more accurate for within-hemifield as compared to across-hemifield sequences. In parallel, we found that population activity in LIP of the split-brain monkeys was significantly stronger for within-hemifield as compared to across-hemifield conditions of the double-step task. In contrast, in the normal monkey, both the average behavioral performance and population activity showed no bias toward the within-hemifield condition. Finally, we found that the difference between within-hemifield and across-hemifield performance in the split-brain monkeys was reflected at the level of single neuron activity in LIP. These findings indicate that remapping activity in area LIP is present in the split-brain monkey for the double-step task and co-varies with spatial behavior on within-hemifield compared to across-hemifield sequences. PMID:17493922

Synaptic multistability and network synchronization induced by the neuron-glial interaction in the brain

NASA Astrophysics Data System (ADS)

Lazarevich, I. A.; Stasenko, S. V.; Kazantsev, V. B.

2017-02-01

The dynamics of a synaptic contact between neurons that forms a feedback loop through the interaction with glial cells of the brain surrounding the neurons is studied. It is shown that, depending on the character of the neuron-glial interaction, the dynamics of the signal transmission frequency in the synaptic contact can be bistable with two stable steady states or spiking with the regular generation of spikes with various amplitudes and durations. It is found that such a synaptic contact at the network level is responsible for the appearance of quasisynchronous network bursts.

Active transport of vesicles in neurons is modulated by mechanical tension.

PubMed

Ahmed, Wylie W; Saif, Taher A

2014-03-27

Effective intracellular transport of proteins and organelles is critical in cells, and is especially important for ensuring proper neuron functionality. In neurons, most proteins are synthesized in the cell body and must be transported through thin structures over long distances where normal diffusion is insufficient. Neurons transport subcellular cargo along axons and neurites through a stochastic interplay of active and passive transport. Mechanical tension is critical in maintaining proper function in neurons, but its role in transport is not well understood. To this end, we investigate the active and passive transport of vesicles in Aplysia neurons while changing neurite tension via applied strain, and quantify the resulting dynamics. We found that tension in neurons modulates active transport of vesicles by increasing the probability of active motion, effective diffusivity, and induces a retrograde bias. We show that mechanical tension modulates active transport processes in neurons and that external forces can couple to internal (subcellular) forces and change the overall transport dynamics.

Active transport of vesicles in neurons is modulated by mechanical tension

PubMed Central

Ahmed, Wylie W.; Saif, Taher A.

2014-01-01

Effective intracellular transport of proteins and organelles is critical in cells, and is especially important for ensuring proper neuron functionality. In neurons, most proteins are synthesized in the cell body and must be transported through thin structures over long distances where normal diffusion is insufficient. Neurons transport subcellular cargo along axons and neurites through a stochastic interplay of active and passive transport. Mechanical tension is critical in maintaining proper function in neurons, but its role in transport is not well understood. To this end, we investigate the active and passive transport of vesicles in Aplysia neurons while changing neurite tension via applied strain, and quantify the resulting dynamics. We found that tension in neurons modulates active transport of vesicles by increasing the probability of active motion, effective diffusivity, and induces a retrograde bias. We show that mechanical tension modulates active transport processes in neurons and that external forces can couple to internal (subcellular) forces and change the overall transport dynamics. PMID:24670781

Stimulus features coded by single neurons of a macaque body category selective patch.

PubMed

Popivanov, Ivo D; Schyns, Philippe G; Vogels, Rufin

2016-04-26

Body category-selective regions of the primate temporal cortex respond to images of bodies, but it is unclear which fragments of such images drive single neurons' responses in these regions. Here we applied the Bubbles technique to the responses of single macaque middle superior temporal sulcus (midSTS) body patch neurons to reveal the image fragments the neurons respond to. We found that local image fragments such as extremities (limbs), curved boundaries, and parts of the torso drove the large majority of neurons. Bubbles revealed the whole body in only a few neurons. Neurons coded the features in a manner that was tolerant to translation and scale changes. Most image fragments were excitatory but for a few neurons both inhibitory and excitatory fragments (opponent coding) were present in the same image. The fragments we reveal here in the body patch with Bubbles differ from those suggested in previous studies of face-selective neurons in face patches. Together, our data indicate that the majority of body patch neurons respond to local image fragments that occur frequently, but not exclusively, in bodies, with a coding that is tolerant to translation and scale. Overall, the data suggest that the body category selectivity of the midSTS body patch depends more on the feature statistics of bodies (e.g., extensions occur more frequently in bodies) than on semantics (bodies as an abstract category).

Dynamics of Competition between Subnetworks of Spiking Neuronal Networks in the Balanced State.

PubMed

Lagzi, Fereshteh; Rotter, Stefan

2015-01-01

We explore and analyze the nonlinear switching dynamics of neuronal networks with non-homogeneous connectivity. The general significance of such transient dynamics for brain function is unclear; however, for instance decision-making processes in perception and cognition have been implicated with it. The network under study here is comprised of three subnetworks of either excitatory or inhibitory leaky integrate-and-fire neurons, of which two are of the same type. The synaptic weights are arranged to establish and maintain a balance between excitation and inhibition in case of a constant external drive. Each subnetwork is randomly connected, where all neurons belonging to a particular population have the same in-degree and the same out-degree. Neurons in different subnetworks are also randomly connected with the same probability; however, depending on the type of the pre-synaptic neuron, the synaptic weight is scaled by a factor. We observed that for a certain range of the "within" versus "between" connection weights (bifurcation parameter), the network activation spontaneously switches between the two sub-networks of the same type. This kind of dynamics has been termed "winnerless competition", which also has a random component here. In our model, this phenomenon is well described by a set of coupled stochastic differential equations of Lotka-Volterra type that imply a competition between the subnetworks. The associated mean-field model shows the same dynamical behavior as observed in simulations of large networks comprising thousands of spiking neurons. The deterministic phase portrait is characterized by two attractors and a saddle node, its stochastic component is essentially given by the multiplicative inherent noise of the system. We find that the dwell time distribution of the active states is exponential, indicating that the noise drives the system randomly from one attractor to the other. A similar model for a larger number of populations might suggest a general approach to study the dynamics of interacting populations of spiking networks.

Dynamics of Competition between Subnetworks of Spiking Neuronal Networks in the Balanced State

PubMed Central

Lagzi, Fereshteh; Rotter, Stefan

2015-01-01

We explore and analyze the nonlinear switching dynamics of neuronal networks with non-homogeneous connectivity. The general significance of such transient dynamics for brain function is unclear; however, for instance decision-making processes in perception and cognition have been implicated with it. The network under study here is comprised of three subnetworks of either excitatory or inhibitory leaky integrate-and-fire neurons, of which two are of the same type. The synaptic weights are arranged to establish and maintain a balance between excitation and inhibition in case of a constant external drive. Each subnetwork is randomly connected, where all neurons belonging to a particular population have the same in-degree and the same out-degree. Neurons in different subnetworks are also randomly connected with the same probability; however, depending on the type of the pre-synaptic neuron, the synaptic weight is scaled by a factor. We observed that for a certain range of the “within” versus “between” connection weights (bifurcation parameter), the network activation spontaneously switches between the two sub-networks of the same type. This kind of dynamics has been termed “winnerless competition”, which also has a random component here. In our model, this phenomenon is well described by a set of coupled stochastic differential equations of Lotka-Volterra type that imply a competition between the subnetworks. The associated mean-field model shows the same dynamical behavior as observed in simulations of large networks comprising thousands of spiking neurons. The deterministic phase portrait is characterized by two attractors and a saddle node, its stochastic component is essentially given by the multiplicative inherent noise of the system. We find that the dwell time distribution of the active states is exponential, indicating that the noise drives the system randomly from one attractor to the other. A similar model for a larger number of populations might suggest a general approach to study the dynamics of interacting populations of spiking networks. PMID:26407178

Chronic multiunit recordings in behaving animals: advantages and limitations.

PubMed

Supèr, Hans; Roelfsema, Pieter R

2005-01-01

By simultaneous recording from neural responses at many different loci at the same time, we can understand the interaction between neurons, and thereby gain insight into the network properties of neural processing, instead of the functioning of individual neurons. Here we will discuss a method for recording in behaving animals that uses chronically implanted micro-electrodes that allow one to track neural responses over a long period of time. In a majority of cases, multiunit activity, which is the aggregate spiking activity of a number of neurons in the vicinity of an electrode tip, is recorded through these electrodes, and occasionally single neurons can be isolated. Here we compare the properties of multiunit responses to the responses of single neurons in the primary visual cortex. We also discuss the advantages and disadvantages of the multiunit signal as opposed to a signal of single neurons. We demonstrate that multiunit recording provides a reliable and useful technique in cases where the neurons at the electrodes have similar response properties. Multiunit recording is therefore especially valuable when task variables have an effect that is consistent across the population of neurons. In the primary visual cortex, this is the case for figure-ground segregation and visual attention. Multiunit recording also has clear advantages for cross-correlation analysis. We show that the cross-correlation function between multiunit signals gives a reliable estimate of the average single-unit cross-correlation function. By the use of multiunit recording, it becomes much easier to detect relatively weak interactions between neurons at different cortical locations.

Neuronal networks with NMDARs and lateral inhibition implement winner-takes-all

PubMed Central

Shoemaker, Patrick A.

2015-01-01

A neural circuit that relies on the electrical properties of NMDA synaptic receptors is shown by numerical and theoretical analysis to be capable of realizing the winner-takes-all function, a powerful computational primitive that is often attributed to biological nervous systems. This biophysically-plausible model employs global lateral inhibition in a simple feedback arrangement. As its inputs increase, high-gain and then bi- or multi-stable equilibrium states may be assumed in which there is significant depolarization of a single neuron and hyperpolarization or very weak depolarization of other neurons in the network. The state of the winning neuron conveys analog information about its input. The winner-takes-all characteristic depends on the nonmonotonic current-voltage relation of NMDA receptor ion channels, as well as neural thresholding, and the gain and nature of the inhibitory feedback. Dynamical regimes vary with input strength. Fixed points may become unstable as the network enters a winner-takes-all regime, which can lead to entrained oscillations. Under some conditions, oscillatory behavior can be interpreted as winner-takes-all in nature. Stable winner-takes-all behavior is typically recovered as inputs increase further, but with still larger inputs, the winner-takes-all characteristic is ultimately lost. Network stability may be enhanced by biologically plausible mechanisms. PMID:25741276

Anticipated synchronization in neuronal circuits unveiled by a phase-response-curve analysis

NASA Astrophysics Data System (ADS)

Matias, Fernanda S.; Carelli, Pedro V.; Mirasso, Claudio R.; Copelli, Mauro

2017-05-01

Anticipated synchronization (AS) is a counterintuitive behavior that has been observed in several systems. When AS occurs in a sender-receiver configuration, the latter can predict the future dynamics of the former for certain parameter values. In particular, in neuroscience AS was proposed to explain the apparent discrepancy between information flow and time lag in the cortical activity recorded in monkeys. Despite its success, a clear understanding of the mechanisms yielding AS in neuronal circuits is still missing. Here we use the well-known phase-response-curve (PRC) approach to study the prototypical sender-receiver-interneuron neuronal motif. Our aim is to better understand how the transitions between delayed to anticipated synchronization and anticipated synchronization to phase-drift regimes occur. We construct a map based on the PRC method to predict the phase-locking regimes and their stability. We find that a PRC function of two variables, accounting simultaneously for the inputs from sender and interneuron into the receiver, is essential to reproduce the numerical results obtained using a Hodgkin-Huxley model for the neurons. On the contrary, the typical approximation that considers a sum of two independent single-variable PRCs fails for intermediate to high values of the inhibitory coupling strength of the interneuron. In particular, it loses the delayed-synchronization to anticipated-synchronization transition.