Switching neuronal state: optimal stimuli revealed using a stochastically-seeded gradient algorithm.

PubMed

Chang, Joshua; Paydarfar, David

2014-12-01

Inducing a switch in neuronal state using energy optimal stimuli is relevant to a variety of problems in neuroscience. Analytical techniques from optimal control theory can identify such stimuli; however, solutions to the optimization problem using indirect variational approaches can be elusive in models that describe neuronal behavior. Here we develop and apply a direct gradient-based optimization algorithm to find stimulus waveforms that elicit a change in neuronal state while minimizing energy usage. We analyze standard models of neuronal behavior, the Hodgkin-Huxley and FitzHugh-Nagumo models, to show that the gradient-based algorithm: (1) enables automated exploration of a wide solution space, using stochastically generated initial waveforms that converge to multiple locally optimal solutions; and (2) finds optimal stimulus waveforms that achieve a physiological outcome condition, without a priori knowledge of the optimal terminal condition of all state variables. Analysis of biological systems using stochastically-seeded gradient methods can reveal salient dynamical mechanisms underlying the optimal control of system behavior. The gradient algorithm may also have practical applications in future work, for example, finding energy optimal waveforms for therapeutic neural stimulation that minimizes power usage and diminishes off-target effects and damage to neighboring tissue.

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.

Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus

PubMed Central

Jehee, Janneke F. M.; Ballard, Dana H.

2009-01-01

Biphasic neural response properties, where the optimal stimulus for driving a neural response changes from one stimulus pattern to the opposite stimulus pattern over short periods of time, have been described in several visual areas, including lateral geniculate nucleus (LGN), primary visual cortex (V1), and middle temporal area (MT). We describe a hierarchical model of predictive coding and simulations that capture these temporal variations in neuronal response properties. We focus on the LGN-V1 circuit and find that after training on natural images the model exhibits the brain's LGN-V1 connectivity structure, in which the structure of V1 receptive fields is linked to the spatial alignment and properties of center-surround cells in the LGN. In addition, the spatio-temporal response profile of LGN model neurons is biphasic in structure, resembling the biphasic response structure of neurons in cat LGN. Moreover, the model displays a specific pattern of influence of feedback, where LGN receptive fields that are aligned over a simple cell receptive field zone of the same polarity decrease their responses while neurons of opposite polarity increase their responses with feedback. This phase-reversed pattern of influence was recently observed in neurophysiology. These results corroborate the idea that predictive feedback is a general coding strategy in the brain. PMID:19412529

Minimal time spiking in various ChR2-controlled neuron models.

PubMed

Renault, Vincent; Thieullen, Michèle; Trélat, Emmanuel

2018-02-01

We use conductance based neuron models, and the mathematical modeling of optogenetics to define controlled neuron models and we address the minimal time control of these affine systems for the first spike from equilibrium. We apply tools of geometric optimal control theory to study singular extremals, and we implement a direct method to compute optimal controls. When the system is too large to theoretically investigate the existence of singular optimal controls, we observe numerically the optimal bang-bang controls.

Exact solution for the optimal neuronal layout problem.

PubMed

Chklovskii, Dmitri B

2004-10-01

Evolution perfected brain design by maximizing its functionality while minimizing costs associated with building and maintaining it. Assumption that brain functionality is specified by neuronal connectivity, implemented by costly biological wiring, leads to the following optimal design problem. For a given neuronal connectivity, find a spatial layout of neurons that minimizes the wiring cost. Unfortunately, this problem is difficult to solve because the number of possible layouts is often astronomically large. We argue that the wiring cost may scale as wire length squared, reducing the optimal layout problem to a constrained minimization of a quadratic form. For biologically plausible constraints, this problem has exact analytical solutions, which give reasonable approximations to actual layouts in the brain. These solutions make the inverse problem of inferring neuronal connectivity from neuronal layout more tractable.

Robust information propagation through noisy neural circuits

PubMed Central

Pouget, Alexandre

2017-01-01

Sensory neurons give highly variable responses to stimulation, which can limit the amount of stimulus information available to downstream circuits. Much work has investigated the factors that affect the amount of information encoded in these population responses, leading to insights about the role of covariability among neurons, tuning curve shape, etc. However, the informativeness of neural responses is not the only relevant feature of population codes; of potentially equal importance is how robustly that information propagates to downstream structures. For instance, to quantify the retina’s performance, one must consider not only the informativeness of the optic nerve responses, but also the amount of information that survives the spike-generating nonlinearity and noise corruption in the next stage of processing, the lateral geniculate nucleus. Our study identifies the set of covariance structures for the upstream cells that optimize the ability of information to propagate through noisy, nonlinear circuits. Within this optimal family are covariances with “differential correlations”, which are known to reduce the information encoded in neural population activities. Thus, covariance structures that maximize information in neural population codes, and those that maximize the ability of this information to propagate, can be very different. Moreover, redundancy is neither necessary nor sufficient to make population codes robust against corruption by noise: redundant codes can be very fragile, and synergistic codes can—in some cases—optimize robustness against noise. PMID:28419098

Sensory Optimization by Stochastic Tuning

PubMed Central

Jurica, Peter; Gepshtein, Sergei; Tyukin, Ivan; van Leeuwen, Cees

2013-01-01

Individually, visual neurons are each selective for several aspects of stimulation, such as stimulus location, frequency content, and speed. Collectively, the neurons implement the visual system’s preferential sensitivity to some stimuli over others, manifested in behavioral sensitivity functions. We ask how the individual neurons are coordinated to optimize visual sensitivity. We model synaptic plasticity in a generic neural circuit, and find that stochastic changes in strengths of synaptic connections entail fluctuations in parameters of neural receptive fields. The fluctuations correlate with uncertainty of sensory measurement in individual neurons: the higher the uncertainty the larger the amplitude of fluctuation. We show that this simple relationship is sufficient for the stochastic fluctuations to steer sensitivities of neurons toward a characteristic distribution, from which follows a sensitivity function observed in human psychophysics, and which is predicted by a theory of optimal allocation of receptive fields. The optimal allocation arises in our simulations without supervision or feedback about system performance and independently of coupling between neurons, making the system highly adaptive and sensitive to prevailing stimulation. PMID:24219849

A flexible, interactive software tool for fitting the parameters of neuronal models.

PubMed

Friedrich, Péter; Vella, Michael; Gulyás, Attila I; Freund, Tamás F; Káli, Szabolcs

2014-01-01

The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.

A flexible, interactive software tool for fitting the parameters of neuronal models

PubMed Central

Friedrich, Péter; Vella, Michael; Gulyás, Attila I.; Freund, Tamás F.; Káli, Szabolcs

2014-01-01

The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool. PMID:25071540

Two-photon microscope for multisite microphotolysis of caged neurotransmitters in acute brain slices

PubMed Central

Losavio, Bradley E.; Iyer, Vijay; Saggau, Peter

2009-01-01

We developed a two-photon microscope optimized for physiologically manipulating single neurons through their postsynaptic receptors. The optical layout fulfills the stringent design criteria required for high-speed, high-resolution imaging in scattering brain tissue with minimal photodamage. We detail the practical compensation of spectral and temporal dispersion inherent in fast laser beam scanning with acousto-optic deflectors, as well as a set of biological protocols for visualizing nearly diffraction-limited structures and delivering physiological synaptic stimuli. The microscope clearly resolves dendritic spines and evokes electrophysiological transients in single neurons that are similar to endogenous responses. This system enables the study of multisynaptic integration and will assist our understanding of single neuron function and dendritic computation. PMID:20059271

Probability density function learning by unsupervised neurons.

PubMed

Fiori, S

2001-10-01

In a recent work, we introduced the concept of pseudo-polynomial adaptive activation function neuron (FAN) and presented an unsupervised information-theoretic learning theory for such structure. The learning model is based on entropy optimization and provides a way of learning probability distributions from incomplete data. The aim of the present paper is to illustrate some theoretical features of the FAN neuron, to extend its learning theory to asymmetrical density function approximation, and to provide an analytical and numerical comparison with other known density function estimation methods, with special emphasis to the universal approximation ability. The paper also provides a survey of PDF learning from incomplete data, as well as results of several experiments performed on real-world problems and signals.

A compact light-sheet microscope for the study of the mammalian central nervous system

PubMed Central

Yang, Zhengyi; Haslehurst, Peter; Scott, Suzanne; Emptage, Nigel; Dholakia, Kishan

2016-01-01

Investigation of the transient processes integral to neuronal function demands rapid and high-resolution imaging techniques over a large field of view, which cannot be achieved with conventional scanning microscopes. Here we describe a compact light sheet fluorescence microscope, featuring a 45° inverted geometry and an integrated photolysis laser, that is optimized for applications in neuroscience, in particular fast imaging of sub-neuronal structures in mammalian brain slices. We demonstrate the utility of this design for three-dimensional morphological reconstruction, activation of a single synapse with localized photolysis, and fast imaging of neuronal Ca2+ signalling across a large field of view. The developed system opens up a host of novel applications for the neuroscience community. PMID:27215692

Tactile orientation perception: an ideal observer analysis of human psychophysical performance in relation to macaque area 3b receptive fields

PubMed Central

Peters, Ryan M.; Staibano, Phillip

2015-01-01

The ability to resolve the orientation of edges is crucial to daily tactile and sensorimotor function, yet the means by which edge perception occurs is not well understood. Primate cortical area 3b neurons have diverse receptive field (RF) spatial structures that may participate in edge orientation perception. We evaluated five candidate RF models for macaque area 3b neurons, previously recorded while an oriented bar contacted the monkey's fingertip. We used a Bayesian classifier to assign each neuron a best-fit RF structure. We generated predictions for human performance by implementing an ideal observer that optimally decoded stimulus-evoked spike counts in the model neurons. The ideal observer predicted a saturating reduction in bar orientation discrimination threshold with increasing bar length. We tested 24 humans on an automated, precision-controlled bar orientation discrimination task and observed performance consistent with that predicted. We next queried the ideal observer to discover the RF structure and number of cortical neurons that best matched each participant's performance. Human perception was matched with a median of 24 model neurons firing throughout a 1-s period. The 10 lowest-performing participants were fit with RFs lacking inhibitory sidebands, whereas 12 of the 14 higher-performing participants were fit with RFs containing inhibitory sidebands. Participants whose discrimination improved as bar length increased to 10 mm were fit with longer RFs; those who performed well on the 2-mm bar, with narrower RFs. These results suggest plausible RF features and computational strategies underlying tactile spatial perception and may have implications for perceptual learning. PMID:26354318

Differential role of molten globule and protein folding in distinguishing unique features of botulinum neurotoxin.

PubMed

Kumar, Raj; Kukreja, Roshan V; Cai, Shuowei; Singh, Bal R

2014-06-01

Botulinum neurotoxins (BoNTs) are proteins of great interest not only because of their extreme toxicity but also paradoxically for their therapeutic applications. All the known serotypes (A-G) have varying degrees of longevity and potency inside the neuronal cell. Differential chemical modifications such as phosphorylation and ubiquitination have been suggested as possible mechanisms for their longevity, but the molecular basis of the longevity remains unclear. Since the endopeptidase domain (light chain; LC) of toxin apparently survives inside the neuronal cells for months, it is important to examine the structural features of this domain to understand its resistance to intracellular degradation. Published crystal structures (both botulinum neurotoxins and endopeptidase domain) have not provided adequate explanation for the intracellular longevity of the domain. Structural features obtained from spectroscopic analysis of LCA and LCB were similar, and a PRIME (PReImminent Molten Globule Enzyme) conformation appears to be responsible for their optimal enzymatic activity at 37°C. LCE, on the other hand, was although optimally active at 37°C, but its active conformation differed from the PRIME conformation of LCA and LCB. This study establishes and confirms our earlier finding that an optimally active conformation of these proteins in the form of PRIME exists for the most poisonous poison, botulinum neurotoxin. There are substantial variations in the structural and functional characteristics of these active molten globule related structures among the three BoNT endopeptidases examined. These differential conformations of LCs are important in understanding the fundamental structural features of proteins, and their possible connection to intracellular longevity could provide significant clues for devising new countermeasures and effective therapeutics. Copyright © 2014 Elsevier B.V. All rights reserved.

Segregation of the Brain into Gray and White Matter: A Design Minimizing Conduction Delays

PubMed Central

Wen, Quan; Chklovskii, Dmitri B

2005-01-01

A ubiquitous feature of the vertebrate anatomy is the segregation of the brain into white and gray matter. Assuming that evolution maximized brain functionality, what is the reason for such segregation? To answer this question, we posit that brain functionality requires high interconnectivity and short conduction delays. Based on this assumption we searched for the optimal brain architecture by comparing different candidate designs. We found that the optimal design depends on the number of neurons, interneuronal connectivity, and axon diameter. In particular, the requirement to connect neurons with many fast axons drives the segregation of the brain into white and gray matter. These results provide a possible explanation for the structure of various regions of the vertebrate brain, such as the mammalian neocortex and neostriatum, the avian telencephalon, and the spinal cord. PMID:16389299

Gene expression profiling of two distinct neuronal populations in the rodent spinal cord.

PubMed

Ryge, Jesper; Westerdahl, Ann-Charlotte; Alstrøm, Preben; Kiehn, Ole

2008-01-01

In the field of neuroscience microarray gene expression profiles on anatomically defined brain structures are being used increasingly to study both normal brain functions as well as pathological states. Fluorescent tracing techniques in brain tissue that identifies distinct neuronal populations can in combination with global gene expression profiling potentially increase the resolution and specificity of such studies to shed new light on neuronal functions at the cellular level. We examine the microarray gene expression profiles of two distinct neuronal populations in the spinal cord of the neonatal rat, the principal motor neurons and specific interneurons involved in motor control. The gene expression profiles of the respective cell populations were obtained from amplified mRNA originating from 50-250 fluorescently identified and laser microdissected cells. In the data analysis we combine a new microarray normalization procedure with a conglomerate measure of significant differential gene expression. Using our methodology we find 32 genes to be more expressed in the interneurons compared to the motor neurons that all except one have not previously been associated with this neuronal population. As a validation of our method we find 17 genes to be more expressed in the motor neurons than in the interneurons and of these only one had not previously been described in this population. We provide an optimized experimental protocol that allows isolation of gene transcripts from fluorescent retrogradely labeled cell populations in fresh tissue, which can be used to generate amplified aRNA for microarray hybridization from as few as 50 laser microdissected cells. Using this optimized experimental protocol in combination with our microarray analysis methodology we find 49 differentially expressed genes between the motor neurons and the interneurons that reflect the functional differences between these two cell populations in generating and transmitting the motor output in the rodent spinal cord.

Gene Expression Profiling of Two Distinct Neuronal Populations in the Rodent Spinal Cord

PubMed Central

Alstrøm, Preben; Kiehn, Ole

2008-01-01

Background In the field of neuroscience microarray gene expression profiles on anatomically defined brain structures are being used increasingly to study both normal brain functions as well as pathological states. Fluorescent tracing techniques in brain tissue that identifies distinct neuronal populations can in combination with global gene expression profiling potentially increase the resolution and specificity of such studies to shed new light on neuronal functions at the cellular level. Methodology/Principal Findings We examine the microarray gene expression profiles of two distinct neuronal populations in the spinal cord of the neonatal rat, the principal motor neurons and specific interneurons involved in motor control. The gene expression profiles of the respective cell populations were obtained from amplified mRNA originating from 50–250 fluorescently identified and laser microdissected cells. In the data analysis we combine a new microarray normalization procedure with a conglomerate measure of significant differential gene expression. Using our methodology we find 32 genes to be more expressed in the interneurons compared to the motor neurons that all except one have not previously been associated with this neuronal population. As a validation of our method we find 17 genes to be more expressed in the motor neurons than in the interneurons and of these only one had not previously been described in this population. Conclusions/Significance We provide an optimized experimental protocol that allows isolation of gene transcripts from fluorescent retrogradely labeled cell populations in fresh tissue, which can be used to generate amplified aRNA for microarray hybridization from as few as 50 laser microdissected cells. Using this optimized experimental protocol in combination with our microarray analysis methodology we find 49 differentially expressed genes between the motor neurons and the interneurons that reflect the functional differences between these two cell populations in generating and transmitting the motor output in the rodent spinal cord. PMID:18923679

Sensory optimization by stochastic tuning.

PubMed

Jurica, Peter; Gepshtein, Sergei; Tyukin, Ivan; van Leeuwen, Cees

2013-10-01

Individually, visual neurons are each selective for several aspects of stimulation, such as stimulus location, frequency content, and speed. Collectively, the neurons implement the visual system's preferential sensitivity to some stimuli over others, manifested in behavioral sensitivity functions. We ask how the individual neurons are coordinated to optimize visual sensitivity. We model synaptic plasticity in a generic neural circuit and find that stochastic changes in strengths of synaptic connections entail fluctuations in parameters of neural receptive fields. The fluctuations correlate with uncertainty of sensory measurement in individual neurons: The higher the uncertainty the larger the amplitude of fluctuation. We show that this simple relationship is sufficient for the stochastic fluctuations to steer sensitivities of neurons toward a characteristic distribution, from which follows a sensitivity function observed in human psychophysics and which is predicted by a theory of optimal allocation of receptive fields. The optimal allocation arises in our simulations without supervision or feedback about system performance and independently of coupling between neurons, making the system highly adaptive and sensitive to prevailing stimulation. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Optimal firing rate estimation

NASA Technical Reports Server (NTRS)

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

2001-01-01

We define a measure for evaluating the quality of a predictive model of the behavior of a spiking neuron. This measure, information gain per spike (Is), indicates how much more information is provided by the model than if the prediction were made by specifying the neuron's average firing rate over the same time period. We apply a maximum Is criterion to optimize the performance of Gaussian smoothing filters for estimating neural firing rates. With data from bullfrog vestibular semicircular canal neurons and data from simulated integrate-and-fire neurons, the optimal bandwidth for firing rate estimation is typically similar to the average firing rate. Precise timing and average rate models are limiting cases that perform poorly. We estimate that bullfrog semicircular canal sensory neurons transmit in the order of 1 bit of stimulus-related information per spike.

Signal processing in local neuronal circuits based on activity-dependent noise and competition

NASA Astrophysics Data System (ADS)

Volman, Vladislav; Levine, Herbert

2009-09-01

We study the characteristics of weak signal detection by a recurrent neuronal network with plastic synaptic coupling. It is shown that in the presence of an asynchronous component in synaptic transmission, the network acquires selectivity with respect to the frequency of weak periodic stimuli. For nonperiodic frequency-modulated stimuli, the response is quantified by the mutual information between input (signal) and output (network's activity) and is optimized by synaptic depression. Introducing correlations in signal structure resulted in the decrease in input-output mutual information. Our results suggest that in neural systems with plastic connectivity, information is not merely carried passively by the signal; rather, the information content of the signal itself might determine the mode of its processing by a local neuronal circuit.

Translational neurocardiology: preclinical models and cardioneural integrative aspects

PubMed Central

Andresen, M. C.; Armour, J. A.; Billman, G. E.; Chen, P.‐S.; Foreman, R. D.; Herring, N.; O'Leary, D. S.; Sabbah, H. N.; Schultz, H. D.; Sunagawa, K.; Zucker, I. H.

2016-01-01

Abstract Neuronal elements distributed throughout the cardiac nervous system, from the level of the insular cortex to the intrinsic cardiac nervous system, are in constant communication with one another to ensure that cardiac output matches the dynamic process of regional blood flow demand. Neural elements in their various ‘levels’ become differentially recruited in the transduction of sensory inputs arising from the heart, major vessels, other visceral organs and somatic structures to optimize neuronal coordination of regional cardiac function. This White Paper will review the relevant aspects of the structural and functional organization for autonomic control of the heart in normal conditions, how these systems remodel/adapt during cardiac disease, and finally how such knowledge can be leveraged in the evolving realm of autonomic regulation therapy for cardiac therapeutics. PMID:27098459

Spiking neuron network Helmholtz machine.

PubMed

Sountsov, Pavel; Miller, Paul

2015-01-01

An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule.

Spiking neuron network Helmholtz machine

PubMed Central

Sountsov, Pavel; Miller, Paul

2015-01-01

An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented in the brain is currently incomplete. There have been many proposed solutions that address how neurons can perform optimal inference but the question of how synaptic plasticity can implement optimal learning is rarely addressed. This paper aims to unify the two fields of probabilistic inference and synaptic plasticity by using a neuronal network of realistic model spiking neurons to implement a well-studied computational model called the Helmholtz Machine. The Helmholtz Machine is amenable to neural implementation as the algorithm it uses to learn its parameters, called the wake-sleep algorithm, uses a local delta learning rule. Our spiking-neuron network implements both the delta rule and a small example of a Helmholtz machine. This neuronal network can learn an internal model of continuous-valued training data sets without supervision. The network can also perform inference on the learned internal models. We show how various biophysical features of the neural implementation constrain the parameters of the wake-sleep algorithm, such as the duration of the wake and sleep phases of learning and the minimal sample duration. We examine the deviations from optimal performance and tie them to the properties of the synaptic plasticity rule. PMID:25954191

Predicting spike occurrence and neuronal responsiveness from LFPs in primary somatosensory cortex.

PubMed

Storchi, Riccardo; Zippo, Antonio G; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E M

2012-01-01

Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neuronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role.

Beyond the frontiers of neuronal types

PubMed Central

Battaglia, Demian; Karagiannis, Anastassios; Gallopin, Thierry; Gutch, Harold W.; Cauli, Bruno

2012-01-01

Cortical neurons and, particularly, inhibitory interneurons display a large diversity of morphological, synaptic, electrophysiological, and molecular properties, as well as diverse embryonic origins. Various authors have proposed alternative classification schemes that rely on the concomitant observation of several multimodal features. However, a broad variability is generally observed even among cells that are grouped into a same class. Furthermore, the attribution of specific neurons to a single defined class is often difficult, because individual properties vary in a highly graded fashion, suggestive of continua of features between types. Going beyond the description of representative traits of distinct classes, we focus here on the analysis of atypical cells. We introduce a novel paradigm for neuronal type classification, assuming explicitly the existence of a structured continuum of diversity. Our approach, grounded on the theory of fuzzy sets, identifies a small optimal number of model archetypes. At the same time, it quantifies the degree of similarity between these archetypes and each considered neuron. This allows highlighting archetypal cells, which bear a clear similarity to a single model archetype, and edge cells, which manifest a convergence of traits from multiple archetypes. PMID:23403725

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.

Streaming parallel GPU acceleration of large-scale filter-based spiking neural networks.

PubMed

Slażyński, Leszek; Bohte, Sander

2012-01-01

The arrival of graphics processing (GPU) cards suitable for massively parallel computing promises affordable large-scale neural network simulation previously only available at supercomputing facilities. While the raw numbers suggest that GPUs may outperform CPUs by at least an order of magnitude, the challenge is to develop fine-grained parallel algorithms to fully exploit the particulars of GPUs. Computation in a neural network is inherently parallel and thus a natural match for GPU architectures: given inputs, the internal state for each neuron can be updated in parallel. We show that for filter-based spiking neurons, like the Spike Response Model, the additive nature of membrane potential dynamics enables additional update parallelism. This also reduces the accumulation of numerical errors when using single precision computation, the native precision of GPUs. We further show that optimizing simulation algorithms and data structures to the GPU's architecture has a large pay-off: for example, matching iterative neural updating to the memory architecture of the GPU speeds up this simulation step by a factor of three to five. With such optimizations, we can simulate in better-than-realtime plausible spiking neural networks of up to 50 000 neurons, processing over 35 million spiking events per second.

Uncluttered Single-Image Visualization of Vascular Structures using GPU and Integer Programming

PubMed Central

Won, Joong-Ho; Jeon, Yongkweon; Rosenberg, Jarrett; Yoon, Sungroh; Rubin, Geoffrey D.; Napel, Sandy

2013-01-01

Direct projection of three-dimensional branching structures, such as networks of cables, blood vessels, or neurons onto a 2D image creates the illusion of intersecting structural parts and creates challenges for understanding and communication. We present a method for visualizing such structures, and demonstrate its utility in visualizing the abdominal aorta and its branches, whose tomographic images might be obtained by computed tomography or magnetic resonance angiography, in a single two-dimensional stylistic image, without overlaps among branches. The visualization method, termed uncluttered single-image visualization (USIV), involves optimization of geometry. This paper proposes a novel optimization technique that utilizes an interesting connection of the optimization problem regarding USIV to the protein structure prediction problem. Adopting the integer linear programming-based formulation for the protein structure prediction problem, we tested the proposed technique using 30 visualizations produced from five patient scans with representative anatomical variants in the abdominal aortic vessel tree. The novel technique can exploit commodity-level parallelism, enabling use of general-purpose graphics processing unit (GPGPU) technology that yields a significant speedup. Comparison of the results with the other optimization technique previously reported elsewhere suggests that, in most aspects, the quality of the visualization is comparable to that of the previous one, with a significant gain in the computation time of the algorithm. PMID:22291148

Translational neurocardiology: preclinical models and cardioneural integrative aspects.

PubMed

Ardell, J L; Andresen, M C; Armour, J A; Billman, G E; Chen, P-S; Foreman, R D; Herring, N; O'Leary, D S; Sabbah, H N; Schultz, H D; Sunagawa, K; Zucker, I H

2016-07-15

Neuronal elements distributed throughout the cardiac nervous system, from the level of the insular cortex to the intrinsic cardiac nervous system, are in constant communication with one another to ensure that cardiac output matches the dynamic process of regional blood flow demand. Neural elements in their various 'levels' become differentially recruited in the transduction of sensory inputs arising from the heart, major vessels, other visceral organs and somatic structures to optimize neuronal coordination of regional cardiac function. This White Paper will review the relevant aspects of the structural and functional organization for autonomic control of the heart in normal conditions, how these systems remodel/adapt during cardiac disease, and finally how such knowledge can be leveraged in the evolving realm of autonomic regulation therapy for cardiac therapeutics. © 2016 The Authors. The Journal of Physiology © 2016 The Physiological Society.

Predicting Spike Occurrence and Neuronal Responsiveness from LFPs in Primary Somatosensory Cortex

PubMed Central

Storchi, Riccardo; Zippo, Antonio G.; Caramenti, Gian Carlo; Valente, Maurizio; Biella, Gabriele E. M.

2012-01-01

Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role. PMID:22586452

Optimal size of stochastic Hodgkin-Huxley neuronal systems for maximal energy efficiency in coding pulse signals

NASA Astrophysics Data System (ADS)

Yu, Lianchun; Liu, Liwei

2014-03-01

The generation and conduction of action potentials (APs) represents a fundamental means of communication in the nervous system and is a metabolically expensive process. In this paper, we investigate the energy efficiency of neural systems in transferring pulse signals with APs. By analytically solving a bistable neuron model that mimics the AP generation with a particle crossing the barrier of a double well, we find the optimal number of ion channels that maximizes the energy efficiency of a neuron. We also investigate the energy efficiency of a neuron population in which the input pulse signals are represented with synchronized spikes and read out with a downstream coincidence detector neuron. We find an optimal number of neurons in neuron population, as well as the number of ion channels in each neuron that maximizes the energy efficiency. The energy efficiency also depends on the characters of the input signals, e.g., the pulse strength and the interpulse intervals. These results are confirmed by computer simulation of the stochastic Hodgkin-Huxley model with a detailed description of the ion channel random gating. We argue that the tradeoff between signal transmission reliability and energy cost may influence the size of the neural systems when energy use is constrained.

Optimal size of stochastic Hodgkin-Huxley neuronal systems for maximal energy efficiency in coding pulse signals.

PubMed

Yu, Lianchun; Liu, Liwei

2014-03-01

The generation and conduction of action potentials (APs) represents a fundamental means of communication in the nervous system and is a metabolically expensive process. In this paper, we investigate the energy efficiency of neural systems in transferring pulse signals with APs. By analytically solving a bistable neuron model that mimics the AP generation with a particle crossing the barrier of a double well, we find the optimal number of ion channels that maximizes the energy efficiency of a neuron. We also investigate the energy efficiency of a neuron population in which the input pulse signals are represented with synchronized spikes and read out with a downstream coincidence detector neuron. We find an optimal number of neurons in neuron population, as well as the number of ion channels in each neuron that maximizes the energy efficiency. The energy efficiency also depends on the characters of the input signals, e.g., the pulse strength and the interpulse intervals. These results are confirmed by computer simulation of the stochastic Hodgkin-Huxley model with a detailed description of the ion channel random gating. We argue that the tradeoff between signal transmission reliability and energy cost may influence the size of the neural systems when energy use is constrained.

Design and optimization of non-clogging counter-flow microconcentrator for enriching epidermoid cervical carcinoma cells.

PubMed

Tran-Minh, Nhut; Dong, Tao; Su, Qianhua; Yang, Zhaochu; Jakobsen, Henrik; Karlsen, Frank

2011-02-01

Clogging failure is common for microfilters in living cells concentration; for instance, the CaSki Cell-lines (Epidermoid cervical carcinoma cells) utilizing the flat membrane structure. In order to avoid the clogging, counter-flow concentration units with turbine blade-like micropillar are proposed in microconcentrator design. Due to the unusual geometrical-profiles and extraordinary microfluidic performance, the cells blocking does not occur even at permeate entrances. A counter-flow microconcentrator was designed, with both processing layer and collecting layer arranged in terms of the fractal based honeycomb structure. The device was optimized by coupling Artificial Neuron Network (ANN) and Computational Fluid Dynamics (CFD). The excellent concentration ratio of a final microconcentrator was presented in numerical results.

Methods for implantation of micro-wire bundles and optimization of single/multiunit recordings from human mesial temporal lobe

PubMed Central

Misra, A; Burke, JF; Ramayya, A; Jacobs, J; Sperling, MR; Moxon, KA; Kahana, MJ; Evans, JJ; Sharan, AD

2014-01-01

Objective The authors report methods developed for the implantation of micro-wire bundles into mesial temporal lobe structures and subsequent single neuron recording in epileptic patients undergoing in-patient diagnostic monitoring. This is done with the intention of lowering the perceived barriers to routine single neuron recording from deep brain structures in the clinical setting. Approach Over a 15 month period, 11 patients were implanted with platinum micro-wire bundles into mesial temporal structures. Protocols were developed for A) monitoring electrode integrity through impedance testing, B) ensuring continuous 24-7 recording, C) localizing micro-wire position and “splay” pattern and D) monitoring grounding and referencing to maintain the quality of recordings. Main Result Five common modes of failure were identified: 1) broken micro-wires from acute tensile force, 2) broken micro-wires from cyclic fatigue at stress points, 3) poor in-vivo micro-electrode separation, 4) motion artifact and 5) deteriorating ground connection and subsequent drop in common mode noise rejection. Single neurons have been observed up to 14 days post implantation and on 40% of micro-wires. Significance Long-term success requires detailed review of each implant by both the clinical and research teams to identify failure modes, and appropriate refinement of techniques while moving forward. This approach leads to reliable unit recordings without prolonging operative times, which will help increase the availability and clinical viability of human single neuron data. PMID:24608589

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.

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

An intracellular analysis of the visual responses of neurones in cat visual cortex.

PubMed Central

Douglas, R J; Martin, K A; Whitteridge, D

1991-01-01

1. Extracellular and intracellular recordings were made from neurones in the visual cortex of the cat in order to compare the subthreshold membrane potentials, reflecting the input to the neurone, with the output from the neurone seen as action potentials. 2. Moving bars and edges, generated under computer control, were used to stimulate the neurones. The membrane potential was digitized and averaged for a number of trials after stripping the action potentials. Comparison of extracellular and intracellular discharge patterns indicated that the intracellular impalement did not alter the neurones' properties. Input resistance of the neurone altered little during stable intracellular recordings (30 min-2 h 50 min). 3. Intracellular recordings showed two distinct patterns of membrane potential changes during optimal visual stimulation. The patterns corresponded closely to the division of S-type (simple) and C-type (complex) receptive fields. Simple cells had a complex pattern of membrane potential fluctuations, involving depolarizations alternating with hyperpolarizations. Complex cells had a simple single sustained plateau of depolarization that was often followed but not preceded by a hyperpolarization. In both simple and complex cells the depolarizations led to action potential discharges. The hyperpolarizations were associated with inhibition of action potential discharge. 4. Stimulating simple cells with non-optimal directions of motion produced little or no hyperpolarization of the membrane in most cases, despite a lack of action potential output. Directional complex cells always produced a single plateau of depolarization leading to action potential discharge in both the optimal and non-optimal directions of motion. The directionality could not be predicted on the basis of the position of the hyperpolarizing inhibitory potentials found in the optimal direction. 5. Stimulation of simple cells with non-optimal orientations occasionally produced slight hyperpolarizations and inhibition of action potential discharge. Complex cells, which had broader orientation tuning than simple cells, could show marked hyperpolarization for non-optimal orientations, but this was not generally the case. 6. The data do not support models of directionality and orientation that rely solely on strong inhibitory mechanisms to produce stimulus selectivity. PMID:1804981

A principle of economy predicts the functional architecture of grid cells.

PubMed

Wei, Xue-Xin; Prentice, Jason; Balasubramanian, Vijay

2015-09-03

Grid cells in the brain respond when an animal occupies a periodic lattice of 'grid fields' during navigation. Grids are organized in modules with different periodicity. We propose that the grid system implements a hierarchical code for space that economizes the number of neurons required to encode location with a given resolution across a range equal to the largest period. This theory predicts that (i) grid fields should lie on a triangular lattice, (ii) grid scales should follow a geometric progression, (iii) the ratio between adjacent grid scales should be √e for idealized neurons, and lie between 1.4 and 1.7 for realistic neurons, (iv) the scale ratio should vary modestly within and between animals. These results explain the measured grid structure in rodents. We also predict optimal organization in one and three dimensions, the number of modules, and, with added assumptions, the ratio between grid periods and field widths.

Optimal sparse approximation with integrate and fire neurons.

PubMed

Shapero, Samuel; Zhu, Mengchen; Hasler, Jennifer; Rozell, Christopher

2014-08-01

Sparse approximation is a hypothesized coding strategy where a population of sensory neurons (e.g. V1) encodes a stimulus using as few active neurons as possible. We present the Spiking LCA (locally competitive algorithm), a rate encoded Spiking Neural Network (SNN) of integrate and fire neurons that calculate sparse approximations. The Spiking LCA is designed to be equivalent to the nonspiking LCA, an analog dynamical system that converges on a ℓ(1)-norm sparse approximations exponentially. We show that the firing rate of the Spiking LCA converges on the same solution as the analog LCA, with an error inversely proportional to the sampling time. We simulate in NEURON a network of 128 neuron pairs that encode 8 × 8 pixel image patches, demonstrating that the network converges to nearly optimal encodings within 20 ms of biological time. We also show that when using more biophysically realistic parameters in the neurons, the gain function encourages additional ℓ(0)-norm sparsity in the encoding, relative both to ideal neurons and digital solvers.

Autonomous Optimization of Targeted Stimulation of Neuronal Networks

PubMed Central

Kumar, Sreedhar S.; Wülfing, Jan; Okujeni, Samora; Boedecker, Joschka; Riedmiller, Martin

2016-01-01

Driven by clinical needs and progress in neurotechnology, targeted interaction with neuronal networks is of increasing importance. Yet, the dynamics of interaction between intrinsic ongoing activity in neuronal networks and their response to stimulation is unknown. Nonetheless, electrical stimulation of the brain is increasingly explored as a therapeutic strategy and as a means to artificially inject information into neural circuits. Strategies using regular or event-triggered fixed stimuli discount the influence of ongoing neuronal activity on the stimulation outcome and are therefore not optimal to induce specific responses reliably. Yet, without suitable mechanistic models, it is hardly possible to optimize such interactions, in particular when desired response features are network-dependent and are initially unknown. In this proof-of-principle study, we present an experimental paradigm using reinforcement-learning (RL) to optimize stimulus settings autonomously and evaluate the learned control strategy using phenomenological models. We asked how to (1) capture the interaction of ongoing network activity, electrical stimulation and evoked responses in a quantifiable ‘state’ to formulate a well-posed control problem, (2) find the optimal state for stimulation, and (3) evaluate the quality of the solution found. Electrical stimulation of generic neuronal networks grown from rat cortical tissue in vitro evoked bursts of action potentials (responses). We show that the dynamic interplay of their magnitudes and the probability to be intercepted by spontaneous events defines a trade-off scenario with a network-specific unique optimal latency maximizing stimulus efficacy. An RL controller was set to find this optimum autonomously. Across networks, stimulation efficacy increased in 90% of the sessions after learning and learned latencies strongly agreed with those predicted from open-loop experiments. Our results show that autonomous techniques can exploit quantitative relationships underlying activity-response interaction in biological neuronal networks to choose optimal actions. Simple phenomenological models can be useful to validate the quality of the resulting controllers. PMID:27509295

Autonomous Optimization of Targeted Stimulation of Neuronal Networks.

PubMed

Kumar, Sreedhar S; Wülfing, Jan; Okujeni, Samora; Boedecker, Joschka; Riedmiller, Martin; Egert, Ulrich

2016-08-01

Driven by clinical needs and progress in neurotechnology, targeted interaction with neuronal networks is of increasing importance. Yet, the dynamics of interaction between intrinsic ongoing activity in neuronal networks and their response to stimulation is unknown. Nonetheless, electrical stimulation of the brain is increasingly explored as a therapeutic strategy and as a means to artificially inject information into neural circuits. Strategies using regular or event-triggered fixed stimuli discount the influence of ongoing neuronal activity on the stimulation outcome and are therefore not optimal to induce specific responses reliably. Yet, without suitable mechanistic models, it is hardly possible to optimize such interactions, in particular when desired response features are network-dependent and are initially unknown. In this proof-of-principle study, we present an experimental paradigm using reinforcement-learning (RL) to optimize stimulus settings autonomously and evaluate the learned control strategy using phenomenological models. We asked how to (1) capture the interaction of ongoing network activity, electrical stimulation and evoked responses in a quantifiable 'state' to formulate a well-posed control problem, (2) find the optimal state for stimulation, and (3) evaluate the quality of the solution found. Electrical stimulation of generic neuronal networks grown from rat cortical tissue in vitro evoked bursts of action potentials (responses). We show that the dynamic interplay of their magnitudes and the probability to be intercepted by spontaneous events defines a trade-off scenario with a network-specific unique optimal latency maximizing stimulus efficacy. An RL controller was set to find this optimum autonomously. Across networks, stimulation efficacy increased in 90% of the sessions after learning and learned latencies strongly agreed with those predicted from open-loop experiments. Our results show that autonomous techniques can exploit quantitative relationships underlying activity-response interaction in biological neuronal networks to choose optimal actions. Simple phenomenological models can be useful to validate the quality of the resulting controllers.

Optimization of the computational load of a hypercube supercomputer onboard a mobile robot

NASA Technical Reports Server (NTRS)

Barhen, Jacob; Toomarian, N.; Protopopescu, V.

1987-01-01

A combinatorial optimization methodology is developed, which enables the efficient use of hypercube multiprocessors onboard mobile intelligent robots dedicated to time-critical missions. The methodology is implemented in terms of large-scale concurrent algorithms based either on fast simulated annealing, or on nonlinear asynchronous neural networks. In particular, analytic expressions are given for the effect of single-neuron perturbations on the systems' configuration energy. Compact neuromorphic data structures are used to model effects such as precedence constraints, processor idling times, and task-schedule overlaps. Results for a typical robot-dynamics benchmark are presented.

Brain-state invariant thalamo-cortical coordination revealed by non-linear encoders.

PubMed

Viejo, Guillaume; Cortier, Thomas; Peyrache, Adrien

2018-03-01

Understanding how neurons cooperate to integrate sensory inputs and guide behavior is a fundamental problem in neuroscience. A large body of methods have been developed to study neuronal firing at the single cell and population levels, generally seeking interpretability as well as predictivity. However, these methods are usually confronted with the lack of ground-truth necessary to validate the approach. Here, using neuronal data from the head-direction (HD) system, we present evidence demonstrating how gradient boosted trees, a non-linear and supervised Machine Learning tool, can learn the relationship between behavioral parameters and neuronal responses with high accuracy by optimizing the information rate. Interestingly, and unlike other classes of Machine Learning methods, the intrinsic structure of the trees can be interpreted in relation to behavior (e.g. to recover the tuning curves) or to study how neurons cooperate with their peers in the network. We show how the method, unlike linear analysis, reveals that the coordination in thalamo-cortical circuits is qualitatively the same during wakefulness and sleep, indicating a brain-state independent feed-forward circuit. Machine Learning tools thus open new avenues for benchmarking model-based characterization of spike trains.

Brain-state invariant thalamo-cortical coordination revealed by non-linear encoders

PubMed Central

Cortier, Thomas; Peyrache, Adrien

2018-01-01

Understanding how neurons cooperate to integrate sensory inputs and guide behavior is a fundamental problem in neuroscience. A large body of methods have been developed to study neuronal firing at the single cell and population levels, generally seeking interpretability as well as predictivity. However, these methods are usually confronted with the lack of ground-truth necessary to validate the approach. Here, using neuronal data from the head-direction (HD) system, we present evidence demonstrating how gradient boosted trees, a non-linear and supervised Machine Learning tool, can learn the relationship between behavioral parameters and neuronal responses with high accuracy by optimizing the information rate. Interestingly, and unlike other classes of Machine Learning methods, the intrinsic structure of the trees can be interpreted in relation to behavior (e.g. to recover the tuning curves) or to study how neurons cooperate with their peers in the network. We show how the method, unlike linear analysis, reveals that the coordination in thalamo-cortical circuits is qualitatively the same during wakefulness and sleep, indicating a brain-state independent feed-forward circuit. Machine Learning tools thus open new avenues for benchmarking model-based characterization of spike trains. PMID:29565979

Biological modelling of a computational spiking neural network with neuronal avalanches.

PubMed

Li, Xiumin; Chen, Qing; Xue, Fangzheng

2017-06-28

In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'. © 2017 The Author(s).

Biological modelling of a computational spiking neural network with neuronal avalanches

NASA Astrophysics Data System (ADS)

Li, Xiumin; Chen, Qing; Xue, Fangzheng

2017-05-01

In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed neuronal avalanches. It has been hypothesized that the homeostatic level balanced between stability and plasticity of this critical state may be the optimal state for performing diverse neural computational tasks. However, the critical region for high performance is narrow and sensitive for spiking neural networks (SNNs). In this paper, we investigated the role of the critical state in neural computations based on liquid-state machines, a biologically plausible computational neural network model for real-time computing. The computational performance of an SNN when operating at the critical state and, in particular, with spike-timing-dependent plasticity for updating synaptic weights is investigated. The network is found to show the best computational performance when it is subjected to critical dynamic states. Moreover, the active-neuron-dominant structure refined from synaptic learning can remarkably enhance the robustness of the critical state and further improve computational accuracy. These results may have important implications in the modelling of spiking neural networks with optimal computational performance. This article is part of the themed issue `Mathematical methods in medicine: neuroscience, cardiology and pathology'.

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.

Long-term culture of pluripotent stem-cell-derived human neurons on diamond--A substrate for neurodegeneration research and therapy.

PubMed

Nistor, Paul A; May, Paul W; Tamagnini, Francesco; Randall, Andrew D; Caldwell, Maeve A

2015-08-01

Brain Computer Interfaces (BCI) currently represent a field of intense research aimed both at understanding neural circuit physiology and at providing functional therapy for traumatic or degenerative neurological conditions. Due to its chemical inertness, biocompatibility and stability, diamond is currently being actively investigated as a potential substrate material for culturing cells and for use as the electrically active component of a neural sensor. Here we provide a protocol for the differentiation of mature, electrically active neurons on microcrystalline synthetic thin-film diamond substrates starting from undifferentiated pluripotent stem cells. Furthermore, we investigate the optimal characteristics of the diamond microstructure for long-term neuronal sustainability. We also analyze the effect of boron as a dopant for such a culture. We found that the diamond crystalline structure has a significant influence on the neuronal culture unlike the boron doping. Specifically, small diamond microcrystals promote higher neurite density formation. We find that boron incorporated into the diamond does not influence the neurite density and has no deleterious effect on cell survival. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Metastability and Inter-Band Frequency Modulation in Networks of Oscillating Spiking Neuron Populations

PubMed Central

Bhowmik, David; Shanahan, Murray

2013-01-01

Groups of neurons firing synchronously are hypothesized to underlie many cognitive functions such as attention, associative learning, memory, and sensory selection. Recent theories suggest that transient periods of synchronization and desynchronization provide a mechanism for dynamically integrating and forming coalitions of functionally related neural areas, and that at these times conditions are optimal for information transfer. Oscillating neural populations display a great amount of spectral complexity, with several rhythms temporally coexisting in different structures and interacting with each other. This paper explores inter-band frequency modulation between neural oscillators using models of quadratic integrate-and-fire neurons and Hodgkin-Huxley neurons. We vary the structural connectivity in a network of neural oscillators, assess the spectral complexity, and correlate the inter-band frequency modulation. We contrast this correlation against measures of metastable coalition entropy and synchrony. Our results show that oscillations in different neural populations modulate each other so as to change frequency, and that the interaction of these fluctuating frequencies in the network as a whole is able to drive different neural populations towards episodes of synchrony. Further to this, we locate an area in the connectivity space in which the system directs itself in this way so as to explore a large repertoire of synchronous coalitions. We suggest that such dynamics facilitate versatile exploration, integration, and communication between functionally related neural areas, and thereby supports sophisticated cognitive processing in the brain. PMID:23614040

Receptive-field subfields of V2 neurons in macaque monkeys are adult-like near birth.

PubMed

Zhang, Bin; Tao, Xiaofeng; Shen, Guofu; Smith, Earl L; Ohzawa, Izumi; Chino, Yuzo M

2013-02-06

Infant primates can discriminate texture-defined form despite their relatively low visual acuity. The neuronal mechanisms underlying this remarkable visual capacity of infants have not been studied in nonhuman primates. Since many V2 neurons in adult monkeys can extract the local features in complex stimuli that are required for form vision, we used two-dimensional dynamic noise stimuli and local spectral reverse correlation to measure whether the spatial map of receptive-field subfields in individual V2 neurons is sufficiently mature near birth to capture local features. As in adults, most V2 neurons in 4-week-old monkeys showed a relatively high degree of homogeneity in the spatial matrix of facilitatory subfields. However, ∼25% of V2 neurons had the subfield map where the neighboring facilitatory subfields substantially differed in their preferred orientations and spatial frequencies. Over 80% of V2 neurons in both infants and adults had "tuned" suppressive profiles in their subfield maps that could alter the tuning properties of facilitatory profiles. The differences in the preferred orientations between facilitatory and suppressive profiles were relatively large but extended over a broad range. Response immaturities in infants were mild; the overall strength of facilitatory subfield responses was lower than that in adults, and the optimal correlation delay ("latency") was longer in 4-week-old infants. These results suggest that as early as 4 weeks of age, the spatial receptive-field structure of V2 neurons is as complex as in adults and the ability of V2 neurons to compare local features of neighboring stimulus elements is nearly adult like.

Growing a hypercubical output space in a self-organizing feature map.

PubMed

Bauer, H U; Villmann, T

1997-01-01

Neural maps project data from an input space onto a neuron position in a (often lower dimensional) output space grid in a neighborhood preserving way, with neighboring neurons in the output space responding to neighboring data points in the input space. A map-learning algorithm can achieve an optimal neighborhood preservation only, if the output space topology roughly matches the effective structure of the data in the input space. We here present a growth algorithm, called the GSOM or growing self-organizing map, which enhances a widespread map self-organization process, Kohonen's self-organizing feature map (SOFM), by an adaptation of the output space grid during learning. The GSOM restricts the output space structure to the shape of a general hypercubical shape, with the overall dimensionality of the grid and its extensions along the different directions being subject of the adaptation. This constraint meets the demands of many larger information processing systems, of which the neural map can be a part. We apply our GSOM-algorithm to three examples, two of which involve real world data. Using recently developed methods for measuring the degree of neighborhood preservation in neural maps, we find the GSOM-algorithm to produce maps which preserve neighborhoods in a nearly optimal fashion.

Bio-inspired spiking neural network for nonlinear systems control.

PubMed

Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

2018-08-01

Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Cell structure and function in the visual cortex of the cat

PubMed Central

Kelly, J. P.; Van Essen, D. C.

1974-01-01

1. The organization of the visual cortex was studied with a technique that allows one to determine the physiology and morphology of individual cells. Micro-electrodes filled with the fluorescent dye Procion yellow were used to record intracellularly from cells in area 17 of the cat. The visual receptive field of each neurone was classified as simple, complex, or hypercomplex, and the cell was then stained by the iontophoretic injection of dye. 2. Fifty neurones were successfully examined in this way, and their structural features were compared to the varieties of cell types seen in Golgi preparations of area 17. The majority of simple units were stellate cells, whereas the majority of complex and hypercomplex units were pyramidal cells. Several neurones belonged to less common morphological types, such as double bouquet cells. Simple cells were concentrated in layer IV, hypercomplex cells in layer II + III, and complex cells in layers II + III, V and VI. 3. Electrically inexcitable cells that had high resting potentials but no impulse activity were stained and identified as glial cells. Glial cells responded to visual stimuli with slow graded depolarizations, and many of them showed a preference for a stimulus orientation similar to the optimal orientation for adjacent neurones. 4. The results show that there is a clear, but not absolute correlation between the major structural and functional classes of cells in the visual cortex. This approach, linking the physiological properties of a single cell to a given morphological type, will help in furthering our understanding of the cerebral cortex. ImagesPlate 4Plate 1Plate 2Plate 3 PMID:4136579

Congruent and Opposite Neurons as Partners in Multisensory Integration and Segregation

NASA Astrophysics Data System (ADS)

Zhang, Wen-Hao; Wong, K. Y. Michael; Wang, He; Wu, Si

Experiments revealed that where visual and vestibular cues are integrated to infer heading direction in the brain, there are two types of neurons with roughly the same number. Respectively, congruent and opposite cells respond similarly and oppositely to visual and vestibular cues. Congruent neurons are known to be responsible for cue integration, but the computational role of opposite neurons remains largely unknown. We propose that opposite neurons may serve to encode the disparity information between cues necessary for multisensory segregation. We build a computational model composed of two reciprocally coupled modules, each consisting of groups of congruent and opposite neurons. Our model reproduces the characteristics of congruent and opposite neurons, and demonstrates that in each module, congruent and opposite neurons can jointly achieve optimal multisensory information integration and segregation. This study sheds light on our understanding of how the brain implements optimal multisensory integration and segregation concurrently in a distributed manner. This work is supported by the Research Grants Council of Hong Kong (N _HKUST606/12, 605813, and 16322616) and National Basic Research Program of China (2014CB846101) and the Natural Science Foundation of China (31261160495).

Motor unit recruitment by size does not provide functional advantages for motor performance

PubMed Central

Dideriksen, Jakob L; Farina, Dario

2013-01-01

It is commonly assumed that the orderly recruitment of motor units by size provides a functional advantage for the performance of movements compared with a random recruitment order. On the other hand, the excitability of a motor neuron depends on its size and this is intrinsically linked to its innervation number. A range of innervation numbers among motor neurons corresponds to a range of sizes and thus to a range of excitabilities ordered by size. Therefore, if the excitation drive is similar among motor neurons, the recruitment by size is inevitably due to the intrinsic properties of motor neurons and may not have arisen to meet functional demands. In this view, we tested the assumption that orderly recruitment is necessarily beneficial by determining if this type of recruitment produces optimal motor output. Using evolutionary algorithms and without any a priori assumptions, the parameters of neuromuscular models were optimized with respect to several criteria for motor performance. Interestingly, the optimized model parameters matched well known neuromuscular properties, but none of the optimization criteria determined a consistent recruitment order by size unless this was imposed by an association between motor neuron size and excitability. Further, when the association between size and excitability was imposed, the resultant model of recruitment did not improve the motor performance with respect to the absence of orderly recruitment. A consistent observation was that optimal solutions for a variety of criteria of motor performance always required a broad range of innervation numbers in the population of motor neurons, skewed towards the small values. These results indicate that orderly recruitment of motor units in itself does not provide substantial functional advantages for motor control. Rather, the reason for its near-universal presence in human movements is that motor functions are optimized by a broad range of innervation numbers. PMID:24144879

Motor unit recruitment by size does not provide functional advantages for motor performance.

PubMed

Dideriksen, Jakob L; Farina, Dario

2013-12-15

It is commonly assumed that the orderly recruitment of motor units by size provides a functional advantage for the performance of movements compared with a random recruitment order. On the other hand, the excitability of a motor neuron depends on its size and this is intrinsically linked to its innervation number. A range of innervation numbers among motor neurons corresponds to a range of sizes and thus to a range of excitabilities ordered by size. Therefore, if the excitation drive is similar among motor neurons, the recruitment by size is inevitably due to the intrinsic properties of motor neurons and may not have arisen to meet functional demands. In this view, we tested the assumption that orderly recruitment is necessarily beneficial by determining if this type of recruitment produces optimal motor output. Using evolutionary algorithms and without any a priori assumptions, the parameters of neuromuscular models were optimized with respect to several criteria for motor performance. Interestingly, the optimized model parameters matched well known neuromuscular properties, but none of the optimization criteria determined a consistent recruitment order by size unless this was imposed by an association between motor neuron size and excitability. Further, when the association between size and excitability was imposed, the resultant model of recruitment did not improve the motor performance with respect to the absence of orderly recruitment. A consistent observation was that optimal solutions for a variety of criteria of motor performance always required a broad range of innervation numbers in the population of motor neurons, skewed towards the small values. These results indicate that orderly recruitment of motor units in itself does not provide substantial functional advantages for motor control. Rather, the reason for its near-universal presence in human movements is that motor functions are optimized by a broad range of innervation numbers.

Macrophages offer a paradigm switch for CNS delivery of therapeutic proteins

PubMed Central

Klyachko, Natalia L; Haney, Matthew J; Zhao, Yuling; Manickam, Devika S; Mahajan, Vivek; Suresh, Poornima; Hingtgen, Shawn D; Mosley, R Lee; Gendelman, Howard E; Kabanov, Alexander V; Batrakova, Elena V

2013-01-01

Aims Active targeted transport of the nanoformulated redox enzyme, catalase, in macrophages attenuates oxidative stress and as such increases survival of dopaminergic neurons in animal models of Parkinson’s disease. Optimization of the drug formulation is crucial for the successful delivery in living cells. We demonstrated earlier that packaging of catalase into a polyion complex micelle (‘nanozyme’) with a synthetic polyelectrolyte block copolymer protected the enzyme against degradation in macrophages and improved therapeutic outcomes. We now report the manufacture of nanozymes with superior structure and therapeutic indices. Methods Synthesis, characterization and therapeutic efficacy of optimal cell-based nanoformulations are evaluated. Results A formulation design for drug carriers typically works to avoid entrapment in monocytes and macrophages focusing on small-sized nanoparticles with a polyethylene glycol corona (to provide a stealth effect). By contrast, the best nanozymes for delivery in macrophages reported in this study have a relatively large size (~200 nm), which resulted in improved loading capacity and release from macrophages. Furthermore, the cross-linking of nanozymes with the excess of a nonbiodegradable linker ensured their low cytotoxicity, and efficient catalase protection in cell carriers. Finally, the ‘alternatively activated’ macrophage phenotype (M2) utilized in these studies did not promote further inflammation in the brain, resulting in a subtle but statistically significant effect on neuronal regeneration and repair in vivo. Conclusion The optimized cross-linked nanozyme loaded into macrophages reduced neuroinflammatory responses and increased neuronal survival in mice. Importantly, the approach for nanoformulation design for cell-mediated delivery is different from the common requirements for injectable formulations. PMID:24237263

Macrophages offer a paradigm switch for CNS delivery of therapeutic proteins.

PubMed

Klyachko, Natalia L; Haney, Matthew J; Zhao, Yuling; Manickam, Devika S; Mahajan, Vivek; Suresh, Poornima; Hingtgen, Shawn D; Mosley, R Lee; Gendelman, Howard E; Kabanov, Alexander V; Batrakova, Elena V

2014-07-01

Active targeted transport of the nanoformulated redox enzyme, catalase, in macrophages attenuates oxidative stress and as such increases survival of dopaminergic neurons in animal models of Parkinson's disease. Optimization of the drug formulation is crucial for the successful delivery in living cells. We demonstrated earlier that packaging of catalase into a polyion complex micelle ('nanozyme') with a synthetic polyelectrolyte block copolymer protected the enzyme against degradation in macrophages and improved therapeutic outcomes. We now report the manufacture of nanozymes with superior structure and therapeutic indices. Synthesis, characterization and therapeutic efficacy of optimal cell-based nanoformulations are evaluated. A formulation design for drug carriers typically works to avoid entrapment in monocytes and macrophages focusing on small-sized nanoparticles with a polyethylene glycol corona (to provide a stealth effect). By contrast, the best nanozymes for delivery in macrophages reported in this study have a relatively large size (≈ 200 nm), which resulted in improved loading capacity and release from macrophages. Furthermore, the cross-linking of nanozymes with the excess of a nonbiodegradable linker ensured their low cytotoxicity, and efficient catalase protection in cell carriers. Finally, the 'alternatively activated' macrophage phenotype (M2) utilized in these studies did not promote further inflammation in the brain, resulting in a subtle but statistically significant effect on neuronal regeneration and repair in vivo. The optimized cross-linked nanozyme loaded into macrophages reduced neuroinflammatory responses and increased neuronal survival in mice. Importantly, the approach for nanoformulation design for cell-mediated delivery is different from the common requirements for injectable formulations.

Comparison of Classifiers for Decoding Sensory and Cognitive Information from Prefrontal Neuronal Populations

PubMed Central

Astrand, Elaine; Enel, Pierre; Ibos, Guilhem; Dominey, Peter Ford; Baraduc, Pierre; Ben Hamed, Suliann

2014-01-01

Decoding neuronal information is important in neuroscience, both as a basic means to understand how neuronal activity is related to cerebral function and as a processing stage in driving neuroprosthetic effectors. Here, we compare the readout performance of six commonly used classifiers at decoding two different variables encoded by the spiking activity of the non-human primate frontal eye fields (FEF): the spatial position of a visual cue, and the instructed orientation of the animal's attention. While the first variable is exogenously driven by the environment, the second variable corresponds to the interpretation of the instruction conveyed by the cue; it is endogenously driven and corresponds to the output of internal cognitive operations performed on the visual attributes of the cue. These two variables were decoded using either a regularized optimal linear estimator in its explicit formulation, an optimal linear artificial neural network estimator, a non-linear artificial neural network estimator, a non-linear naïve Bayesian estimator, a non-linear Reservoir recurrent network classifier or a non-linear Support Vector Machine classifier. Our results suggest that endogenous information such as the orientation of attention can be decoded from the FEF with the same accuracy as exogenous visual information. All classifiers did not behave equally in the face of population size and heterogeneity, the available training and testing trials, the subject's behavior and the temporal structure of the variable of interest. In most situations, the regularized optimal linear estimator and the non-linear Support Vector Machine classifiers outperformed the other tested decoders. PMID:24466019

Cationic amino acid based lipids as effective nonviral gene delivery vectors for primary cultured neurons.

PubMed

Aoshima, Yumiko; Hokama, Ryosuke; Sou, Keitaro; Sarker, Satya Ranjan; Iida, Kabuto; Nakamura, Hideki; Inoue, Takafumi; Takeoka, Shinji

2013-12-18

The delivery of specific genes into neurons offers a potent approach for treatment of diseases as well as for the study of neuronal cell biology. Here we investigated the capabilities of cationic amino acid based lipid assemblies to act as nonviral gene delivery vectors in primary cultured neurons. An arginine-based lipid, Arg-C3-Glu2C14, and a lysine-based lipid, Lys-C3-Glu2C14, with two different types of counterion, chloride ion (Cl-) and trifluoroacetic acid (TFA-), were shown to successfully mediate transfection of primary cultured neurons with plasmid DNA encoding green fluorescent protein. Among four types of lipids, we optimized their conditions such as the lipid-to-DNA ratio and the amount of pDNA and conducted a cytotoxicity assay at the same time. Overall, Arg-C3-Glu2C14 with TFA- induced a rate of transfection in primary cultured neurons higher than that of Lys-C3-Glu2C14 using an optimal weight ratio of lipid-to-plasmid DNA of 1. Moreover, it was suggested that Arg-C3-Glu2C14 with TFA- showed the optimized value higher than that of Lipofectamine2000 in experimental conditions. Thus, Arg-C3-Glu2C14 with TFA- is a promising candidate as a reliable transfection reagent for primary cultured neurons with a relatively low cytotoxicity.

Cationic Amino Acid Based Lipids as Effective Nonviral Gene Delivery Vectors for Primary Cultured Neurons

PubMed Central

2013-01-01

The delivery of specific genes into neurons offers a potent approach for treatment of diseases as well as for the study of neuronal cell biology. Here we investigated the capabilities of cationic amino acid based lipid assemblies to act as nonviral gene delivery vectors in primary cultured neurons. An arginine-based lipid, Arg-C3-Glu2C14, and a lysine-based lipid, Lys-C3-Glu2C14, with two different types of counterion, chloride ion (Cl–) and trifluoroacetic acid (TFA–), were shown to successfully mediate transfection of primary cultured neurons with plasmid DNA encoding green fluorescent protein. Among four types of lipids, we optimized their conditions such as the lipid-to-DNA ratio and the amount of pDNA and conducted a cytotoxicity assay at the same time. Overall, Arg-C3-Glu2C14 with TFA– induced a rate of transfection in primary cultured neurons higher than that of Lys-C3-Glu2C14 using an optimal weight ratio of lipid-to-plasmid DNA of 1. Moreover, it was suggested that Arg-C3-Glu2C14 with TFA– showed the optimized value higher than that of Lipofectamine2000 in experimental conditions. Thus, Arg-C3-Glu2C14 with TFA– is a promising candidate as a reliable transfection reagent for primary cultured neurons with a relatively low cytotoxicity. PMID:24087930

Pharmacogenetic stimulation of cholinergic pedunculopontine neurons reverses motor deficits in a rat model of Parkinson's disease.

PubMed

Pienaar, Ilse S; Gartside, Sarah E; Sharma, Puneet; De Paola, Vincenzo; Gretenkord, Sabine; Withers, Dominic; Elson, Joanna L; Dexter, David T

2015-09-23

Patients with advanced Parkinson's disease (PD) often present with axial symptoms, including postural- and gait difficulties that respond poorly to dopaminergic agents. Although deep brain stimulation (DBS) of a highly heterogeneous brain structure, the pedunculopontine nucleus (PPN), improves such symptoms, the underlying neuronal substrate responsible for the clinical benefits remains largely unknown, thus hampering optimization of DBS interventions. Choline acetyltransferase (ChAT)::Cre(+) transgenic rats were sham-lesioned or rendered parkinsonian through intranigral, unihemispheric stereotaxic administration of the ubiquitin-proteasomal system inhibitor, lactacystin, combined with designer receptors exclusively activated by designer drugs (DREADD), to activate the cholinergic neurons of the nucleus tegmenti pedunculopontine (PPTg), the rat equivalent of the human PPN. We have previously shown that the lactacystin rat model accurately reflects aspects of PD, including a partial loss of PPTg cholinergic neurons, similar to what is seen in the post-mortem brains of advanced PD patients. In this manuscript, we show that transient activation of the remaining PPTg cholinergic neurons in the lactacystin rat model of PD, via peripheral administration of the cognate DREADD ligand, clozapine-N-oxide (CNO), dramatically improved motor symptoms, as was assessed by behavioral tests that measured postural instability, gait, sensorimotor integration, forelimb akinesia and general motor activity. In vivo electrophysiological recordings revealed increased spiking activity of PPTg putative cholinergic neurons during CNO-induced activation. c-Fos expression in DREADD overexpressed ChAT-immunopositive (ChAT+) neurons of the PPTg was also increased by CNO administration, consistent with upregulated neuronal activation in this defined neuronal population. Overall, these findings provide evidence that functional modulation of PPN cholinergic neurons alleviates parkinsonian motor symptoms.

Optimal Detection of a Localized Perturbation in Random Networks of Integrate-and-Fire Neurons.

PubMed

Bernardi, Davide; Lindner, Benjamin

2017-06-30

Experimental and theoretical studies suggest that cortical networks are chaotic and coding relies on averages over large populations. However, there is evidence that rats can respond to the short stimulation of a single cortical cell, a theoretically unexplained fact. We study effects of single-cell stimulation on a large recurrent network of integrate-and-fire neurons and propose a simple way to detect the perturbation. Detection rates obtained from simulations and analytical estimates are similar to experimental response rates if the readout is slightly biased towards specific neurons. Near-optimal detection is attained for a broad range of intermediate values of the mean coupling between neurons.

Optimal Detection of a Localized Perturbation in Random Networks of Integrate-and-Fire Neurons

NASA Astrophysics Data System (ADS)

Bernardi, Davide; Lindner, Benjamin

2017-06-01

Experimental and theoretical studies suggest that cortical networks are chaotic and coding relies on averages over large populations. However, there is evidence that rats can respond to the short stimulation of a single cortical cell, a theoretically unexplained fact. We study effects of single-cell stimulation on a large recurrent network of integrate-and-fire neurons and propose a simple way to detect the perturbation. Detection rates obtained from simulations and analytical estimates are similar to experimental response rates if the readout is slightly biased towards specific neurons. Near-optimal detection is attained for a broad range of intermediate values of the mean coupling between neurons.

Error-based analysis of optimal tuning functions explains phenomena observed in sensory neurons.

PubMed

Yaeli, Steve; Meir, Ron

2010-01-01

Biological systems display impressive capabilities in effectively responding to environmental signals in real time. There is increasing evidence that organisms may indeed be employing near optimal Bayesian calculations in their decision-making. An intriguing question relates to the properties of optimal encoding methods, namely determining the properties of neural populations in sensory layers that optimize performance, subject to physiological constraints. Within an ecological theory of neural encoding/decoding, we show that optimal Bayesian performance requires neural adaptation which reflects environmental changes. Specifically, we predict that neuronal tuning functions possess an optimal width, which increases with prior uncertainty and environmental noise, and decreases with the decoding time window. Furthermore, even for static stimuli, we demonstrate that dynamic sensory tuning functions, acting at relatively short time scales, lead to improved performance. Interestingly, the narrowing of tuning functions as a function of time was recently observed in several biological systems. Such results set the stage for a functional theory which may explain the high reliability of sensory systems, and the utility of neuronal adaptation occurring at multiple time scales.

Population activity structure of excitatory and inhibitory neurons

PubMed Central

Doiron, Brent

2017-01-01

Many studies use population analysis approaches, such as dimensionality reduction, to characterize the activity of large groups of neurons. To date, these methods have treated each neuron equally, without taking into account whether neurons are excitatory or inhibitory. We studied population activity structure as a function of neuron type by applying factor analysis to spontaneous activity from spiking networks with balanced excitation and inhibition. Throughout the study, we characterized population activity structure by measuring its dimensionality and the percentage of overall activity variance that is shared among neurons. First, by sampling only excitatory or only inhibitory neurons, we found that the activity structures of these two populations in balanced networks are measurably different. We also found that the population activity structure is dependent on the ratio of excitatory to inhibitory neurons sampled. Finally we classified neurons from extracellular recordings in the primary visual cortex of anesthetized macaques as putative excitatory or inhibitory using waveform classification, and found similarities with the neuron type-specific population activity structure of a balanced network with excitatory clustering. These results imply that knowledge of neuron type is important, and allows for stronger statistical tests, when interpreting population activity structure. PMID:28817581

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.

Improved detection of soma location and morphology in fluorescence microscopy images of neurons.

PubMed

Kayasandik, Cihan Bilge; Labate, Demetrio

2016-12-01

Automated detection and segmentation of somas in fluorescent images of neurons is a major goal in quantitative studies of neuronal networks, including applications of high-content-screenings where it is required to quantify multiple morphological properties of neurons. Despite recent advances in image processing targeted to neurobiological applications, existing algorithms of soma detection are often unreliable, especially when processing fluorescence image stacks of neuronal cultures. In this paper, we introduce an innovative algorithm for the detection and extraction of somas in fluorescent images of networks of cultured neurons where somas and other structures exist in the same fluorescent channel. Our method relies on a new geometrical descriptor called Directional Ratio and a collection of multiscale orientable filters to quantify the level of local isotropy in an image. To optimize the application of this approach, we introduce a new construction of multiscale anisotropic filters that is implemented by separable convolution. Extensive numerical experiments using 2D and 3D confocal images show that our automated algorithm reliably detects somas, accurately segments them, and separates contiguous ones. We include a detailed comparison with state-of-the-art existing methods to demonstrate that our algorithm is extremely competitive in terms of accuracy, reliability and computational efficiency. Our algorithm will facilitate the development of automated platforms for high content neuron image processing. A Matlab code is released open-source and freely available to the scientific community. Copyright © 2016 Elsevier B.V. All rights reserved.

Communication in neuronal networks.

PubMed

Laughlin, Simon B; Sejnowski, Terrence J

2003-09-26

Brains perform with remarkable efficiency, are capable of prodigious computation, and are marvels of communication. We are beginning to understand some of the geometric, biophysical, and energy constraints that have governed the evolution of cortical networks. To operate efficiently within these constraints, nature has optimized the structure and function of cortical networks with design principles similar to those used in electronic networks. The brain also exploits the adaptability of biological systems to reconfigure in response to changing needs.

A principle of economy predicts the functional architecture of grid cells

PubMed Central

Wei, Xue-Xin; Prentice, Jason; Balasubramanian, Vijay

2015-01-01

Grid cells in the brain respond when an animal occupies a periodic lattice of ‘grid fields’ during navigation. Grids are organized in modules with different periodicity. We propose that the grid system implements a hierarchical code for space that economizes the number of neurons required to encode location with a given resolution across a range equal to the largest period. This theory predicts that (i) grid fields should lie on a triangular lattice, (ii) grid scales should follow a geometric progression, (iii) the ratio between adjacent grid scales should be √e for idealized neurons, and lie between 1.4 and 1.7 for realistic neurons, (iv) the scale ratio should vary modestly within and between animals. These results explain the measured grid structure in rodents. We also predict optimal organization in one and three dimensions, the number of modules, and, with added assumptions, the ratio between grid periods and field widths. DOI: http://dx.doi.org/10.7554/eLife.08362.001 PMID:26335200

Complexity Optimization and High-Throughput Low-Latency Hardware Implementation of a Multi-Electrode Spike-Sorting Algorithm

PubMed Central

Dragas, Jelena; Jäckel, David; Hierlemann, Andreas; Franke, Felix

2017-01-01

Reliable real-time low-latency spike sorting with large data throughput is essential for studies of neural network dynamics and for brain-machine interfaces (BMIs), in which the stimulation of neural networks is based on the networks' most recent activity. However, the majority of existing multi-electrode spike-sorting algorithms are unsuited for processing high quantities of simultaneously recorded data. Recording from large neuronal networks using large high-density electrode sets (thousands of electrodes) imposes high demands on the data-processing hardware regarding computational complexity and data transmission bandwidth; this, in turn, entails demanding requirements in terms of chip area, memory resources and processing latency. This paper presents computational complexity optimization techniques, which facilitate the use of spike-sorting algorithms in large multi-electrode-based recording systems. The techniques are then applied to a previously published algorithm, on its own, unsuited for large electrode set recordings. Further, a real-time low-latency high-performance VLSI hardware architecture of the modified algorithm is presented, featuring a folded structure capable of processing the activity of hundreds of neurons simultaneously. The hardware is reconfigurable “on-the-fly” and adaptable to the nonstationarities of neuronal recordings. By transmitting exclusively spike time stamps and/or spike waveforms, its real-time processing offers the possibility of data bandwidth and data storage reduction. PMID:25415989

Complexity optimization and high-throughput low-latency hardware implementation of a multi-electrode spike-sorting algorithm.

PubMed

Dragas, Jelena; Jackel, David; Hierlemann, Andreas; Franke, Felix

2015-03-01

Reliable real-time low-latency spike sorting with large data throughput is essential for studies of neural network dynamics and for brain-machine interfaces (BMIs), in which the stimulation of neural networks is based on the networks' most recent activity. However, the majority of existing multi-electrode spike-sorting algorithms are unsuited for processing high quantities of simultaneously recorded data. Recording from large neuronal networks using large high-density electrode sets (thousands of electrodes) imposes high demands on the data-processing hardware regarding computational complexity and data transmission bandwidth; this, in turn, entails demanding requirements in terms of chip area, memory resources and processing latency. This paper presents computational complexity optimization techniques, which facilitate the use of spike-sorting algorithms in large multi-electrode-based recording systems. The techniques are then applied to a previously published algorithm, on its own, unsuited for large electrode set recordings. Further, a real-time low-latency high-performance VLSI hardware architecture of the modified algorithm is presented, featuring a folded structure capable of processing the activity of hundreds of neurons simultaneously. The hardware is reconfigurable “on-the-fly” and adaptable to the nonstationarities of neuronal recordings. By transmitting exclusively spike time stamps and/or spike waveforms, its real-time processing offers the possibility of data bandwidth and data storage reduction.

Optimization of Neuronal-Computer Interface

DTIC Science & Technology

2009-06-23

for the inhibitory neurotransmitter gamma-aminobutyric acid ( GABA ) receptor subunit as a general marker of inhibitory neurons (Beck et al., 1993...These analyses confirmed the presence of GABA -positive neurons (Fig. 4). Fig 4: Cultures contain inhibitory neurons. Cultures were subjected to double...immunofluorescent analyses for neurofilaments (anti-NF) using monoclonal antibody SMI-32 and a polyclonal antibody directed against the GABA receptor

A Physiological Neural Controller of a Muscle Fiber Oculomotor Plant in Horizontal Monkey Saccades

PubMed Central

Enderle, John D.

2014-01-01

A neural network model of biophysical neurons in the midbrain is presented to drive a muscle fiber oculomotor plant during horizontal monkey saccades. Neural circuitry, including omnipause neuron, premotor excitatory and inhibitory burst neurons, long lead burst neuron, tonic neuron, interneuron, abducens nucleus, and oculomotor nucleus, is developed to examine saccade dynamics. The time-optimal control strategy by realization of agonist and antagonist controller models is investigated. In consequence, each agonist muscle fiber is stimulated by an agonist neuron, while an antagonist muscle fiber is unstimulated by a pause and step from the antagonist neuron. It is concluded that the neural network is constrained by a minimum duration of the agonist pulse and that the most dominant factor in determining the saccade magnitude is the number of active neurons for the small saccades. For the large saccades, however, the duration of agonist burst firing significantly affects the control of saccades. The proposed saccadic circuitry establishes a complete model of saccade generation since it not only includes the neural circuits at both the premotor and motor stages of the saccade generator, but also uses a time-optimal controller to yield the desired saccade magnitude. PMID:24944832

Analog "neuronal" networks in early vision.

PubMed Central

Koch, C; Marroquin, J; Yuille, A

1986-01-01

Many problems in early vision can be formulated in terms of minimizing a cost function. Examples are shape from shading, edge detection, motion analysis, structure from motion, and surface interpolation. As shown by Poggio and Koch [Poggio, T. & Koch, C. (1985) Proc. R. Soc. London, Ser. B 226, 303-323], quadratic variational problems, an important subset of early vision tasks, can be "solved" by linear, analog electrical, or chemical networks. However, in the presence of discontinuities, the cost function is nonquadratic, raising the question of designing efficient algorithms for computing the optimal solution. Recently, Hopfield and Tank [Hopfield, J. J. & Tank, D. W. (1985) Biol. Cybern. 52, 141-152] have shown that networks of nonlinear analog "neurons" can be effective in computing the solution of optimization problems. We show how these networks can be generalized to solve the nonconvex energy functionals of early vision. We illustrate this approach by implementing a specific analog network, solving the problem of reconstructing a smooth surface from sparse data while preserving its discontinuities. These results suggest a novel computational strategy for solving early vision problems in both biological and real-time artificial vision systems. PMID:3459172

Integrated nanoscale tools for interrogating living cells

NASA Astrophysics Data System (ADS)

Jorgolli, Marsela

The development of next-generation, nanoscale technologies that interface biological systems will pave the way towards new understanding of such complex systems. Nanowires -- one-dimensional nanoscale structures -- have shown unique potential as an ideal physical interface to biological systems. Herein, we focus on the development of nanowire-based devices that can enable a wide variety of biological studies. First, we built upon standard nanofabrication techniques to optimize nanowire devices, resulting in perfectly ordered arrays of both opaque (Silicon) and transparent (Silicon dioxide) nanowires with user defined structural profile, densities, and overall patterns, as well as high sample consistency and large scale production. The high-precision and well-controlled fabrication method in conjunction with additional technologies laid the foundation for the generation of highly specialized platforms for imaging, electrochemical interrogation, and molecular biology. Next, we utilized nanowires as the fundamental structure in the development of integrated nanoelectronic platforms to directly interrogate the electrical activity of biological systems. Initially, we generated a scalable intracellular electrode platform based on vertical nanowires that allows for parallel electrical interfacing to multiple mammalian neurons. Our prototype device consisted of 16 individually addressable stimulation/recording sites, each containing an array of 9 electrically active silicon nanowires. We showed that these vertical nanowire electrode arrays could intracellularly record and stimulate neuronal activity in dissociated cultures of rat cortical neurons similar to patch clamp electrodes. In addition, we used our intracellular electrode platform to measure multiple individual synaptic connections, which enables the reconstruction of the functional connectivity maps of neuronal circuits. In order to expand and improve the capability of this functional prototype device we designed and fabricated a new hybrid chip that combines a front-side nanowire-based interface for neuronal recording with backside complementary metal oxide semiconductor (CMOS) circuits for on-chip multiplexing, voltage control for stimulation, signal amplification, and signal processing. Individual chips contain 1024 stimulation/recording sites enabling large-scale interfacing of neuronal networks with single cell resolution. Through electrical and electrochemical characterization of the devices, we demonstrated their enhanced functionality at a massively parallel scale. In our initial cell experiments, we achieved intracellular stimulations and recordings of changes in the membrane potential in a variety of cells including: HEK293T, cardiomyocytes, and rat cortical neurons. This demonstrated the device capability for single-cell-resolution recording/stimulation which when extended to a large number of neurons in a massively parallel fashion will enable the functional mapping of a complex neuronal network.

A wire length minimization approach to ocular dominance patterns in mammalian visual cortex

NASA Astrophysics Data System (ADS)

Chklovskii, Dmitri B.; Koulakov, Alexei A.

2000-09-01

The primary visual area (V1) of the mammalian brain is a thin sheet of neurons. Because each neuron is dominated by either right or left eye one can treat V1 as a binary mixture of neurons. The spatial arrangement of neurons dominated by different eyes is known as the ocular dominance (OD) pattern. We propose a theory for OD patterns based on the premise that they are evolutionary adaptations to minimize the length of intra-cortical connections. Thus, the existing OD patterns are obtained by solving a wire length minimization problem. We divide all the neurons into two classes: right- and left-eye dominated. We find that if the number of connections of each neuron with the neurons of the same class differs from that with the other class, the segregation of neurons into monocular regions indeed reduces the wire length. The shape of the regions depends on the relative number of neurons in the two classes. If both classes are equally represented we find that the optimal OD pattern consists of alternating stripes. If one class is less numerous than the other, the optimal OD pattern consists of patches of the underrepresented (ipsilateral) eye dominated neurons surrounded by the neurons of the other class. We predict the transition from stripes to patches when the fraction of neurons dominated by the ipsilateral eye is about 40%. This prediction agrees with the data in macaque and Cebus monkeys. Our theory can be applied to other binary cortical systems.

Maximization of Learning Speed Due to Neuronal Redundancy in Reinforcement Learning

NASA Astrophysics Data System (ADS)

Takiyama, Ken

2016-11-01

Adaptable neural activity contributes to the flexibility of human behavior, which is optimized in situations such as motor learning and decision making. Although learning signals in motor learning and decision making are low-dimensional, neural activity, which is very high dimensional, must be modified to achieve optimal performance based on the low-dimensional signal, resulting in a severe credit-assignment problem. Despite this problem, the human brain contains a vast number of neurons, leaving an open question: what is the functional significance of the huge number of neurons? Here, I address this question by analyzing a redundant neural network with a reinforcement-learning algorithm in which the numbers of neurons and output units are N and M, respectively. Because many combinations of neural activity can generate the same output under the condition of N ≫ M, I refer to the index N - M as neuronal redundancy. Although greater neuronal redundancy makes the credit-assignment problem more severe, I demonstrate that a greater degree of neuronal redundancy facilitates learning speed. Thus, in an apparent contradiction of the credit-assignment problem, I propose the hypothesis that a functional role of a huge number of neurons or a huge degree of neuronal redundancy is to facilitate learning speed.

Calcium phosphate transfection of primary hippocampal neurons.

PubMed

Sun, Miao; Bernard, Laura P; Dibona, Victoria L; Wu, Qian; Zhang, Huaye

2013-11-12

Calcium phosphate precipitation is a convenient and economical method for transfection of cultured cells. With optimization, it is possible to use this method on hard-to-transfect cells like primary neurons. Here we describe our detailed protocol for calcium phosphate transfection of hippocampal neurons cocultured with astroglial cells.

The Contingency of Cocaine Administration Accounts for Structural and Functional Medial Prefrontal Deficits and Increased Adrenocortical Activation

PubMed Central

Anderson, Rachel M.; Cosme, Caitlin V.; Glanz, Ryan M.; Miller, Mary C.; Romig-Martin, Sara A.; LaLumiere, Ryan T.

2015-01-01

The prelimbic region (PL) of the medial prefrontal cortex (mPFC) is implicated in the relapse of drug-seeking behavior. Optimal mPFC functioning relies on synaptic connections involving dendritic spines in pyramidal neurons, whereas prefrontal dysfunction resulting from elevated glucocorticoids, stress, aging, and mental illness are each linked to decreased apical dendritic branching and spine density in pyramidal neurons in these cortical fields. The fact that cocaine use induces activation of the stress-responsive hypothalamo-pituitary-adrenal axis raises the possibility that cocaine-related impairments in mPFC functioning may be manifested by similar changes in neuronal architecture in mPFC. Nevertheless, previous studies have generally identified increases, rather than decreases, in structural plasticity in mPFC after cocaine self-administration. Here, we use 3D imaging and analysis of dendritic spine morphometry to show that chronic cocaine self-administration leads to mild decreases of apical dendritic branching, prominent dendritic spine attrition in PL pyramidal neurons, and working memory deficits. Importantly, these impairments were largely accounted for in groups of rats that self-administered cocaine compared with yoked-cocaine- and saline-matched counterparts. Follow-up experiments failed to demonstrate any effects of either experimenter-administered cocaine or food self-administration on structural alterations in PL neurons. Finally, we verified that the cocaine self-administration group was distinguished by more protracted increases in adrenocortical activity compared with yoked-cocaine- and saline-matched controls. These studies suggest a mechanism whereby increased adrenocortical activity resulting from chronic cocaine self-administration may contribute to regressive prefrontal structural and functional plasticity. SIGNIFICANCE STATEMENT Stress, aging, and mental illness are each linked to decreased prefrontal plasticity. Here, we show that chronic cocaine self-administration in rats leads to decrements in medial prefrontal structural and functional plasticity. Notably, these impairments were largely accounted for in rats that self-administered cocaine compared with yoked counterparts. Moreover, we verified previous reports showing that adrenocortical output is augmented by cocaine administration and is more protracted in rats that were permitted to receive the drug contingently instead of passively. These studies suggest that increased adrenocortical activity resulting from cocaine self-administration may contribute to regressive prefrontal structural and functional plasticity. PMID:26311772

Decision-Related Activity in Macaque V2 for Fine Disparity Discrimination Is Not Compatible with Optimal Linear Readout

PubMed Central

Clery, Stephane; Cumming, Bruce G.

2017-01-01

Fine judgments of stereoscopic depth rely mainly on relative judgments of depth (relative binocular disparity) between objects, rather than judgments of the distance to where the eyes are fixating (absolute disparity). In macaques, visual area V2 is the earliest site in the visual processing hierarchy for which neurons selective for relative disparity have been observed (Thomas et al., 2002). Here, we found that, in macaques trained to perform a fine disparity discrimination task, disparity-selective neurons in V2 were highly selective for the task, and their activity correlated with the animals' perceptual decisions (unexplained by the stimulus). This may partially explain similar correlations reported in downstream areas. Although compatible with a perceptual role of these neurons for the task, the interpretation of such decision-related activity is complicated by the effects of interneuronal “noise” correlations between sensory neurons. Recent work has developed simple predictions to differentiate decoding schemes (Pitkow et al., 2015) without needing measures of noise correlations, and found that data from early sensory areas were compatible with optimal linear readout of populations with information-limiting correlations. In contrast, our data here deviated significantly from these predictions. We additionally tested this prediction for previously reported results of decision-related activity in V2 for a related task, coarse disparity discrimination (Nienborg and Cumming, 2006), thought to rely on absolute disparity. Although these data followed the predicted pattern, they violated the prediction quantitatively. This suggests that optimal linear decoding of sensory signals is not generally a good predictor of behavior in simple perceptual tasks. SIGNIFICANCE STATEMENT Activity in sensory neurons that correlates with an animal's decision is widely believed to provide insights into how the brain uses information from sensory neurons. Recent theoretical work developed simple predictions to differentiate decoding schemes, and found support for optimal linear readout of early sensory populations with information-limiting correlations. Here, we observed decision-related activity for neurons in visual area V2 of macaques performing fine disparity discrimination, as yet the earliest site for this task. These findings, and previously reported results from V2 in a different task, deviated from the predictions for optimal linear readout of a population with information-limiting correlations. Our results suggest that optimal linear decoding of early sensory information is not a general decoding strategy used by the brain. PMID:28100751

Optimizing growth and post treatment of diamond for high capacitance neural interfaces.

PubMed

Tong, Wei; Fox, Kate; Zamani, Akram; Turnley, Ann M; Ganesan, Kumaravelu; Ahnood, Arman; Cicione, Rosemary; Meffin, Hamish; Prawer, Steven; Stacey, Alastair; Garrett, David J

2016-10-01

Electrochemical and biological properties are two crucial criteria in the selection of the materials to be used as electrodes for neural interfaces. For neural stimulation, materials are required to exhibit high capacitance and to form intimate contact with neurons for eliciting effective neural responses at acceptably low voltages. Here we report on a new high capacitance material fabricated using nitrogen included ultrananocrystalline diamond (N-UNCD). After exposure to oxygen plasma for 3 h, the activated N-UNCD exhibited extremely high electrochemical capacitance greater than 1 mF/cm(2), which originates from the special hybrid sp(2)/sp(3) structure of N-UNCD. The in vitro biocompatibility of the activated N-UNCD was then assessed using rat cortical neurons and surface roughness was found to be critical for healthy neuron growth, with best results observed on surfaces with a roughness of approximately 20 nm. Therefore, by using oxygen plasma activated N-UNCD with appropriate surface roughness, and considering the chemical and mechanical stability of diamond, the fabricated neural interfaces are expected to exhibit high efficacy, long-term stability and a healthy neuron/electrode interface. Copyright © 2016 Elsevier Ltd. All rights reserved.

Evaluation of extra virgin olive oil stability by artificial neural network.

PubMed

Silva, Simone Faria; Anjos, Carlos Alberto Rodrigues; Cavalcanti, Rodrigo Nunes; Celeghini, Renata Maria dos Santos

2015-07-15

The stability of extra virgin olive oil in polyethylene terephthalate bottles and tinplate cans stored for 6 months under dark and light conditions was evaluated. The following analyses were carried out: free fatty acids, peroxide value, specific extinction at 232 and 270 nm, chlorophyll, L(∗)C(∗)h color, total phenolic compounds, tocopherols and squalene. The physicochemical changes were evaluated by artificial neural network (ANN) modeling with respect to light exposure conditions and packaging material. The optimized ANN structure consists of 11 input neurons, 18 hidden neurons and 5 output neurons using hyperbolic tangent and softmax activation functions in hidden and output layers, respectively. The five output neurons correspond to five possible classifications according to packaging material (PET amber, PET transparent and tinplate can) and light exposure (dark and light storage). The predicted physicochemical changes agreed very well with the experimental data showing high classification accuracy for test (>90%) and training set (>85). Sensitivity analysis showed that free fatty acid content, peroxide value, L(∗)Cab(∗)hab(∗) color parameters, tocopherol and chlorophyll contents were the physicochemical attributes with the most discriminative power. Copyright © 2015 Elsevier Ltd. All rights reserved.

A neural-network-based approach to the double traveling salesman problem.

PubMed

Plebe, Alessio; Anile, Angelo Marcello

2002-02-01

The double traveling salesman problem is a variation of the basic traveling salesman problem where targets can be reached by two salespersons operating in parallel. The real problem addressed by this work concerns the optimization of the harvest sequence for the two independent arms of a fruit-harvesting robot. This application poses further constraints, like a collision-avoidance function. The proposed solution is based on a self-organizing map structure, initialized with as many artificial neurons as the number of targets to be reached. One of the key components of the process is the combination of competitive relaxation with a mechanism for deleting and creating artificial neurons. Moreover, in the competitive relaxation process, information about the trajectory connecting the neurons is combined with the distance of neurons from the target. This strategy prevents tangles in the trajectory and collisions between the two tours. Results of tests indicate that the proposed approach is efficient and reliable for harvest sequence planning. Moreover, the enhancements added to the pure self-organizing map concept are of wider importance, as proved by a traveling salesman problem version of the program, simplified from the double version for comparison.

Calcium Phosphate Transfection of Primary Hippocampal Neurons

PubMed Central

DiBona, Victoria L.; Wu, Qian; Zhang, Huaye

2013-01-01

Calcium phosphate precipitation is a convenient and economical method for transfection of cultured cells. With optimization, it is possible to use this method on hard-to-transfect cells like primary neurons. Here we describe our detailed protocol for calcium phosphate transfection of hippocampal neurons cocultured with astroglial cells. PMID:24300106

Direct mapping of 19F in 19FDG-6P in brain tissue at subcellular resolution using soft X-ray fluorescence

NASA Astrophysics Data System (ADS)

Poitry-Yamate, C.; Gianoncelli, A.; Kourousias, G.; Kaulich, B.; Lepore, M.; Gruetter, R.; Kiskinova, M.

2013-10-01

Low energy x-ray fluorescence (LEXRF) detection was optimized for imaging cerebral glucose metabolism by mapping the fluorine LEXRF signal of 19F in 19FDG, trapped as intracellular 19F-deoxyglucose-6-phosphate (19FDG-6P) at 1μm spatial resolution from 3μm thick brain slices. 19FDG metabolism was evaluated in brain structures closely resembling the general cerebral cytoarchitecture following formalin fixation of brain slices and their inclusion in an epon matrix. 2-dimensional distribution maps of 19FDG-6P were placed in a cytoarchitectural and morphological context by simultaneous LEXRF mapping of N and O, and scanning transmission x-ray (STXM) imaging. A disproportionately high uptake and metabolism of glucose was found in neuropil relative to intracellular domains of the cell body of hypothalamic neurons, showing directly that neurons, like glial cells, also metabolize glucose. As 19F-deoxyglucose-6P is structurally identical to 18F-deoxyglucose-6P, LEXRF of subcellular 19F provides a link to in vivo 18FDG PET, forming a novel basis for understanding the physiological mechanisms underlying the 18FDG PET image, and the contribution of neurons and glia to the PET signal.

Optimal staining methods for delineation of cortical areas and neuron counts in human brains.

PubMed

Uylings, H B; Zilles, K; Rajkowska, G

1999-04-01

For cytoarchitectonic delineation of cortical areas in human brain, the Gallyas staining for somata with its sharp contrast between cell bodies and neuropil is preferable to the classical Nissl staining, the more so when an image analysis system is used. This Gallyas staining, however, does not appear to be appropriate for counting neuron numbers in pertinent brain areas, due to the lack of distinct cytological features between small neurons and glial cells. For cell counting Nissl is preferable. In an optimal design for cell counting at least both the Gallyas and the Nissl staining must be applied, the former staining for cytoarchitectural delineaton of cortical areas and the latter for counting the number of neurons in the pertinent cortical areas. Copyright 1999 Academic Press.

Age-related reduction in microcolumnar structure correlates with cognitive decline in ventral but not dorsal area 46 of the rhesus monkey.

PubMed

Cruz, L; Roe, D L; Urbanc, B; Inglis, A; Stanley, H E; Rosene, D L

2009-02-18

The age-related decline in cognitive function that is observed in normal aging monkeys and humans occurs without significant loss of cortical neurons. This suggests that cognitive impairment results from subtle, sub-lethal changes in the cortex. Recently, changes in the structural coherence in mini- or microcolumns without loss of neurons have been linked to loss of function. Here we use a density map method to quantify microcolumnar structure in both banks of the sulcus principalis (prefrontal cortical area 46) of 16 (ventral) and 19 (dorsal) behaviorally tested female rhesus monkeys from 6 to 33 years of age. While total neuronal density does not change with age in either of these banks, there is a significant age-related reduction in the strength of microcolumns in both regions on the order of 40%. This likely reflects a subtle but definite loss of organization in the structure of the cortical microcolumn. The reduction in strength in ventral area 46 correlates with cognitive impairments in learning and memory while the reduction in dorsal area 46 does not. This result is congruent with published data attributing cognitive functions to ventral area 46 that are similar to our particular cognitive battery which does not optimally tap cognitive functions attributed to dorsal area 46. While the exact mechanisms underlying this loss of microcolumnar organization remain to be determined, it is plausible that they reflect age-related alterations in dendritic and/or axonal organization which alter connectivity and may contribute to age-related declines in cognitive performance.

Noise-induced escape in an excitable system

NASA Astrophysics Data System (ADS)

Khovanov, I. A.; Polovinkin, A. V.; Luchinsky, D. G.; McClintock, P. V. E.

2013-03-01

We consider the stochastic dynamics of escape in an excitable system, the FitzHugh-Nagumo (FHN) neuronal model, for different classes of excitability. We discuss, first, the threshold structure of the FHN model as an example of a system without a saddle state. We then develop a nonlinear (nonlocal) stability approach based on the theory of large fluctuations, including a finite-noise correction, to describe noise-induced escape in the excitable regime. We show that the threshold structure is revealed via patterns of most probable (optimal) fluctuational paths. The approach allows us to estimate the escape rate and the exit location distribution. We compare the responses of a monostable resonator and monostable integrator to stochastic input signals and to a mixture of periodic and stochastic stimuli. Unlike the commonly used local analysis of the stable state, our nonlocal approach based on optimal paths yields results that are in good agreement with direct numerical simulations of the Langevin equation.

Elements of an algorithm for optimizing a parameter-structural neural network

NASA Astrophysics Data System (ADS)

Mrówczyńska, Maria

2016-06-01

The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development of this field improves classic algorithms for numerical calculations in the aspect of analytical solutions that are difficult to achieve. Algorithms based on artificial intelligence in the form of artificial neural networks, including the topology of connections between neurons have become an important instrument connected to the problem of processing and modelling processes. This concept results from the integration of neural networks and parameter optimization methods and makes it possible to avoid the necessity to arbitrarily define the structure of a network. This kind of extension of the training process is exemplified by the algorithm called the Group Method of Data Handling (GMDH), which belongs to the class of evolutionary algorithms. The article presents a GMDH type network, used for modelling deformations of the geometrical axis of a steel chimney during its operation.

Baseline estimation in flame's spectra by using neural networks and robust statistics

NASA Astrophysics Data System (ADS)

Garces, Hugo; Arias, Luis; Rojas, Alejandro

2014-09-01

This work presents a baseline estimation method in flame spectra based on artificial intelligence structure as a neural network, combining robust statistics with multivariate analysis to automatically discriminate measured wavelengths belonging to continuous feature for model adaptation, surpassing restriction of measuring target baseline for training. The main contributions of this paper are: to analyze a flame spectra database computing Jolliffe statistics from Principal Components Analysis detecting wavelengths not correlated with most of the measured data corresponding to baseline; to systematically determine the optimal number of neurons in hidden layers based on Akaike's Final Prediction Error; to estimate baseline in full wavelength range sampling measured spectra; and to train an artificial intelligence structure as a Neural Network which allows to generalize the relation between measured and baseline spectra. The main application of our research is to compute total radiation with baseline information, allowing to diagnose combustion process state for optimization in early stages.

Sparse bursts optimize information transmission in a multiplexed neural code.

PubMed

Naud, Richard; Sprekeler, Henning

2018-06-22

Many cortical neurons combine the information ascending and descending the cortical hierarchy. In the classical view, this information is combined nonlinearly to give rise to a single firing-rate output, which collapses all input streams into one. We analyze the extent to which neurons can simultaneously represent multiple input streams by using a code that distinguishes spike timing patterns at the level of a neural ensemble. Using computational simulations constrained by experimental data, we show that cortical neurons are well suited to generate such multiplexing. Interestingly, this neural code maximizes information for short and sparse bursts, a regime consistent with in vivo recordings. Neurons can also demultiplex this information, using specific connectivity patterns. The anatomy of the adult mammalian cortex suggests that these connectivity patterns are used by the nervous system to maintain sparse bursting and optimal multiplexing. Contrary to firing-rate coding, our findings indicate that the physiology and anatomy of the cortex may be interpreted as optimizing the transmission of multiple independent signals to different targets. Copyright © 2018 the Author(s). Published by PNAS.

Minimum energy control for a two-compartment neuron to extracellular electric fields

NASA Astrophysics Data System (ADS)

Yi, Guo-Sheng; Wang, Jiang; Li, Hui-Yan; Wei, Xi-Le; Deng, Bin

2016-11-01

The energy optimization of extracellular electric field (EF) stimulus for a neuron is considered in this paper. We employ the optimal control theory to design a low energy EF input for a reduced two-compartment model. It works by driving the neuron to closely track a prescriptive spike train. A cost function is introduced to balance the contradictory objectives, i.e., tracking errors and EF stimulus energy. By using the calculus of variations, we transform the minimization of cost function to a six-dimensional two-point boundary value problem (BVP). Through solving the obtained BVP in the cases of three fundamental bifurcations, it is shown that the control method is able to provide an optimal EF stimulus of reduced energy for the neuron to effectively track a prescriptive spike train. Further, the feasibility of the adopted method is interpreted from the point of view of the biophysical basis of spike initiation. These investigations are conducive to designing stimulating dose for extracellular neural stimulation, which are also helpful to interpret the effects of extracellular field on neural activity.

Stimulation of GABA-Induced Ca2+ Influx Enhances Maturation of Human Induced Pluripotent Stem Cell-Derived Neurons

PubMed Central

Rushton, David J.; Mattis, Virginia B.; Svendsen, Clive N.; Allen, Nicholas D.; Kemp, Paul J.

2013-01-01

Optimal use of patient-derived, induced pluripotent stem cells for modeling neuronal diseases is crucially dependent upon the proper physiological maturation of derived neurons. As a strategy to develop defined differentiation protocols that optimize electrophysiological function, we investigated the role of Ca2+ channel regulation by astrocyte conditioned medium in neuronal maturation, using whole-cell patch clamp and Ca2+ imaging. Standard control medium supported basic differentiation of induced pluripotent stem cell-derived neurons, as assayed by the ability to fire simple, single, induced action potentials. In contrast, treatment with astrocyte conditioned medium elicited complex and spontaneous neuronal activity, often with rhythmic and biphasic characteristics. Such augmented spontaneous activity correlated with astrocyte conditioned medium-evoked hyperpolarization and was dependent upon regulated function of L-, N- and R-type Ca2+ channels. The requirement for astrocyte conditioned medium could be substituted by simply supplementing control differentiation medium with high Ca2+ or γ-amino butyric acid (GABA). Importantly, even in the absence of GABA signalling, opening Ca2+ channels directly using Bay K8644 was able to hyperpolarise neurons and enhance excitability, producing fully functional neurons. These data provide mechanistic insight into how secreted astrocyte factors control differentiation and, importantly, suggest that pharmacological modulation of Ca2+ channel function leads to the development of a defined protocol for improved maturation of induced pluripotent stem cell-derived neurons. PMID:24278369

Estimation of key parameters in adaptive neuron model according to firing patterns based on improved particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Yuan, Chunhua; Wang, Jiang; Yi, Guosheng

2017-03-01

Estimation of ion channel parameters is crucial to spike initiation of neurons. The biophysical neuron models have numerous ion channel parameters, but only a few of them play key roles in the firing patterns of the models. So we choose three parameters featuring the adaptation in the Ermentrout neuron model to be estimated. However, the traditional particle swarm optimization (PSO) algorithm is still easy to fall into local optimum and has the premature convergence phenomenon in the study of some problems. In this paper, we propose an improved method that uses a concave function and dynamic logistic chaotic mapping mixed to adjust the inertia weights of the fitness value, effectively improve the global convergence ability of the algorithm. The perfect predicting firing trajectories of the rebuilt model using the estimated parameters prove that only estimating a few important ion channel parameters can establish the model well and the proposed algorithm is effective. Estimations using two classic PSO algorithms are also compared to the improved PSO to verify that the algorithm proposed in this paper can avoid local optimum and quickly converge to the optimal value. The results provide important theoretical foundations for building biologically realistic neuron models.

Optimizing the 3D-reconstruction technique for serial block-face scanning electron microscopy.

PubMed

Wernitznig, Stefan; Sele, Mariella; Urschler, Martin; Zankel, Armin; Pölt, Peter; Rind, F Claire; Leitinger, Gerd

2016-05-01

Elucidating the anatomy of neuronal circuits and localizing the synaptic connections between neurons, can give us important insights in how the neuronal circuits work. We are using serial block-face scanning electron microscopy (SBEM) to investigate the anatomy of a collision detection circuit including the Lobula Giant Movement Detector (LGMD) neuron in the locust, Locusta migratoria. For this, thousands of serial electron micrographs are produced that allow us to trace the neuronal branching pattern. The reconstruction of neurons was previously done manually by drawing cell outlines of each cell in each image separately. This approach was very time consuming and troublesome. To make the process more efficient a new interactive software was developed. It uses the contrast between the neuron under investigation and its surrounding for semi-automatic segmentation. For segmentation the user sets starting regions manually and the algorithm automatically selects a volume within the neuron until the edges corresponding to the neuronal outline are reached. Internally the algorithm optimizes a 3D active contour segmentation model formulated as a cost function taking the SEM image edges into account. This reduced the reconstruction time, while staying close to the manual reference segmentation result. Our algorithm is easy to use for a fast segmentation process, unlike previous methods it does not require image training nor an extended computing capacity. Our semi-automatic segmentation algorithm led to a dramatic reduction in processing time for the 3D-reconstruction of identified neurons. Copyright © 2016 Elsevier B.V. All rights reserved.

3D printing biodegradable scaffolds with chitosan materials for tissue engineering

NASA Astrophysics Data System (ADS)

Bardakova, K. N.; Demina, T. S.; Grebenik, E. A.; Minaev, N. V.; Akopova, T. A.; Bagratashvili, V. N.; Timashev, P. S.

2018-04-01

Chitosan-g-oligo (L,L-lactide) copolymer was synthesized through a solvent-free reaction in an extruder. Three-dimensional scaffolds based on photosensitive composition contained the synthetized copolymer were formed by two-photon polymerization. The optimum ratio of components, methods of preparation of photopolymerizable mixtures, parameters of the laser structuring and procedure of washing from unbound crosslinkers have been optimized. Chitosan scaffolds were non-cytotoxic and might therefore be a suitable candidate for treating spinal cord injuries and other neuronal degenerative diseases.

Decision-Related Activity in Macaque V2 for Fine Disparity Discrimination Is Not Compatible with Optimal Linear Readout.

PubMed

Clery, Stephane; Cumming, Bruce G; Nienborg, Hendrikje

2017-01-18

Fine judgments of stereoscopic depth rely mainly on relative judgments of depth (relative binocular disparity) between objects, rather than judgments of the distance to where the eyes are fixating (absolute disparity). In macaques, visual area V2 is the earliest site in the visual processing hierarchy for which neurons selective for relative disparity have been observed (Thomas et al., 2002). Here, we found that, in macaques trained to perform a fine disparity discrimination task, disparity-selective neurons in V2 were highly selective for the task, and their activity correlated with the animals' perceptual decisions (unexplained by the stimulus). This may partially explain similar correlations reported in downstream areas. Although compatible with a perceptual role of these neurons for the task, the interpretation of such decision-related activity is complicated by the effects of interneuronal "noise" correlations between sensory neurons. Recent work has developed simple predictions to differentiate decoding schemes (Pitkow et al., 2015) without needing measures of noise correlations, and found that data from early sensory areas were compatible with optimal linear readout of populations with information-limiting correlations. In contrast, our data here deviated significantly from these predictions. We additionally tested this prediction for previously reported results of decision-related activity in V2 for a related task, coarse disparity discrimination (Nienborg and Cumming, 2006), thought to rely on absolute disparity. Although these data followed the predicted pattern, they violated the prediction quantitatively. This suggests that optimal linear decoding of sensory signals is not generally a good predictor of behavior in simple perceptual tasks. Activity in sensory neurons that correlates with an animal's decision is widely believed to provide insights into how the brain uses information from sensory neurons. Recent theoretical work developed simple predictions to differentiate decoding schemes, and found support for optimal linear readout of early sensory populations with information-limiting correlations. Here, we observed decision-related activity for neurons in visual area V2 of macaques performing fine disparity discrimination, as yet the earliest site for this task. These findings, and previously reported results from V2 in a different task, deviated from the predictions for optimal linear readout of a population with information-limiting correlations. Our results suggest that optimal linear decoding of early sensory information is not a general decoding strategy used by the brain. Copyright © 2017 the authors 0270-6474/17/370715-11$15.00/0.

Small Molecule Anticonvulsant Agents with Potent In Vitro Neuroprotection

PubMed Central

Smith, Garry R.; Zhang, Yan; Du, Yanming; Kondaveeti, Sandeep K.; Zdilla, Michael J.; Reitz, Allen B.

2012-01-01

Severe seizure activity is associated with recurring cycles of excitotoxicity and oxidative stress that result in progressive neuronal damage and death. Intervention to halt these pathological processes is a compelling disease-modifying strategy for the treatment of seizure disorders. In the present study, a core small molecule with anticonvulsant activity has been structurally optimized for neuroprotection. Phenotypic screening of rat hippocampal cultures with nutrient medium depleted of antioxidants was utilized as a disease model. Increased cell death and decreased neuronal viability produced by acute treatment with glutamate or hydrogen peroxide were prevented by our novel molecules. The neuroprotection associated with this chemical series has marked structure activity relationships that focus on modification of the benzylic position of a 2-phenyl-2-hydroxyethyl sulfamide core structure. Complete separation between anticonvulsant activity and neuroprotective action was dependent on substitution at the benzylic carbon. Chiral selectivity was evident in that the S-enantiomer of the benzylic hydroxy group had neither neuroprotective nor anticonvulsant activity, while the R-enantiomer of the lead compound had full neuroprotective action at ≤40 nM and antiseizure activity in three animal models. These studies indicate that potent, multifunctional neuroprotective anticonvulsants are feasible within a single molecular entity. PMID:22535312

Optimal number of stimulation contacts for coordinated reset neuromodulation

PubMed Central

Lysyansky, Borys; Popovych, Oleksandr V.; Tass, Peter A.

2013-01-01

In this computational study we investigate coordinated reset (CR) neuromodulation designed for an effective control of synchronization by multi-site stimulation of neuronal target populations. This method was suggested to effectively counteract pathological neuronal synchrony characteristic for several neurological disorders. We study how many stimulation sites are required for optimal CR-induced desynchronization. We found that a moderate increase of the number of stimulation sites may significantly prolong the post-stimulation desynchronized transient after the stimulation is completely switched off. This can, in turn, reduce the amount of the administered stimulation current for the intermittent ON–OFF CR stimulation protocol, where time intervals with stimulation ON are recurrently followed by time intervals with stimulation OFF. In addition, we found that the optimal number of stimulation sites essentially depends on how strongly the administered current decays within the neuronal tissue with increasing distance from the stimulation site. In particular, for a broad spatial stimulation profile, i.e., for a weak spatial decay rate of the stimulation current, CR stimulation can optimally be delivered via a small number of stimulation sites. Our findings may contribute to an optimization of therapeutic applications of CR neuromodulation. PMID:23885239

The Three-Dimensional Culture System with Matrigel and Neurotrophic Factors Preserves the Structure and Function of Spiral Ganglion Neuron In Vitro.

PubMed

Sun, Gaoying; Liu, Wenwen; Fan, Zhaomin; Zhang, Daogong; Han, Yuechen; Xu, Lei; Qi, Jieyu; Zhang, Shasha; Gao, Bradley T; Bai, Xiaohui; Li, Jianfeng; Chai, Renjie; Wang, Haibo

2016-01-01

Whole organ culture of the spiral ganglion region is a resourceful model system facilitating manipulation and analysis of live sprial ganglion neurons (SGNs). Three-dimensional (3D) cultures have been demonstrated to have many biomedical applications, but the effect of 3D culture in maintaining the SGNs structure and function in explant culture remains uninvestigated. In this study, we used the matrigel to encapsulate the spiral ganglion region isolated from neonatal mice. First, we optimized the matrigel concentration for the 3D culture system and found the 3D culture system protected the SGNs against apoptosis, preserved the structure of spiral ganglion region, and promoted the sprouting and outgrowth of SGNs neurites. Next, we found the 3D culture system promoted growth cone growth as evidenced by a higher average number and a longer average length of filopodia and a larger growth cone area. 3D culture system also significantly elevated the synapse density of SGNs. Last, we found that the 3D culture system combined with neurotrophic factors had accumulated effects in promoting the neurites outgrowth compared with 3D culture or NFs treatment only groups. Together, we conclude that the 3D culture system preserves the structure and function of SGN in explant culture.

Brain-region–specific alterations of the trajectories of neuronal volume growth throughout the lifespan in autism

PubMed Central

2014-01-01

Several morphometric studies have revealed smaller than normal neurons in the neocortex of autistic subjects. To test the hypothesis that abnormal neuronal growth is a marker of an autism-associated global encephalopathy, neuronal volumes were estimated in 16 brain regions, including various subcortical structures, Ammon’s horn, archicortex, cerebellum, and brainstem in 14 brains from individuals with autism 4 to 60 years of age and 14 age-matched control brains. This stereological study showed a significantly smaller volume of neuronal soma in 14 of 16 regions in the 4- to 8-year-old autistic brains than in the controls. Arbitrary classification revealed a very severe neuronal volume deficit in 14.3% of significantly altered structures, severe in 50%, moderate in 21.4%, and mild in 14.3% structures. This pattern suggests desynchronized neuronal growth in the interacting neuronal networks involved in the autistic phenotype. The comparative study of the autistic and control subject brains revealed that the number of structures with a significant volume deficit decreased from 14 in the 4- to 8-year-old autistic subjects to 4 in the 36- to 60-year-old. Neuronal volumes in 75% of the structures examined in the older adults with autism are comparable to neuronal volume in age-matched controls. This pattern suggests defects of neuronal growth in early childhood and delayed up-regulation of neuronal growth during adolescence and adulthood reducing neuron soma volume deficit in majority of examined regions. However, significant correction of neuron size but limited clinical improvements suggests that delayed correction does not restore functional deficits. PMID:24612906

All brains are made of this: a fundamental building block of brain matter with matching neuronal and glial masses.

PubMed

Mota, Bruno; Herculano-Houzel, Suzana

2014-01-01

How does the size of the glial and neuronal cells that compose brain tissue vary across brain structures and species? Our previous studies indicate that average neuronal size is highly variable, while average glial cell size is more constant. Measuring whole cell sizes in vivo, however, is a daunting task. Here we use chi-square minimization of the relationship between measured neuronal and glial cell densities in the cerebral cortex, cerebellum, and rest of brain in 27 mammalian species to model neuronal and glial cell mass, as well as the neuronal mass fraction of the tissue (the fraction of tissue mass composed by neurons). Our model shows that while average neuronal cell mass varies by over 500-fold across brain structures and species, average glial cell mass varies only 1.4-fold. Neuronal mass fraction varies typically between 0.6 and 0.8 in all structures. Remarkably, we show that two fundamental, universal relationships apply across all brain structures and species: (1) the glia/neuron ratio varies with the total neuronal mass in the tissue (which in turn depends on variations in average neuronal cell mass), and (2) the neuronal mass per glial cell, and with it the neuronal mass fraction and neuron/glia mass ratio, varies with average glial cell mass in the tissue. We propose that there is a fundamental building block of brain tissue: the glial mass that accompanies a unit of neuronal mass. We argue that the scaling of this glial mass is a consequence of a universal mechanism whereby numbers of glial cells are added to the neuronal parenchyma during development, irrespective of whether the neurons composing it are large or small, but depending on the average mass of the glial cells being added. We also show how evolutionary variations in neuronal cell mass, glial cell mass and number of neurons suffice to determine the most basic characteristics of brain structures, such as mass, glia/neuron ratio, neuron/glia mass ratio, and cell densities.

Significance of Input Correlations in Striatal Function

PubMed Central

Yim, Man Yi; Aertsen, Ad; Kumar, Arvind

2011-01-01

The striatum is the main input station of the basal ganglia and is strongly associated with motor and cognitive functions. Anatomical evidence suggests that individual striatal neurons are unlikely to share their inputs from the cortex. Using a biologically realistic large-scale network model of striatum and cortico-striatal projections, we provide a functional interpretation of the special anatomical structure of these projections. Specifically, we show that weak pairwise correlation within the pool of inputs to individual striatal neurons enhances the saliency of signal representation in the striatum. By contrast, correlations among the input pools of different striatal neurons render the signal representation less distinct from background activity. We suggest that for the network architecture of the striatum, there is a preferred cortico-striatal input configuration for optimal signal representation. It is further enhanced by the low-rate asynchronous background activity in striatum, supported by the balance between feedforward and feedback inhibitions in the striatal network. Thus, an appropriate combination of rates and correlations in the striatal input sets the stage for action selection presumably implemented in the basal ganglia. PMID:22125480

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.

DHA Effects in Brain Development and Function

PubMed Central

Lauritzen, Lotte; Brambilla, Paolo; Mazzocchi, Alessandra; Harsløf, Laurine B. S.; Ciappolino, Valentina; Agostoni, Carlo

2016-01-01

Docosahexaenoic acid (DHA) is a structural constituent of membranes specifically in the central nervous system. Its accumulation in the fetal brain takes place mainly during the last trimester of pregnancy and continues at very high rates up to the end of the second year of life. Since the endogenous formation of DHA seems to be relatively low, DHA intake may contribute to optimal conditions for brain development. We performed a narrative review on research on the associations between DHA levels and brain development and function throughout the lifespan. Data from cell and animal studies justify the indication of DHA in relation to brain function for neuronal cell growth and differentiation as well as in relation to neuronal signaling. Most data from human studies concern the contribution of DHA to optimal visual acuity development. Accumulating data indicate that DHA may have effects on the brain in infancy, and recent studies indicate that the effect of DHA may depend on gender and genotype of genes involved in the endogenous synthesis of DHA. While DHA levels may affect early development, potential effects are also increasingly recognized during childhood and adult life, suggesting a role of DHA in cognitive decline and in relation to major psychiatric disorders. PMID:26742060

DHA Effects in Brain Development and Function.

PubMed

Lauritzen, Lotte; Brambilla, Paolo; Mazzocchi, Alessandra; Harsløf, Laurine B S; Ciappolino, Valentina; Agostoni, Carlo

2016-01-04

Docosahexaenoic acid (DHA) is a structural constituent of membranes specifically in the central nervous system. Its accumulation in the fetal brain takes place mainly during the last trimester of pregnancy and continues at very high rates up to the end of the second year of life. Since the endogenous formation of DHA seems to be relatively low, DHA intake may contribute to optimal conditions for brain development. We performed a narrative review on research on the associations between DHA levels and brain development and function throughout the lifespan. Data from cell and animal studies justify the indication of DHA in relation to brain function for neuronal cell growth and differentiation as well as in relation to neuronal signaling. Most data from human studies concern the contribution of DHA to optimal visual acuity development. Accumulating data indicate that DHA may have effects on the brain in infancy, and recent studies indicate that the effect of DHA may depend on gender and genotype of genes involved in the endogenous synthesis of DHA. While DHA levels may affect early development, potential effects are also increasingly recognized during childhood and adult life, suggesting a role of DHA in cognitive decline and in relation to major psychiatric disorders.

Vasopressin regularizes the phasic firing pattern of rat hypothalamic magnocellular vasopressin neurons.

PubMed

Gouzènes, L; Desarménien, M G; Hussy, N; Richard, P; Moos, F C

1998-03-01

Vasopressin (AVP) magnocellular neurons of hypothalamic nuclei express specific phasic firing (successive periods of activity and silence), which conditions the mode of neurohypophyseal vasopression release. In situations favoring plasmatic secretion of AVP, the hormone is also released at the somatodendritic level, at which it is believed to modulate the activity of AVP neurons. We investigated the nature of this autocontrol by testing the effects of juxtamembrane applications of AVP on the extracellular activity of presumed AVP neurons in paraventricular and supraoptic nuclei of anesthetized rats. AVP had three effects depending on the initial firing pattern: (1) excitation of faintly active neurons (periods of activity of <10 sec), which acquired or reinforced their phasic pattern; (2) inhibition of quasi-continuously active neurons (periods of silences of <10 sec), which became clearly phasic; and (3) no effect on neurons already showing an intermediate phasic pattern (active and silent periods of 10-30 sec). Consequently, AVP application resulted in a narrower range of activity patterns of the population of AVP neurons, with a Gaussian distribution centered around a mode of 57% of time in activity, indicating a homogenization of the firing pattern. The resulting phasic pattern had characteristics close to those established previously for optimal release of AVP from neurohypophyseal endings. These results suggest a new role for AVP as an optimizing factor that would foster the population of AVP neurons to discharge with a phasic pattern known to be most efficient for hormone release.

Temporal neural networks and transient analysis of complex engineering systems

NASA Astrophysics Data System (ADS)

Uluyol, Onder

A theory is introduced for a multi-layered Local Output Gamma Feedback (LOGF) neural network within the paradigm of Locally-Recurrent Globally-Feedforward neural networks. It is developed for the identification, prediction, and control tasks of spatio-temporal systems and allows for the presentation of different time scales through incorporation of a gamma memory. It is initially applied to the tasks of sunspot and Mackey-Glass series prediction as benchmarks, then it is extended to the task of power level control of a nuclear reactor at different fuel cycle conditions. The developed LOGF neuron model can also be viewed as a Transformed Input and State (TIS) Gamma memory for neural network architectures for temporal processing. The novel LOGF neuron model extends the static neuron model by incorporating into it a short-term memory structure in the form of a digital gamma filter. A feedforward neural network made up of LOGF neurons can thus be used to model dynamic systems. A learning algorithm based upon the Backpropagation-Through-Time (BTT) approach is derived. It is applicable for training a general L-layer LOGF neural network. The spatial and temporal weights and parameters of the network are iteratively optimized for a given problem using the derived learning algorithm.

Directional hearing by linear summation of binaural inputs at the medial superior olive

PubMed Central

van der Heijden, Marcel; Lorteije, Jeannette A. M.; Plauška, Andrius; Roberts, Michael T.; Golding, Nace L.; Borst, J. Gerard G.

2013-01-01

SUMMARY Neurons in the medial superior olive (MSO) enable sound localization by their remarkable sensitivity to submillisecond interaural time differences (ITDs). Each MSO neuron has its own “best ITD” to which it responds optimally. A difference in physical path length of the excitatory inputs from both ears cannot fully account for the ITD tuning of MSO neurons. As a result, it is still debated how these inputs interact and whether the segregation of inputs to opposite dendrites, well-timed synaptic inhibition, or asymmetries in synaptic potentials or cellular morphology further optimize coincidence detection or ITD tuning. Using in vivo whole-cell and juxtacellular recordings, we show here that ITD tuning of MSO neurons is determined by the timing of their excitatory inputs. The inputs from both ears sum linearly, whereas spike probability depends nonlinearly on the size of synaptic inputs. This simple coincidence detection scheme thus makes accurate sound localization possible. PMID:23764292

Channel Noise-Enhanced Synchronization Transitions Induced by Time Delay in Adaptive Neuronal Networks with Spike-Timing-Dependent Plasticity

NASA Astrophysics Data System (ADS)

Xie, Huijuan; Gong, Yubing; Wang, Baoying

In this paper, we numerically study the effect of channel noise on synchronization transitions induced by time delay in adaptive scale-free Hodgkin-Huxley neuronal networks with spike-timing-dependent plasticity (STDP). It is found that synchronization transitions by time delay vary as channel noise intensity is changed and become most pronounced when channel noise intensity is optimal. This phenomenon depends on STDP and network average degree, and it can be either enhanced or suppressed as network average degree increases depending on channel noise intensity. These results show that there are optimal channel noise and network average degree that can enhance the synchronization transitions by time delay in the adaptive neuronal networks. These findings could be helpful for better understanding of the regulation effect of channel noise on synchronization of neuronal networks. They could find potential implications for information transmission in neural systems.

Stochastic Resonance in Signal Detection and Human Perception

DTIC Science & Technology

2006-07-05

learning scheme performing a stochastic gradient ascent on the SNR to determine the optimal noise level based on the samples from the process. Rather than...produce some SR effect in threshold neurons and a new statistically robust learning law was proposed to find the optimal noise level. [McDonnell...Ultimately, we know that it is the brain that responds to a visual stimulus causing neurons to fire. Conceivably if we understood the effect of the noise PDF

A Reward-Maximizing Spiking Neuron as a Bounded Rational Decision Maker.

PubMed

Leibfried, Felix; Braun, Daniel A

2015-08-01

Rate distortion theory describes how to communicate relevant information most efficiently over a channel with limited capacity. One of the many applications of rate distortion theory is bounded rational decision making, where decision makers are modeled as information channels that transform sensory input into motor output under the constraint that their channel capacity is limited. Such a bounded rational decision maker can be thought to optimize an objective function that trades off the decision maker's utility or cumulative reward against the information processing cost measured by the mutual information between sensory input and motor output. In this study, we interpret a spiking neuron as a bounded rational decision maker that aims to maximize its expected reward under the computational constraint that the mutual information between the neuron's input and output is upper bounded. This abstract computational constraint translates into a penalization of the deviation between the neuron's instantaneous and average firing behavior. We derive a synaptic weight update rule for such a rate distortion optimizing neuron and show in simulations that the neuron efficiently extracts reward-relevant information from the input by trading off its synaptic strengths against the collected reward.

α7 nicotinic ACh receptors as a ligand-gated source of Ca(2+) ions: the search for a Ca(2+) optimum.

PubMed

Uteshev, Victor V

2012-01-01

The spatiotemporal distribution of cytosolic Ca(2+) ions is a key determinant of neuronal behavior and survival. Distinct sources of Ca(2+) ions including ligand- and voltage-gated Ca(2+) channels contribute to intracellular Ca(2+) homeostasis. Many normal physiological and therapeutic neuronal functions are Ca(2+)-dependent, however an excess of cytosolic Ca(2+) or a lack of the appropriate balance between Ca(2+) entry and clearance may destroy cellular integrity and cause cellular death. Therefore, the existence of optimal spatiotemporal patterns of cytosolic Ca(2+) elevations and thus, optimal activation of ligand- and voltage-gated Ca(2+) ion channels are postulated to benefit neuronal function and survival. Alpha7 nicotinic -acetylcholine receptors (nAChRs) are highly permeable to Ca(2+) ions and play an important role in modulation of neurotransmitter release, gene expression and neuroprotection in a variety of neuronal and non-neuronal cells. In this review, the focus is placed on α7 nAChR-mediated currents and Ca(2+) influx and how this source of Ca(2+) entry compares to NMDA receptors in supporting cytosolic Ca(2+) homeostasis, neuronal function and survival.

Constructing Neuronal Network Models in Massively Parallel Environments.

PubMed

Ippen, Tammo; Eppler, Jochen M; Plesser, Hans E; Diesmann, Markus

2017-01-01

Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers.

Constructing Neuronal Network Models in Massively Parallel Environments

PubMed Central

Ippen, Tammo; Eppler, Jochen M.; Plesser, Hans E.; Diesmann, Markus

2017-01-01

Recent advances in the development of data structures to represent spiking neuron network models enable us to exploit the complete memory of petascale computers for a single brain-scale network simulation. In this work, we investigate how well we can exploit the computing power of such supercomputers for the creation of neuronal networks. Using an established benchmark, we divide the runtime of simulation code into the phase of network construction and the phase during which the dynamical state is advanced in time. We find that on multi-core compute nodes network creation scales well with process-parallel code but exhibits a prohibitively large memory consumption. Thread-parallel network creation, in contrast, exhibits speedup only up to a small number of threads but has little overhead in terms of memory. We further observe that the algorithms creating instances of model neurons and their connections scale well for networks of ten thousand neurons, but do not show the same speedup for networks of millions of neurons. Our work uncovers that the lack of scaling of thread-parallel network creation is due to inadequate memory allocation strategies and demonstrates that thread-optimized memory allocators recover excellent scaling. An analysis of the loop order used for network construction reveals that more complex tests on the locality of operations significantly improve scaling and reduce runtime by allowing construction algorithms to step through large networks more efficiently than in existing code. The combination of these techniques increases performance by an order of magnitude and harnesses the increasingly parallel compute power of the compute nodes in high-performance clusters and supercomputers. PMID:28559808

Super-linear Precision in Simple Neural Population Codes

NASA Astrophysics Data System (ADS)

Schwab, David; Fiete, Ila

2015-03-01

A widely used tool for quantifying the precision with which a population of noisy sensory neurons encodes the value of an external stimulus is the Fisher Information (FI). Maximizing the FI is also a commonly used objective for constructing optimal neural codes. The primary utility and importance of the FI arises because it gives, through the Cramer-Rao bound, the smallest mean-squared error achievable by any unbiased stimulus estimator. However, it is well-known that when neural firing is sparse, optimizing the FI can result in codes that perform very poorly when considering the resulting mean-squared error, a measure with direct biological relevance. Here we construct optimal population codes by minimizing mean-squared error directly and study the scaling properties of the resulting network, focusing on the optimal tuning curve width. We then extend our results to continuous attractor networks that maintain short-term memory of external stimuli in their dynamics. Here we find similar scaling properties in the structure of the interactions that minimize diffusive information loss.

Action understanding and active inference

PubMed Central

Mattout, Jérémie; Kilner, James

2012-01-01

We have suggested that the mirror-neuron system might be usefully understood as implementing Bayes-optimal perception of actions emitted by oneself or others. To substantiate this claim, we present neuronal simulations that show the same representations can prescribe motor behavior and encode motor intentions during action–observation. These simulations are based on the free-energy formulation of active inference, which is formally related to predictive coding. In this scheme, (generalised) states of the world are represented as trajectories. When these states include motor trajectories they implicitly entail intentions (future motor states). Optimizing the representation of these intentions enables predictive coding in a prospective sense. Crucially, the same generative models used to make predictions can be deployed to predict the actions of self or others by simply changing the bias or precision (i.e. attention) afforded to proprioceptive signals. We illustrate these points using simulations of handwriting to illustrate neuronally plausible generation and recognition of itinerant (wandering) motor trajectories. We then use the same simulations to produce synthetic electrophysiological responses to violations of intentional expectations. Our results affirm that a Bayes-optimal approach provides a principled framework, which accommodates current thinking about the mirror-neuron system. Furthermore, it endorses the general formulation of action as active inference. PMID:21327826

DeepNeuron: an open deep learning toolbox for neuron tracing.

PubMed

Zhou, Zhi; Kuo, Hsien-Chi; Peng, Hanchuan; Long, Fuhui

2018-06-06

Reconstructing three-dimensional (3D) morphology of neurons is essential for understanding brain structures and functions. Over the past decades, a number of neuron tracing tools including manual, semiautomatic, and fully automatic approaches have been developed to extract and analyze 3D neuronal structures. Nevertheless, most of them were developed based on coding certain rules to extract and connect structural components of a neuron, showing limited performance on complicated neuron morphology. Recently, deep learning outperforms many other machine learning methods in a wide range of image analysis and computer vision tasks. Here we developed a new Open Source toolbox, DeepNeuron, which uses deep learning networks to learn features and rules from data and trace neuron morphology in light microscopy images. DeepNeuron provides a family of modules to solve basic yet challenging problems in neuron tracing. These problems include but not limited to: (1) detecting neuron signal under different image conditions, (2) connecting neuronal signals into tree(s), (3) pruning and refining tree morphology, (4) quantifying the quality of morphology, and (5) classifying dendrites and axons in real time. We have tested DeepNeuron using light microscopy images including bright-field and confocal images of human and mouse brain, on which DeepNeuron demonstrates robustness and accuracy in neuron tracing.

Optimal voltage stimulation parameters for network-mediated responses in wild type and rd10 mouse retinal ganglion cells

NASA Astrophysics Data System (ADS)

Jalligampala, Archana; Sekhar, Sudarshan; Zrenner, Eberhart; Rathbun, Daniel L.

2017-04-01

To further improve the quality of visual percepts elicited by microelectronic retinal prosthetics, substantial efforts have been made to understand how retinal neurons respond to electrical stimulation. It is generally assumed that a sufficiently strong stimulus will recruit most retinal neurons. However, recent evidence has shown that the responses of some retinal neurons decrease with excessively strong stimuli (a non-monotonic response function). Therefore, it is necessary to identify stimuli that can be used to activate the majority of retinal neurons even when such non-monotonic cells are part of the neuronal population. Taking these non-monotonic responses into consideration, we establish the optimal voltage stimulation parameters (amplitude, duration, and polarity) for epiretinal stimulation of network-mediated (indirect) ganglion cell responses. We recorded responses from 3958 mouse retinal ganglion cells (RGCs) in both healthy (wild type, WT) and a degenerating (rd10) mouse model of retinitis pigmentosa—using flat-mounted retina on a microelectrode array. Rectangular monophasic voltage-controlled pulses were presented with varying voltage, duration, and polarity. We found that in 4-5 weeks old rd10 mice the RGC thresholds were comparable to those of WT. There was a marked response variability among mouse RGCs. To account for this variability, we interpolated the percentage of RGCs activated at each point in the voltage-polarity-duration stimulus space, thus identifying the optimal voltage-controlled pulse (-2.4 V, 0.88 ms). The identified optimal voltage pulse can activate at least 65% of potentially responsive RGCs in both mouse strains. Furthermore, this pulse is well within the range of stimuli demonstrated to be safe and effective for retinal implant patients. Such optimized stimuli and the underlying method used to identify them support a high yield of responsive RGCs and will serve as an effective guideline for future in vitro investigations of retinal electrostimulation by establishing standard stimuli for each unique experimental condition.

Simulating synchronization in neuronal networks

NASA Astrophysics Data System (ADS)

Fink, Christian G.

2016-06-01

We discuss several techniques used in simulating neuronal networks by exploring how a network's connectivity structure affects its propensity for synchronous spiking. Network connectivity is generated using the Watts-Strogatz small-world algorithm, and two key measures of network structure are described. These measures quantify structural characteristics that influence collective neuronal spiking, which is simulated using the leaky integrate-and-fire model. Simulations show that adding a small number of random connections to an otherwise lattice-like connectivity structure leads to a dramatic increase in neuronal synchronization.

A real-time hybrid neuron network for highly parallel cognitive systems.

PubMed

Christiaanse, Gerrit Jan; Zjajo, Amir; Galuzzi, Carlo; van Leuken, Rene

2016-08-01

For comprehensive understanding of how neurons communicate with each other, new tools need to be developed that can accurately mimic the behaviour of such neurons and neuron networks under `real-time' constraints. In this paper, we propose an easily customisable, highly pipelined, neuron network design, which executes optimally scheduled floating-point operations for maximal amount of biophysically plausible neurons per FPGA family type. To reduce the required amount of resources without adverse effect on the calculation latency, a single exponent instance is used for multiple neuron calculation operations. Experimental results indicate that the proposed network design allows the simulation of up to 1188 neurons on Virtex7 (XC7VX550T) device in brain real-time yielding a speed-up of x12.4 compared to the state-of-the art.

Inferring neural activity from BOLD signals through nonlinear optimization.

PubMed

Vakorin, Vasily A; Krakovska, Olga O; Borowsky, Ron; Sarty, Gordon E

2007-11-01

The blood oxygen level-dependent (BOLD) fMRI signal does not measure neuronal activity directly. This fact is a key concern for interpreting functional imaging data based on BOLD. Mathematical models describing the path from neural activity to the BOLD response allow us to numerically solve the inverse problem of estimating the timing and amplitude of the neuronal activity underlying the BOLD signal. In fact, these models can be viewed as an advanced substitute for the impulse response function. In this work, the issue of estimating the dynamics of neuronal activity from the observed BOLD signal is considered within the framework of optimization problems. The model is based on the extended "balloon" model and describes the conversion of neuronal signals into the BOLD response through the transitional dynamics of the blood flow-inducing signal, cerebral blood flow, cerebral blood volume and deoxyhemoglobin concentration. Global optimization techniques are applied to find a control input (the neuronal activity and/or the biophysical parameters in the model) that causes the system to follow an admissible solution to minimize discrepancy between model and experimental data. As an alternative to a local linearization (LL) filtering scheme, the optimization method escapes the linearization of the transition system and provides a possibility to search for the global optimum, avoiding spurious local minima. We have found that the dynamics of the neural signals and the physiological variables as well as the biophysical parameters can be robustly reconstructed from the BOLD responses. Furthermore, it is shown that spiking off/on dynamics of the neural activity is the natural mathematical solution of the model. Incorporating, in addition, the expansion of the neural input by smooth basis functions, representing a low-pass filtering, allows us to model local field potential (LFP) solutions instead of spiking solutions.

Binocular Neurons in Parastriate Cortex: Interocular ‘Matching’ of Receptive Field Properties, Eye Dominance and Strength of Silent Suppression

PubMed Central

Wang, Chun; Dreher, Bogdan

2014-01-01

Spike-responses of single binocular neurons were recorded from a distinct part of primary visual cortex, the parastriate cortex (cytoarchitectonic area 18) of anaesthetized and immobilized domestic cats. Functional identification of neurons was based on the ratios of phase-variant (F1) component to the mean firing rate (F0) of their spike-responses to optimized (orientation, direction, spatial and temporal frequencies and size) sine-wave-luminance-modulated drifting grating patches presented separately via each eye. In over 95% of neurons, the interocular differences in the phase-sensitivities (differences in F1/F0 spike-response ratios) were small (≤0.3) and in over 80% of neurons, the interocular differences in preferred orientations were ≤10°. The interocular correlations of the direction selectivity indices and optimal spatial frequencies, like those of the phase sensitivies and optimal orientations, were also strong (coefficients of correlation r ≥0.7005). By contrast, the interocular correlations of the optimal temporal frequencies, the diameters of summation areas of the excitatory responses and suppression indices were weak (coefficients of correlation r ≤0.4585). In cells with high eye dominance indices (HEDI cells), the mean magnitudes of suppressions evoked by stimulation of silent, extra-classical receptive fields via the non-dominant eyes, were significantly greater than those when the stimuli were presented via the dominant eyes. We argue that the well documented ‘eye-origin specific’ segregation of the lateral geniculate inputs underpinning distinct eye dominance columns in primary visual cortices of mammals with frontally positioned eyes (distinct eye dominance columns), combined with significant interocular differences in the strength of silent suppressive fields, putatively contribute to binocular stereoscopic vision. PMID:24927276

Cholinergic suppression of visual responses in primate V1 is mediated by GABAergic inhibition

PubMed Central

Aoki, Chiye; Hawken, Michael J.

2012-01-01

Acetylcholine (ACh) has been implicated in selective attention. To understand the local circuit action of ACh, we iontophoresed cholinergic agonists into the primate primary visual cortex (V1) while presenting optimal visual stimuli. Consistent with our previous anatomical studies showing that GABAergic neurons in V1 express ACh receptors to a greater extent than do excitatory neurons, we observed suppressed visual responses in 36% of recorded neurons outside V1's primary thalamorecipient layer (4c). This suppression is blocked by the GABAA receptor antagonist gabazine. Within layer 4c, ACh release produces a response gain enhancement (Disney AA, Aoki C, Hawken MJ. Neuron 56: 701–713, 2007); elsewhere, ACh suppresses response gain by strengthening inhibition. Our finding contrasts with the observation that the dominant mechanism of suppression in the neocortex of rats is reduced glutamate release. We propose that in primates, distinct cholinergic receptor subtypes are recruited on specific cell types and in specific lamina to yield opposing modulatory effects that together increase neurons' responsiveness to optimal stimuli without changing tuning width. PMID:22786955

Cholinergic suppression of visual responses in primate V1 is mediated by GABAergic inhibition.

PubMed

Disney, Anita A; Aoki, Chiye; Hawken, Michael J

2012-10-01

Acetylcholine (ACh) has been implicated in selective attention. To understand the local circuit action of ACh, we iontophoresed cholinergic agonists into the primate primary visual cortex (V1) while presenting optimal visual stimuli. Consistent with our previous anatomical studies showing that GABAergic neurons in V1 express ACh receptors to a greater extent than do excitatory neurons, we observed suppressed visual responses in 36% of recorded neurons outside V1's primary thalamorecipient layer (4c). This suppression is blocked by the GABA(A) receptor antagonist gabazine. Within layer 4c, ACh release produces a response gain enhancement (Disney AA, Aoki C, Hawken MJ. Neuron 56: 701-713, 2007); elsewhere, ACh suppresses response gain by strengthening inhibition. Our finding contrasts with the observation that the dominant mechanism of suppression in the neocortex of rats is reduced glutamate release. We propose that in primates, distinct cholinergic receptor subtypes are recruited on specific cell types and in specific lamina to yield opposing modulatory effects that together increase neurons' responsiveness to optimal stimuli without changing tuning width.

Phase transitions in Pareto optimal complex networks

NASA Astrophysics Data System (ADS)

Seoane, Luís F.; Solé, Ricard

2015-09-01

The organization of interactions in complex systems can be described by networks connecting different units. These graphs are useful representations of the local and global complexity of the underlying systems. The origin of their topological structure can be diverse, resulting from different mechanisms including multiplicative processes and optimization. In spatial networks or in graphs where cost constraints are at work, as it occurs in a plethora of situations from power grids to the wiring of neurons in the brain, optimization plays an important part in shaping their organization. In this paper we study network designs resulting from a Pareto optimization process, where different simultaneous constraints are the targets of selection. We analyze three variations on a problem, finding phase transitions of different kinds. Distinct phases are associated with different arrangements of the connections, but the need of drastic topological changes does not determine the presence or the nature of the phase transitions encountered. Instead, the functions under optimization do play a determinant role. This reinforces the view that phase transitions do not arise from intrinsic properties of a system alone, but from the interplay of that system with its external constraints.

Vestibular signals in the parasolitary nucleus.

PubMed

Barmack, N H; Yakhnitsa, V

2000-06-01

Vestibular primary afferents project to secondary vestibular neurons located in the vestibular complex. Vestibular primary afferents also project to the uvula-nodulus of the cerebellum where they terminate on granule cells. In this report we describe the physiological properties of neurons in a "new" vestibular nucleus, the parasolitary nucleus (Psol). This nucleus consists of 2,300 GABAergic neurons that project onto the ipsilateral inferior olive (beta-nucleus and dorsomedial cell column) as well as the nucleus reticularis gigantocellularis. These olivary neurons are the exclusive source of vestibularly modulated climbing fiber inputs to the cerebellum. We recorded the activity of Psol neurons during natural vestibular stimulation in anesthetized rabbits. The rabbits were placed in a three-axis rate table at the center of a large sphere, permitting vestibular and optokinetic stimulation. We recorded from 74 neurons in the Psol and from 23 neurons in the regions bordering Psol. The activity of 72/74 Psol neurons and 4/23 non-Psol neurons was modulated by vestibular stimulation in either the pitch or roll planes but not the horizontal plane. Psol neurons responded in phase with ipsilateral side-down head position or velocity during sinusoidal stimulation. Approximately 80% of the recorded Psol neurons responded to static roll-tilt. The optimal response planes of evoked vestibular responses were inferred from measurement of null planes. Optimal response planes usually were aligned with the anatomical orientation of one of the two ipsilateral vertical semicircular canals. The frequency dependence of null plane measurements indicated a convergence of vestibular information from otoliths and semicircular canals. None of the recorded neurons evinced optokinetic sensitivity. These results are consistent with the view that Psol neurons provide the vestibular signals to the inferior olive that eventually reached the cerebellum in the form of modulated climbing fiber discharges. These signals provide information about spatial orientation about the longitudinal axis.

Bioluminescence Monitoring of Neuronal Activity in Freely Moving Zebrafish Larvae

PubMed Central

Knafo, Steven; Prendergast, Andrew; Thouvenin, Olivier; Figueiredo, Sophie Nunes; Wyart, Claire

2017-01-01

The proof of concept for bioluminescence monitoring of neural activity in zebrafish with the genetically encoded calcium indicator GFP-aequorin has been previously described (Naumann et al., 2010) but challenges remain. First, bioluminescence signals originating from a single muscle fiber can constitute a major pitfall. Second, bioluminescence signals emanating from neurons only are very small. To improve signals while verifying specificity, we provide an optimized 4 steps protocol achieving: 1) selective expression of a zebrafish codon-optimized GFP-aequorin, 2) efficient soaking of larvae in GFP-aequorin substrate coelenterazine, 3) bioluminescence monitoring of neural activity from motor neurons in free-tailed moving animals performing acoustic escapes and 4) verification of the absence of muscle expression using immunohistochemistry. PMID:29130058

Coupling of semiconductor nanowires with neurons and their interfacial structure.

PubMed

Lee, Ki-Young; Shim, Sojung; Kim, Il-Soo; Oh, Hwangyou; Kim, Sunoh; Ahn, Jae-Pyeong; Park, Seung-Han; Rhim, Hyewhon; Choi, Heon-Jin

2009-12-04

We report on the compatibility of various nanowires with hippocampal neurons and the structural study of the neuron-nanowire interface. Si, Ge, SiGe, and GaN nanowires are compatible with hippocampal neurons due to their native oxide, but ZnO nanowires are toxic to neuron due to a release of Zn ion. The interfaces of fixed Si nanowire and hippocampal neuron, cross-sectional samples, were prepared by focused ion beam and observed by transmission electron microscopy. The results showed that the processes of neuron were adhered well on the nanowire without cleft.

Structure-activity relationship study of vitamin k derivatives yields highly potent neuroprotective agents.

PubMed

Josey, Benjamin J; Inks, Elizabeth S; Wen, Xuejun; Chou, C James

2013-02-14

Historically known for its role in blood coagulation and bone formation, vitamin K (VK) has begun to emerge as an important nutrient for brain function. While VK involvement in the brain has not been fully explored, it is well-known that oxidative stress plays a critical role in neurodegenerative diseases. It was recently reported that VK protects neurons and oligodendrocytes from oxidative injury and rescues Drosophila from mitochondrial defects associated with Parkinson's disease. In this study, we take a chemical approach to define the optimal and minimum pharmacophore responsible for the neuroprotective effects of VK. In doing so, we have developed a series of potent VK analogues with favorable drug characteristics that provide full protection at nanomolar concentrations in a well-defined model of neuronal oxidative stress. Additionally, we have characterized key cellular responses and biomarkers consistent with the compounds' ability to rescue cells from oxidative stress induced cell death.

Cerebral morphology and functional sparing after prenatal frontal cortex lesions in rats.

PubMed

Kolb, B; Cioe, J; Muirhead, D

1998-03-01

Rats were given suction lesions of the presumptive frontal cortex on embryonic day 18 (E18) and subsequently tested, as adults, on tests of spatial navigation (Morris water task, radial arm maze), motor tasks (Whishaw reaching task, beam walking), and locomotor activity. Frontal cortical lesions at E18 affected cerebral morphogenesis, producing unusual morphological structures including abnormal patches of neurons in the cortex and white matter as well as neuronal bridges between the hemispheres. A small sample of E18 operates also had hydrocephaly. The animals with E18 lesions without hydrocephalus were behaviorally indistinguishable from littermate controls. The results demonstrate that animals with focal lesions of the presumptive frontal cortex have gross abnormalities in cerebral morphology but the lesions leave the functions normally subserved by the frontal cortex in adult rats unaffected. The results are discussed in the context of a hypothesis regarding the optimal times for functional recovery from cortical injury.

A mixed analog/digital chaotic neuro-computer system for quadratic assignment problems.

PubMed

Horio, Yoshihiko; Ikeguchi, Tohru; Aihara, Kazuyuki

2005-01-01

We construct a mixed analog/digital chaotic neuro-computer prototype system for quadratic assignment problems (QAPs). The QAP is one of the difficult NP-hard problems, and includes several real-world applications. Chaotic neural networks have been used to solve combinatorial optimization problems through chaotic search dynamics, which efficiently searches optimal or near optimal solutions. However, preliminary experiments have shown that, although it obtained good feasible solutions, the Hopfield-type chaotic neuro-computer hardware system could not obtain the optimal solution of the QAP. Therefore, in the present study, we improve the system performance by adopting a solution construction method, which constructs a feasible solution using the analog internal state values of the chaotic neurons at each iteration. In order to include the construction method into our hardware, we install a multi-channel analog-to-digital conversion system to observe the internal states of the chaotic neurons. We show experimentally that a great improvement in the system performance over the original Hopfield-type chaotic neuro-computer is obtained. That is, we obtain the optimal solution for the size-10 QAP in less than 1000 iterations. In addition, we propose a guideline for parameter tuning of the chaotic neuro-computer system according to the observation of the internal states of several chaotic neurons in the network.

Properties of Neurons in External Globus Pallidus Can Support Optimal Action Selection

PubMed Central

Bogacz, Rafal; Martin Moraud, Eduardo; Abdi, Azzedine; Magill, Peter J.; Baufreton, Jérôme

2016-01-01

The external globus pallidus (GPe) is a key nucleus within basal ganglia circuits that are thought to be involved in action selection. A class of computational models assumes that, during action selection, the basal ganglia compute for all actions available in a given context the probabilities that they should be selected. These models suggest that a network of GPe and subthalamic nucleus (STN) neurons computes the normalization term in Bayes’ equation. In order to perform such computation, the GPe needs to send feedback to the STN equal to a particular function of the activity of STN neurons. However, the complex form of this function makes it unlikely that individual GPe neurons, or even a single GPe cell type, could compute it. Here, we demonstrate how this function could be computed within a network containing two types of GABAergic GPe projection neuron, so-called ‘prototypic’ and ‘arkypallidal’ neurons, that have different response properties in vivo and distinct connections. We compare our model predictions with the experimentally-reported connectivity and input-output functions (f-I curves) of the two populations of GPe neurons. We show that, together, these dichotomous cell types fulfil the requirements necessary to compute the function needed for optimal action selection. We conclude that, by virtue of their distinct response properties and connectivities, a network of arkypallidal and prototypic GPe neurons comprises a neural substrate capable of supporting the computation of the posterior probabilities of actions. PMID:27389780

Wireless Sensor Network Congestion Control Based on Standard Particle Swarm Optimization and Single Neuron PID

PubMed Central

Yang, Xiaoping; Chen, Xueying; Xia, Riting; Qian, Zhihong

2018-01-01

Aiming at the problem of network congestion caused by the large number of data transmissions in wireless routing nodes of wireless sensor network (WSN), this paper puts forward an algorithm based on standard particle swarm–neural PID congestion control (PNPID). Firstly, PID control theory was applied to the queue management of wireless sensor nodes. Then, the self-learning and self-organizing ability of neurons was used to achieve online adjustment of weights to adjust the proportion, integral and differential parameters of the PID controller. Finally, the standard particle swarm optimization to neural PID (NPID) algorithm of initial values of proportion, integral and differential parameters and neuron learning rates were used for online optimization. This paper describes experiments and simulations which show that the PNPID algorithm effectively stabilized queue length near the expected value. At the same time, network performance, such as throughput and packet loss rate, was greatly improved, which alleviated network congestion and improved network QoS. PMID:29671822

Wireless Sensor Network Congestion Control Based on Standard Particle Swarm Optimization and Single Neuron PID.

PubMed

Yang, Xiaoping; Chen, Xueying; Xia, Riting; Qian, Zhihong

2018-04-19

Aiming at the problem of network congestion caused by the large number of data transmissions in wireless routing nodes of wireless sensor network (WSN), this paper puts forward an algorithm based on standard particle swarm⁻neural PID congestion control (PNPID). Firstly, PID control theory was applied to the queue management of wireless sensor nodes. Then, the self-learning and self-organizing ability of neurons was used to achieve online adjustment of weights to adjust the proportion, integral and differential parameters of the PID controller. Finally, the standard particle swarm optimization to neural PID (NPID) algorithm of initial values of proportion, integral and differential parameters and neuron learning rates were used for online optimization. This paper describes experiments and simulations which show that the PNPID algorithm effectively stabilized queue length near the expected value. At the same time, network performance, such as throughput and packet loss rate, was greatly improved, which alleviated network congestion and improved network QoS.

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.

Temporal structure of neuronal population oscillations with empirical model decomposition

NASA Astrophysics Data System (ADS)

Li, Xiaoli

2006-08-01

Frequency analysis of neuronal oscillation is very important for understanding the neural information processing and mechanism of disorder in the brain. This Letter addresses a new method to analyze the neuronal population oscillations with empirical mode decomposition (EMD). Following EMD of neuronal oscillation, a series of intrinsic mode functions (IMFs) are obtained, then Hilbert transform of IMFs can be used to extract the instantaneous time frequency structure of neuronal oscillation. The method is applied to analyze the neuronal oscillation in the hippocampus of epileptic rats in vivo, the results show the neuronal oscillations have different descriptions during the pre-ictal, seizure onset and ictal periods of the epileptic EEG at the different frequency band. This new method is very helpful to provide a view for the temporal structure of neural oscillation.

Hierarchical winner-take-all particle swarm optimization social network for neural model fitting.

PubMed

Coventry, Brandon S; Parthasarathy, Aravindakshan; Sommer, Alexandra L; Bartlett, Edward L

2017-02-01

Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models.

Feedback and feedforward control of frequency tuning to naturalistic stimuli.

PubMed

Chacron, Maurice J; Maler, Leonard; Bastian, Joseph

2005-06-08

Sensory neurons must respond to a wide variety of natural stimuli that can have very different spatiotemporal characteristics. Optimal responsiveness to subsets of these stimuli can be achieved by devoting specialized neural circuitry to different stimulus categories, or, alternatively, this circuitry can be modulated or tuned to optimize responsiveness to current stimulus conditions. This study explores the mechanisms that enable neurons within the initial processing station of the electrosensory system of weakly electric fish to shift their tuning properties based on the spatial extent of the stimulus. These neurons are tuned to low frequencies when the stimulus is restricted to a small region within the receptive field center but are tuned to higher frequencies when the stimulus impinges on large regions of the sensory epithelium. Through a combination of modeling and in vivo electrophysiology, we reveal the respective contributions of the filtering characteristics of extended dendritic structures and feedback circuitry to this shift in tuning. Our results show that low-frequency tuning can result from the cable properties of an extended dendrite that conveys receptor-afferent information to the cell body. The shift from low- to high-frequency tuning, seen in response to spatially extensive stimuli, results from increased wide-band input attributable to activation of larger populations of receptor afferents, as well as the activation of parallel fiber feedback from the cerebellum. This feedback provides a cancellation signal with low-pass characteristics that selectively attenuates low-frequency responsiveness. Thus, with spatially extensive stimuli, these cells preferentially respond to the higher-frequency components of the receptor-afferent input.

The Reconstruction of Three-Dimensional Morphological and Electrical Paraneters from Two-Dimensional Sections of Neurones

NASA Astrophysics Data System (ADS)

Brawn, A. D.; Wheal, H. V.

1986-07-01

A system is described which can be used to create a three-dimensional model of a neurone from the central nervous system. This model can then be used to obtain quantitative data on the physical and electrical pro, perties of the neurone. Living neurones are either raised in culture, or taken from in vitro preparations of brain tissue and optically sectioned. These two-dimensional sections are digitised, and input to a 68008-based microcomputer. The system reconstructs the three-dimensional structure of the neurone, both geanetrically and electrically. The user can a) View the structure fran any point at any angle b) "Move through" the structure along any given vector c) Nave through" the structure following a neurone process d) Fire the neurone at any point, and "watch" the action potentials propagate e) Vary the parameters of the electrical model of a process element. The system is targeted to a research programme on epilepsy, which makes frequent use of both geometric and electrical neurone modelling. Current techniques which may involve crude histology and two-dimensional drawings have considerable short camings.

Population coding in sparsely connected networks of noisy neurons.

PubMed

Tripp, Bryan P; Orchard, Jeff

2012-01-01

This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and because the information they encode must be decoded by other neurons, if it is to affect behavior. However, population coding theory has often ignored network structure, or assumed discrete, fully connected populations (in contrast with the sparsely connected, continuous sheet of the cortex). In this study, we modeled a sheet of cortical neurons with sparse, primarily local connections, and found that a network with this structure could encode multiple internal state variables with high signal-to-noise ratio. However, we were unable to create high-fidelity networks by instantiating connections at random according to spatial connection probabilities. In our models, high-fidelity networks required additional structure, with higher cluster factors and correlations between the inputs to nearby neurons.

Efficient transformation of an auditory population code in a small sensory system.

PubMed

Clemens, Jan; Kutzki, Olaf; Ronacher, Bernhard; Schreiber, Susanne; Wohlgemuth, Sandra

2011-08-16

Optimal coding principles are implemented in many large sensory systems. They include the systematic transformation of external stimuli into a sparse and decorrelated neuronal representation, enabling a flexible readout of stimulus properties. Are these principles also applicable to size-constrained systems, which have to rely on a limited number of neurons and may only have to fulfill specific and restricted tasks? We studied this question in an insect system--the early auditory pathway of grasshoppers. Grasshoppers use genetically fixed songs to recognize mates. The first steps of neural processing of songs take place in a small three-layer feed-forward network comprising only a few dozen neurons. We analyzed the transformation of the neural code within this network. Indeed, grasshoppers create a decorrelated and sparse representation, in accordance with optimal coding theory. Whereas the neuronal input layer is best read out as a summed population, a labeled-line population code for temporal features of the song is established after only two processing steps. At this stage, information about song identity is maximal for a population decoder that preserves neuronal identity. We conclude that optimal coding principles do apply to the early auditory system of the grasshopper, despite its size constraints. The inputs, however, are not encoded in a systematic, map-like fashion as in many larger sensory systems. Already at its periphery, part of the grasshopper auditory system seems to focus on behaviorally relevant features, and is in this property more reminiscent of higher sensory areas in vertebrates.

Single Neuron Optimization as a Basis for Accurate Biophysical Modeling: The Case of Cerebellar Granule Cells.

PubMed

Masoli, Stefano; Rizza, Martina F; Sgritta, Martina; Van Geit, Werner; Schürmann, Felix; D'Angelo, Egidio

2017-01-01

In realistic neuronal modeling, once the ionic channel complement has been defined, the maximum ionic conductance (G i-max ) values need to be tuned in order to match the firing pattern revealed by electrophysiological recordings. Recently, selection/mutation genetic algorithms have been proposed to efficiently and automatically tune these parameters. Nonetheless, since similar firing patterns can be achieved through different combinations of G i-max values, it is not clear how well these algorithms approximate the corresponding properties of real cells. Here we have evaluated the issue by exploiting a unique opportunity offered by the cerebellar granule cell (GrC), which is electrotonically compact and has therefore allowed the direct experimental measurement of ionic currents. Previous models were constructed using empirical tuning of G i-max values to match the original data set. Here, by using repetitive discharge patterns as a template, the optimization procedure yielded models that closely approximated the experimental G i-max values. These models, in addition to repetitive firing, captured additional features, including inward rectification, near-threshold oscillations, and resonance, which were not used as features. Thus, parameter optimization using genetic algorithms provided an efficient modeling strategy for reconstructing the biophysical properties of neurons and for the subsequent reconstruction of large-scale neuronal network models.

Optimality in mono- and multisensory map formation.

PubMed

Bürck, Moritz; Friedel, Paul; Sichert, Andreas B; Vossen, Christine; van Hemmen, J Leo

2010-07-01

In the struggle for survival in a complex and dynamic environment, nature has developed a multitude of sophisticated sensory systems. In order to exploit the information provided by these sensory systems, higher vertebrates reconstruct the spatio-temporal environment from each of the sensory systems they have at their disposal. That is, for each modality the animal computes a neuronal representation of the outside world, a monosensory neuronal map. Here we present a universal framework that allows to calculate the specific layout of the involved neuronal network by means of a general mathematical principle, viz., stochastic optimality. In order to illustrate the use of this theoretical framework, we provide a step-by-step tutorial of how to apply our model. In so doing, we present a spatial and a temporal example of optimal stimulus reconstruction which underline the advantages of our approach. That is, given a known physical signal transmission and rudimental knowledge of the detection process, our approach allows to estimate the possible performance and to predict neuronal properties of biological sensory systems. Finally, information from different sensory modalities has to be integrated so as to gain a unified perception of reality for further processing, e.g., for distinct motor commands. We briefly discuss concepts of multimodal interaction and how a multimodal space can evolve by alignment of monosensory maps.

Designing optimal stimuli to control neuronal spike timing

PubMed Central

Packer, Adam M.; Yuste, Rafael; Paninski, Liam

2011-01-01

Recent advances in experimental stimulation methods have raised the following important computational question: how can we choose a stimulus that will drive a neuron to output a target spike train with optimal precision, given physiological constraints? Here we adopt an approach based on models that describe how a stimulating agent (such as an injected electrical current or a laser light interacting with caged neurotransmitters or photosensitive ion channels) affects the spiking activity of neurons. Based on these models, we solve the reverse problem of finding the best time-dependent modulation of the input, subject to hardware limitations as well as physiologically inspired safety measures, that causes the neuron to emit a spike train that with highest probability will be close to a target spike train. We adopt fast convex constrained optimization methods to solve this problem. Our methods can potentially be implemented in real time and may also be generalized to the case of many cells, suitable for neural prosthesis applications. With the use of biologically sensible parameters and constraints, our method finds stimulation patterns that generate very precise spike trains in simulated experiments. We also tested the intracellular current injection method on pyramidal cells in mouse cortical slices, quantifying the dependence of spiking reliability and timing precision on constraints imposed on the applied currents. PMID:21511704

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.

A unified internal model theory to resolve the paradox of active versus passive self-motion sensation

PubMed Central

Angelaki, Dora E

2017-01-01

Brainstem and cerebellar neurons implement an internal model to accurately estimate self-motion during externally generated (‘passive’) movements. However, these neurons show reduced responses during self-generated (‘active’) movements, indicating that predicted sensory consequences of motor commands cancel sensory signals. Remarkably, the computational processes underlying sensory prediction during active motion and their relationship to internal model computations during passive movements remain unknown. We construct a Kalman filter that incorporates motor commands into a previously established model of optimal passive self-motion estimation. The simulated sensory error and feedback signals match experimentally measured neuronal responses during active and passive head and trunk rotations and translations. We conclude that a single sensory internal model can combine motor commands with vestibular and proprioceptive signals optimally. Thus, although neurons carrying sensory prediction error or feedback signals show attenuated modulation, the sensory cues and internal model are both engaged and critically important for accurate self-motion estimation during active head movements. PMID:29043978

A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations.

PubMed

Hahne, Jan; Helias, Moritz; Kunkel, Susanne; Igarashi, Jun; Bolten, Matthias; Frommer, Andreas; Diesmann, Markus

2015-01-01

Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers. Finally, we discuss limitations of the novel technology.

A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations

PubMed Central

Hahne, Jan; Helias, Moritz; Kunkel, Susanne; Igarashi, Jun; Bolten, Matthias; Frommer, Andreas; Diesmann, Markus

2015-01-01

Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers. Finally, we discuss limitations of the novel technology. PMID:26441628

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.

The Isolation of Pure Populations of Neurons by Laser Capture Microdissection: Methods and Application in Neuroscience.

PubMed

Morris, Renée; Mehta, Prachi

2018-01-01

In mammals, the central nervous system (CNS) is constituted of various cellular elements, posing a challenge to isolating specific cell types to investigate their expression profile. As a result, tissue homogenization is not amenable to analyses of motor neurons profiling as these represent less than 10% of the total spinal cord cell population. One way to tackle the problem of tissue heterogeneity and obtain meaningful genomic, proteomic, and transcriptomic profiling is to use laser capture microdissection technology (LCM). In this chapter, we describe protocols for the capture of isolated populations of motor neurons from spinal cord tissue sections and for downstream transcriptomic analysis of motor neurons with RT-PCR. We have also included a protocol for the immunological confirmation that the captured neurons are indeed motor neurons. Although focused on spinal cord motor neurons, these protocols can be easily optimized for the isolation of any CNS neurons.

Dementia of frontal lobe type and motor neuron disease. A Golgi study of the frontal cortex.

PubMed Central

Ferrer, I; Roig, C; Espino, A; Peiro, G; Matias Guiu, X

1991-01-01

Neuropathological findings in a 38 year old patient with dementia of frontal lobe type and motor neuron disease included pyramidal tracts, myelin pallor and neuron loss, gliosis and chromatolysis in the hypoglossal nucleus, together with frontal atrophy, neuron loss, gliosis and spongiosis in the upper cortical layers of the frontal (and temporal) lobes. Most remaining pyramidal and non-pyramidal neurons (multipolar, bitufted and bipolar cells) in the upper layers (layers II and III) of the frontal cortex (area B) had reduced dendritic arbors, proximal dendritic varicosities and amputation of dendrites as revealed in optimally stained rapid Golgi sections. Pyramidal cells in these layers also showed depletion of dendritic spines. Neurons in the inner layers were preserved. Loss of receptive surfaces in neurons of the upper cortical layers in the frontal cortex are indicative of neuronal disconnection, and are "hidden" contributory morphological substrates for the development of dementia. Images PMID:1744652

A Neuron-Based Screening Platform for Optimizing Genetically-Encoded Calcium Indicators

PubMed Central

Schreiter, Eric R.; Hasseman, Jeremy P.; Tsegaye, Getahun; Fosque, Benjamin F.; Behnam, Reza; Shields, Brenda C.; Ramirez, Melissa; Kimmel, Bruce E.; Kerr, Rex A.; Jayaraman, Vivek; Looger, Loren L.; Svoboda, Karel; Kim, Douglas S.

2013-01-01

Fluorescent protein-based sensors for detecting neuronal activity have been developed largely based on non-neuronal screening systems. However, the dynamics of neuronal state variables (e.g., voltage, calcium, etc.) are typically very rapid compared to those of non-excitable cells. We developed an electrical stimulation and fluorescence imaging platform based on dissociated rat primary neuronal cultures. We describe its use in testing genetically-encoded calcium indicators (GECIs). Efficient neuronal GECI expression was achieved using lentiviruses containing a neuronal-selective gene promoter. Action potentials (APs) and thus neuronal calcium levels were quantitatively controlled by electrical field stimulation, and fluorescence images were recorded. Images were segmented to extract fluorescence signals corresponding to individual GECI-expressing neurons, which improved sensitivity over full-field measurements. We demonstrate the superiority of screening GECIs in neurons compared with solution measurements. Neuronal screening was useful for efficient identification of variants with both improved response kinetics and high signal amplitudes. This platform can be used to screen many types of sensors with cellular resolution under realistic conditions where neuronal state variables are in relevant ranges with respect to timing and amplitude. PMID:24155972

Intraoperative Neurophysiological Monitoring in Spine Surgery: A Significant Tool for Neuronal Protection and Functional Restoration.

PubMed

Scibilia, Antonino; Raffa, Giovanni; Rizzo, Vincenzo; Quartarone, Angelo; Visocchi, Massimiliano; Germanò, Antonino; Tomasello, Francesco

2017-01-01

Although there is recent evidence for the role of intraoperative neurophysiological monitoring (IONM) in spine surgery, there are no uniform opinions on the optimal combination of the different tools. At our institution, multimodal IONM (mIONM) approach in spine surgery involves the evaluation of somatosensory evoked potentials (SEPs) and motor evoked potentials (MEPs) with electrical transcranial stimulation, including the use of a multipulse technique with multiple myomeric registration of responses from limbs, and a single-pulse technique with D-wave registration through epi- and intradural recording, and free running and evoked electromyography (frEMG and eEMG) with bilateral recording from segmental target muscles. We analyzed the impact of the mIONM on the preservation of neuronal structures and on functional restoration in a prospective series of patients who underwent spine surgery. We observed an improvement of neurological status in 50 % of the patients. The D-wave registration was the most useful intraoperative tool, especially when MEP and SEP responses were absent or poorly recordable. Our preliminary data confirm that mIONM plays a fundamental role in the identification and functional preservation of the spinal cord and nerve roots. It is highly sensitive and specific for detecting and avoiding neurological injury during spine surgery and represents a helpful tool for achieving optimal postoperative functional outcome.

Resolving the detailed structure of cortical and thalamic neurons in the adult rat brain with refined biotinylated dextran amine labeling.

PubMed

Ling, Changying; Hendrickson, Michael L; Kalil, Ronald E

2012-01-01

Biotinylated dextran amine (BDA) has been used frequently for both anterograde and retrograde pathway tracing in the central nervous system. Typically, BDA labels axons and cell somas in sufficient detail to identify their topographical location accurately. However, BDA labeling often has proved to be inadequate to resolve the fine structural details of axon arbors or the dendrites of neurons at a distance from the site of BDA injection. To overcome this limitation, we varied several experimental parameters associated with the BDA labeling of neurons in the adult rat brain in order to improve the sensitivity of the method. Specifically, we compared the effect on labeling sensitivity of: (a) using 3,000 or 10,000 MW BDA; (b) injecting different volumes of BDA; (c) co-injecting BDA with NMDA; and (d) employing various post-injection survival times. Following the extracellular injection of BDA into the visual cortex, labeled cells and axons were observed in both cortical and thalamic areas of all animals studied. However, the detailed morphology of axon arbors and distal dendrites was evident only under optimal conditions for BDA labeling that take into account the: molecular weight of the BDA used, concentration and volume of BDA injected, post-injection survival time, and toning of the resolved BDA with gold and silver. In these instances, anterogradely labeled axons and retrogradely labeled dendrites were resolved in fine detail, approximating that which can be achieved with intracellularly injected compounds such as biocytin or fluorescent dyes.

Electronic neural network for dynamic resource allocation

NASA Technical Reports Server (NTRS)

Thakoor, A. P.; Eberhardt, S. P.; Daud, T.

1991-01-01

A VLSI implementable neural network architecture for dynamic assignment is presented. The resource allocation problems involve assigning members of one set (e.g. resources) to those of another (e.g. consumers) such that the global 'cost' of the associations is minimized. The network consists of a matrix of sigmoidal processing elements (neurons), where the rows of the matrix represent resources and columns represent consumers. Unlike previous neural implementations, however, association costs are applied directly to the neurons, reducing connectivity of the network to VLSI-compatible 0 (number of neurons). Each row (and column) has an additional neuron associated with it to independently oversee activations of all the neurons in each row (and each column), providing a programmable 'k-winner-take-all' function. This function simultaneously enforces blocking (excitatory/inhibitory) constraints during convergence to control the number of active elements in each row and column within desired boundary conditions. Simulations show that the network, when implemented in fully parallel VLSI hardware, offers optimal (or near-optimal) solutions within only a fraction of a millisecond, for problems up to 128 resources and 128 consumers, orders of magnitude faster than conventional computing or heuristic search methods.

Synchronous firing patterns of induced pluripotent stem cell-derived cortical neurons depend on the network structure consisting of excitatory and inhibitory neurons.

PubMed

Iida, Shoko; Shimba, Kenta; Sakai, Koji; Kotani, Kiyoshi; Jimbo, Yasuhiko

2018-06-18

The balance between glutamate-mediated excitation and GABA-mediated inhibition is critical to cortical functioning. However, the contribution of network structure consisting of the both neurons to cortical functioning has not been elucidated. We aimed to evaluate the relationship between the network structure and functional activity patterns in vitro. We used mouse induced pluripotent stem cells (iPSCs) to construct three types of neuronal populations; excitatory-rich (Exc), inhibitory-rich (Inh), and control (Cont). Then, we analyzed the activity patterns of these neuronal populations using microelectrode arrays (MEAs). Inhibitory synaptic densities differed between the three types of iPSC-derived neuronal populations, and the neurons showed spontaneously synchronized bursting activity with functional maturation for one month. Moreover, different firing patterns were observed between the three populations; Exc demonstrated the highest firing rates, including frequent, long, and dominant bursts. In contrast, Inh demonstrated the lowest firing rates and the least dominant bursts. Synchronized bursts were enhanced by disinhibition via GABA A receptor blockade. The present study, using iPSC-derived neurons and MEAs, for the first time show that synchronized bursting of cortical networks in vitro depends on the network structure consisting of excitatory and inhibitory neurons. Copyright © 2018 Elsevier Inc. All rights reserved.

Metrics for comparing neuronal tree shapes based on persistent homology.

PubMed

Li, Yanjie; Wang, Dingkang; Ascoli, Giorgio A; Mitra, Partha; Wang, Yusu

2017-01-01

As more and more neuroanatomical data are made available through efforts such as NeuroMorpho.Org and FlyCircuit.org, the need to develop computational tools to facilitate automatic knowledge discovery from such large datasets becomes more urgent. One fundamental question is how best to compare neuron structures, for instance to organize and classify large collection of neurons. We aim to develop a flexible yet powerful framework to support comparison and classification of large collection of neuron structures efficiently. Specifically we propose to use a topological persistence-based feature vectorization framework. Existing methods to vectorize a neuron (i.e, convert a neuron to a feature vector so as to support efficient comparison and/or searching) typically rely on statistics or summaries of morphometric information, such as the average or maximum local torque angle or partition asymmetry. These simple summaries have limited power in encoding global tree structures. Based on the concept of topological persistence recently developed in the field of computational topology, we vectorize each neuron structure into a simple yet informative summary. In particular, each type of information of interest can be represented as a descriptor function defined on the neuron tree, which is then mapped to a simple persistence-signature. Our framework can encode both local and global tree structure, as well as other information of interest (electrophysiological or dynamical measures), by considering multiple descriptor functions on the neuron. The resulting persistence-based signature is potentially more informative than simple statistical summaries (such as average/mean/max) of morphometric quantities-Indeed, we show that using a certain descriptor function will give a persistence-based signature containing strictly more information than the classical Sholl analysis. At the same time, our framework retains the efficiency associated with treating neurons as points in a simple Euclidean feature space, which would be important for constructing efficient searching or indexing structures over them. We present preliminary experimental results to demonstrate the effectiveness of our persistence-based neuronal feature vectorization framework.

Metrics for comparing neuronal tree shapes based on persistent homology

PubMed Central

Li, Yanjie; Wang, Dingkang; Ascoli, Giorgio A.; Mitra, Partha

2017-01-01

As more and more neuroanatomical data are made available through efforts such as NeuroMorpho.Org and FlyCircuit.org, the need to develop computational tools to facilitate automatic knowledge discovery from such large datasets becomes more urgent. One fundamental question is how best to compare neuron structures, for instance to organize and classify large collection of neurons. We aim to develop a flexible yet powerful framework to support comparison and classification of large collection of neuron structures efficiently. Specifically we propose to use a topological persistence-based feature vectorization framework. Existing methods to vectorize a neuron (i.e, convert a neuron to a feature vector so as to support efficient comparison and/or searching) typically rely on statistics or summaries of morphometric information, such as the average or maximum local torque angle or partition asymmetry. These simple summaries have limited power in encoding global tree structures. Based on the concept of topological persistence recently developed in the field of computational topology, we vectorize each neuron structure into a simple yet informative summary. In particular, each type of information of interest can be represented as a descriptor function defined on the neuron tree, which is then mapped to a simple persistence-signature. Our framework can encode both local and global tree structure, as well as other information of interest (electrophysiological or dynamical measures), by considering multiple descriptor functions on the neuron. The resulting persistence-based signature is potentially more informative than simple statistical summaries (such as average/mean/max) of morphometric quantities—Indeed, we show that using a certain descriptor function will give a persistence-based signature containing strictly more information than the classical Sholl analysis. At the same time, our framework retains the efficiency associated with treating neurons as points in a simple Euclidean feature space, which would be important for constructing efficient searching or indexing structures over them. We present preliminary experimental results to demonstrate the effectiveness of our persistence-based neuronal feature vectorization framework. PMID:28809960

Loss of neuronal 3D chromatin organization causes transcriptional and behavioural deficits related to serotonergic dysfunction.

PubMed

Ito, Satomi; Magalska, Adriana; Alcaraz-Iborra, Manuel; Lopez-Atalaya, Jose P; Rovira, Victor; Contreras-Moreira, Bruno; Lipinski, Michal; Olivares, Roman; Martinez-Hernandez, Jose; Ruszczycki, Blazej; Lujan, Rafael; Geijo-Barrientos, Emilio; Wilczynski, Grzegorz M; Barco, Angel

2014-07-18

The interior of the neuronal cell nucleus is a highly organized three-dimensional (3D) structure where regions of the genome that are linearly millions of bases apart establish sub-structures with specialized functions. To investigate neuronal chromatin organization and dynamics in vivo, we generated bitransgenic mice expressing GFP-tagged histone H2B in principal neurons of the forebrain. Surprisingly, the expression of this chimeric histone in mature neurons caused chromocenter declustering and disrupted the association of heterochromatin with the nuclear lamina. The loss of these structures did not affect neuronal viability but was associated with specific transcriptional and behavioural deficits related to serotonergic dysfunction. Overall, our results demonstrate that the 3D organization of chromatin within neuronal cells provides an additional level of epigenetic regulation of gene expression that critically impacts neuronal function. This in turn suggests that some loci associated with neuropsychiatric disorders may be particularly sensitive to changes in chromatin architecture.

Neuronal avalanches of a self-organized neural network with active-neuron-dominant structure.

PubMed

Li, Xiumin; Small, Michael

2012-06-01

Neuronal avalanche is a spontaneous neuronal activity which obeys a power-law distribution of population event sizes with an exponent of -3/2. It has been observed in the superficial layers of cortex both in vivo and in vitro. In this paper, we analyze the information transmission of a novel self-organized neural network with active-neuron-dominant structure. Neuronal avalanches can be observed in this network with appropriate input intensity. We find that the process of network learning via spike-timing dependent plasticity dramatically increases the complexity of network structure, which is finally self-organized to be active-neuron-dominant connectivity. Both the entropy of activity patterns and the complexity of their resulting post-synaptic inputs are maximized when the network dynamics are propagated as neuronal avalanches. This emergent topology is beneficial for information transmission with high efficiency and also could be responsible for the large information capacity of this network compared with alternative archetypal networks with different neural connectivity.

Dynamic state estimation based on Poisson spike trains—towards a theory of optimal encoding

NASA Astrophysics Data System (ADS)

Susemihl, Alex; Meir, Ron; Opper, Manfred

2013-03-01

Neurons in the nervous system convey information to higher brain regions by the generation of spike trains. An important question in the field of computational neuroscience is how these sensory neurons encode environmental information in a way which may be simply analyzed by subsequent systems. Many aspects of the form and function of the nervous system have been understood using the concepts of optimal population coding. Most studies, however, have neglected the aspect of temporal coding. Here we address this shortcoming through a filtering theory of inhomogeneous Poisson processes. We derive exact relations for the minimal mean squared error of the optimal Bayesian filter and, by optimizing the encoder, obtain optimal codes for populations of neurons. We also show that a class of non-Markovian, smooth stimuli are amenable to the same treatment, and provide results for the filtering and prediction error which hold for a general class of stochastic processes. This sets a sound mathematical framework for a population coding theory that takes temporal aspects into account. It also formalizes a number of studies which discussed temporal aspects of coding using time-window paradigms, by stating them in terms of correlation times and firing rates. We propose that this kind of analysis allows for a systematic study of temporal coding and will bring further insights into the nature of the neural code.

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.

Fractal dimension of EEG activity senses neuronal impairment in acute stroke.

PubMed

Zappasodi, Filippo; Olejarczyk, Elzbieta; Marzetti, Laura; Assenza, Giovanni; Pizzella, Vittorio; Tecchio, Franca

2014-01-01

The brain is a self-organizing system which displays self-similarities at different spatial and temporal scales. Thus, the complexity of its dynamics, associated to efficient processing and functional advantages, is expected to be captured by a measure of its scale-free (fractal) properties. Under the hypothesis that the fractal dimension (FD) of the electroencephalographic signal (EEG) is optimally sensitive to the neuronal dysfunction secondary to a brain lesion, we tested the FD's ability in assessing two key processes in acute stroke: the clinical impairment and the recovery prognosis. Resting EEG was collected in 36 patients 4-10 days after a unilateral ischemic stroke in the middle cerebral artery territory and 19 healthy controls. National Health Institute Stroke Scale (NIHss) was collected at T0 and 6 months later. Highuchi FD, its inter-hemispheric asymmetry (FDasy) and spectral band powers were calculated for EEG signals. FD was smaller in patients than in controls (1.447±0.092 vs 1.525±0.105) and its reduction was paired to a worse acute clinical status. FD decrease was associated to alpha increase and beta decrease of oscillatory activity power. Larger FDasy in acute phase was paired to a worse clinical recovery at six months. FD in our patients captured the loss of complexity reflecting the global system dysfunction resulting from the structural damage. This decrease seems to reveal the intimate nature of structure-function unity, where the regional neural multi-scale self-similar activity is impaired by the anatomical lesion. This picture is coherent with neuronal activity complexity decrease paired to a reduced repertoire of functional abilities. FDasy result highlights the functional relevance of the balance between homologous brain structures' activities in stroke recovery.

Track structure model of microscopic energy deposition by protons and heavy ions in segments of neuronal cell dendrites represented by cylinders or spheres

PubMed Central

Alp, Murat; Cucinotta, Francis A.

2017-01-01

Changes to cognition, including memory, following radiation exposure are a concern for cosmic ray exposures to astronauts and in Hadron therapy with proton and heavy ion beams. The purpose of the present work is to develop computational methods to evaluate microscopic energy deposition (ED) in volumes representative of neuron cell structures, including segments of dendrites and spines, using a stochastic track structure model. A challenge for biophysical models of neuronal damage is the large sizes (>100 μm) and variability in volumes of possible dendritic segments and pre-synaptic elements (spines and filopodia). We consider cylindrical and spherical microscopic volumes of varying geometric parameters and aspect ratios from 0.5 to 5 irradiated by protons, and 3He and 12C particles at energies corresponding to a distance of 1 cm to the Bragg peak, which represent particles of interest in Hadron therapy as well as space radiation exposure. We investigate the optimal axis length of dendritic segments to evaluate microscopic ED and hit probabilities along the dendritic branches at a given macroscopic dose. Because of large computation times to analyze ED in volumes of varying sizes, we developed an analytical method to find the mean primary dose in spheres that can guide numerical methods to find the primary dose distribution for cylinders. Considering cylindrical segments of varying aspect ratio at constant volume, we assess the chord length distribution, mean number of hits and ED profiles by primary particles and secondary electrons (δ-rays). For biophysical modeling applications, segments on dendritic branches are proposed to have equal diameters and axes lengths along the varying diameter of a dendritic branch. PMID:28554507

Track structure model of microscopic energy deposition by protons and heavy ions in segments of neuronal cell dendrites represented by cylinders or spheres

NASA Astrophysics Data System (ADS)

Alp, Murat; Cucinotta, Francis A.

2017-05-01

Changes to cognition, including memory, following radiation exposure are a concern for cosmic ray exposures to astronauts and in Hadron therapy with proton and heavy ion beams. The purpose of the present work is to develop computational methods to evaluate microscopic energy deposition (ED) in volumes representative of neuron cell structures, including segments of dendrites and spines, using a stochastic track structure model. A challenge for biophysical models of neuronal damage is the large sizes (> 100 μm) and variability in volumes of possible dendritic segments and pre-synaptic elements (spines and filopodia). We consider cylindrical and spherical microscopic volumes of varying geometric parameters and aspect ratios from 0.5 to 5 irradiated by protons, and 3He and 12C particles at energies corresponding to a distance of 1 cm to the Bragg peak, which represent particles of interest in Hadron therapy as well as space radiation exposure. We investigate the optimal axis length of dendritic segments to evaluate microscopic ED and hit probabilities along the dendritic branches at a given macroscopic dose. Because of large computation times to analyze ED in volumes of varying sizes, we developed an analytical method to find the mean primary dose in spheres that can guide numerical methods to find the primary dose distribution for cylinders. Considering cylindrical segments of varying aspect ratio at constant volume, we assess the chord length distribution, mean number of hits and ED profiles by primary particles and secondary electrons (δ-rays). For biophysical modeling applications, segments on dendritic branches are proposed to have equal diameters and axes lengths along the varying diameter of a dendritic branch.

The Sign Rule and Beyond: Boundary Effects, Flexibility, and Noise Correlations in Neural Population Codes

PubMed Central

Hu, Yu; Zylberberg, Joel; Shea-Brown, Eric

2014-01-01

Over repeat presentations of the same stimulus, sensory neurons show variable responses. This “noise” is typically correlated between pairs of cells, and a question with rich history in neuroscience is how these noise correlations impact the population's ability to encode the stimulus. Here, we consider a very general setting for population coding, investigating how information varies as a function of noise correlations, with all other aspects of the problem – neural tuning curves, etc. – held fixed. This work yields unifying insights into the role of noise correlations. These are summarized in the form of theorems, and illustrated with numerical examples involving neurons with diverse tuning curves. Our main contributions are as follows. (1) We generalize previous results to prove a sign rule (SR) — if noise correlations between pairs of neurons have opposite signs vs. their signal correlations, then coding performance will improve compared to the independent case. This holds for three different metrics of coding performance, and for arbitrary tuning curves and levels of heterogeneity. This generality is true for our other results as well. (2) As also pointed out in the literature, the SR does not provide a necessary condition for good coding. We show that a diverse set of correlation structures can improve coding. Many of these violate the SR, as do experimentally observed correlations. There is structure to this diversity: we prove that the optimal correlation structures must lie on boundaries of the possible set of noise correlations. (3) We provide a novel set of necessary and sufficient conditions, under which the coding performance (in the presence of noise) will be as good as it would be if there were no noise present at all. PMID:24586128

Track structure model of microscopic energy deposition by protons and heavy ions in segments of neuronal cell dendrites represented by cylinders or spheres.

PubMed

Alp, Murat; Cucinotta, Francis A

2017-05-01

Changes to cognition, including memory, following radiation exposure are a concern for cosmic ray exposures to astronauts and in Hadron therapy with proton and heavy ion beams. The purpose of the present work is to develop computational methods to evaluate microscopic energy deposition (ED) in volumes representative of neuron cell structures, including segments of dendrites and spines, using a stochastic track structure model. A challenge for biophysical models of neuronal damage is the large sizes (> 100µm) and variability in volumes of possible dendritic segments and pre-synaptic elements (spines and filopodia). We consider cylindrical and spherical microscopic volumes of varying geometric parameters and aspect ratios from 0.5 to 5 irradiated by protons, and 3 He and 12 C particles at energies corresponding to a distance of 1cm to the Bragg peak, which represent particles of interest in Hadron therapy as well as space radiation exposure. We investigate the optimal axis length of dendritic segments to evaluate microscopic ED and hit probabilities along the dendritic branches at a given macroscopic dose. Because of large computation times to analyze ED in volumes of varying sizes, we developed an analytical method to find the mean primary dose in spheres that can guide numerical methods to find the primary dose distribution for cylinders. Considering cylindrical segments of varying aspect ratio at constant volume, we assess the chord length distribution, mean number of hits and ED profiles by primary particles and secondary electrons (δ-rays). For biophysical modeling applications, segments on dendritic branches are proposed to have equal diameters and axes lengths along the varying diameter of a dendritic branch. Copyright © 2017. Published by Elsevier Ltd.

OPTOGENETICS, SEX AND VIOLENCE IN THE BRAIN: IMPLICATIONS FOR PSYCHIATRY

PubMed Central

Anderson, David J.

2012-01-01

Pathological aggression, and the inability to control aggressive impulses, takes a tremendous toll on society. Yet aggression is a normal component of the innate behavior repertoire of most vertebrate animal species, as well as of many invertebrates. Progress in understanding the etiology of disorders of aggressive behavior, whether genetic or environmental in nature, therefore requires an understanding of the brain circuitry that controls normal aggression. Efforts to understand this circuitry at the level of specific neuronal populations have been constrained by the limited resolution of classical methodologies, such as electrical stimulation and electrolytic lesion. The availability of new, genetically based tools for mapping and manipulating neural circuits at the level of specific, genetically defined neuronal subtypes provides an opportunity to investigate the functional organization of aggression circuitry with cellular resolution. However these technologies are optimally applied in the mouse, where there has been surprisingly little traditional work on the functional neuroanatomy of aggression. Here we discuss recent, initial efforts to apply optogenetics and other state-of-the-art methods to the dissection of aggression circuitry in the mouse. We find, surprisingly, that neurons necessary and sufficient for inter-male aggression are located within the ventrolateral subdivision of the ventromedial hypothalamic nucleus (VMHvl), a structure traditionally associated with reproductive behavior. These neurons are intermingled with neurons activated during male-female mating, with ~20% overlap between the populations. We discuss the significance of these findings with respect to neuroethological and neuroanatomical perspectives on the functional organization of innate behaviors, and their potential implications for psychiatry. PMID:22209636

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

Dystrophic (senescent) rather than activated microglial cells are associated with tau pathology and likely precede neurodegeneration in Alzheimer's disease.

PubMed

Streit, Wolfgang J; Braak, Heiko; Xue, Qing-Shan; Bechmann, Ingo

2009-10-01

The role of microglial cells in the pathogenesis of Alzheimer's disease (AD) neurodegeneration is unknown. Although several works suggest that chronic neuroinflammation caused by activated microglia contributes to neurofibrillary degeneration, anti-inflammatory drugs do not prevent or reverse neuronal tau pathology. This raises the question if indeed microglial activation occurs in the human brain at sites of neurofibrillary degeneration. In view of the recent work demonstrating presence of dystrophic (senescent) microglia in aged human brain, the purpose of this study was to investigate microglial cells in situ and at high resolution in the immediate vicinity of tau-positive structures in order to determine conclusively whether degenerating neuronal structures are associated with activated or with dystrophic microglia. We used a newly optimized immunohistochemical method for visualizing microglial cells in human archival brain together with Braak staging of neurofibrillary pathology to ascertain the morphology of microglia in the vicinity of tau-positive structures. We now report histopathological findings from 19 humans covering the spectrum from none to severe AD pathology, including patients with Down's syndrome, showing that degenerating neuronal structures positive for tau (neuropil threads, neurofibrillary tangles, neuritic plaques) are invariably colocalized with severely dystrophic (fragmented) rather than with activated microglial cells. Using Braak staging of Alzheimer neuropathology we demonstrate that microglial dystrophy precedes the spread of tau pathology. Deposits of amyloid-beta protein (Abeta) devoid of tau-positive structures were found to be colocalized with non-activated, ramified microglia, suggesting that Abeta does not trigger microglial activation. Our findings also indicate that when microglial activation does occur in the absence of an identifiable acute central nervous system insult, it is likely to be the result of systemic infectious disease. The findings reported here strongly argue against the hypothesis that neuroinflammatory changes contribute to AD dementia. Instead, they offer an alternative hypothesis of AD pathogenesis that takes into consideration: (1) the notion that microglia are neuron-supporting cells and neuroprotective; (2) the fact that development of non-familial, sporadic AD is inextricably linked to aging. They support the idea that progressive, aging-related microglial degeneration and loss of microglial neuroprotection rather than induction of microglial activation contributes to the onset of sporadic Alzheimer's disease. The results have far-reaching implications in terms of reevaluating current treatment approaches towards AD.

Comprehensive optical and data management infrastructure for high-throughput light-sheet microscopy of whole mouse brains.

PubMed

Müllenbroich, M Caroline; Silvestri, Ludovico; Onofri, Leonardo; Costantini, Irene; Hoff, Marcel Van't; Sacconi, Leonardo; Iannello, Giulio; Pavone, Francesco S

2015-10-01

Comprehensive mapping and quantification of neuronal projections in the central nervous system requires high-throughput imaging of large volumes with microscopic resolution. To this end, we have developed a confocal light-sheet microscope that has been optimized for three-dimensional (3-D) imaging of structurally intact clarified whole-mount mouse brains. We describe the optical and electromechanical arrangement of the microscope and give details on the organization of the microscope management software. The software orchestrates all components of the microscope, coordinates critical timing and synchronization, and has been written in a versatile and modular structure using the LabVIEW language. It can easily be adapted and integrated to other microscope systems and has been made freely available to the light-sheet community. The tremendous amount of data routinely generated by light-sheet microscopy further requires novel strategies for data handling and storage. To complete the full imaging pipeline of our high-throughput microscope, we further elaborate on big data management from streaming of raw images up to stitching of 3-D datasets. The mesoscale neuroanatomy imaged at micron-scale resolution in those datasets allows characterization and quantification of neuronal projections in unsectioned mouse brains.

The application of the multi-alternative approach in active neural network models

NASA Astrophysics Data System (ADS)

Podvalny, S.; Vasiljev, E.

2017-02-01

The article refers to the construction of intelligent systems based artificial neuron networks are used. We discuss the basic properties of the non-compliance of artificial neuron networks and their biological prototypes. It is shown here that the main reason for these discrepancies is the structural immutability of the neuron network models in the learning process, that is, their passivity. Based on the modern understanding of the biological nervous system as a structured ensemble of nerve cells, it is proposed to abandon the attempts to simulate its work at the level of the elementary neurons functioning processes and proceed to the reproduction of the information structure of data storage and processing on the basis of the general enough evolutionary principles of multialternativity, i.e. the multi-level structural model, diversity and modularity. The implementation method of these principles is offered, using the faceted memory organization in the neuron network with the rearranging active structure. An example of the implementation of the active facet-type neuron network in the intellectual decision-making system in the conditions of critical events development in the electrical distribution system.

Toward an Integration of Deep Learning and Neuroscience

PubMed Central

Marblestone, Adam H.; Wayne, Greg; Kording, Konrad P.

2016-01-01

Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) the cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. In support of these hypotheses, we argue that a range of implementations of credit assignment through multiple layers of neurons are compatible with our current knowledge of neural circuitry, and that the brain's specialized systems can be interpreted as enabling efficient optimization for specific problem classes. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses. PMID:27683554

Optimizing neuronal differentiation from induced pluripotent stem cells to model ASD

PubMed Central

Kim, Dae-Sung; Ross, P. Joel; Zaslavsky, Kirill; Ellis, James

2014-01-01

Autism spectrum disorder (ASD) is an early-onset neurodevelopmental disorder characterized by deficits in social communication, and restricted and repetitive patterns of behavior. Despite its high prevalence, discovery of pathophysiological mechanisms underlying ASD has lagged due to a lack of appropriate model systems. Recent advances in induced pluripotent stem cell (iPSC) technology and neural differentiation techniques allow for detailed functional analyses of neurons generated from living individuals with ASD. Refinement of cortical neuron differentiation methods from iPSCs will enable mechanistic studies of specific neuronal subpopulations that may be preferentially impaired in ASD. In this review, we summarize recent accomplishments in differentiation of cortical neurons from human pluripotent stems cells and efforts to establish in vitro model systems to study ASD using personalized neurons. PMID:24782713

Impaired neurogenesis of the dentate gyrus is associated with pattern separation deficits: A computational study.

PubMed

Faghihi, Faramarz; Moustafa, Ahmed A

2016-09-01

The separation of input patterns received from the entorhinal cortex (EC) by the dentate gyrus (DG) is a well-known critical step of information processing in the hippocampus. Although the role of interneurons in separation pattern efficiency of the DG has been theoretically known, the balance of neurogenesis of excitatory neurons and interneurons as well as its potential role in information processing in the DG is not fully understood. In this work, we study separation efficiency of the DG for different rates of neurogenesis of interneurons and excitatory neurons using a novel computational model in which we assume an increase in the synaptic efficacy between excitatory neurons and interneurons and then its decay over time. Information processing in the EC and DG was simulated as information flow in a two layer feed-forward neural network. The neurogenesis rate was modeled as the percentage of new born neurons added to the neuronal population in each time bin. The results show an important role of an optimal neurogenesis rate of interneurons and excitatory neurons in the DG in efficient separation of inputs from the EC in pattern separation tasks. The model predicts that any deviation of the optimal values of neurogenesis rates leads to different decreased levels of the separation deficits of the DG which influences its function to encode memory.

Generation of optimal artificial neural networks using a pattern search algorithm: application to approximation of chemical systems.

PubMed

Ihme, Matthias; Marsden, Alison L; Pitsch, Heinz

2008-02-01

A pattern search optimization method is applied to the generation of optimal artificial neural networks (ANNs). Optimization is performed using a mixed variable extension to the generalized pattern search method. This method offers the advantage that categorical variables, such as neural transfer functions and nodal connectivities, can be used as parameters in optimization. When used together with a surrogate, the resulting algorithm is highly efficient for expensive objective functions. Results demonstrate the effectiveness of this method in optimizing an ANN for the number of neurons, the type of transfer function, and the connectivity among neurons. The optimization method is applied to a chemistry approximation of practical relevance. In this application, temperature and a chemical source term are approximated as functions of two independent parameters using optimal ANNs. Comparison of the performance of optimal ANNs with conventional tabulation methods demonstrates equivalent accuracy by considerable savings in memory storage. The architecture of the optimal ANN for the approximation of the chemical source term consists of a fully connected feedforward network having four nonlinear hidden layers and 117 synaptic weights. An equivalent representation of the chemical source term using tabulation techniques would require a 500 x 500 grid point discretization of the parameter space.

Cortical membrane potential signature of optimal states for sensory signal detection

PubMed Central

McGinley, Matthew J.; David, Stephen V.; McCormick, David A.

2015-01-01

The neural correlates of optimal states for signal detection task performance are largely unknown. One hypothesis holds that optimal states exhibit tonically depolarized cortical neurons with enhanced spiking activity, such as occur during movement. We recorded membrane potentials of auditory cortical neurons in mice trained on a challenging tone-in-noise detection task while assessing arousal with simultaneous pupillometry and hippocampal recordings. Arousal measures accurately predicted multiple modes of membrane potential activity, including: rhythmic slow oscillations at low arousal, stable hyperpolarization at intermediate arousal, and depolarization during phasic or tonic periods of hyper-arousal. Walking always occurred during hyper-arousal. Optimal signal detection behavior and sound-evoked responses, at both sub-threshold and spiking levels, occurred at intermediate arousal when pre-decision membrane potentials were stably hyperpolarized. These results reveal a cortical physiological signature of the classically-observed inverted-U relationship between task performance and arousal, and that optimal detection exhibits enhanced sensory-evoked responses and reduced background synaptic activity. PMID:26074005

Hierarchical Winner-Take-All Particle Swarm Optimization Social Network for Neural Model Fitting

PubMed Central

Coventry, Brandon S.; Parthasarathy, Aravindakshan; Sommer, Alexandra L.; Bartlett, Edward L.

2016-01-01

Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models. PMID:27726048

Brain scaling in mammalian evolution as a consequence of concerted and mosaic changes in numbers of neurons and average neuronal cell size

PubMed Central

Herculano-Houzel, Suzana; Manger, Paul R.; Kaas, Jon H.

2014-01-01

Enough species have now been subject to systematic quantitative analysis of the relationship between the morphology and cellular composition of their brain that patterns begin to emerge and shed light on the evolutionary path that led to mammalian brain diversity. Based on an analysis of the shared and clade-specific characteristics of 41 modern mammalian species in 6 clades, and in light of the phylogenetic relationships among them, here we propose that ancestral mammal brains were composed and scaled in their cellular composition like modern afrotherian and glire brains: with an addition of neurons that is accompanied by a decrease in neuronal density and very little modification in glial cell density, implying a significant increase in average neuronal cell size in larger brains, and the allocation of approximately 2 neurons in the cerebral cortex and 8 neurons in the cerebellum for every neuron allocated to the rest of brain. We also propose that in some clades the scaling of different brain structures has diverged away from the common ancestral layout through clade-specific (or clade-defining) changes in how average neuronal cell mass relates to numbers of neurons in each structure, and how numbers of neurons are differentially allocated to each structure relative to the number of neurons in the rest of brain. Thus, the evolutionary expansion of mammalian brains has involved both concerted and mosaic patterns of scaling across structures. This is, to our knowledge, the first mechanistic model that explains the generation of brains large and small in mammalian evolution, and it opens up new horizons for seeking the cellular pathways and genes involved in brain evolution. PMID:25157220

Phase Transitions in Living Neural Networks

NASA Astrophysics Data System (ADS)

Williams-Garcia, Rashid Vladimir

Our nervous systems are composed of intricate webs of interconnected neurons interacting in complex ways. These complex interactions result in a wide range of collective behaviors with implications for features of brain function, e.g., information processing. Under certain conditions, such interactions can drive neural network dynamics towards critical phase transitions, where power-law scaling is conjectured to allow optimal behavior. Recent experimental evidence is consistent with this idea and it seems plausible that healthy neural networks would tend towards optimality. This hypothesis, however, is based on two problematic assumptions, which I describe and for which I present alternatives in this thesis. First, critical transitions may vanish due to the influence of an environment, e.g., a sensory stimulus, and so living neural networks may be incapable of achieving "critical" optimality. I develop a framework known as quasicriticality, in which a relative optimality can be achieved depending on the strength of the environmental influence. Second, the power-law scaling supporting this hypothesis is based on statistical analysis of cascades of activity known as neuronal avalanches, which conflate causal and non-causal activity, thus confounding important dynamical information. In this thesis, I present a new method to unveil causal links, known as causal webs, between neuronal activations, thus allowing for experimental tests of the quasicriticality hypothesis and other practical applications.

Optimization behavior of brainstem respiratory neurons. A cerebral neural network model.

PubMed

Poon, C S

1991-01-01

A recent model of respiratory control suggested that the steady-state respiratory responses to CO2 and exercise may be governed by an optimal control law in the brainstem respiratory neurons. It was not certain, however, whether such complex optimization behavior could be accomplished by a realistic biological neural network. To test this hypothesis, we developed a hybrid computer-neural model in which the dynamics of the lung, brain and other tissue compartments were simulated on a digital computer. Mimicking the "controller" was a human subject who pedalled on a bicycle with varying speed (analog of ventilatory output) with a view to minimize an analog signal of the total cost of breathing (chemical and mechanical) which was computed interactively and displayed on an oscilloscope. In this manner, the visuomotor cortex served as a proxy (homolog) of the brainstem respiratory neurons in the model. Results in 4 subjects showed a linear steady-state ventilatory CO2 response to arterial PCO2 during simulated CO2 inhalation and a nearly isocapnic steady-state response during simulated exercise. Thus, neural optimization is a plausible mechanism for respiratory control during exercise and can be achieved by a neural network with cognitive computational ability without the need for an exercise stimulus.

Adaptive coupling optimized spiking coherence and synchronization in Newman-Watts neuronal networks

NASA Astrophysics Data System (ADS)

Gong, Yubing; Xu, Bo; Wu, Ya'nan

2013-09-01

In this paper, we have numerically studied the effect of adaptive coupling on the temporal coherence and synchronization of spiking activity in Newman-Watts Hodgkin-Huxley neuronal networks. It is found that random shortcuts can enhance the spiking synchronization more rapidly when the increment speed of adaptive coupling is increased and can optimize the temporal coherence of spikes only when the increment speed of adaptive coupling is appropriate. It is also found that adaptive coupling strength can enhance the synchronization of spikes and can optimize the temporal coherence of spikes when random shortcuts are appropriate. These results show that adaptive coupling has a big influence on random shortcuts related spiking activity and can enhance and optimize the temporal coherence and synchronization of spiking activity of the network. These findings can help better understand the roles of adaptive coupling for improving the information processing and transmission in neural systems.

Evaluation of thermal conductivity of MgO-MWCNTs/EG hybrid nanofluids based on experimental data by selecting optimal artificial neural networks

NASA Astrophysics Data System (ADS)

Vafaei, Masoud; Afrand, Masoud; Sina, Nima; Kalbasi, Rasool; Sourani, Forough; Teimouri, Hamid

2017-01-01

In this paper, the thermal conductivity ratio of MgO-MWCNTs/EG hybrid nanofluids has been predicted by an optimal artificial neural network at solid volume fractions of 0.05%, 0.1%, 0.15%, 0.2%, 0.4% and 0.6% in the temperature range of 25-50 °C. In this way, at the first, thirty six experimental data was presented to determine the thermal conductivity ratio of the hybrid nanofluid. Then, four optimal artificial neural networks with 6, 8, 10 and 12 neurons in hidden layer were designed to predict the thermal conductivity ratio of the nanofluid. The comparison between four optimal ANN results and experimental showed that the ANN with 12 neurons in hidden layer was the best model. Moreover, the results obtained from the best ANN indicated the maximum deviation margin of 0.8%.

The Vertebrate Brain, Evidence of Its Modular Organization and Operating System: Insights into the Brain's Basic Units of Structure, Function, and Operation and How They Influence Neuronal Signaling and Behavior.

PubMed

Baslow, Morris H

2011-01-01

The human brain is a complex organ made up of neurons and several other cell types, and whose role is processing information for use in eliciting behaviors. However, the composition of its repeating cellular units for both structure and function are unresolved. Based on recent descriptions of the brain's physiological "operating system", a function of the tri-cellular metabolism of N-acetylaspartate (NAA) and N-acetylaspartylglutamate (NAAG) for supply of energy, and on the nature of "neuronal words and languages" for intercellular communication, insights into the brain's modular structural and functional units have been gained. In this article, it is proposed that the basic structural unit in brain is defined by its physiological operating system, and that it consists of a single neuron, and one or more astrocytes, oligodendrocytes, and vascular system endothelial cells. It is also proposed that the basic functional unit in the brain is defined by how neurons communicate, and consists of two neurons and their interconnecting dendritic-synaptic-dendritic field. Since a functional unit is composed of two neurons, it requires two structural units to form a functional unit. Thus, the brain can be envisioned as being made up of the three-dimensional stacking and intertwining of myriad structural units which results not only in its gross structure, but also in producing a uniform distribution of binary functional units. Since the physiological NAA-NAAG operating system for supply of energy is repeated in every structural unit, it is positioned to control global brain function.

Multidimensional density shaping by sigmoids.

PubMed

Roth, Z; Baram, Y

1996-01-01

An estimate of the probability density function of a random vector is obtained by maximizing the output entropy of a feedforward network of sigmoidal units with respect to the input weights. Classification problems can be solved by selecting the class associated with the maximal estimated density. Newton's optimization method, applied to the estimated density, yields a recursive estimator for a random variable or a random sequence. A constrained connectivity structure yields a linear estimator, which is particularly suitable for "real time" prediction. A Gaussian nonlinearity yields a closed-form solution for the network's parameters, which may also be used for initializing the optimization algorithm when other nonlinearities are employed. A triangular connectivity between the neurons and the input, which is naturally suggested by the statistical setting, reduces the number of parameters. Applications to classification and forecasting problems are demonstrated.

Effect of Boric Acid Supplementation on the Expression of BDNF in African Ostrich Chick Brain.

PubMed

Tang, Juan; Zheng, Xing-ting; Xiao, Ke; Wang, Kun-lun; Wang, Jing; Wang, Yun-xiao; Wang, Ke; Wang, Wei; Lu, Shun; Yang, Ke-li; Sun, Peng-Peng; Khaliq, Haseeb; Zhong, Juming; Peng, Ke-Mei

2016-03-01

The degree of brain development can be expressed by the levels of brain brain-derived neurotrophic factor (BDNF). BDNF plays an irreplaceable role in the process of neuronal development, protection, and restoration. The aim of the present study was to evaluate the effects of boric acid supplementation in water on the ostrich chick neuronal development. One-day-old healthy animals were supplemented with boron in drinking water at various concentrations, and the potential effects of boric acid on brain development were tested by a series of experiments. The histological changes in brain were observed by hematoxylin and eosin (HE) staining and Nissl staining. Expression of BDNF was analyzed by immunohistochemistry, quantitative real-time PCR (QRT-PCR), and enzyme linked immunosorbent assay (ELISA). Apoptosis was evaluated with Dutp-biotin nick end labeling (TUNEL) reaction, and caspase-3 was detected with QRT-PCR. The results were as follows: (1) under the light microscope, the neuron structure was well developed with abundance of neurites and intact cell morphology when animals were fed with less than 160 mg/L of boric acid (groups II, III, IV). Adversely, when boric acid doses were higher than 320 mg/L(groups V, VI), the high-dose boric acid neuron structure was damaged with less neurites, particularly at 640 mg/L; (2) the quantity of BDNF expression in groups II, III, and IV was increased while it was decreased in groups V and VI when compared with that in group I; (3) TUNEL reaction and the caspase-3 mRNA level showed that the amount of cell apoptosis in group II, group III, and group IV were decreased, but increased in group V and group VI significantly. These results indicated that appropriate supplementation of boric acid, especially at 160 mg/L, could promote ostrich chicks' brain development by promoting the BDNF expression and reducing cell apoptosis. Conversely, high dose of boric acid particularly in 640 mg/L would damage the neuron structure of ostrich chick brain by inhibiting the BDNF expression and increasing cell apoptosis. Taken together, the 160 mg/L boric acid supplementation may be the optimal dose for the brain development of ostrich chicks.

Minimum energy control for in vitro neurons.

PubMed

Nabi, Ali; Stigen, Tyler; Moehlis, Jeff; Netoff, Theoden

2013-06-01

To demonstrate the applicability of optimal control theory for designing minimum energy charge-balanced input waveforms for single periodically-firing in vitro neurons from brain slices of Long-Evans rats. The method of control uses the phase model of a neuron and does not require prior knowledge of the neuron's biological details. The phase model of a neuron is a one-dimensional model that is characterized by the neuron's phase response curve (PRC), a sensitivity measure of the neuron to a stimulus applied at different points in its firing cycle. The PRC for each neuron is experimentally obtained by measuring the shift in phase due to a short-duration pulse injected into the periodically-firing neuron at various phase values. Based on the measured PRC, continuous-time, charge-balanced, minimum energy control waveforms have been designed to regulate the next firing time of the neuron upon application at the onset of an action potential. The designed waveforms can achieve the inter-spike-interval regulation for in vitro neurons with energy levels that are lower than those of conventional monophasic pulsatile inputs of past studies by at least an order of magnitude. They also provide the advantage of being charge-balanced. The energy efficiency of these waveforms is also shown by performing several supporting simulations that compare the performance of the designed waveforms against that of phase shuffled surrogate inputs, variants of the minimum energy waveforms obtained from suboptimal PRCs, as well as pulsatile stimuli that are applied at the point of maximum PRC. It was found that the minimum energy waveforms perform better than all other stimuli both in terms of control and in the amount of energy used. Specifically, it was seen that these charge-balanced waveforms use at least an order of magnitude less energy than conventional monophasic pulsatile stimuli. The significance of this work is that it uses concepts from the theory of optimal control and introduces a novel approach in designing minimum energy charge-balanced input waveforms for neurons that are robust to noise and implementable in electrophysiological experiments.

Optimization of neural network architecture for classification of radar jamming FM signals

NASA Astrophysics Data System (ADS)

Soto, Alberto; Mendoza, Ariadna; Flores, Benjamin C.

2017-05-01

The purpose of this study is to investigate several artificial Neural Network (NN) architectures in order to design a cognitive radar system capable of optimally distinguishing linear Frequency-Modulated (FM) signals from bandlimited Additive White Gaussian Noise (AWGN). The goal is to create a theoretical framework to determine an optimal NN architecture to achieve a Probability of Detection (PD) of 95% or higher and a Probability of False Alarm (PFA) of 1.5% or lower at 5 dB Signal to Noise Ratio (SNR). Literature research reveals that the frequency-domain power spectral densities characterize a signal more efficiently than its time-domain counterparts. Therefore, the input data is preprocessed by calculating the magnitude square of the Discrete Fourier Transform of the digitally sampled bandlimited AWGN and linear FM signals to populate a matrix containing N number of samples and M number of spectra. This matrix is used as input for the NN, and the spectra are divided as follows: 70% for training, 15% for validation, and 15% for testing. The study begins by experimentally deducing the optimal number of hidden neurons (1-40 neurons), then the optimal number of hidden layers (1-5 layers), and lastly, the most efficient learning algorithm. The training algorithms examined are: Resilient Backpropagation, Scaled Conjugate Gradient, Conjugate Gradient with Powell/Beale Restarts, Polak-Ribiére Conjugate Gradient, and Variable Learning Rate Backpropagation. We determine that an architecture with ten hidden neurons (or higher), one hidden layer, and a Scaled Conjugate Gradient for training algorithm encapsulates an optimal architecture for our application.

Superresolution imaging of Drosophila tissues using expansion microscopy.

PubMed

Jiang, Nan; Kim, Hyeon-Jin; Chozinski, Tyler J; Azpurua, Jorge E; Eaton, Benjamin A; Vaughan, Joshua C; Parrish, Jay Z

2018-06-15

The limited resolving power of conventional diffraction-limited microscopy hinders analysis of small, densely packed structural elements in cells. Expansion microscopy (ExM) provides an elegant solution to this problem, allowing for increased resolution with standard microscopes via physical expansion of the specimen in a swellable polymer hydrogel. Here, we apply, validate, and optimize ExM protocols that enable the study of Drosophila embryos, larval brains, and larval and adult body walls. We achieve a lateral resolution of ∼70 nm in Drosophila tissues using a standard confocal microscope, and we use ExM to analyze fine intracellular structures and intercellular interactions. First, we find that ExM reveals features of presynaptic active zone (AZ) structure that are observable with other superresolution imaging techniques but not with standard confocal microscopy. We further show that synapses known to exhibit age-dependent changes in activity also exhibit age-dependent changes in AZ structure. Finally, we use the significantly improved axial resolution of ExM to show that dendrites of somatosensory neurons are inserted into epithelial cells at a higher frequency than previously reported in confocal microscopy studies. Altogether, our study provides a foundation for the application of ExM to Drosophila tissues and underscores the importance of tissue-specific optimization of ExM procedures.

Boosting Your Baby's Brain Power

ERIC Educational Resources Information Center

Engel-Smothers, Holly; Heim, Susan M.

2009-01-01

With more than 100 billion neurons that would stretch more than 60,000 miles, a newborn baby's brain is quite phenomenal! These neurons must generally form connections within the first eight months of a baby's life to foster optimal brain growth and lifelong learning. Mommies, daddies, and caregivers are extremely vital to ensuring babies reach…

Fine thermotactic discrimination between the optimal and slightly cooler temperatures via a TRPV channel in chordotonal neurons.

PubMed

Kwon, Young; Shen, Wei L; Shim, Hye-Seok; Montell, Craig

2010-08-04

Animals select their optimal environmental temperature, even when faced with alternatives that differ only slightly. This behavior is critical as small differences in temperature of only several degrees can have a profound effect on the survival and rate of development of poikilothermic animals, such as the fruit fly. Here, we demonstrate that Drosophila larvae choose their preferred temperature of 17.5 degrees C over slightly cooler temperatures (14-16 degrees C) through activation of chordotonal neurons. Mutations affecting a transient receptor potential (TRP) vanilloid channel, Inactive (Iav), which is expressed specifically in chordotonal neurons, eliminated the ability to choose 17.5 degrees C over 14-16 degrees C. The impairment in selecting 17.5 degrees C resulted from absence of an avoidance response, which is normally mediated by an increase in turns at the lower temperatures. We conclude that the decision to select the preferred over slightly cooler temperatures requires iav and is achieved by activating chordotonal neurons, which in turn induces repulsive behaviors, due to an increase in high angle turns.

Fine Thermotactic Discrimination between the Optimal and Slightly Cooler Temperatures via a TRPV Channel in Chordotonal Neurons

PubMed Central

Kwon, Young; Shen, Wei L.; Shim, Hye-Seok; Montell, Craig

2012-01-01

Animals select their optimal environmental temperature, even when faced with alternatives that differ only slightly. This behavior is critical as small differences in temperature of only several degrees can have a profound effect on the survival and rate of development of poikilothermic animals, such as the fruit fly. Here, we demonstrate that Drosophila larvae choose their preferred temperature of 17.5°C over slightly cooler temperatures (14–16°C) through activation of chordotonal neurons. Mutations affecting a transient receptor potential (TRP) vanilloid channel, Inactive (Iav), which is expressed specifically in chordotonal neurons, eliminated the ability to choose 17.5°C over 14–16°C. The impairment in selecting 17.5°C resulted from absence of an avoidance response, which is normally mediated by an increase in turns at the lower temperatures. We conclude that the decision to select the preferred over slightly cooler temperatures requires iav and is achieved by activating chordotonal neurons, which in turn induces repulsive behaviors, due to an increase in high angle turns. PMID:20685989

Neurons in the Frontal Lobe Encode the Value of Multiple Decision Variables

PubMed Central

Kennerley, Steven W.; Dahmubed, Aspandiar F.; Lara, Antonio H.; Wallis, Jonathan D.

2009-01-01

A central question in behavioral science is how we select among choice alternatives to obtain consistently the most beneficial outcomes. Three variables are particularly important when making a decision: the potential payoff, the probability of success, and the cost in terms of time and effort. A key brain region in decision making is the frontal cortex as damage here impairs the ability to make optimal choices across a range of decision types. We simultaneously recorded the activity of multiple single neurons in the frontal cortex while subjects made choices involving the three aforementioned decision variables. This enabled us to contrast the relative contribution of the anterior cingulate cortex (ACC), the orbito-frontal cortex, and the lateral prefrontal cortex to the decision-making process. Neurons in all three areas encoded value relating to choices involving probability, payoff, or cost manipulations. However, the most significant signals were in the ACC, where neurons encoded multiplexed representations of the three different decision variables. This supports the notion that the ACC is an important component of the neural circuitry underlying optimal decision making. PMID:18752411

Cortical Dynamics in Presence of Assemblies of Densely Connected Weight-Hub Neurons

PubMed Central

Setareh, Hesam; Deger, Moritz; Petersen, Carl C. H.; Gerstner, Wulfram

2017-01-01

Experimental measurements of pairwise connection probability of pyramidal neurons together with the distribution of synaptic weights have been used to construct randomly connected model networks. However, several experimental studies suggest that both wiring and synaptic weight structure between neurons show statistics that differ from random networks. Here we study a network containing a subset of neurons which we call weight-hub neurons, that are characterized by strong inward synapses. We propose a connectivity structure for excitatory neurons that contain assemblies of densely connected weight-hub neurons, while the pairwise connection probability and synaptic weight distribution remain consistent with experimental data. Simulations of such a network with generalized integrate-and-fire neurons display regular and irregular slow oscillations akin to experimentally observed up/down state transitions in the activity of cortical neurons with a broad distribution of pairwise spike correlations. Moreover, stimulation of a model network in the presence or absence of assembly structure exhibits responses similar to light-evoked responses of cortical layers in optogenetically modified animals. We conclude that a high connection probability into and within assemblies of excitatory weight-hub neurons, as it likely is present in some but not all cortical layers, changes the dynamics of a layer of cortical microcircuitry significantly. PMID:28690508

C. elegans model of neuronal aging

PubMed Central

Peng, Chiu-Ying; Chen, Chun-Hao; Hsu, Jiun-Min

2011-01-01

Aging of the nervous system underlies the behavioral and cognitive decline associated with senescence. Understanding the molecular and cellular basis of neuronal aging will therefore contribute to the development of effective treatments for aging and age-associated neurodegenerative disorders. Despite this pressing need, there are surprisingly few animal models that aim at recapitulating neuronal aging in a physiological context. We recently developed a C. elegans model of neuronal aging, and showed that age-dependent neuronal defects are regulated by insulin signaling. We identified electrical activity and epithelial attachment as two critical factors in the maintenance of structural integrity of C. elegans touch receptor neurons. These findings open a new avenue for elucidating the molecular mechanisms that maintain neuronal structures during the course of aging. PMID:22446530

Stochastic multiresonance in coupled excitable FHN neurons

NASA Astrophysics Data System (ADS)

Li, Huiyan; Sun, Xiaojuan; Xiao, Jinghua

2018-04-01

In this paper, effects of noise on Watts-Strogatz small-world neuronal networks, which are stimulated by a subthreshold signal, have been investigated. With the numerical simulations, it is surprisingly found that there exist several optimal noise intensities at which the subthreshold signal can be detected efficiently. This indicates the occurrence of stochastic multiresonance in the studied neuronal networks. Moreover, it is revealed that the occurrence of stochastic multiresonance has close relationship with the period of subthreshold signal Te and the noise-induced mean period of the neuronal networks T0. In detail, we find that noise could induce the neuronal networks to generate stochastic resonance for M times if Te is not very large and falls into the interval ( M × T 0 , ( M + 1 ) × T 0 ) with M being a positive integer. In real neuronal system, subthreshold signal detection is very meaningful. Thus, the obtained results in this paper could give some important implications on detecting subthreshold signal and propagating neuronal information in neuronal systems.

Optimal decoding and information transmission in Hodgkin-Huxley neurons under metabolic cost constraints.

PubMed

Kostal, Lubomir; Kobayashi, Ryota

2015-10-01

Information theory quantifies the ultimate limits on reliable information transfer by means of the channel capacity. However, the channel capacity is known to be an asymptotic quantity, assuming unlimited metabolic cost and computational power. We investigate a single-compartment Hodgkin-Huxley type neuronal model under the spike-rate coding scheme and address how the metabolic cost and the decoding complexity affects the optimal information transmission. We find that the sub-threshold stimulation regime, although attaining the smallest capacity, allows for the most efficient balance between the information transmission and the metabolic cost. Furthermore, we determine post-synaptic firing rate histograms that are optimal from the information-theoretic point of view, which enables the comparison of our results with experimental data. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Programmed to learn? The ontogeny of mirror neurons.

PubMed

Del Giudice, Marco; Manera, Valeria; Keysers, Christian

2009-03-01

Mirror neurons are increasingly recognized as a crucial substrate for many developmental processes, including imitation and social learning. Although there has been considerable progress in describing their function and localization in the primate and adult human brain, we still know little about their ontogeny. The idea that mirror neurons result from Hebbian learning while the child observes/hears his/her own actions has received remarkable empirical support in recent years. Here we add a new element to this proposal, by suggesting that the infant's perceptual-motor system is optimized to provide the brain with the correct input for Hebbian learning, thus facilitating the association between the perception of actions and their corresponding motor programs. We review evidence that infants (1) have a marked visual preference for hands, (2) show cyclic movement patterns with a frequency that could be in the optimal range for enhanced Hebbian learning, and (3) show synchronized theta EEG (also known to favour synaptic Hebbian learning) in mirror cortical areas during self-observation of grasping. These conditions, taken together, would allow mirror neurons for manual actions to develop quickly and reliably through experiential canalization. Our hypothesis provides a plausible pathway for the emergence of mirror neurons that integrates learning with genetic pre-programming, suggesting new avenues for research on the link between synaptic processes and behaviour in ontogeny.

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.

A computational model of the respiratory network challenged and optimized by data from optogenetic manipulation of glycinergic neurons.

PubMed

Oku, Yoshitaka; Hülsmann, Swen

2017-04-07

The topology of the respiratory network in the brainstem has been addressed using different computational models, which help to understand the functional properties of the system. We tested a neural mass model by comparing the result of activation and inhibition of inhibitory neurons in silico with recently published results of optogenetic manipulation of glycinergic neurons [Sherman, et al. (2015) Nat Neurosci 18:408]. The comparison revealed that a five-cell type model consisting of three classes of inhibitory neurons [I-DEC, E-AUG, E-DEC (PI)] and two excitatory populations (pre-I/I) and (I-AUG) neurons can be applied to explain experimental observations made by stimulating or inhibiting inhibitory neurons by light sensitive ion channels. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

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.

A cholinergic feedback circuit to regulate striatal population uncertainty and optimize reinforcement learning.

PubMed

Franklin, Nicholas T; Frank, Michael J

2015-12-25

Convergent evidence suggests that the basal ganglia support reinforcement learning by adjusting action values according to reward prediction errors. However, adaptive behavior in stochastic environments requires the consideration of uncertainty to dynamically adjust the learning rate. We consider how cholinergic tonically active interneurons (TANs) may endow the striatum with such a mechanism in computational models spanning three Marr's levels of analysis. In the neural model, TANs modulate the excitability of spiny neurons, their population response to reinforcement, and hence the effective learning rate. Long TAN pauses facilitated robustness to spurious outcomes by increasing divergence in synaptic weights between neurons coding for alternative action values, whereas short TAN pauses facilitated stochastic behavior but increased responsiveness to change-points in outcome contingencies. A feedback control system allowed TAN pauses to be dynamically modulated by uncertainty across the spiny neuron population, allowing the system to self-tune and optimize performance across stochastic environments.

The Vertebrate Brain, Evidence of Its Modular Organization and Operating System: Insights into the Brain's Basic Units of Structure, Function, and Operation and How They Influence Neuronal Signaling and Behavior

PubMed Central

Baslow, Morris H.

2011-01-01

The human brain is a complex organ made up of neurons and several other cell types, and whose role is processing information for use in eliciting behaviors. However, the composition of its repeating cellular units for both structure and function are unresolved. Based on recent descriptions of the brain's physiological “operating system”, a function of the tri-cellular metabolism of N-acetylaspartate (NAA) and N-acetylaspartylglutamate (NAAG) for supply of energy, and on the nature of “neuronal words and languages” for intercellular communication, insights into the brain's modular structural and functional units have been gained. In this article, it is proposed that the basic structural unit in brain is defined by its physiological operating system, and that it consists of a single neuron, and one or more astrocytes, oligodendrocytes, and vascular system endothelial cells. It is also proposed that the basic functional unit in the brain is defined by how neurons communicate, and consists of two neurons and their interconnecting dendritic–synaptic–dendritic field. Since a functional unit is composed of two neurons, it requires two structural units to form a functional unit. Thus, the brain can be envisioned as being made up of the three-dimensional stacking and intertwining of myriad structural units which results not only in its gross structure, but also in producing a uniform distribution of binary functional units. Since the physiological NAA–NAAG operating system for supply of energy is repeated in every structural unit, it is positioned to control global brain function. PMID:21720525

Neuronal Tracing with Magnetic Labels: NMR Imaging Methods, Preliminary Results, and New Optimized Coils.

NASA Astrophysics Data System (ADS)

Ghosh, Pratik

1992-01-01

The investigations focussed on in vivo NMR imaging studies of magnetic particles with and within neural cells. NMR imaging methods, both Fourier transform and projection reconstruction, were implemented and new protocols were developed to perform "Neuronal Tracing with Magnetic Labels" on small animal brains. Having performed the preliminary experiments with neuronal tracing, new optimized coils and experimental set-up were devised. A novel gradient coil technology along with new rf-coils were implemented, and optimized for future use with small animals in them. A new magnetic labelling procedure was developed that allowed labelling of billions of cells with ultra -small magnetite particles in a short time. The relationships among the viability of such cells, the amount of label and the contrast in the images were studied as quantitatively as possible. Intracerebral grafting of magnetite labelled fetal rat brain cells made it possible for the first time to attempt monitoring in vivo the survival, differentiation, and possible migration of both host and grafted cells in the host rat brain. This constituted the early steps toward future experiments that may lead to the monitoring of human brain grafts of fetal brain cells. Preliminary experiments with direct injection of horse radish peroxidase-conjugated magnetite particles into neurons, followed by NMR imaging, revealed a possible non-invasive alternative, allowing serial study of the dynamic transport pattern of tracers in single living animals. New gradient coils were built by using parallel solid-conductor ribbon cables that could be wrapped easily and quickly. Rapid rise times provided by these coils allowed implementation of fast imaging methods. Optimized rf-coil circuit development made it possible to understand better the sample-coil properties and the associated trade -offs in cases of small but conducting samples.

Evaluation of helper-dependent canine adenovirus vectors in a 3D human CNS model

PubMed Central

Simão, Daniel; Pinto, Catarina; Fernandes, Paulo; Peddie, Christopher J.; Piersanti, Stefania; Collinson, Lucy M.; Salinas, Sara; Saggio, Isabella; Schiavo, Giampietro; Kremer, Eric J.; Brito, Catarina; Alves, Paula M.

2017-01-01

Gene therapy is a promising approach with enormous potential for treatment of neurodegenerative disorders. Viral vectors derived from canine adenovirus type 2 (CAV-2) present attractive features for gene delivery strategies in the human brain, by preferentially transducing neurons, are capable of efficient axonal transport to afferent brain structures, have a 30-kb cloning capacity and have low innate and induced immunogenicity in pre-clinical tests. For clinical translation, in-depth pre-clinical evaluation of efficacy and safety in a human setting is primordial. Stem cell-derived human neural cells have a great potential as complementary tools by bridging the gap between animal models, which often diverge considerably from human phenotype, and clinical trials. Herein, we explore helper-dependent CAV-2 (hd-CAV-2) efficacy and safety for gene delivery in a human stem cell-derived 3D neural in vitro model. Assessment of hd-CAV-2 vector efficacy was performed at different multiplicities of infection, by evaluating transgene expression and impact on cell viability, ultrastructural cellular organization and neuronal gene expression. Under optimized conditions, hd-CAV-2 transduction led to stable long-term transgene expression with minimal toxicity. hd-CAV-2 preferentially transduced neurons, while human adenovirus type 5 (HAdV5) showed increased tropism towards glial cells. This work demonstrates, in a physiologically relevant 3D model, that hd-CAV-2 vectors are efficient tools for gene delivery to human neurons, with stable long-term transgene expression and minimal cytotoxicity. PMID:26181626

Evaluation of helper-dependent canine adenovirus vectors in a 3D human CNS model.

PubMed

Simão, D; Pinto, C; Fernandes, P; Peddie, C J; Piersanti, S; Collinson, L M; Salinas, S; Saggio, I; Schiavo, G; Kremer, E J; Brito, C; Alves, P M

2016-01-01

Gene therapy is a promising approach with enormous potential for treatment of neurodegenerative disorders. Viral vectors derived from canine adenovirus type 2 (CAV-2) present attractive features for gene delivery strategies in the human brain, by preferentially transducing neurons, are capable of efficient axonal transport to afferent brain structures, have a 30-kb cloning capacity and have low innate and induced immunogenicity in preclinical tests. For clinical translation, in-depth preclinical evaluation of efficacy and safety in a human setting is primordial. Stem cell-derived human neural cells have a great potential as complementary tools by bridging the gap between animal models, which often diverge considerably from human phenotype, and clinical trials. Herein, we explore helper-dependent CAV-2 (hd-CAV-2) efficacy and safety for gene delivery in a human stem cell-derived 3D neural in vitro model. Assessment of hd-CAV-2 vector efficacy was performed at different multiplicities of infection, by evaluating transgene expression and impact on cell viability, ultrastructural cellular organization and neuronal gene expression. Under optimized conditions, hd-CAV-2 transduction led to stable long-term transgene expression with minimal toxicity. hd-CAV-2 preferentially transduced neurons, whereas human adenovirus type 5 (HAdV5) showed increased tropism toward glial cells. This work demonstrates, in a physiologically relevant 3D model, that hd-CAV-2 vectors are efficient tools for gene delivery to human neurons, with stable long-term transgene expression and minimal cytotoxicity.

Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex

PubMed Central

Logiaco, Laureline; Quilodran, René; Procyk, Emmanuel; Arleo, Angelo

2015-01-01

The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC) of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70–200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys’ behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators. PMID:26266537

Activity-Induced Synaptic Structural Modifications by an Activator of Integrin Signaling at the Drosophila Neuromuscular Junction.

PubMed

Lee, Joo Yeun; Geng, Junhua; Lee, Juhyun; Wang, Andrew R; Chang, Karen T

2017-03-22

Activity-induced synaptic structural modification is crucial for neural development and synaptic plasticity, but the molecular players involved in this process are not well defined. Here, we report that a protein named Shriveled (Shv) regulates synaptic growth and activity-dependent synaptic remodeling at the Drosophila neuromuscular junction. Depletion of Shv causes synaptic overgrowth and an accumulation of immature boutons. We find that Shv physically and genetically interacts with βPS integrin. Furthermore, Shv is secreted during intense, but not mild, neuronal activity to acutely activate integrin signaling, induce synaptic bouton enlargement, and increase postsynaptic glutamate receptor abundance. Consequently, loss of Shv prevents activity-induced synapse maturation and abolishes post-tetanic potentiation, a form of synaptic plasticity. Our data identify Shv as a novel trans-synaptic signal secreted upon intense neuronal activity to promote synapse remodeling through integrin receptor signaling. SIGNIFICANCE STATEMENT The ability of neurons to rapidly modify synaptic structure in response to neuronal activity, a process called activity-induced structural remodeling, is crucial for neuronal development and complex brain functions. The molecular players that are important for this fundamental biological process are not well understood. Here we show that the Shriveled (Shv) protein is required during development to maintain normal synaptic growth. We further demonstrate that Shv is selectively released during intense neuronal activity, but not mild neuronal activity, to acutely activate integrin signaling and trigger structural modifications at the Drosophila neuromuscular junction. This work identifies Shv as a key modulator of activity-induced structural remodeling and suggests that neurons use distinct molecular cues to differentially modulate synaptic growth and remodeling to meet synaptic demand. Copyright © 2017 the authors 0270-6474/17/373246-18$15.00/0.

Resolving the Detailed Structure of Cortical and Thalamic Neurons in the Adult Rat Brain with Refined Biotinylated Dextran Amine Labeling

PubMed Central

Ling, Changying; Hendrickson, Michael L.; Kalil, Ronald E.

2012-01-01

Biotinylated dextran amine (BDA) has been used frequently for both anterograde and retrograde pathway tracing in the central nervous system. Typically, BDA labels axons and cell somas in sufficient detail to identify their topographical location accurately. However, BDA labeling often has proved to be inadequate to resolve the fine structural details of axon arbors or the dendrites of neurons at a distance from the site of BDA injection. To overcome this limitation, we varied several experimental parameters associated with the BDA labeling of neurons in the adult rat brain in order to improve the sensitivity of the method. Specifically, we compared the effect on labeling sensitivity of: (a) using 3,000 or 10,000 MW BDA; (b) injecting different volumes of BDA; (c) co-injecting BDA with NMDA; and (d) employing various post-injection survival times. Following the extracellular injection of BDA into the visual cortex, labeled cells and axons were observed in both cortical and thalamic areas of all animals studied. However, the detailed morphology of axon arbors and distal dendrites was evident only under optimal conditions for BDA labeling that take into account the: molecular weight of the BDA used, concentration and volume of BDA injected, post-injection survival time, and toning of the resolved BDA with gold and silver. In these instances, anterogradely labeled axons and retrogradely labeled dendrites were resolved in fine detail, approximating that which can be achieved with intracellularly injected compounds such as biocytin or fluorescent dyes. PMID:23144777

Differentiation and Characterization of Dopaminergic Neurons From Baboon Induced Pluripotent Stem Cells.

PubMed

Grow, Douglas A; Simmons, DeNard V; Gomez, Jorge A; Wanat, Matthew J; McCarrey, John R; Paladini, Carlos A; Navara, Christopher S

2016-09-01

: The progressive death of dopamine producing neurons in the substantia nigra pars compacta is the principal cause of symptoms of Parkinson's disease (PD). Stem cells have potential therapeutic use in replacing these cells and restoring function. To facilitate development of this approach, we sought to establish a preclinical model based on a large nonhuman primate for testing the efficacy and safety of stem cell-based transplantation. To this end, we differentiated baboon fibroblast-derived induced pluripotent stem cells (biPSCs) into dopaminergic neurons with the application of specific morphogens and growth factors. We confirmed that biPSC-derived dopaminergic neurons resemble those found in the human midbrain based on cell type-specific expression of dopamine markers TH and GIRK2. Using the reverse transcriptase quantitative polymerase chain reaction, we also showed that biPSC-derived dopaminergic neurons express PAX6, FOXA2, LMX1A, NURR1, and TH genes characteristic of this cell type in vivo. We used perforated patch-clamp electrophysiology to demonstrate that biPSC-derived dopaminergic neurons fired spontaneous rhythmic action potentials and high-frequency action potentials with spike frequency adaption upon injection of depolarizing current. Finally, we showed that biPSC-derived neurons released catecholamines in response to electrical stimulation. These results demonstrate the utility of the baboon model for testing and optimizing the efficacy and safety of stem cell-based therapeutic approaches for the treatment of PD. Functional dopamine neurons were produced from baboon induced pluripotent stem cells, and their properties were compared to baboon midbrain cells in vivo. The baboon has advantages as a clinically relevant model in which to optimize the efficacy and safety of stem cell-based therapies for neurodegenerative diseases, such as Parkinson's disease. Baboons possess crucial neuroanatomical and immunological similarities to humans, and baboon pluripotent stem cells can be differentiated into functional neurons that mimic those in the human brain, thus laying the foundation for the utility of the baboon model for evaluating stem cell therapies. ©AlphaMed Press.

Dynamic Excitatory and Inhibitory Gain Modulation Can Produce Flexible, Robust and Optimal Decision-making

PubMed Central

Niyogi, Ritwik K.; Wong-Lin, KongFatt

2013-01-01

Behavioural and neurophysiological studies in primates have increasingly shown the involvement of urgency signals during the temporal integration of sensory evidence in perceptual decision-making. Neuronal correlates of such signals have been found in the parietal cortex, and in separate studies, demonstrated attention-induced gain modulation of both excitatory and inhibitory neurons. Although previous computational models of decision-making have incorporated gain modulation, their abstract forms do not permit an understanding of the contribution of inhibitory gain modulation. Thus, the effects of co-modulating both excitatory and inhibitory neuronal gains on decision-making dynamics and behavioural performance remain unclear. In this work, we incorporate time-dependent co-modulation of the gains of both excitatory and inhibitory neurons into our previous biologically based decision circuit model. We base our computational study in the context of two classic motion-discrimination tasks performed in animals. Our model shows that by simultaneously increasing the gains of both excitatory and inhibitory neurons, a variety of the observed dynamic neuronal firing activities can be replicated. In particular, the model can exhibit winner-take-all decision-making behaviour with higher firing rates and within a significantly more robust model parameter range. It also exhibits short-tailed reaction time distributions even when operating near a dynamical bifurcation point. The model further shows that neuronal gain modulation can compensate for weaker recurrent excitation in a decision neural circuit, and support decision formation and storage. Higher neuronal gain is also suggested in the more cognitively demanding reaction time than in the fixed delay version of the task. Using the exact temporal delays from the animal experiments, fast recruitment of gain co-modulation is shown to maximize reward rate, with a timescale that is surprisingly near the experimentally fitted value. Our work provides insights into the simultaneous and rapid modulation of excitatory and inhibitory neuronal gains, which enables flexible, robust, and optimal decision-making. PMID:23825935

Decoding sound level in the marmoset primary auditory cortex.

PubMed

Sun, Wensheng; Marongelli, Ellisha N; Watkins, Paul V; Barbour, Dennis L

2017-10-01

Neurons that respond favorably to a particular sound level have been observed throughout the central auditory system, becoming steadily more common at higher processing areas. One theory about the role of these level-tuned or nonmonotonic neurons is the level-invariant encoding of sounds. To investigate this theory, we simulated various subpopulations of neurons by drawing from real primary auditory cortex (A1) neuron responses and surveyed their performance in forming different sound level representations. Pure nonmonotonic subpopulations did not provide the best level-invariant decoding; instead, mixtures of monotonic and nonmonotonic neurons provided the most accurate decoding. For level-fidelity decoding, the inclusion of nonmonotonic neurons slightly improved or did not change decoding accuracy until they constituted a high proportion. These results indicate that nonmonotonic neurons fill an encoding role complementary to, rather than alternate to, monotonic neurons. NEW & NOTEWORTHY Neurons with nonmonotonic rate-level functions are unique to the central auditory system. These level-tuned neurons have been proposed to account for invariant sound perception across sound levels. Through systematic simulations based on real neuron responses, this study shows that neuron populations perform sound encoding optimally when containing both monotonic and nonmonotonic neurons. The results indicate that instead of working independently, nonmonotonic neurons complement the function of monotonic neurons in different sound-encoding contexts. Copyright © 2017 the American Physiological Society.

Communication and wiring in the cortical connectome

PubMed Central

Budd, Julian M. L.; Kisvárday, Zoltán F.

2012-01-01

In cerebral cortex, the huge mass of axonal wiring that carries information between near and distant neurons is thought to provide the neural substrate for cognitive and perceptual function. The goal of mapping the connectivity of cortical axons at different spatial scales, the cortical connectome, is to trace the paths of information flow in cerebral cortex. To appreciate the relationship between the connectome and cortical function, we need to discover the nature and purpose of the wiring principles underlying cortical connectivity. A popular explanation has been that axonal length is strictly minimized both within and between cortical regions. In contrast, we have hypothesized the existence of a multi-scale principle of cortical wiring where to optimize communication there is a trade-off between spatial (construction) and temporal (routing) costs. Here, using recent evidence concerning cortical spatial networks we critically evaluate this hypothesis at neuron, local circuit, and pathway scales. We report three main conclusions. First, the axonal and dendritic arbor morphology of single neocortical neurons may be governed by a similar wiring principle, one that balances the conservation of cellular material and conduction delay. Second, the same principle may be observed for fiber tracts connecting cortical regions. Third, the absence of sufficient local circuit data currently prohibits any meaningful assessment of the hypothesis at this scale of cortical organization. To avoid neglecting neuron and microcircuit levels of cortical organization, the connectome framework should incorporate more morphological description. In addition, structural analyses of temporal cost for cortical circuits should take account of both axonal conduction and neuronal integration delays, which appear mostly of the same order of magnitude. We conclude the hypothesized trade-off between spatial and temporal costs may potentially offer a powerful explanation for cortical wiring patterns. PMID:23087619

Neuronal plasticity and neurotrophic factors in drug responses

PubMed Central

Castrén, Eero; Antila, Hanna

2017-01-01

Neurotrophic factors, particularly brain-derived neurotrophic factor (BDNF) and other members of the neurotrophin family, are central mediators of the activity-dependent plasticity through which environmental experiences, such as sensory information are translated into the structure and function of neuronal networks. Synthesis, release and action of BDNF is regulated by neuronal activity and BDNF in turn leads to trophic effects such as formation, stabilization and potentiation of synapses through its high-affinity TrkB receptors. Several clinically available drugs directly activate neurotrophins and neuronal plasticity. In particular, antidepressant drugs rapidly activate TrkB signaling and gradually increase BDNF expression, and the behavioral effects of antidepressants are mediated by and dependent on BDNF signaling through TrkB at least in rodents. These findings indicate that antidepressants, widely used drugs, effectively act as TrkB activators. They further imply that neuronal plasticity is a central mechanism in the action of antidepressant drugs. Indeed, it was recently discovered that antidepressants reactivate a state of plasticity in the adult cerebral cortex that closely resembles the enhanced plasticity normally observed during postnatal critical periods. This state of induced plasticity, known as iPlasticity, allows environmental stimuli to beneficially reorganize networks abnormally wired during early life. iPlasticity has been observed in cortical as well as subcortical networks and is induced by several pharmacological and non-pharmacological treatments. iPlasticity is a new pharmacological principle where drug treatment and rehabilitation cooperate: the drug acts permissively to enhance plasticity and rehabilitation provides activity to guide the appropriate wiring of the plastic network. Optimization of iPlastic drug treatment with novel means of rehabilitation may help improve the efficacy of available drug treatments and expand the use of currently existing drugs into new indications. PMID:28397840

Optimization of a GCaMP calcium indicator for neural activity imaging.

PubMed

Akerboom, Jasper; Chen, Tsai-Wen; Wardill, Trevor J; Tian, Lin; Marvin, Jonathan S; Mutlu, Sevinç; Calderón, Nicole Carreras; Esposti, Federico; Borghuis, Bart G; Sun, Xiaonan Richard; Gordus, Andrew; Orger, Michael B; Portugues, Ruben; Engert, Florian; Macklin, John J; Filosa, Alessandro; Aggarwal, Aman; Kerr, Rex A; Takagi, Ryousuke; Kracun, Sebastian; Shigetomi, Eiji; Khakh, Baljit S; Baier, Herwig; Lagnado, Leon; Wang, Samuel S-H; Bargmann, Cornelia I; Kimmel, Bruce E; Jayaraman, Vivek; Svoboda, Karel; Kim, Douglas S; Schreiter, Eric R; Looger, Loren L

2012-10-03

Genetically encoded calcium indicators (GECIs) are powerful tools for systems neuroscience. Recent efforts in protein engineering have significantly increased the performance of GECIs. The state-of-the art single-wavelength GECI, GCaMP3, has been deployed in a number of model organisms and can reliably detect three or more action potentials in short bursts in several systems in vivo. Through protein structure determination, targeted mutagenesis, high-throughput screening, and a battery of in vitro assays, we have increased the dynamic range of GCaMP3 by severalfold, creating a family of "GCaMP5" sensors. We tested GCaMP5s in several systems: cultured neurons and astrocytes, mouse retina, and in vivo in Caenorhabditis chemosensory neurons, Drosophila larval neuromuscular junction and adult antennal lobe, zebrafish retina and tectum, and mouse visual cortex. Signal-to-noise ratio was improved by at least 2- to 3-fold. In the visual cortex, two GCaMP5 variants detected twice as many visual stimulus-responsive cells as GCaMP3. By combining in vivo imaging with electrophysiology we show that GCaMP5 fluorescence provides a more reliable measure of neuronal activity than its predecessor GCaMP3. GCaMP5 allows more sensitive detection of neural activity in vivo and may find widespread applications for cellular imaging in general.

Efficient coding of spectrotemporal binaural sounds leads to emergence of the auditory space representation

PubMed Central

Młynarski, Wiktor

2014-01-01

To date a number of studies have shown that receptive field shapes of early sensory neurons can be reproduced by optimizing coding efficiency of natural stimulus ensembles. A still unresolved question is whether the efficient coding hypothesis explains formation of neurons which explicitly represent environmental features of different functional importance. This paper proposes that the spatial selectivity of higher auditory neurons emerges as a direct consequence of learning efficient codes for natural binaural sounds. Firstly, it is demonstrated that a linear efficient coding transform—Independent Component Analysis (ICA) trained on spectrograms of naturalistic simulated binaural sounds extracts spatial information present in the signal. A simple hierarchical ICA extension allowing for decoding of sound position is proposed. Furthermore, it is shown that units revealing spatial selectivity can be learned from a binaural recording of a natural auditory scene. In both cases a relatively small subpopulation of learned spectrogram features suffices to perform accurate sound localization. Representation of the auditory space is therefore learned in a purely unsupervised way by maximizing the coding efficiency and without any task-specific constraints. This results imply that efficient coding is a useful strategy for learning structures which allow for making behaviorally vital inferences about the environment. PMID:24639644

Multiregional Age-Associated Reduction of Brain Neuronal Reserve Without Association With Neurofibrillary Degeneration or β-Amyloidosis.

PubMed

Wegiel, Jerzy; Flory, Michael; Kuchna, Izabela; Nowicki, Krzysztof; Yong Ma, Shuang; Wegiel, Jarek; Badmaev, Eulalia; Silverman, Wayne P; de Leon, Mony; Reisberg, Barry; Wisniewski, Thomas

2017-06-01

Increase in human life expectancy has resulted in the rapid growth of the elderly population with minimal or no intellectual deterioration. The aim of this stereological study of 10 structures and 5 subdivisions with and without neurofibrillary degeneration in the brains of 28 individuals 25-102-years-old was to establish the pattern of age-associated neurodegeneration and neuronal loss in the brains of nondemented adults and elderly. The study revealed the absence of significant neuronal loss in 7 regions and topographically selective reduction of neuronal reserve over 77 years in 8 brain structures including the entorhinal cortex (EC) (-33.3%), the second layer of the EC (-54%), cornu Ammonis sector 1 (CA1) (-28.5%), amygdala, (-45.8%), thalamus (-40.5%), caudate nucleus (-35%), Purkinje cells (-48.3%), and neurons in the dentate nucleus (40.1%). A similar rate of neuronal loss in adults and elderly, without signs of accelerating neuronal loss in agers or super-agers, appears to indicate age-associated brain remodeling with significant reduction of neuronal reserve in 8 brain regions. Multivariate analysis demonstrates the absence of a significant association between neuronal loss and the severity of neurofibrillary degeneration and β-amyloidosis, and a similar rate of age-associated neuronal loss in structures with and without neurofibrillary degeneration. © 2017 American Association of Neuropathologists, Inc. All rights reserved.

The Nanoscale Observation of the Three-Dimensional Structures of Neurosynapses, Membranous Conjunctions Between Cultured Hippocampal Neurons and Their Significance in the Development of Epilepsy.

PubMed

Sun, Lan; Jiang, Shuang; Tang, Xianhua; Zhang, Yingge; Qin, Luye; Jiang, Xia; Yu, Albert Cheung Hoi

2016-12-01

The nanoscale three-dimensional structures of neurosynapses are unknown, and the neuroanatomical basis of epilepsy remains to be elucidated. Here, we studied the nanoscale three-dimensional synapses between hippocampal neurons, and membranous conjunctions between neurons were found with atomic force microscopy (AFM) and confirmed by transmission electron microscope (TEM), and their pathophysiological significance was primarily investigated. The neurons and dendrites were marked by MAP-2, axons by neurofilament 200, and synapses by synapsin I immunological staining. In the synapsin I-positive neurite ends of the neurons positively stained with MAP-2 and neurofilament 200, neurosynapses with various nanoscale morphology and structure could be found by AFM. The neurosynapses had typical three-dimensional structures of synaptic triplet including the presynaptic neurite end, synaptic cleft of 30 ∼ 40 in chemical synapses and 2 ∼ 6 nm in electrical ones, the postsynaptic neurite or dendrite spine, the typical neurite end button, the distinct pre- and postsynaptic membranes, and the obvious thickening of the postsynaptic membranes or neurites. Some membranous connections including membrane-like junctions (MLJ) and fiber-tube links (FTL) without triplet structures and cleft were found between neurons. The development frequencies of the two membranous conjunctions increased while those of the synaptic conjunctions decreased between the neurons from Otx1 knock-out mice in comparison with those between the neurons from normal mice. These results suggested that the neuroanatomical basis of Otx1 knock-out epilepsy is the combination of the decreased synaptic conjunctions and the increased membranous conjunctions.

Life and Understanding: The Origins of “Understanding” in Self-Organizing Nervous Systems

PubMed Central

Yufik, Yan M.; Friston, Karl

2016-01-01

This article is motivated by a formulation of biotic self-organization in Friston (2013), where the emergence of “life” in coupled material entities (e.g., macromolecules) was predicated on bounded subsets that maintain a degree of statistical independence from the rest of the network. Boundary elements in such systems constitute a Markov blanket; separating the internal states of a system from its surrounding states. In this article, we ask whether Markov blankets operate in the nervous system and underlie the development of intelligence, enabling a progression from the ability to sense the environment to the ability to understand it. Markov blankets have been previously hypothesized to form in neuronal networks as a result of phase transitions that cause network subsets to fold into bounded assemblies, or packets (Yufik and Sheridan, 1997; Yufik, 1998a). The ensuing neuronal packets hypothesis builds on the notion of neuronal assemblies (Hebb, 1949, 1980), treating such assemblies as flexible but stable biophysical structures capable of withstanding entropic erosion. In other words, structures that maintain their integrity under changing conditions. In this treatment, neuronal packets give rise to perception of “objects”; i.e., quasi-stable (stimulus bound) feature groupings that are conserved over multiple presentations (e.g., the experience of perceiving “apple” can be interrupted and resumed many times). Monitoring the variations in such groups enables the apprehension of behavior; i.e., attributing to objects the ability to undergo changes without loss of self-identity. Ultimately, “understanding” involves self-directed composition and manipulation of the ensuing “mental models” that are constituted by neuronal packets, whose dynamics capture relationships among objects: that is, dependencies in the behavior of objects under varying conditions. For example, movement is known to involve rotation of population vectors in the motor cortex (Georgopoulos et al., 1988, 1993). The neuronal packet hypothesis associates “understanding” with the ability to detect and generate coordinated rotation of population vectors—in neuronal packets—in associative cortex and other regions in the brain. The ability to coordinate vector representations in this way is assumed to have developed in conjunction with the ability to postpone overt motor expression of implicit movement, thus creating a mechanism for prediction and behavioral optimization via mental modeling that is unique to higher species. This article advances the notion that Markov blankets—necessary for the emergence of life—have been subsequently exploited by evolution and thus ground the ways that living organisms adapt to their environment, culminating in their ability to understand it. PMID:28018185

Optimal control of directional deep brain stimulation in the parkinsonian neuronal network

NASA Astrophysics Data System (ADS)

Fan, Denggui; Wang, Zhihui; Wang, Qingyun

2016-07-01

The effect of conventional deep brain stimulation (DBS) on debilitating symptoms of Parkinson's disease can be limited because it can only yield the spherical field. And, some side effects are clearly induced with influencing their adjacent ganglia. Recent experimental evidence for patients with Parkinson's disease has shown that a novel DBS electrode with 32 independent stimulation source contacts can effectively optimize the clinical therapy by enlarging the therapeutic windows, when it is applied on the subthalamic nucleus (STN). This is due to the selective activation in clusters of various stimulation contacts which can be steered directionally and accurately on the targeted regions of interest. In addition, because of the serious damage to the neural tissues, the charge-unbalanced stimulation is not typically indicated and the real DBS utilizes charge-balanced bi-phasic (CBBP) pulses. Inspired by this, we computationally investigate the optimal control of directional CBBP-DBS from the proposed parkinsonian neuronal network of basal ganglia-thalamocortical circuit. By appropriately tuning stimulation for different neuronal populations, it can be found that directional steering CBBP-DBS paradigms are superior to the spherical case in improving parkinsonian dynamical properties including the synchronization of neuronal populations and the reliability of thalamus relaying the information from cortex, which is in a good agreement with the physiological experiments. Furthermore, it can be found that directional steering stimulations can increase the optimal stimulation intensity of desynchronization by more than 1 mA compared to the spherical case. This is consistent with the experimental result with showing that there exists at least one steering direction that can allow increasing the threshold of side effects by 1 mA. In addition, we also simulate the local field potential (LFP) and dominant frequency (DF) of the STN neuronal population induced by the activation of 32 different contacts with optimal stimulation intensity and immediately after the stimulation, respectively. These can reveal regional differences in pathological activity within STN nucleus. It is shown that in line with the experimental results directional steering stimulation can induce the low-amplitude LFP which implies the occurrence of desynchronizing regime, as well as the distribution of DF can locate at the 13-40 Hz of beta frequency range. Hopefully, the obtained results can provide theoretical evidences in exploring pathophysiologic activity of brain.

Live-Cell, Label-Free Identification of GABAergic and Non-GABAergic Neurons in Primary Cortical Cultures Using Micropatterned Surface

PubMed Central

Kono, Sho; Kushida, Takatoshi; Hirano-Iwata, Ayumi; Niwano, Michio; Tanii, Takashi

2016-01-01

Excitatory and inhibitory neurons have distinct roles in cortical dynamics. Here we present a novel method for identifying inhibitory GABAergic neurons from non-GABAergic neurons, which are mostly excitatory glutamatergic neurons, in primary cortical cultures. This was achieved using an asymmetrically designed micropattern that directs an axonal process to the longest pathway. In the current work, we first modified the micropattern geometry to improve cell viability and then studied the axon length from 2 to 7 days in vitro (DIV). The cell types of neurons were evaluated retrospectively based on immunoreactivity against GAD67, a marker for inhibitory GABAergic neurons. We found that axons of non-GABAergic neurons grow significantly longer than those of GABAergic neurons in the early stages of development. The optimal threshold for identifying GABAergic and non-GABAergic neurons was evaluated to be 110 μm at 6 DIV. The method does not require any fluorescence labelling and can be carried out on live cells. The accuracy of identification was 98.2%. We confirmed that the high accuracy was due to the use of a micropattern, which standardized the development of cultured neurons. The method promises to be beneficial both for engineering neuronal networks in vitro and for basic cellular neuroscience research. PMID:27513933

Structure-function analysis of genetically defined neuronal populations.

PubMed

Groh, Alexander; Krieger, Patrik

2013-10-01

Morphological and functional classification of individual neurons is a crucial aspect of the characterization of neuronal networks. Systematic structural and functional analysis of individual neurons is now possible using transgenic mice with genetically defined neurons that can be visualized in vivo or in brain slice preparations. Genetically defined neurons are useful for studying a particular class of neurons and also for more comprehensive studies of the neuronal content of a network. Specific subsets of neurons can be identified by fluorescence imaging of enhanced green fluorescent protein (eGFP) or another fluorophore expressed under the control of a cell-type-specific promoter. The advantages of such genetically defined neurons are not only their homogeneity and suitability for systematic descriptions of networks, but also their tremendous potential for cell-type-specific manipulation of neuronal networks in vivo. This article describes a selection of procedures for visualizing and studying the anatomy and physiology of genetically defined neurons in transgenic mice. We provide information about basic equipment, reagents, procedures, and analytical approaches for obtaining three-dimensional (3D) cell morphologies and determining the axonal input and output of genetically defined neurons. We exemplify with genetically labeled cortical neurons, but the procedures are applicable to other brain regions with little or no alterations.

Autaptic effects on synchrony of neurons coupled by electrical synapses

NASA Astrophysics Data System (ADS)

Kim, Youngtae

2017-07-01

In this paper, we numerically study the effects of a special synapse known as autapse on synchronization of population of Morris-Lecar (ML) neurons coupled by electrical synapses. Several configurations of the ML neuronal populations such as a pair or a ring or a globally coupled network with and without autapses are examined. While most of the papers on the autaptic effects on synchronization have used networks of neurons of same spiking rate, we use the network of neurons of different spiking rates. We find that the optimal autaptic coupling strength and the autaptic time delay enhance synchronization in our neural networks. We use the phase response curve analysis to explain the enhanced synchronization by autapses. Our findings reveal the important relationship between the intraneuronal feedback loop and the interneuronal coupling.

Three Types of Cortical L5 Neurons that Differ in Brain-Wide Connectivity and Function

PubMed Central

Kim, Euiseok J.; Juavinett, Ashley L.; Kyubwa, Espoir M.; Jacobs, Matthew W.; Callaway, Edward M.

2015-01-01

SUMMARY Cortical layer 5 (L5) pyramidal neurons integrate inputs from many sources and distribute outputs to cortical and subcortical structures. Previous studies demonstrate two L5 pyramid types: cortico-cortical (CC) and cortico-subcortical (CS). We characterize connectivity and function of these cell types in mouse primary visual cortex and reveal a new subtype. Unlike previously described L5 CC and CS neurons, this new subtype does not project to striatum [cortico-cortical, non-striatal (CC-NS)] and has distinct morphology, physiology and visual responses. Monosynaptic rabies tracing reveals that CC neurons preferentially receive input from higher visual areas, while CS neurons receive more input from structures implicated in top-down modulation of brain states. CS neurons are also more direction-selective and prefer faster stimuli than CC neurons. These differences suggest distinct roles as specialized output channels, with CS neurons integrating information and generating responses more relevant to movement control and CC neurons being more important in visual perception. PMID:26671462

Three Types of Cortical Layer 5 Neurons That Differ in Brain-wide Connectivity and Function.

PubMed

Kim, Euiseok J; Juavinett, Ashley L; Kyubwa, Espoir M; Jacobs, Matthew W; Callaway, Edward M

2015-12-16

Cortical layer 5 (L5) pyramidal neurons integrate inputs from many sources and distribute outputs to cortical and subcortical structures. Previous studies demonstrate two L5 pyramid types: cortico-cortical (CC) and cortico-subcortical (CS). We characterize connectivity and function of these cell types in mouse primary visual cortex and reveal a new subtype. Unlike previously described L5 CC and CS neurons, this new subtype does not project to striatum [cortico-cortical, non-striatal (CC-NS)] and has distinct morphology, physiology, and visual responses. Monosynaptic rabies tracing reveals that CC neurons preferentially receive input from higher visual areas, while CS neurons receive more input from structures implicated in top-down modulation of brain states. CS neurons are also more direction-selective and prefer faster stimuli than CC neurons. These differences suggest distinct roles as specialized output channels, with CS neurons integrating information and generating responses more relevant to movement control and CC neurons being more important in visual perception. Copyright © 2015 Elsevier Inc. All rights reserved.

3D printing of layered brain-like structures using peptide modified gellan gum substrates.

PubMed

Lozano, Rodrigo; Stevens, Leo; Thompson, Brianna C; Gilmore, Kerry J; Gorkin, Robert; Stewart, Elise M; in het Panhuis, Marc; Romero-Ortega, Mario; Wallace, Gordon G

2015-10-01

The brain is an enormously complex organ structured into various regions of layered tissue. Researchers have attempted to study the brain by modeling the architecture using two dimensional (2D) in vitro cell culturing methods. While those platforms attempt to mimic the in vivo environment, they do not truly resemble the three dimensional (3D) microstructure of neuronal tissues. Development of an accurate in vitro model of the brain remains a significant obstacle to our understanding of the functioning of the brain at the tissue or organ level. To address these obstacles, we demonstrate a new method to bioprint 3D brain-like structures consisting of discrete layers of primary neural cells encapsulated in hydrogels. Brain-like structures were constructed using a bio-ink consisting of a novel peptide-modified biopolymer, gellan gum-RGD (RGD-GG), combined with primary cortical neurons. The ink was optimized for a modified reactive printing process and developed for use in traditional cell culturing facilities without the need for extensive bioprinting equipment. Furthermore the peptide modification of the gellan gum hydrogel was found to have a profound positive effect on primary cell proliferation and network formation. The neural cell viability combined with the support of neural network formation demonstrated the cell supportive nature of the matrix. The facile ability to form discrete cell-containing layers validates the application of this novel printing technique to form complex, layered and viable 3D cell structures. These brain-like structures offer the opportunity to reproduce more accurate 3D in vitro microstructures with applications ranging from cell behavior studies to improving our understanding of brain injuries and neurodegenerative diseases. Copyright © 2015 Elsevier Ltd. All rights reserved.

A failure in energy metabolism and antioxidant uptake precede symptoms of Huntington's disease in mice.

PubMed

Acuña, Aníbal I; Esparza, Magdalena; Kramm, Carlos; Beltrán, Felipe A; Parra, Alejandra V; Cepeda, Carlos; Toro, Carlos A; Vidal, René L; Hetz, Claudio; Concha, Ilona I; Brauchi, Sebastián; Levine, Michael S; Castro, Maite A

2013-01-01

Huntington's disease has been associated with a failure in energy metabolism and oxidative damage. Ascorbic acid is a powerful antioxidant highly concentrated in the brain where it acts as a messenger, modulating neuronal metabolism. Using an electrophysiological approach in R6/2 HD slices, we observe an abnormal ascorbic acid flux from astrocytes to neurons, which is responsible for alterations in neuronal metabolic substrate preferences. Here using striatal neurons derived from knock-in mice expressing mutant huntingtin (STHdhQ cells), we study ascorbic acid transport. When extracellular ascorbic acid concentration increases, as occurs during synaptic activity, ascorbic acid transporter 2 (SVCT2) translocates to the plasma membrane, ensuring optimal ascorbic acid uptake for neurons. In contrast, SVCT2 from cells that mimic HD symptoms (dubbed HD cells) fails to reach the plasma membrane under the same conditions. We reason that an early impairment of ascorbic acid uptake in HD neurons could lead to early metabolic failure promoting neuronal death.

Meigo governs dendrite targeting specificity by modulating Ephrin level and N-glycosylation

PubMed Central

Sekine, Sayaka U; Haraguchi, Shuka; Chao, Kinhong; Kato, Tomoko; Luo, Liqun; Miura, Masayuki; Chihara, Takahiro

2016-01-01

Neural circuit assembly requires precise dendrite and axon targeting. We identified an evolutionarily conserved endoplasmic reticulum (ER) protein, Meigo, from a mosaic genetic screen in Drosophila melanogaster. Meigo was cell-autonomously required in olfactory receptor neurons and projection neurons to target their axons and dendrites to the lateral antennal lobe and to refine projection neuron dendrites into individual glomeruli. Loss of Meigo induced an unfolded protein response and reduced the amount of neuronal cell surface proteins, including Ephrin. Ephrin overexpression specifically suppressed the projection neuron dendrite refinement defect present in meigo mutant flies, and ephrin knockdown caused a similar projection neuron dendrite refinement defect. Meigo positively regulated the level of Ephrin N-glycosylation, which was required for its optimal function in vivo. Thus, Meigo, an ER-resident protein, governs neuronal targeting specificity by regulating ER folding capacity and protein N-glycosylation. Furthermore, Ephrin appears to be an important substrate that mediates Meigo’s function in refinement of glomerular targeting. PMID:23624514

A failure in energy metabolism and antioxidant uptake precede symptoms of Huntington’s disease in mice

PubMed Central

Acuña, Aníbal I.; Esparza, Magdalena; Kramm, Carlos; Beltrán, Felipe A.; Parra, Alejandra V.; Cepeda, Carlos; Toro, Carlos A.; Vidal, René L.; Hetz, Claudio; Concha, Ilona I.; Brauchi, Sebastián; Levine, Michael S.; Castro, Maite A.

2013-01-01

Huntington’s disease has been associated with a failure in energy metabolism and oxidative damage. Ascorbic acid is a powerful antioxidant highly concentrated in the brain where it acts as a messenger, modulating neuronal metabolism. Using an electrophysiological approach in R6/2 HD slices, we observe an abnormal ascorbic acid flux from astrocytes to neurons, which is responsible for alterations in neuronal metabolic substrate preferences. Here using striatal neurons derived from knock-in mice expressing mutant huntingtin (STHdhQ cells), we study ascorbic acid transport. When extracellular ascorbic acid concentration increases, as occurs during synaptic activity, ascorbic acid transporter 2 (SVCT2) translocates to the plasma membrane, ensuring optimal ascorbic acid uptake for neurons. In contrast, SVCT2 from cells that mimic HD symptoms (dubbed HD cells) fails to reach the plasma membrane under the same conditions. We reason that an early impairment of ascorbic acid uptake in HD neurons could lead to early metabolic failure promoting neuronal death. PMID:24336051

A failure in energy metabolism and antioxidant uptake precede symptoms of Huntington’s disease in mice

NASA Astrophysics Data System (ADS)

Acuña, Aníbal I.; Esparza, Magdalena; Kramm, Carlos; Beltrán, Felipe A.; Parra, Alejandra V.; Cepeda, Carlos; Toro, Carlos A.; Vidal, René L.; Hetz, Claudio; Concha, Ilona I.; Brauchi, Sebastián; Levine, Michael S.; Castro, Maite A.

2013-12-01

Huntington’s disease has been associated with a failure in energy metabolism and oxidative damage. Ascorbic acid is a powerful antioxidant highly concentrated in the brain where it acts as a messenger, modulating neuronal metabolism. Using an electrophysiological approach in R6/2 HD slices, we observe an abnormal ascorbic acid flux from astrocytes to neurons, which is responsible for alterations in neuronal metabolic substrate preferences. Here using striatal neurons derived from knock-in mice expressing mutant huntingtin (STHdhQ cells), we study ascorbic acid transport. When extracellular ascorbic acid concentration increases, as occurs during synaptic activity, ascorbic acid transporter 2 (SVCT2) translocates to the plasma membrane, ensuring optimal ascorbic acid uptake for neurons. In contrast, SVCT2 from cells that mimic HD symptoms (dubbed HD cells) fails to reach the plasma membrane under the same conditions. We reason that an early impairment of ascorbic acid uptake in HD neurons could lead to early metabolic failure promoting neuronal death.

Is Spinal Muscular Atrophy a disease of the motor neurons only: pathogenesis and therapeutic implications?

PubMed Central

Simone, Chiara; Ramirez, Agnese; Bucchia, Monica; Rinchetti, Paola; Rideout, Hardy; Papadimitriou, Dimitra; Re, Diane B.; Corti, Stefania

2016-01-01

Spinal Muscular Atrophy (SMA) is a genetic neurological disease that causes infant mortality; no effective therapies are currently available. SMA is due to homozygous mutations and/or deletions in the Survival Motor Neuron 1 (SMN1) gene and subsequent reduction of the SMN protein, leading to the death of motor neurons. However, there is increasing evidence that in addition to motor neurons, other cell types are contributing to SMA pathology. In this review, we will discuss the involvement of non-motor neuronal cells, located both inside and outside the central nervous system, in disease onset and progression. These contribution of non-motor neuronal cells to disease pathogenesis has important therapeutic implications: in fact, even if SMN restoration in motor neurons is needed, it has been shown that optimal phenotypic amelioration in animal models of SMA requires a more widespread SMN correction. It will be crucial to take this evidence into account before clinical translation of the novel therapeutic approaches that are currently under development. PMID:26681261

Wide field-of-view, multi-region two-photon imaging of neuronal activity in the mammalian brain

PubMed Central

Stirman, Jeffrey N.; Smith, Ikuko T.; Kudenov, Michael W.; Smith, Spencer L.

2016-01-01

Two-photon calcium imaging provides an optical readout of neuronal activity in populations of neurons with subcellular resolution. However, conventional two-photon imaging systems are limited in their field of view to ~1 mm2, precluding the visualization of multiple cortical areas simultaneously. Here, we demonstrate a two-photon microscope with an expanded field of view (>9.5 mm2) for rapidly reconfigurable simultaneous scanning of widely separated populations of neurons. We custom designed and assembled an optimized scan engine, objective, and two independently positionable, temporally multiplexed excitation pathways. We used this new microscope to measure activity correlations between two cortical visual areas in mice during visual processing. PMID:27347754

Cascaded VLSI Chips Help Neural Network To Learn

NASA Technical Reports Server (NTRS)

Duong, Tuan A.; Daud, Taher; Thakoor, Anilkumar P.

1993-01-01

Cascading provides 12-bit resolution needed for learning. Using conventional silicon chip fabrication technology of VLSI, fully connected architecture consisting of 32 wide-range, variable gain, sigmoidal neurons along one diagonal and 7-bit resolution, electrically programmable, synaptic 32 x 31 weight matrix implemented on neuron-synapse chip. To increase weight nominally from 7 to 13 bits, synapses on chip individually cascaded with respective synapses on another 32 x 32 matrix chip with 7-bit resolution synapses only (without neurons). Cascade correlation algorithm varies number of layers effectively connected into network; adds hidden layers one at a time during learning process in such way as to optimize overall number of neurons and complexity and configuration of network.

Pattern separation: a common function for new neurons in hippocampus and olfactory bulb.

PubMed

Sahay, Amar; Wilson, Donald A; Hen, René

2011-05-26

While adult-born neurons in the olfactory bulb (OB) and the dentate gyrus (DG) subregion of the hippocampus have fundamentally different properties, they may have more in common than meets the eye. Here, we propose that new granule cells in the OB and DG may function as modulators of principal neurons to influence pattern separation and that adult neurogenesis constitutes an adaptive mechanism to optimally encode contextual or olfactory information. See the related Perspective from Aimone, Deng, and Gage, "Resolving New Memories: A Critical Look at the Dentate Gyrus, Adult Neurogenesis, and Pattern Separation," in this issue of Neuron. Copyright © 2011 Elsevier Inc. All rights reserved.

Investigating the Slow Axonal Transport of Neurofilaments: A Precursor for Optimal Neuronal Signaling

NASA Astrophysics Data System (ADS)

Johnson, Christopher M.

Neurofilaments are the intermediate filaments of neurons and are the most abundant structure of the neuronal cytoskeleton. Once synthesized within the cell body they are then transported throughout the axon along microtubule tracks, driven by the molecular motors kinesin and dynein. This movement is characterized by long pauses with no movement interrupted by infrequent bouts of rapid movement, resulting in an aggregate dense cytoskeletal structure, which serves to regulate an axon's shape and size. Curiously, the modulated kinetics of these polymers produces a very regular, yet non-uniform, morphology in myelinated axons which are composed of discretely spaced myelin-ensheathed segments that are separated by short constricted regions called "nodes of Ranvier". This unique design optimizes the conduction velocity of myelinated axons at minimal fiber size. Hence, neurofilaments regulate the axon caliber to optimize neuron function. The goal of this dissertation is to investigate the motile mechanism of neurofilament transport as well as the resulting electrophysiological effects that follow. We start by examining highly time-resolved kymograph images generated from recorded neurofilament movement via epifluorescence microscopy. Using kymograph analysis, edge detection algorithms, and pixel smoothing tactics, neurofilament trajectories are extracted and used to obtain statistical distributions for the characteristics of how these filaments move within cells. The results suggest that the observed intermittent and bidirectional motions of these filaments might be explained by a model in which dynein and kinesin motors attach to a single neurofilament cargo and interact through mechanical forces only (i.e. a "tug-of-war" model). We test this hypothesis by developing two discrete-state stochastic models for the kinetic cycles of kinesin and dynein, which are then incorporated into a separate stochastic model that represents the posed tug-of-war scenario. We then systematically vary the number of motors in the model and attempt to identify those combinations of motors that show an agreement with the motility characteristic found from the above mentioned kymographs. By pruning the modeled data in accordance with the experimental results, our model can render an estimate of how many motors are attached to the cargo during transport. The model predicts that, on average, the total number of active motors on each neurofilament is relatively small and relatively independent of polymer length, which suggests that the motors may not be distributed uniformly along the filaments. Finally, we develop a model to explore the physiological function of axon morphology sculpted by neurofilament kinetics. Specifically, nodal constrictions are generated by slowing of neurofilaments in the internodal domain (Monsma et al., 2014), but the physiological function of these constrictions is unknown. To address this, we develop a computational model to investigate the effect of nodal constrictions on the axonal conduction velocity. For a fixed number of ion channels, we find that there is an optimal extent of nodal constriction which minimizes the internodal axon caliber that is required to achieve a given target conduction velocity, and we show that this is sensitive to the precise geometry of the axon and myelin sheath in the flanking paranodal regions. Thus axonal constrictions appear to be a biological adaptation that serves to minimize axonal volume, thereby maximizing the spatial and metabolic efficiency of these processes.

Cytoskeleton Molecular Motors: Structures and Their Functions in Neuron.

PubMed

Xiao, Qingpin; Hu, Xiaohui; Wei, Zhiyi; Tam, Kin Yip

2016-01-01

Cells make use of molecular motors to transport small molecules, macromolecules and cellular organelles to target region to execute biological functions, which is utmost important for polarized cells, such as neurons. In particular, cytoskeleton motors play fundamental roles in neuron polarization, extension, shape and neurotransmission. Cytoskeleton motors comprise of myosin, kinesin and cytoplasmic dynein. F-actin filaments act as myosin track, while kinesin and cytoplasmic dynein move on microtubules. Cytoskeleton motors work together to build a highly polarized and regulated system in neuronal cells via different molecular mechanisms and functional regulations. This review discusses the structures and working mechanisms of the cytoskeleton motors in neurons.

LASER BIOLOGY: Peculiarities of studying an isolated neuron by the method of laser interference microscopy

NASA Astrophysics Data System (ADS)

Yusipovich, Alexander I.; Novikov, Sergey M.; Kazakova, Tatiana A.; Erokhova, Liudmila A.; Brazhe, Nadezda A.; Lazarev, Grigory L.; Maksimov, Georgy V.

2006-09-01

Actual aspects of using a new method of laser interference microscopy (LIM) for studying nerve cells are discussed. The peculiarities of the LIM display of neurons are demonstrated by the example of isolated neurons of a pond snail Lymnaea stagnalis. A comparative analysis of the images of the cell and subcellular structures of a neuron obtained by the methods of interference microscopy, optical transmission microscopy, and confocal microscopy is performed. Various aspects of the application of LIM for studying the lateral dimensions and internal structure of the cytoplasm and organelles of a neuron in cytology and cell physiology are discussed.

Minimum energy control for in vitro neurons

NASA Astrophysics Data System (ADS)

Nabi, Ali; Stigen, Tyler; Moehlis, Jeff; Netoff, Theoden

2013-06-01

Objective. To demonstrate the applicability of optimal control theory for designing minimum energy charge-balanced input waveforms for single periodically-firing in vitro neurons from brain slices of Long-Evans rats. Approach. The method of control uses the phase model of a neuron and does not require prior knowledge of the neuron’s biological details. The phase model of a neuron is a one-dimensional model that is characterized by the neuron’s phase response curve (PRC), a sensitivity measure of the neuron to a stimulus applied at different points in its firing cycle. The PRC for each neuron is experimentally obtained by measuring the shift in phase due to a short-duration pulse injected into the periodically-firing neuron at various phase values. Based on the measured PRC, continuous-time, charge-balanced, minimum energy control waveforms have been designed to regulate the next firing time of the neuron upon application at the onset of an action potential. Main result. The designed waveforms can achieve the inter-spike-interval regulation for in vitro neurons with energy levels that are lower than those of conventional monophasic pulsatile inputs of past studies by at least an order of magnitude. They also provide the advantage of being charge-balanced. The energy efficiency of these waveforms is also shown by performing several supporting simulations that compare the performance of the designed waveforms against that of phase shuffled surrogate inputs, variants of the minimum energy waveforms obtained from suboptimal PRCs, as well as pulsatile stimuli that are applied at the point of maximum PRC. It was found that the minimum energy waveforms perform better than all other stimuli both in terms of control and in the amount of energy used. Specifically, it was seen that these charge-balanced waveforms use at least an order of magnitude less energy than conventional monophasic pulsatile stimuli. Significance. The significance of this work is that it uses concepts from the theory of optimal control and introduces a novel approach in designing minimum energy charge-balanced input waveforms for neurons that are robust to noise and implementable in electrophysiological experiments.

The intriguing nature of dorsal root ganglion neurons: linking structure with polarity and function.

PubMed

Nascimento, Ana Isabel; Mar, Fernando Milhazes; Sousa, Mónica Mendes

2018-05-02

Dorsal root ganglion (DRG) neurons are the first neurons of the sensory pathway. They are activated by a variety of sensory stimuli that are then transmitted to the central nervous system. An important feature of DRG neurons is their unique morphology where a single process -the stem axon- bifurcates into a peripheral and a central axonal branch, with different functions and cellular properties. Distinctive structural aspects of the two DRG neuron branches may have important implications for their function in health and disease. However, the link between DRG axonal branch structure, polarity and function has been largely neglected in the field, and relevant information is rather scattered across the literature. In particular, ultrastructural differences between the two axonal branches are likely to account for the higher transport and regenerative ability of the peripheral DRG neuron axon when compared to the central one. Nevertheless, the cell intrinsic factors contributing to this central-peripheral asymmetry are still unknown. Here we critically review the factors that may underlie the functional asymmetry between the peripheral and central DRG axonal branches. Also, we discuss the hypothesis that DRG neurons may assemble a structure resembling the axon initial segment that may be responsible, at least in part, for their polarity and electrophysiological features. Ultimately, we suggest that the clarification of the axonal ultrastructure of DRG neurons using state-of-the-art techniques will be crucial to understand the physiology of this peculiar cell type. Copyright © 2018. Published by Elsevier Ltd.

Automatic classification of canine PRG neuronal discharge patterns using K-means clustering.

PubMed

Zuperku, Edward J; Prkic, Ivana; Stucke, Astrid G; Miller, Justin R; Hopp, Francis A; Stuth, Eckehard A

2015-02-01

Respiratory-related neurons in the parabrachial-Kölliker-Fuse (PB-KF) region of the pons play a key role in the control of breathing. The neuronal activities of these pontine respiratory group (PRG) neurons exhibit a variety of inspiratory (I), expiratory (E), phase spanning and non-respiratory related (NRM) discharge patterns. Due to the variety of patterns, it can be difficult to classify them into distinct subgroups according to their discharge contours. This report presents a method that automatically classifies neurons according to their discharge patterns and derives an average subgroup contour of each class. It is based on the K-means clustering technique and it is implemented via SigmaPlot User-Defined transform scripts. The discharge patterns of 135 canine PRG neurons were classified into seven distinct subgroups. Additional methods for choosing the optimal number of clusters are described. Analysis of the results suggests that the K-means clustering method offers a robust objective means of both automatically categorizing neuron patterns and establishing the underlying archetypical contours of subtypes based on the discharge patterns of group of neurons. Published by Elsevier B.V.

Advancing multiscale structural mapping of the brain through fluorescence imaging and analysis across length scales

PubMed Central

Hogstrom, L. J.; Guo, S. M.; Murugadoss, K.; Bathe, M.

2016-01-01

Brain function emerges from hierarchical neuronal structure that spans orders of magnitude in length scale, from the nanometre-scale organization of synaptic proteins to the macroscopic wiring of neuronal circuits. Because the synaptic electrochemical signal transmission that drives brain function ultimately relies on the organization of neuronal circuits, understanding brain function requires an understanding of the principles that determine hierarchical neuronal structure in living or intact organisms. Recent advances in fluorescence imaging now enable quantitative characterization of neuronal structure across length scales, ranging from single-molecule localization using super-resolution imaging to whole-brain imaging using light-sheet microscopy on cleared samples. These tools, together with correlative electron microscopy and magnetic resonance imaging at the nanoscopic and macroscopic scales, respectively, now facilitate our ability to probe brain structure across its full range of length scales with cellular and molecular specificity. As these imaging datasets become increasingly accessible to researchers, novel statistical and computational frameworks will play an increasing role in efforts to relate hierarchical brain structure to its function. In this perspective, we discuss several prominent experimental advances that are ushering in a new era of quantitative fluorescence-based imaging in neuroscience along with novel computational and statistical strategies that are helping to distil our understanding of complex brain structure. PMID:26855758

Adult neurogenesis: optimizing hippocampal function to suit the environment.

PubMed

Glasper, Erica R; Schoenfeld, Timothy J; Gould, Elizabeth

2012-02-14

Numerous studies have attempted to determine the function of adult neurogenesis in the hippocampus using methods to deplete new neurons and examine changes in behaviors associated with this brain region. This approach has produced a set of findings that, although not entirely consistent, suggest new neurons are associated with improved learning and reduced anxiety. This paper attempts to synthesize some of these findings into a model that proposes adaptive significance to experience-dependent alterations in new neuron formation. We suggest that the modulation of adult neurogenesis, as well as of the microcircuitry associated with new neurons, by experience prepares the hippocampus to meet the specific demands of an environment that is predictably similar to one that existed previously. Reduced neurogenesis that occurs with persistent exposure to a high threat environment produces a hippocampus that is more likely to respond with behavior that maximizes the chance of survival. Conversely, enhanced neurogenesis that occurs with continual exposure to a rewarding environment leads to behavior that optimizes the chances of successful reproduction. The persistence of this form of plasticity throughout adulthood may provide the neural substrate for adaptive responding to both stable and dynamic environmental conditions. Copyright © 2011. Published by Elsevier B.V.

Flow cytometry for receptor analysis from ex-vivo brain tissue in adult rat.

PubMed

Benoit, A; Guillamin, M; Aitken, P; Smith, P F; Philoxene, B; Sola, B; Poulain, L; Coquerel, A; Besnard, S

2018-07-01

Flow cytometry allows single-cell analysis of peripheral biological samples and is useful in many fields of research and clinical applications, mainly in hematology, immunology, and oncology. In the neurosciences, the flow cytometry separation method was first applied to stem cell extraction from healthy or cerebral tumour tissue and was more recently tested in order to phenotype brain cells, hippocampal neurogenesis, and to detect prion proteins. However, it remains sparsely applied in quantifying membrane receptors in relation to synaptic plasticity. We aimed to optimize a flow cytometric procedure for receptor quantification in neurons and non-neurons. A neural dissociation process, myelin separation, fixation, and membrane permeability procedures were optimized to maximize cell survival and analysis in hippocampal tissue obtained from adult rodents. We then aimed to quantify membrane muscarinic acetylcholine receptors (mAChRs) in rats with and without bilateral vestibular loss (BVL). mAChR's were quantified for neuronal and non-neuronal cells in the hippocampus and striatum following BVL. At day 30 but not at day 7 following BVL, there was a significant increase (P ≤ 0.05) in the percentage of neurons expressing M 2/4 mAChRs in both the hippocampus and the striatum. Here, we showed that flow cytometry appears to be a reliable method of membrane receptor quantification in ex-vivo brain tissue. Copyright © 2018 Elsevier B.V. All rights reserved.

Optimization Methods for Spiking Neurons and Networks

PubMed Central

Russell, Alexander; Orchard, Garrick; Dong, Yi; Mihalaş, Ştefan; Niebur, Ernst; Tapson, Jonathan; Etienne-Cummings, Ralph

2011-01-01

Spiking neurons and spiking neural circuits are finding uses in a multitude of tasks such as robotic locomotion control, neuroprosthetics, visual sensory processing, and audition. The desired neural output is achieved through the use of complex neuron models, or by combining multiple simple neurons into a network. In either case, a means for configuring the neuron or neural circuit is required. Manual manipulation of parameters is both time consuming and non-intuitive due to the nonlinear relationship between parameters and the neuron’s output. The complexity rises even further as the neurons are networked and the systems often become mathematically intractable. In large circuits, the desired behavior and timing of action potential trains may be known but the timing of the individual action potentials is unknown and unimportant, whereas in single neuron systems the timing of individual action potentials is critical. In this paper, we automate the process of finding parameters. To configure a single neuron we derive a maximum likelihood method for configuring a neuron model, specifically the Mihalas–Niebur Neuron. Similarly, to configure neural circuits, we show how we use genetic algorithms (GAs) to configure parameters for a network of simple integrate and fire with adaptation neurons. The GA approach is demonstrated both in software simulation and hardware implementation on a reconfigurable custom very large scale integration chip. PMID:20959265

Observations of synaptic structures: origins of the neuron doctrine and its current status

PubMed Central

Guillery, R.W

2004-01-01

The neuron doctrine represents nerve cells as polarized structures that contact each other at specialized (synaptic) junctions and form the developmental, functional, structural and trophic units of nervous systems. The doctrine provided a powerful analytical tool in the past, but is now seldom used in educating neuroscientists. Early observations of, and speculations about, sites of neuronal communication, which were made in the early 1860s, almost 30 years before the neuron doctrine was developed, are presented in relation to later accounts, particularly those made in support of, or opposition to, the neuron doctrine. These markedly differing accounts are considered in relation to limitations imposed by preparative and microscopical methods, and are discussed briefly as representing a post-Darwinian, reductionist view, on the one hand, opposed to a holistic view of mankind as a special part of creation, on the other. The widely misunderstood relationship of the neuron doctrine to the cell theory is discussed, as is the degree to which the neuron doctrine is still strictly applicable to an analysis of nervous systems. Current research represents a ‘post-neuronist’ era. The neuron doctrine provided a strong analytical approach in the past, but can no longer be seen as central to contemporary advances in neuroscience. PMID:16147523

Neuroprotective intervention by interferon-γ blockade prevents CD8+ T cell–mediated dendrite and synapse loss

PubMed Central

Kreutzfeldt, Mario; Bergthaler, Andreas; Fernandez, Marylise; Brück, Wolfgang; Steinbach, Karin; Vorm, Mariann; Coras, Roland; Blümcke, Ingmar; Bonilla, Weldy V.; Fleige, Anne; Forman, Ruth; Müller, Werner; Becher, Burkhard; Misgeld, Thomas; Kerschensteiner, Martin; Pinschewer, Daniel D.

2013-01-01

Neurons are postmitotic and thus irreplaceable cells of the central nervous system (CNS). Accordingly, CNS inflammation with resulting neuronal damage can have devastating consequences. We investigated molecular mediators and structural consequences of CD8+ T lymphocyte (CTL) attack on neurons in vivo. In a viral encephalitis model in mice, disease depended on CTL-derived interferon-γ (IFN-γ) and neuronal IFN-γ signaling. Downstream STAT1 phosphorylation and nuclear translocation in neurons were associated with dendrite and synapse loss (deafferentation). Analogous molecular and structural alterations were also found in human Rasmussen encephalitis, a CTL-mediated human autoimmune disorder of the CNS. Importantly, therapeutic intervention by IFN-γ blocking antibody prevented neuronal deafferentation and clinical disease without reducing CTL responses or CNS infiltration. These findings identify neuronal IFN-γ signaling as a novel target for neuroprotective interventions in CTL-mediated CNS disease. PMID:23999498

Segregated cholinergic transmission modulates dopamine neurons integrated in distinct functional circuits.

PubMed

Dautan, Daniel; Souza, Albert S; Huerta-Ocampo, Icnelia; Valencia, Miguel; Assous, Maxime; Witten, Ilana B; Deisseroth, Karl; Tepper, James M; Bolam, J Paul; Gerdjikov, Todor V; Mena-Segovia, Juan

2016-08-01

Dopamine neurons in the ventral tegmental area (VTA) receive cholinergic innervation from brainstem structures that are associated with either movement or reward. Whereas cholinergic neurons of the pedunculopontine nucleus (PPN) carry an associative/motor signal, those of the laterodorsal tegmental nucleus (LDT) convey limbic information. We used optogenetics and in vivo juxtacellular recording and labeling to examine the influence of brainstem cholinergic innervation of distinct neuronal subpopulations in the VTA. We found that LDT cholinergic axons selectively enhanced the bursting activity of mesolimbic dopamine neurons that were excited by aversive stimulation. In contrast, PPN cholinergic axons activated and changed the discharge properties of VTA neurons that were integrated in distinct functional circuits and were inhibited by aversive stimulation. Although both structures conveyed a reinforcing signal, they had opposite roles in locomotion. Our results demonstrate that two modes of cholinergic transmission operate in the VTA and segregate the neurons involved in different reward circuits.

Toward functional classification of neuronal types.

PubMed

Sharpee, Tatyana O

2014-09-17

How many types of neurons are there in the brain? This basic neuroscience question remains unsettled despite many decades of research. Classification schemes have been proposed based on anatomical, electrophysiological, or molecular properties. However, different schemes do not always agree with each other. This raises the question of whether one can classify neurons based on their function directly. For example, among sensory neurons, can a classification scheme be devised that is based on their role in encoding sensory stimuli? Here, theoretical arguments are outlined for how this can be achieved using information theory by looking at optimal numbers of cell types and paying attention to two key properties: correlations between inputs and noise in neural responses. This theoretical framework could help to map the hierarchical tree relating different neuronal classes within and across species. Copyright © 2014 Elsevier Inc. All rights reserved.

Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters

NASA Astrophysics Data System (ADS)

Oby, Emily R.; Perel, Sagi; Sadtler, Patrick T.; Ruff, Douglas A.; Mischel, Jessica L.; Montez, David F.; Cohen, Marlene R.; Batista, Aaron P.; Chase, Steven M.

2016-06-01

Objective. A traditional goal of neural recording with extracellular electrodes is to isolate action potential waveforms of an individual neuron. Recently, in brain-computer interfaces (BCIs), it has been recognized that threshold crossing events of the voltage waveform also convey rich information. To date, the threshold for detecting threshold crossings has been selected to preserve single-neuron isolation. However, the optimal threshold for single-neuron identification is not necessarily the optimal threshold for information extraction. Here we introduce a procedure to determine the best threshold for extracting information from extracellular recordings. We apply this procedure in two distinct contexts: the encoding of kinematic parameters from neural activity in primary motor cortex (M1), and visual stimulus parameters from neural activity in primary visual cortex (V1). Approach. We record extracellularly from multi-electrode arrays implanted in M1 or V1 in monkeys. Then, we systematically sweep the voltage detection threshold and quantify the information conveyed by the corresponding threshold crossings. Main Results. The optimal threshold depends on the desired information. In M1, velocity is optimally encoded at higher thresholds than speed; in both cases the optimal thresholds are lower than are typically used in BCI applications. In V1, information about the orientation of a visual stimulus is optimally encoded at higher thresholds than is visual contrast. A conceptual model explains these results as a consequence of cortical topography. Significance. How neural signals are processed impacts the information that can be extracted from them. Both the type and quality of information contained in threshold crossings depend on the threshold setting. There is more information available in these signals than is typically extracted. Adjusting the detection threshold to the parameter of interest in a BCI context should improve our ability to decode motor intent, and thus enhance BCI control. Further, by sweeping the detection threshold, one can gain insights into the topographic organization of the nearby neural tissue.

Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters

PubMed Central

Oby, Emily R; Perel, Sagi; Sadtler, Patrick T; Ruff, Douglas A; Mischel, Jessica L; Montez, David F; Cohen, Marlene R; Batista, Aaron P; Chase, Steven M

2018-01-01

Objective A traditional goal of neural recording with extracellular electrodes is to isolate action potential waveforms of an individual neuron. Recently, in brain–computer interfaces (BCIs), it has been recognized that threshold crossing events of the voltage waveform also convey rich information. To date, the threshold for detecting threshold crossings has been selected to preserve single-neuron isolation. However, the optimal threshold for single-neuron identification is not necessarily the optimal threshold for information extraction. Here we introduce a procedure to determine the best threshold for extracting information from extracellular recordings. We apply this procedure in two distinct contexts: the encoding of kinematic parameters from neural activity in primary motor cortex (M1), and visual stimulus parameters from neural activity in primary visual cortex (V1). Approach We record extracellularly from multi-electrode arrays implanted in M1 or V1 in monkeys. Then, we systematically sweep the voltage detection threshold and quantify the information conveyed by the corresponding threshold crossings. Main Results The optimal threshold depends on the desired information. In M1, velocity is optimally encoded at higher thresholds than speed; in both cases the optimal thresholds are lower than are typically used in BCI applications. In V1, information about the orientation of a visual stimulus is optimally encoded at higher thresholds than is visual contrast. A conceptual model explains these results as a consequence of cortical topography. Significance How neural signals are processed impacts the information that can be extracted from them. Both the type and quality of information contained in threshold crossings depend on the threshold setting. There is more information available in these signals than is typically extracted. Adjusting the detection threshold to the parameter of interest in a BCI context should improve our ability to decode motor intent, and thus enhance BCI control. Further, by sweeping the detection threshold, one can gain insights into the topographic organization of the nearby neural tissue. PMID:27097901

A stimulus-dependent spike threshold is an optimal neural coder

PubMed Central

Jones, Douglas L.; Johnson, Erik C.; Ratnam, Rama

2015-01-01

A neural code based on sequences of spikes can consume a significant portion of the brain's energy budget. Thus, energy considerations would dictate that spiking activity be kept as low as possible. However, a high spike-rate improves the coding and representation of signals in spike trains, particularly in sensory systems. These are competing demands, and selective pressure has presumably worked to optimize coding by apportioning a minimum number of spikes so as to maximize coding fidelity. The mechanisms by which a neuron generates spikes while maintaining a fidelity criterion are not known. Here, we show that a signal-dependent neural threshold, similar to a dynamic or adapting threshold, optimizes the trade-off between spike generation (encoding) and fidelity (decoding). The threshold mimics a post-synaptic membrane (a low-pass filter) and serves as an internal decoder. Further, it sets the average firing rate (the energy constraint). The decoding process provides an internal copy of the coding error to the spike-generator which emits a spike when the error equals or exceeds a spike threshold. When optimized, the trade-off leads to a deterministic spike firing-rule that generates optimally timed spikes so as to maximize fidelity. The optimal coder is derived in closed-form in the limit of high spike-rates, when the signal can be approximated as a piece-wise constant signal. The predicted spike-times are close to those obtained experimentally in the primary electrosensory afferent neurons of weakly electric fish (Apteronotus leptorhynchus) and pyramidal neurons from the somatosensory cortex of the rat. We suggest that KCNQ/Kv7 channels (underlying the M-current) are good candidates for the decoder. They are widely coupled to metabolic processes and do not inactivate. We conclude that the neural threshold is optimized to generate an energy-efficient and high-fidelity neural code. PMID:26082710

Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters.

PubMed

Oby, Emily R; Perel, Sagi; Sadtler, Patrick T; Ruff, Douglas A; Mischel, Jessica L; Montez, David F; Cohen, Marlene R; Batista, Aaron P; Chase, Steven M

2016-06-01

A traditional goal of neural recording with extracellular electrodes is to isolate action potential waveforms of an individual neuron. Recently, in brain-computer interfaces (BCIs), it has been recognized that threshold crossing events of the voltage waveform also convey rich information. To date, the threshold for detecting threshold crossings has been selected to preserve single-neuron isolation. However, the optimal threshold for single-neuron identification is not necessarily the optimal threshold for information extraction. Here we introduce a procedure to determine the best threshold for extracting information from extracellular recordings. We apply this procedure in two distinct contexts: the encoding of kinematic parameters from neural activity in primary motor cortex (M1), and visual stimulus parameters from neural activity in primary visual cortex (V1). We record extracellularly from multi-electrode arrays implanted in M1 or V1 in monkeys. Then, we systematically sweep the voltage detection threshold and quantify the information conveyed by the corresponding threshold crossings. The optimal threshold depends on the desired information. In M1, velocity is optimally encoded at higher thresholds than speed; in both cases the optimal thresholds are lower than are typically used in BCI applications. In V1, information about the orientation of a visual stimulus is optimally encoded at higher thresholds than is visual contrast. A conceptual model explains these results as a consequence of cortical topography. How neural signals are processed impacts the information that can be extracted from them. Both the type and quality of information contained in threshold crossings depend on the threshold setting. There is more information available in these signals than is typically extracted. Adjusting the detection threshold to the parameter of interest in a BCI context should improve our ability to decode motor intent, and thus enhance BCI control. Further, by sweeping the detection threshold, one can gain insights into the topographic organization of the nearby neural tissue.

ADP-ribosylation Factor 6 (ARF6) Bidirectionally Regulates Dendritic Spine Formation Depending on Neuronal Maturation and Activity*

PubMed Central

Kim, Yoonju; Lee, Sang-Eun; Park, Joohyun; Kim, Minhyung; Lee, Boyoon; Hwang, Daehee; Chang, Sunghoe

2015-01-01

Recent studies have reported conflicting results regarding the role of ARF6 in dendritic spine development, but no clear answer for the controversy has been suggested. We found that ADP-ribosylation factor 6 (ARF6) either positively or negatively regulates dendritic spine formation depending on neuronal maturation and activity. ARF6 activation increased the spine formation in developing neurons, whereas it decreased spine density in mature neurons. Genome-wide microarray analysis revealed that ARF6 activation in each stage leads to opposite patterns of expression of a subset of genes that are involved in neuronal morphology. ARF6-mediated Rac1 activation via the phospholipase D pathway is the coincident factor in both stages, but the antagonistic RhoA pathway becomes involved in the mature stage. Furthermore, blocking neuronal activity in developing neurons using tetrodotoxin or enhancing the activity in mature neurons using picrotoxin or chemical long term potentiation reversed the effect of ARF6 on each stage. Thus, activity-dependent dynamic changes in ARF6-mediated spine structures may play a role in structural plasticity of mature neurons. PMID:25605715

Learning and Generalization under Ambiguity: An fMRI Study

PubMed Central

Chumbley, J. R.; Flandin, G.; Bach, D. R.; Daunizeau, J.; Fehr, E.; Dolan, R. J.; Friston, K. J.

2012-01-01

Adaptive behavior often exploits generalizations from past experience by applying them judiciously in new situations. This requires a means of quantifying the relative importance of prior experience and current information, so they can be balanced optimally. In this study, we ask whether the brain generalizes in an optimal way. Specifically, we used Bayesian learning theory and fMRI to test whether neuronal responses reflect context-sensitive changes in ambiguity or uncertainty about experience-dependent beliefs. We found that the hippocampus expresses clear ambiguity-dependent responses that are associated with an augmented rate of learning. These findings suggest candidate neuronal systems that may be involved in aberrations of generalization, such as over-confidence. PMID:22275857

A biologically inspired neural network for dynamic programming.

PubMed

Francelin Romero, R A; Kacpryzk, J; Gomide, F

2001-12-01

An artificial neural network with a two-layer feedback topology and generalized recurrent neurons, for solving nonlinear discrete dynamic optimization problems, is developed. A direct method to assign the weights of neural networks is presented. The method is based on Bellmann's Optimality Principle and on the interchange of information which occurs during the synaptic chemical processing among neurons. The neural network based algorithm is an advantageous approach for dynamic programming due to the inherent parallelism of the neural networks; further it reduces the severity of computational problems that can occur in methods like conventional methods. Some illustrative application examples are presented to show how this approach works out including the shortest path and fuzzy decision making problems.

Learning and generalization under ambiguity: an fMRI study.

PubMed

Chumbley, J R; Flandin, G; Bach, D R; Daunizeau, J; Fehr, E; Dolan, R J; Friston, K J

2012-01-01

Adaptive behavior often exploits generalizations from past experience by applying them judiciously in new situations. This requires a means of quantifying the relative importance of prior experience and current information, so they can be balanced optimally. In this study, we ask whether the brain generalizes in an optimal way. Specifically, we used Bayesian learning theory and fMRI to test whether neuronal responses reflect context-sensitive changes in ambiguity or uncertainty about experience-dependent beliefs. We found that the hippocampus expresses clear ambiguity-dependent responses that are associated with an augmented rate of learning. These findings suggest candidate neuronal systems that may be involved in aberrations of generalization, such as over-confidence.

Peculiarities of studying an isolated neuron by the method of laser interference microscopy

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

Yusipovich, Alexander I; Kazakova, Tatiana A; Erokhova, Liudmila A

2006-09-30

Actual aspects of using a new method of laser interference microscopy (LIM) for studying nerve cells are discussed. The peculiarities of the LIM display of neurons are demonstrated by the example of isolated neurons of a pond snail Lymnaea stagnalis. A comparative analysis of the images of the cell and subcellular structures of a neuron obtained by the methods of interference microscopy, optical transmission microscopy, and confocal microscopy is performed. Various aspects of the application of LIM for studying the lateral dimensions and internal structure of the cytoplasm and organelles of a neuron in cytology and cell physiology are discussed.more » (laser biology)« less

Morphological evidence for novel enteric neuronal circuitry in guinea pig distal colon.

PubMed

Smolilo, D J; Costa, M; Hibberd, T J; Wattchow, D A; Spencer, Nick J

2018-07-01

The gastrointestinal (GI) tract is unique compared to all other internal organs; it is the only organ with its own nervous system and its own population of intrinsic sensory neurons, known as intrinsic primary afferent neurons (IPANs). How these IPANs form neuronal circuits with other functional classes of neurons in the enteric nervous system (ENS) is incompletely understood. We used a combination of light microscopy, immunohistochemistry and confocal microscopy to examine the topographical distribution of specific classes of neurons in the myenteric plexus of guinea-pig colon, including putative IPANs, with other classes of enteric neurons. These findings were based on immunoreactivity to the neuronal markers, calbindin, calretinin and nitric oxide synthase. We then correlated the varicose outputs formed by putative IPANs with subclasses of excitatory interneurons and motor neurons. We revealed that calbindin-immunoreactive varicosities form specialized structures resembling 'baskets' within the majority of myenteric ganglia, which were arranged in clusters around calretinin-immunoreactive neurons. These calbindin baskets directly arose from projections of putative IPANs and represent morphological evidence of preferential input from sensory neurons directly to a select group of calretinin neurons. Our findings uncovered that these neurons are likely to be ascending excitatory interneurons and excitatory motor neurons. Our study reveals for the first time in the colon, a novel enteric neural circuit, whereby calbindin-immunoreactive putative sensory neurons form specialized varicose structures that likely direct synaptic outputs to excitatory interneurons and motor neurons. This circuit likely forms the basis of polarized neuronal pathways underlying motility. © 2018 Wiley Periodicals, Inc.

Emergent spatial synaptic structure from diffusive plasticity.

PubMed

Sweeney, Yann; Clopath, Claudia

2017-04-01

Some neurotransmitters can diffuse freely across cell membranes, influencing neighbouring neurons regardless of their synaptic coupling. This provides a means of neural communication, alternative to synaptic transmission, which can influence the way in which neural networks process information. Here, we ask whether diffusive neurotransmission can also influence the structure of synaptic connectivity in a network undergoing plasticity. We propose a form of Hebbian synaptic plasticity which is mediated by a diffusive neurotransmitter. Whenever a synapse is modified at an individual neuron through our proposed mechanism, similar but smaller modifications occur in synapses connecting to neighbouring neurons. The effects of this diffusive plasticity are explored in networks of rate-based neurons. This leads to the emergence of spatial structure in the synaptic connectivity of the network. We show that this spatial structure can coexist with other forms of structure in the synaptic connectivity, such as with groups of strongly interconnected neurons that form in response to correlated external drive. Finally, we explore diffusive plasticity in a simple feedforward network model of receptive field development. We show that, as widely observed across sensory cortex, the preferred stimulus identity of neurons in our network become spatially correlated due to diffusion. Our proposed mechanism of diffusive plasticity provides an efficient mechanism for generating these spatial correlations in stimulus preference which can flexibly interact with other forms of synaptic organisation. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

A cholinergic feedback circuit to regulate striatal population uncertainty and optimize reinforcement learning

PubMed Central

Franklin, Nicholas T; Frank, Michael J

2015-01-01

Convergent evidence suggests that the basal ganglia support reinforcement learning by adjusting action values according to reward prediction errors. However, adaptive behavior in stochastic environments requires the consideration of uncertainty to dynamically adjust the learning rate. We consider how cholinergic tonically active interneurons (TANs) may endow the striatum with such a mechanism in computational models spanning three Marr's levels of analysis. In the neural model, TANs modulate the excitability of spiny neurons, their population response to reinforcement, and hence the effective learning rate. Long TAN pauses facilitated robustness to spurious outcomes by increasing divergence in synaptic weights between neurons coding for alternative action values, whereas short TAN pauses facilitated stochastic behavior but increased responsiveness to change-points in outcome contingencies. A feedback control system allowed TAN pauses to be dynamically modulated by uncertainty across the spiny neuron population, allowing the system to self-tune and optimize performance across stochastic environments. DOI: http://dx.doi.org/10.7554/eLife.12029.001 PMID:26705698

Local excitation-inhibition ratio for synfire chain propagation in feed-forward neuronal networks

NASA Astrophysics Data System (ADS)

Guo, Xinmeng; Yu, Haitao; Wang, Jiang; Liu, Jing; Cao, Yibin; Deng, Bin

2017-09-01

A leading hypothesis holds that spiking activity propagates along neuronal sub-populations which are connected in a feed-forward manner, and the propagation efficiency would be affected by the dynamics of sub-populations. In this paper, how the interaction between local excitation and inhibition effects on synfire chain propagation in feed-forward network (FFN) is investigated. The simulation results show that there is an appropriate excitation-inhibition (EI) ratio maximizing the performance of synfire chain propagation. The optimal EI ratio can significantly enhance the selectivity of FFN to synchronous signals, which thereby increases the stability to background noise. Moreover, the effect of network topology on synfire chain propagation is also investigated. It is found that synfire chain propagation can be maximized by an optimal interlayer linking probability. We also find that external noise is detrimental to synchrony propagation by inducing spiking jitter. The results presented in this paper may provide insights into the effects of network dynamics on neuronal computations.

Optimized ratiometric calcium sensors for functional in vivo imaging of neurons and T lymphocytes.

PubMed

Thestrup, Thomas; Litzlbauer, Julia; Bartholomäus, Ingo; Mues, Marsilius; Russo, Luigi; Dana, Hod; Kovalchuk, Yuri; Liang, Yajie; Kalamakis, Georgios; Laukat, Yvonne; Becker, Stefan; Witte, Gregor; Geiger, Anselm; Allen, Taylor; Rome, Lawrence C; Chen, Tsai-Wen; Kim, Douglas S; Garaschuk, Olga; Griesinger, Christian; Griesbeck, Oliver

2014-02-01

The quality of genetically encoded calcium indicators (GECIs) has improved dramatically in recent years, but high-performing ratiometric indicators are still rare. Here we describe a series of fluorescence resonance energy transfer (FRET)-based calcium biosensors with a reduced number of calcium binding sites per sensor. These 'Twitch' sensors are based on the C-terminal domain of Opsanus troponin C. Their FRET responses were optimized by a large-scale functional screen in bacterial colonies, refined by a secondary screen in rat hippocampal neuron cultures. We tested the in vivo performance of the most sensitive variants in the brain and lymph nodes of mice. The sensitivity of the Twitch sensors matched that of synthetic calcium dyes and allowed visualization of tonic action potential firing in neurons and high resolution functional tracking of T lymphocytes. Given their ratiometric readout, their brightness, large dynamic range and linear response properties, Twitch sensors represent versatile tools for neuroscience and immunology.

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.

Qualitative-Modeling-Based Silicon Neurons and Their Networks

PubMed Central

Kohno, Takashi; Sekikawa, Munehisa; Li, Jing; Nanami, Takuya; Aihara, Kazuyuki

2016-01-01

The ionic conductance models of neuronal cells can finely reproduce a wide variety of complex neuronal activities. However, the complexity of these models has prompted the development of qualitative neuron models. They are described by differential equations with a reduced number of variables and their low-dimensional polynomials, which retain the core mathematical structures. Such simple models form the foundation of a bottom-up approach in computational and theoretical neuroscience. We proposed a qualitative-modeling-based approach for designing silicon neuron circuits, in which the mathematical structures in the polynomial-based qualitative models are reproduced by differential equations with silicon-native expressions. This approach can realize low-power-consuming circuits that can be configured to realize various classes of neuronal cells. In this article, our qualitative-modeling-based silicon neuron circuits for analog and digital implementations are quickly reviewed. One of our CMOS analog silicon neuron circuits can realize a variety of neuronal activities with a power consumption less than 72 nW. The square-wave bursting mode of this circuit is explained. Another circuit can realize Class I and II neuronal activities with about 3 nW. Our digital silicon neuron circuit can also realize these classes. An auto-associative memory realized on an all-to-all connected network of these silicon neurons is also reviewed, in which the neuron class plays important roles in its performance. PMID:27378842

An artificial network model for estimating the network structure underlying partially observed neuronal signals.

PubMed

Komatsu, Misako; Namikawa, Jun; Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka; Nakamura, Kiyohiko; Tani, Jun

2014-01-01

Many previous studies have proposed methods for quantifying neuronal interactions. However, these methods evaluated the interactions between recorded signals in an isolated network. In this study, we present a novel approach for estimating interactions between observed neuronal signals by theorizing that those signals are observed from only a part of the network that also includes unobserved structures. We propose a variant of the recurrent network model that consists of both observable and unobservable units. The observable units represent recorded neuronal activity, and the unobservable units are introduced to represent activity from unobserved structures in the network. The network structures are characterized by connective weights, i.e., the interaction intensities between individual units, which are estimated from recorded signals. We applied this model to multi-channel brain signals recorded from monkeys, and obtained robust network structures with physiological relevance. Furthermore, the network exhibited common features that portrayed cortical dynamics as inversely correlated interactions between excitatory and inhibitory populations of neurons, which are consistent with the previous view of cortical local circuits. Our results suggest that the novel concept of incorporating an unobserved structure into network estimations has theoretical advantages and could provide insights into brain dynamics beyond what can be directly observed. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

The influence of hubs in the structure of a neuronal network during an epileptic seizure

NASA Astrophysics Data System (ADS)

Rodrigues, Abner Cardoso; Cerdeira, Hilda A.; Machado, Birajara Soares

2016-02-01

In this work, we propose changes in the structure of a neuronal network with the intention to provoke strong synchronization to simulate episodes of epileptic seizure. Starting with a network of Izhikevich neurons we slowly increase the number of connections in selected nodes in a controlled way, to produce (or not) hubs. We study how these structures alter the synchronization on the spike firings interval, on individual neurons as well as on mean values, as a function of the concentration of connections for random and non-random (hubs) distribution. We also analyze how the post-ictal signal varies for the different distributions. We conclude that a network with hubs is more appropriate to represent an epileptic state.

Fast and robust estimation of spectro-temporal receptive fields using stochastic approximations.

PubMed

Meyer, Arne F; Diepenbrock, Jan-Philipp; Ohl, Frank W; Anemüller, Jörn

2015-05-15

The receptive field (RF) represents the signal preferences of sensory neurons and is the primary analysis method for understanding sensory coding. While it is essential to estimate a neuron's RF, finding numerical solutions to increasingly complex RF models can become computationally intensive, in particular for high-dimensional stimuli or when many neurons are involved. Here we propose an optimization scheme based on stochastic approximations that facilitate this task. The basic idea is to derive solutions on a random subset rather than computing the full solution on the available data set. To test this, we applied different optimization schemes based on stochastic gradient descent (SGD) to both the generalized linear model (GLM) and a recently developed classification-based RF estimation approach. Using simulated and recorded responses, we demonstrate that RF parameter optimization based on state-of-the-art SGD algorithms produces robust estimates of the spectro-temporal receptive field (STRF). Results on recordings from the auditory midbrain demonstrate that stochastic approximations preserve both predictive power and tuning properties of STRFs. A correlation of 0.93 with the STRF derived from the full solution may be obtained in less than 10% of the full solution's estimation time. We also present an on-line algorithm that allows simultaneous monitoring of STRF properties of more than 30 neurons on a single computer. The proposed approach may not only prove helpful for large-scale recordings but also provides a more comprehensive characterization of neural tuning in experiments than standard tuning curves. Copyright © 2015 Elsevier B.V. All rights reserved.

Desynchronization boost by non-uniform coordinated reset stimulation in ensembles of pulse-coupled neurons

PubMed Central

Lücken, Leonhard; Yanchuk, Serhiy; Popovych, Oleksandr V.; Tass, Peter A.

2013-01-01

Several brain diseases are characterized by abnormal neuronal synchronization. Desynchronization of abnormal neural synchrony is theoretically compelling because of the complex dynamical mechanisms involved. We here present a novel type of coordinated reset (CR) stimulation. CR means to deliver phase resetting stimuli at different neuronal sub-populations sequentially, i.e., at times equidistantly distributed in a stimulation cycle. This uniform timing pattern seems to be intuitive and actually applies to the neural network models used for the study of CR so far. CR resets the population to an unstable cluster state from where it passes through a desynchronized transient, eventually resynchronizing if left unperturbed. In contrast, we show that the optimal stimulation times are non-uniform. Using the model of weakly pulse-coupled neurons with phase response curves, we provide an approach that enables to determine optimal stimulation timing patterns that substantially maximize the desynchronized transient time following the application of CR stimulation. This approach includes an optimization search for clusters in a low-dimensional pulse coupled map. As a consequence, model-specific non-uniformly spaced cluster states cause considerably longer desynchronization transients. Intriguingly, such a desynchronization boost with non-uniform CR stimulation can already be achieved by only slight modifications of the uniform CR timing pattern. Our results suggest that the non-uniformness of the stimulation times can be a medically valuable parameter in the calibration procedure for CR stimulation, where the latter has successfully been used in clinical and pre-clinical studies for the treatment of Parkinson's disease and tinnitus. PMID:23750134

Multilayer perceptron architecture optimization using parallel computing techniques.

PubMed

Castro, Wilson; Oblitas, Jimy; Santa-Cruz, Roberto; Avila-George, Himer

2017-01-01

The objective of this research was to develop a methodology for optimizing multilayer-perceptron-type neural networks by evaluating the effects of three neural architecture parameters, namely, number of hidden layers (HL), neurons per hidden layer (NHL), and activation function type (AF), on the sum of squares error (SSE). The data for the study were obtained from quality parameters (physicochemical and microbiological) of milk samples. Architectures or combinations were organized in groups (G1, G2, and G3) generated upon interspersing one, two, and three layers. Within each group, the networks had three neurons in the input layer, six neurons in the output layer, three to twenty-seven NHL, and three AF (tan-sig, log-sig, and linear) types. The number of architectures was determined using three factorial-type experimental designs, which reached 63, 2 187, and 50 049 combinations for G1, G2 and G3, respectively. Using MATLAB 2015a, a logical sequence was designed and implemented for constructing, training, and evaluating multilayer-perceptron-type neural networks using parallel computing techniques. The results show that HL and NHL have a statistically relevant effect on SSE, and from two hidden layers, AF also has a significant effect; thus, both AF and NHL can be evaluated to determine the optimal combination per group. Moreover, in the three study groups, it is observed that there is an inverse relationship between the number of processors and the total optimization time.

A distributed, dynamic, parallel computational model: the role of noise in velocity storage

PubMed Central

Merfeld, Daniel M.

2012-01-01

Networks of neurons perform complex calculations using distributed, parallel computation, including dynamic “real-time” calculations required for motion control. The brain must combine sensory signals to estimate the motion of body parts using imperfect information from noisy neurons. Models and experiments suggest that the brain sometimes optimally minimizes the influence of noise, although it remains unclear when and precisely how neurons perform such optimal computations. To investigate, we created a model of velocity storage based on a relatively new technique–“particle filtering”–that is both distributed and parallel. It extends existing observer and Kalman filter models of vestibular processing by simulating the observer model many times in parallel with noise added. During simulation, the variance of the particles defining the estimator state is used to compute the particle filter gain. We applied our model to estimate one-dimensional angular velocity during yaw rotation, which yielded estimates for the velocity storage time constant, afferent noise, and perceptual noise that matched experimental data. We also found that the velocity storage time constant was Bayesian optimal by comparing the estimate of our particle filter with the estimate of the Kalman filter, which is optimal. The particle filter demonstrated a reduced velocity storage time constant when afferent noise increased, which mimics what is known about aminoglycoside ablation of semicircular canal hair cells. This model helps bridge the gap between parallel distributed neural computation and systems-level behavioral responses like the vestibuloocular response and perception. PMID:22514288

Multilayer perceptron architecture optimization using parallel computing techniques

PubMed Central

Castro, Wilson; Oblitas, Jimy; Santa-Cruz, Roberto; Avila-George, Himer

2017-01-01

The objective of this research was to develop a methodology for optimizing multilayer-perceptron-type neural networks by evaluating the effects of three neural architecture parameters, namely, number of hidden layers (HL), neurons per hidden layer (NHL), and activation function type (AF), on the sum of squares error (SSE). The data for the study were obtained from quality parameters (physicochemical and microbiological) of milk samples. Architectures or combinations were organized in groups (G1, G2, and G3) generated upon interspersing one, two, and three layers. Within each group, the networks had three neurons in the input layer, six neurons in the output layer, three to twenty-seven NHL, and three AF (tan-sig, log-sig, and linear) types. The number of architectures was determined using three factorial-type experimental designs, which reached 63, 2 187, and 50 049 combinations for G1, G2 and G3, respectively. Using MATLAB 2015a, a logical sequence was designed and implemented for constructing, training, and evaluating multilayer-perceptron-type neural networks using parallel computing techniques. The results show that HL and NHL have a statistically relevant effect on SSE, and from two hidden layers, AF also has a significant effect; thus, both AF and NHL can be evaluated to determine the optimal combination per group. Moreover, in the three study groups, it is observed that there is an inverse relationship between the number of processors and the total optimization time. PMID:29236744

Transcranial current stimulation focality using disc and ring electrode configurations: FEM analysis

NASA Astrophysics Data System (ADS)

Datta, Abhishek; Elwassif, Maged; Battaglia, Fortunato; Bikson, Marom

2008-06-01

We calculated the electric fields induced in the brain during transcranial current stimulation (TCS) using a finite-element concentric spheres human head model. A range of disc electrode configurations were simulated: (1) distant-bipolar; (2) adjacent-bipolar; (3) tripolar; and three ring designs, (4) belt, (5) concentric ring, and (6) double concentric ring. We compared the focality of each configuration targeting cortical structures oriented normal to the surface ('surface-radial' and 'cross-section radial'), cortical structures oriented along the brain surface ('surface-tangential' and 'cross-section tangential') and non-oriented cortical surface structures ('surface-magnitude' and 'cross-section magnitude'). For surface-radial fields, we further considered the 'polarity' of modulation (e.g. superficial cortical neuron soma hyper/depolarizing). The distant-bipolar configuration, which is comparable with commonly used TCS protocols, resulted in diffuse (un-focal) modulation with bi-directional radial modulation under each electrode and tangential modulation between electrodes. Increasing the proximity of the two electrodes (adjacent-bipolar electrode configuration) increased focality, at the cost of more surface current. At similar electrode distances, the tripolar-electrodes configuration produced comparable peak focality, but reduced radial bi-directionality. The concentric-ring configuration resulted in the highest spatial focality and uni-directional radial modulation, at the expense of increased total surface current. Changing ring dimensions, or use of two concentric rings, allow titration of this balance. The concentric-ring design may thus provide an optimized configuration for targeted modulation of superficial cortical neurons.

Automated system for analyzing the activity of individual neurons

NASA Technical Reports Server (NTRS)

Bankman, Isaac N.; Johnson, Kenneth O.; Menkes, Alex M.; Diamond, Steve D.; Oshaughnessy, David M.

1993-01-01

This paper presents a signal processing system that: (1) provides an efficient and reliable instrument for investigating the activity of neuronal assemblies in the brain; and (2) demonstrates the feasibility of generating the command signals of prostheses using the activity of relevant neurons in disabled subjects. The system operates online, in a fully automated manner and can recognize the transient waveforms of several neurons in extracellular neurophysiological recordings. Optimal algorithms for detection, classification, and resolution of overlapping waveforms are developed and evaluated. Full automation is made possible by an algorithm that can set appropriate decision thresholds and an algorithm that can generate templates on-line. The system is implemented with a fast IBM PC compatible processor board that allows on-line operation.

Quantitative analysis of serotonin secreted by human embryonic stem cells-derived serotonergic neurons via pH-mediated online stacking-CE-ESI-MRM.

PubMed

Zhong, Xuefei; Hao, Ling; Lu, Jianfeng; Ye, Hui; Zhang, Su-Chun; Li, Lingjun

2016-04-01

A CE-ESI-MRM-based assay was developed for targeted analysis of serotonin released by human embryonic stem cells-derived serotonergic neurons in a chemically defined environment. A discontinuous electrolyte system was optimized for pH-mediated online stacking of serotonin. Combining with a liquid-liquid extraction procedure, LOD of serotonin in the Krebs'-Ringer's solution by CE-ESI-MS/MS on a 3D ion trap MS was0.15 ng/mL. The quantitative results confirmed the serotonergic identity of the in vitro developed neurons and the capacity of these neurons to release serotonin in response to stimulus. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

Fractal Dimension of EEG Activity Senses Neuronal Impairment in Acute Stroke

PubMed Central

Zappasodi, Filippo; Olejarczyk, Elzbieta; Marzetti, Laura; Assenza, Giovanni; Pizzella, Vittorio; Tecchio, Franca

2014-01-01

The brain is a self-organizing system which displays self-similarities at different spatial and temporal scales. Thus, the complexity of its dynamics, associated to efficient processing and functional advantages, is expected to be captured by a measure of its scale-free (fractal) properties. Under the hypothesis that the fractal dimension (FD) of the electroencephalographic signal (EEG) is optimally sensitive to the neuronal dysfunction secondary to a brain lesion, we tested the FD’s ability in assessing two key processes in acute stroke: the clinical impairment and the recovery prognosis. Resting EEG was collected in 36 patients 4–10 days after a unilateral ischemic stroke in the middle cerebral artery territory and 19 healthy controls. National Health Institute Stroke Scale (NIHss) was collected at T0 and 6 months later. Highuchi FD, its inter-hemispheric asymmetry (FDasy) and spectral band powers were calculated for EEG signals. FD was smaller in patients than in controls (1.447±0.092 vs 1.525±0.105) and its reduction was paired to a worse acute clinical status. FD decrease was associated to alpha increase and beta decrease of oscillatory activity power. Larger FDasy in acute phase was paired to a worse clinical recovery at six months. FD in our patients captured the loss of complexity reflecting the global system dysfunction resulting from the structural damage. This decrease seems to reveal the intimate nature of structure-function unity, where the regional neural multi-scale self-similar activity is impaired by the anatomical lesion. This picture is coherent with neuronal activity complexity decrease paired to a reduced repertoire of functional abilities. FDasy result highlights the functional relevance of the balance between homologous brain structures’ activities in stroke recovery. PMID:24967904

Theory of Arachnid Prey Localization

NASA Astrophysics Data System (ADS)

Stürzl, W.; Kempter, R.; van Hemmen, J. L.

2000-06-01

Sand scorpions and many other arachnids locate their prey through highly sensitive slit sensilla at the tips (tarsi) of their eight legs. This sensor array responds to vibrations with stimulus-locked action potentials encoding the target direction. We present a neuronal model to account for stimulus angle determination using a population of second-order neurons, each receiving excitatory input from one tarsus and inhibition from a triad opposite to it. The input opens a time window whose width determines a neuron's firing probability. Stochastic optimization is realized through tuning the balance between excitation and inhibition. The agreement with experiments on the sand scorpion is excellent.

Optimal channel efficiency in a sensory network

NASA Astrophysics Data System (ADS)

Mosqueiro, Thiago S.; Maia, Leonardo P.

2013-07-01

Spontaneous neural activity has been increasingly recognized as a subject of key relevance in neuroscience. It exhibits nontrivial spatiotemporal structure reflecting the organization of the underlying neural network and has proved to be closely intertwined with stimulus-induced activity patterns. As an additional contribution in this regard, we report computational studies that strongly suggest that a stimulus-free feature rules the behavior of an important psychophysical measure of the sensibility of a sensory system to a stimulus, the so-called dynamic range. Indeed in this paper we show that the entropy of the distribution of avalanche lifetimes (information efficiency, since it can be interpreted as the efficiency of the network seen as a communication channel) always accompanies the dynamic range in the benchmark model for sensory systems. Specifically, by simulating the Kinouchi-Copelli (KC) model on two broad families of model networks, we generically observed that both quantities always increase or decrease together as functions of the average branching ratio (the control parameter of the KC model) and that the information efficiency typically exhibits critical optimization jointly with the dynamic range (i.e., both quantities are optimized at the same value of that control parameter, that turns out to be the critical point of a nonequilibrium phase transition). In contrast with the practice of taking power laws to identify critical points in most studies describing measured neuronal avalanches, we rely on data collapses as more robust signatures of criticality to claim that critical optimization may happen even when the distribution of avalanche lifetimes is not a power law, as suggested by a recent experiment. Finally, we note that the entropy of the size distribution of avalanches (information capacity) does not always follow the dynamic range and the information efficiency when they are critically optimized, despite being more widely used than the latter to describe the computational capabilities of a neural network. This strongly suggests that dynamical rules allowing a proper temporal matching of the states of the interacting neurons is the key for achieving good performance in information processing, rather than increasing the number of available units.

Three-dimensional mapping of microcircuit correlation structure

PubMed Central

Cotton, R. James; Froudarakis, Emmanouil; Storer, Patrick; Saggau, Peter; Tolias, Andreas S.

2013-01-01

Great progress has been made toward understanding the properties of single neurons, yet the principles underlying interactions between neurons remain poorly understood. Given that connectivity in the neocortex is locally dense through both horizontal and vertical connections, it is of particular importance to characterize the activity structure of local populations of neurons arranged in three dimensions. However, techniques for simultaneously measuring microcircuit activity are lacking. We developed an in vivo 3D high-speed, random-access two-photon microscope that is capable of simultaneous 3D motion tracking. This allows imaging from hundreds of neurons at several hundred Hz, while monitoring tissue movement. Given that motion will induce common artifacts across the population, accurate motion tracking is absolutely necessary for studying population activity with random-access based imaging methods. We demonstrate the potential of this imaging technique by measuring the correlation structure of large populations of nearby neurons in the mouse visual cortex, and find that the microcircuit correlation structure is stimulus-dependent. Three-dimensional random access multiphoton imaging with concurrent motion tracking provides a novel, powerful method to characterize the microcircuit activity in vivo. PMID:24133414

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV: structuring synaptic pathways among recurrent connections.

PubMed

Gilson, Matthieu; Burkitt, Anthony N; Grayden, David B; Thomas, Doreen A; van Hemmen, J Leo

2009-12-01

In neuronal networks, the changes of synaptic strength (or weight) performed by spike-timing-dependent plasticity (STDP) are hypothesized to give rise to functional network structure. This article investigates how this phenomenon occurs for the excitatory recurrent connections of a network with fixed input weights that is stimulated by external spike trains. We develop a theoretical framework based on the Poisson neuron model to analyze the interplay between the neuronal activity (firing rates and the spike-time correlations) and the learning dynamics, when the network is stimulated by correlated pools of homogeneous Poisson spike trains. STDP can lead to both a stabilization of all the neuron firing rates (homeostatic equilibrium) and a robust weight specialization. The pattern of specialization for the recurrent weights is determined by a relationship between the input firing-rate and correlation structures, the network topology, the STDP parameters and the synaptic response properties. We find conditions for feed-forward pathways or areas with strengthened self-feedback to emerge in an initially homogeneous recurrent network.

Alpha6-Containing Nicotinic Acetylcholine Receptors Mediate Nicotine-Induced Structural Plasticity in Mouse and Human iPSC-Derived Dopaminergic Neurons.

PubMed

Collo, Ginetta; Cavalleri, Laura; Zoli, Michele; Maskos, Uwe; Ratti, Emiliangelo; Merlo Pich, Emilio

2018-01-01

Midbrain dopamine (DA) neurons are considered a critical substrate for the reinforcing and sensitizing effects of nicotine and tobacco dependence. While the role of the α4 and β2 subunit containing nicotinic acetylcholine receptors (α4β2 ∗ nAChRs) in mediating nicotine effects on DA release and DA neuron activity has been widely explored, less information is available on their role in the morphological adaptation of the DA system to nicotine, eventually leading to dysfunctional behaviors observed in nicotine dependence. In particular, no information is available on the role of α6 ∗ nAChRs in nicotine-induced structural plasticity in rodents and no direct evidence exists regarding the occurrence of structural plasticity in human DA neurons exposed to nicotine. To approach this problem, we used two parallel in vitro systems, mouse primary DA neuron cultures from E12.5 embryos and human DA neurons differentiated from induced pluripotent stem cells (iPSCs) of healthy donors, identified using TH + immunoreactivity. In both systems, nicotine 1-10 μM produced a dose-dependent increase of maximal dendrite length, number of primary dendrites, and soma size when measured after 3 days in culture. These effects were blocked by pretreatments with the α6 ∗ nAChR antagonists α-conotoxin MII and α-conotoxin PIA, as well as by the α4β2nAChR antagonist dihydro-β-erythroidine (DHβE) in both mouse and human DA neurons. Nicotine was also ineffective when the primary DA neurons were obtained from null mutant mice for either the α6 subunit or both the α4 and α6 subunits of nAChR. When pregnant mice were exposed to nicotine from gestational day 15, structural plasticity was also observed in the midbrain DA neurons of postnatal day 1 offspring only in wild-type mice and not in both null mutant mice. This study confirmed the critical role of α4α6 ∗ nAChRs in mediating nicotine-induced structural plasticity in both mouse and human DA neurons, supporting the translational relevance of neurons differentiated from human iPSCs for pharmacological studies.

Operant Conditioning of Primate Prefrontal Neurons

PubMed Central

Schultz, Wolfram; Sakagami, Masamichi

2010-01-01

An operant is a behavioral act that has an impact on the environment to produce an outcome, constituting an important component of voluntary behavior. Because the environment can be volatile, the same action may cause different consequences. Thus to obtain an optimal outcome, it is crucial to detect action–outcome relationships and adapt the behavior accordingly. Although prefrontal neurons are known to change activity depending on expected reward, it remains unknown whether prefrontal activity contributes to obtaining reward. We investigated this issue by setting variable relationships between levels of single-neuron activity and rewarding outcomes. Lateral prefrontal neurons changed their spiking activity according to the specific requirements for gaining reward, without the animals making a motor response. Thus spiking activity constituted an operant response. Data from a control task suggested that these changes were unlikely to reflect simple reward predictions. These data demonstrate a remarkable capacity of prefrontal neurons to adapt to specific operant requirements at the single-neuron level. PMID:20107129

Bioorthogonal Metabolic Labeling of Nascent RNA in Neurons Improves the Sensitivity of Transcriptome-Wide Profiling.

PubMed

Zajaczkowski, Esmi L; Zhao, Qiong-Yi; Zhang, Zong Hong; Li, Xiang; Wei, Wei; Marshall, Paul R; Leighton, Laura J; Nainar, Sarah; Feng, Chao; Spitale, Robert C; Bredy, Timothy W

2018-06-15

Transcriptome-wide expression profiling of neurons has provided important insights into the underlying molecular mechanisms and gene expression patterns that transpire during learning and memory formation. However, there is a paucity of tools for profiling stimulus-induced RNA within specific neuronal cell populations. A bioorthogonal method to chemically label nascent (i.e., newly transcribed) RNA in a cell-type-specific and temporally controlled manner, which is also amenable to bioconjugation via click chemistry, was recently developed and optimized within conventional immortalized cell lines. However, its value within a more fragile and complicated cellular system such as neurons, as well as for transcriptome-wide expression profiling, has yet to be demonstrated. Here, we report the visualization and sequencing of activity-dependent nascent RNA derived from neurons using this labeling method. This work has important implications for improving transcriptome-wide expression profiling and visualization of nascent RNA in neurons, which has the potential to provide valuable insights into the mechanisms underlying neural plasticity, learning, and memory.

Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems.

PubMed

Shehzad, Danish; Bozkuş, Zeki

2016-01-01

Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks, interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI) is used. MPI_Allgather collective is exercised for spikes exchange after each interval across distributed memory systems. The increase in number of processors though results in achieving concurrency and better performance but it inversely affects MPI_Allgather which increases communication time between processors. This necessitates improving communication methodology to decrease the spikes exchange time over distributed memory systems. This work has improved MPI_Allgather method using Remote Memory Access (RMA) by moving two-sided communication to one-sided communication, and use of recursive doubling mechanism facilitates achieving efficient communication between the processors in precise steps. This approach enhanced communication concurrency and has improved overall runtime making NEURON more efficient for simulation of large neuronal network models.

Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems

PubMed Central

Bozkuş, Zeki

2016-01-01

Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks, interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI) is used. MPI_Allgather collective is exercised for spikes exchange after each interval across distributed memory systems. The increase in number of processors though results in achieving concurrency and better performance but it inversely affects MPI_Allgather which increases communication time between processors. This necessitates improving communication methodology to decrease the spikes exchange time over distributed memory systems. This work has improved MPI_Allgather method using Remote Memory Access (RMA) by moving two-sided communication to one-sided communication, and use of recursive doubling mechanism facilitates achieving efficient communication between the processors in precise steps. This approach enhanced communication concurrency and has improved overall runtime making NEURON more efficient for simulation of large neuronal network models. PMID:27413363

ATF3 expression improves motor function in the ALS mouse model by promoting motor neuron survival and retaining muscle innervation.

PubMed

Seijffers, Rhona; Zhang, Jiangwen; Matthews, Jonathan C; Chen, Adam; Tamrazian, Eric; Babaniyi, Olusegun; Selig, Martin; Hynynen, Meri; Woolf, Clifford J; Brown, Robert H

2014-01-28

ALS is a fatal neurodegenerative disease characterized by a progressive loss of motor neurons and atrophy of distal axon terminals in muscle, resulting in loss of motor function. Motor end plates denervated by axonal retraction of dying motor neurons are partially reinnervated by remaining viable motor neurons; however, this axonal sprouting is insufficient to compensate for motor neuron loss. Activating transcription factor 3 (ATF3) promotes neuronal survival and axonal growth. Here, we reveal that forced expression of ATF3 in motor neurons of transgenic SOD1(G93A) ALS mice delays neuromuscular junction denervation by inducing axonal sprouting and enhancing motor neuron viability. Maintenance of neuromuscular junction innervation during the course of the disease in ATF3/SOD1(G93A) mice is associated with a substantial delay in muscle atrophy and improved motor performance. Although disease onset and mortality are delayed, disease duration is not affected. This study shows that adaptive axonal growth-promoting mechanisms can substantially improve motor function in ALS and importantly, that augmenting viability of the motor neuron soma and maintaining functional neuromuscular junction connections are both essential elements in therapy for motor neuron disease in the SOD1(G93A) mice. Accordingly, effective protection of optimal motor neuron function requires restitution of multiple dysregulated cellular pathways.

ACTIVITY-DEPENDENT, STRESS-RESPONSIVE BDNF SIGNALING AND THE QUEST FOR OPTIMAL BRAIN HEALTH AND RESILIENCE THROUGHOUT THE LIFESPAN

PubMed Central

Rothman, S. M.; Mattson, M. P.

2013-01-01

During development of the nervous system, the formation of connections (synapses) between neurons is dependent upon electrical activity in those neurons, and neurotrophic factors produced by target cells play a pivotal role in such activity-dependent sculpting of the neural networks. A similar interplay between neurotransmitter and neurotrophic factor signaling pathways mediates adaptive responses of neural networks to environmental demands in adult mammals, with the excitatory neurotransmitter glutamate and brain-derived neurotrophic factor (BDNF) being particularly prominent regulators of synaptic plasticity throughout the central nervous system. Optimal brain health throughout the lifespan is promoted by intermittent challenges such as exercise, cognitive stimulation and dietary energy restriction, that subject neurons to activity-related metabolic stress. At the molecular level, such challenges to neurons result in the production of proteins involved in neurogenesis, learning and memory and neuronal survival; examples include proteins that regulate mitochondrial biogenesis, protein quality control, and resistance of cells to oxidative, metabolic and proteotoxic stress. BDNF signaling mediates up-regulation of several such proteins including the protein chaperone GRP-78, antioxidant enzymes, the cell survival protein Bcl-2, and the DNA repair enzyme APE1. Insufficient exposure to such challenges, genetic factors may conspire to impair BDNF production and/or signaling resulting in the vulnerability of the brain to injury and neurodegenerative disorders including Alzheimer’s, Parkinson’s and Huntington’s diseases. Further, BDNF signaling is negatively regulated by glucocorticoids. Glucocorticoids impair synaptic plasticity in the brain by negatively regulating spine density, neurogenesis and long-term potentiation, effects that are potentially linked to glucocorticoid regulation of BDNF. Findings suggest that BDNF signaling in specific brain regions mediates some of the beneficial effects of exercise and energy restriction on peripheral energy metabolism and the cardiovascular system. Collectively, the findings described in this article suggest the possibility of developing prescriptions for optimal brain health based on activity-dependent BDNF signaling. PMID:23079624

An Information Transmission Measure for the Analysis of Effective Connectivity among Cortical Neurons

PubMed Central

Law, Andrew J.; Sharma, Gaurav; Schieber, Marc H.

2014-01-01

We present a methodology for detecting effective connections between simultaneously recorded neurons using an information transmission measure to identify the presence and direction of information flow from one neuron to another. Using simulated and experimentally-measured data, we evaluate the performance of our proposed method and compare it to the traditional transfer entropy approach. In simulations, our measure of information transmission outperforms transfer entropy in identifying the effective connectivity structure of a neuron ensemble. For experimentally recorded data, where ground truth is unavailable, the proposed method also yields a more plausible connectivity structure than transfer entropy. PMID:21096617

Towards deep learning with segregated dendrites

PubMed Central

Guerguiev, Jordan; Lillicrap, Timothy P

2017-01-01

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations—the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons. PMID:29205151

Towards deep learning with segregated dendrites.

PubMed

Guerguiev, Jordan; Lillicrap, Timothy P; Richards, Blake A

2017-12-05

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations-the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons.

Optimal compensation for neuron loss

PubMed Central

Barrett, David GT; Denève, Sophie; Machens, Christian K

2016-01-01

The brain has an impressive ability to withstand neural damage. Diseases that kill neurons can go unnoticed for years, and incomplete brain lesions or silencing of neurons often fail to produce any behavioral effect. How does the brain compensate for such damage, and what are the limits of this compensation? We propose that neural circuits instantly compensate for neuron loss, thereby preserving their function as much as possible. We show that this compensation can explain changes in tuning curves induced by neuron silencing across a variety of systems, including the primary visual cortex. We find that compensatory mechanisms can be implemented through the dynamics of networks with a tight balance of excitation and inhibition, without requiring synaptic plasticity. The limits of this compensatory mechanism are reached when excitation and inhibition become unbalanced, thereby demarcating a recovery boundary, where signal representation fails and where diseases may become symptomatic. DOI: http://dx.doi.org/10.7554/eLife.12454.001 PMID:27935480

Modeling of inter-neuronal coupling medium and its impact on neuronal synchronization

PubMed Central

Iqbal, Muhammad; Hong, Keum-Shik

2017-01-01

In this paper, modeling of the coupling medium between two neurons, the effects of the model parameters on the synchronization of those neurons, and compensation of coupling strength deficiency in synchronization are studied. Our study exploits the inter-neuronal coupling medium and investigates its intrinsic properties in order to get insight into neuronal-information transmittance and, there from, brain-information processing. A novel electrical model of the coupling medium that represents a well-known RLC circuit attributable to the coupling medium’s intrinsic resistive, inductive, and capacitive properties is derived. Surprisingly, the integration of such properties reveals the existence of a natural three-term control strategy, referred to in the literature as the proportional integral derivative (PID) controller, which can be responsible for synchronization between two neurons. Consequently, brain-information processing can rely on a large number of PID controllers based on the coupling medium properties responsible for the coherent behavior of neurons in a neural network. Herein, the effects of the coupling model (or natural PID controller) parameters are studied and, further, a supervisory mechanism is proposed that follows a learning and adaptation policy based on the particle swarm optimization algorithm for compensation of the coupling strength deficiency. PMID:28486505

Single-cell transcriptional analysis of taste sensory neuron pair in Caenorhabditis elegans.

PubMed

Takayama, Jun; Faumont, Serge; Kunitomo, Hirofumi; Lockery, Shawn R; Iino, Yuichi

2010-01-01

The nervous system is composed of a wide variety of neurons. A description of the transcriptional profiles of each neuron would yield enormous information about the molecular mechanisms that define morphological or functional characteristics. Here we show that RNA isolation from single neurons is feasible by using an optimized mRNA tagging method. This method extracts transcripts in the target cells by co-immunoprecipitation of the complexes of RNA and epitope-tagged poly(A) binding protein expressed specifically in the cells. With this method and genome-wide microarray, we compared the transcriptional profiles of two functionally different neurons in the main C. elegans gustatory neuron class ASE. Eight of the 13 known subtype-specific genes were successfully detected. Additionally, we identified nine novel genes including a receptor guanylyl cyclase, secreted proteins, a TRPC channel and uncharacterized genes conserved among nematodes, suggesting the two neurons are substantially different than previously thought. The expression of these novel genes was controlled by the previously known regulatory network for subtype differentiation. We also describe unique motif organization within individual gene groups classified by the expression patterns in ASE. Our study paves the way to the complete catalog of the expression profiles of individual C. elegans neurons.

Structure, Distribution, and Function of Neuronal/Synaptic Spinules and Related Invaginating Projections

PubMed Central

Petralia, Ronald S.; Wang, Ya-Xian; Mattson, Mark P.; Yao, Pamela J.

2015-01-01

Neurons and especially their synapses often project long thin processes that can invaginate neighboring neuronal or glial cells. These “invaginating projections” can occur in almost any combination of postsynaptic, presynaptic, and glial processes. Invaginating projections provide a precise mechanism for one neuron to communicate or exchange material exclusively at a highly localized site on another neuron, e.g., to regulate synaptic plasticity. The best-known types are postsynaptic projections called “spinules” that invaginate into presynaptic terminals. Spinules seem to be most prevalent at large very active synapses. Here, we present a comprehensive review of all kinds of invaginating projections associated with both neurons in general and more specifically with synapses; we describe them in all animals including simple, basal metazoans. These structures may have evolved into more elaborate structures in some higher animal groups exhibiting greater synaptic plasticity. In addition to classic spinules and filopodial invaginations, we describe a variety of lesser-known structures such as amphid microvilli, spinules in giant mossy terminals and en marron/brush synapses, the highly specialized fish retinal spinules, the trophospongium, capitate projections, and fly gnarls, as well as examples in which the entire presynaptic or postsynaptic process is invaginated. These various invaginating projections have evolved to modify the function of a particular synapse, or to channel an effect to one specific synapse or neuron, without affecting those nearby. We discuss how they function in membrane recycling, nourishment, and cell signaling and explore how they might change in aging and disease. PMID:26007200

THE CHOICE OF OPTIMAL STRUCTURE OF ARTIFICIAL NEURAL NETWORK CLASSIFIER INTENDED FOR CLASSIFICATION OF WELDING FLAWS

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

Sikora, R.; Chady, T.; Baniukiewicz, P.

2010-02-22

Nondestructive testing and evaluation are under continuous development. Currently researches are concentrated on three main topics: advancement of existing methods, introduction of novel methods and development of artificial intelligent systems for automatic defect recognition (ADR). Automatic defect classification algorithm comprises of two main tasks: creating a defect database and preparing a defect classifier. Here, the database was built using defect features that describe all geometrical and texture properties of the defect. Almost twenty carefully selected features calculated for flaws extracted from real radiograms were used. The radiograms were obtained from shipbuilding industry and they were verified by qualified operator. Twomore » weld defect's classifiers based on artificial neural networks were proposed and compared. First model consisted of one neural network model, where each output neuron corresponded to different defect group. The second model contained five neural networks. Each neural network had one neuron on output and was responsible for detection of defects from one group. In order to evaluate the effectiveness of the neural networks classifiers, the mean square errors were calculated for test radiograms and compared.« less

The Choice of Optimal Structure of Artificial Neural Network Classifier Intended for Classification of Welding Flaws

NASA Astrophysics Data System (ADS)

Sikora, R.; Chady, T.; Baniukiewicz, P.; Caryk, M.; Piekarczyk, B.

2010-02-01

Nondestructive testing and evaluation are under continuous development. Currently researches are concentrated on three main topics: advancement of existing methods, introduction of novel methods and development of artificial intelligent systems for automatic defect recognition (ADR). Automatic defect classification algorithm comprises of two main tasks: creating a defect database and preparing a defect classifier. Here, the database was built using defect features that describe all geometrical and texture properties of the defect. Almost twenty carefully selected features calculated for flaws extracted from real radiograms were used. The radiograms were obtained from shipbuilding industry and they were verified by qualified operator. Two weld defect's classifiers based on artificial neural networks were proposed and compared. First model consisted of one neural network model, where each output neuron corresponded to different defect group. The second model contained five neural networks. Each neural network had one neuron on output and was responsible for detection of defects from one group. In order to evaluate the effectiveness of the neural networks classifiers, the mean square errors were calculated for test radiograms and compared.

Maximization of the connectivity repertoire as a statistical principle governing the shapes of dendritic arbors

PubMed Central

Wen, Quan; Stepanyants, Armen; Elston, Guy N.; Grosberg, Alexander Y.; Chklovskii, Dmitri B.

2009-01-01

The shapes of dendritic arbors are fascinating and important, yet the principles underlying these complex and diverse structures remain unclear. Here, we analyzed basal dendritic arbors of 2,171 pyramidal neurons sampled from mammalian brains and discovered 3 statistical properties: the dendritic arbor size scales with the total dendritic length, the spatial correlation of dendritic branches within an arbor has a universal functional form, and small parts of an arbor are self-similar. We proposed that these properties result from maximizing the repertoire of possible connectivity patterns between dendrites and surrounding axons while keeping the cost of dendrites low. We solved this optimization problem by drawing an analogy with maximization of the entropy for a given energy in statistical physics. The solution is consistent with the above observations and predicts scaling relations that can be tested experimentally. In addition, our theory explains why dendritic branches of pyramidal cells are distributed more sparsely than those of Purkinje cells. Our results represent a step toward a unifying view of the relationship between neuronal morphology and function. PMID:19622738

Optimization of a GCaMP calcium indicator for neural activity imaging

PubMed Central

Akerboom, Jasper; Chen, Tsai-Wen; Wardill, Trevor J.; Tian, Lin; Marvin, Jonathan S.; Mutlu, Sevinç; Calderón, Nicole Carreras; Esposti, Federico; Borghuis, Bart G.; Sun, Xiaonan Richard; Gordus, Andrew; Orger, Michael B.; Portugues, Ruben; Engert, Florian; Macklin, John J.; Filosa, Alessandro; Aggarwal, Aman; Kerr, Rex; Takagi, Ryousuke; Kracun, Sebastian; Shigetomi, Eiji; Khakh, Baljit S.; Baier, Herwig; Lagnado, Leon; Wang, Samuel S.-H.; Bargmann, Cornelia I.; Kimmel, Bruce E.; Jayaraman, Vivek; Svoboda, Karel; Kim, Douglas S.; Schreiter, Eric R.; Looger, Loren L.

2012-01-01

Genetically encoded calcium indicators (GECIs) are powerful tools for systems neuroscience. Recent efforts in protein engineering have significantly increased the performance of GECIs. The state-of-the art single-wavelength GECI, GCaMP3, has been deployed in a number of model organisms and can reliably detect three or more action potentials (APs) in short bursts in several systems in vivo. Through protein structure determination, targeted mutagenesis, high-throughput screening, and a battery of in vitro assays, we have increased the dynamic range of GCaMP3 by several-fold, creating a family of “GCaMP5” sensors. We tested GCaMP5s in several systems: cultured neurons and astrocytes, mouse retina, and in vivo in Caenorhabditis chemosensory neurons, Drosophila larval neuromuscular junction and adult antennal lobe, zebrafish retina and tectum, and mouse visual cortex. Signal-to-noise ratio was improved by at least 2–3-fold. In the visual cortex, two GCaMP5 variants detected twice as many visual stimulus-responsive cells as GCaMP3. By combining in vivo imaging with electrophysiology we show that GCaMP5 fluorescence provides a more reliable measure of neuronal activity than its predecessor GCaMP3. GCaMP5 allows more sensitive detection of neural activity in vivo and may find widespread applications for cellular imaging in general. PMID:23035093

Neuroarchitecture and neuroanatomy of the Drosophila central complex: A GAL4-based dissection of protocerebral bridge neurons and circuits.

PubMed

Wolff, Tanya; Iyer, Nirmala A; Rubin, Gerald M

2015-05-01

Insects exhibit an elaborate repertoire of behaviors in response to environmental stimuli. The central complex plays a key role in combining various modalities of sensory information with an insect's internal state and past experience to select appropriate responses. Progress has been made in understanding the broad spectrum of outputs from the central complex neuropils and circuits involved in numerous behaviors. Many resident neurons have also been identified. However, the specific roles of these intricate structures and the functional connections between them remain largely obscure. Significant gains rely on obtaining a comprehensive catalog of the neurons and associated GAL4 lines that arborize within these brain regions, and on mapping neuronal pathways connecting these structures. To this end, small populations of neurons in the Drosophila melanogaster central complex were stochastically labeled using the multicolor flip-out technique and a catalog was created of the neurons, their morphologies, trajectories, relative arrangements, and corresponding GAL4 lines. This report focuses on one structure of the central complex, the protocerebral bridge, and identifies just 17 morphologically distinct cell types that arborize in this structure. This work also provides new insights into the anatomical structure of the four components of the central complex and its accessory neuropils. Most strikingly, we found that the protocerebral bridge contains 18 glomeruli, not 16, as previously believed. Revised wiring diagrams that take into account this updated architectural design are presented. This updated map of the Drosophila central complex will facilitate a deeper behavioral and physiological dissection of this sophisticated set of structures. © 2014 Wiley Periodicals, Inc.

Action Potential Energy Efficiency Varies Among Neuron Types in Vertebrates and Invertebrates

PubMed Central

Sengupta, Biswa; Stemmler, Martin; Laughlin, Simon B.; Niven, Jeremy E.

2010-01-01

The initiation and propagation of action potentials (APs) places high demands on the energetic resources of neural tissue. Each AP forces ATP-driven ion pumps to work harder to restore the ionic concentration gradients, thus consuming more energy. Here, we ask whether the ionic currents underlying the AP can be predicted theoretically from the principle of minimum energy consumption. A long-held supposition that APs are energetically wasteful, based on theoretical analysis of the squid giant axon AP, has recently been overturned by studies that measured the currents contributing to the AP in several mammalian neurons. In the single compartment models studied here, AP energy consumption varies greatly among vertebrate and invertebrate neurons, with several mammalian neuron models using close to the capacitive minimum of energy needed. Strikingly, energy consumption can increase by more than ten-fold simply by changing the overlap of the Na+ and K+ currents during the AP without changing the APs shape. As a consequence, the height and width of the AP are poor predictors of energy consumption. In the Hodgkin–Huxley model of the squid axon, optimizing the kinetics or number of Na+ and K+ channels can whittle down the number of ATP molecules needed for each AP by a factor of four. In contrast to the squid AP, the temporal profile of the currents underlying APs of some mammalian neurons are nearly perfectly matched to the optimized properties of ionic conductances so as to minimize the ATP cost. PMID:20617202

Action potential energy efficiency varies among neuron types in vertebrates and invertebrates.

PubMed

Sengupta, Biswa; Stemmler, Martin; Laughlin, Simon B; Niven, Jeremy E

2010-07-01

The initiation and propagation of action potentials (APs) places high demands on the energetic resources of neural tissue. Each AP forces ATP-driven ion pumps to work harder to restore the ionic concentration gradients, thus consuming more energy. Here, we ask whether the ionic currents underlying the AP can be predicted theoretically from the principle of minimum energy consumption. A long-held supposition that APs are energetically wasteful, based on theoretical analysis of the squid giant axon AP, has recently been overturned by studies that measured the currents contributing to the AP in several mammalian neurons. In the single compartment models studied here, AP energy consumption varies greatly among vertebrate and invertebrate neurons, with several mammalian neuron models using close to the capacitive minimum of energy needed. Strikingly, energy consumption can increase by more than ten-fold simply by changing the overlap of the Na(+) and K(+) currents during the AP without changing the APs shape. As a consequence, the height and width of the AP are poor predictors of energy consumption. In the Hodgkin-Huxley model of the squid axon, optimizing the kinetics or number of Na(+) and K(+) channels can whittle down the number of ATP molecules needed for each AP by a factor of four. In contrast to the squid AP, the temporal profile of the currents underlying APs of some mammalian neurons are nearly perfectly matched to the optimized properties of ionic conductances so as to minimize the ATP cost.

Evaluating choices by single neurons in the frontal lobe: outcome value encoded across multiple decision variables

PubMed Central

Kennerley, Steven W.; Wallis, Jonathan D.

2009-01-01

Damage to the frontal lobe can cause severe decision-making impairments. A mechanism that may underlie this is that neurons in the frontal cortex encode many variables that contribute to the valuation of a choice, such as its costs, benefits and probability of success. However, optimal decision-making requires that one considers these variables, not only when faced with the choice, but also when evaluating the outcome of the choice, in order to adapt future behaviour appropriately. To examine the role of the frontal cortex in encoding the value of different choice outcomes, we simultaneously recorded the activity of multiple single neurons in the anterior cingulate cortex (ACC), orbitofrontal cortex (OFC) and lateral prefrontal cortex (LPFC) while subjects evaluated the outcome of choices involving manipulations of probability, payoff and cost. Frontal neurons encoded many of the parameters that enabled the calculation of the value of these variables, including the onset and offset of reward and the amount of work performed, and often encoded the value of outcomes across multiple decision variables. In addition, many neurons encoded both the predicted outcome during the choice phase of the task as well as the experienced outcome in the outcome phase of the task. These patterns of selectivity were more prevalent in ACC relative to OFC and LPFC. These results support a role for the frontal cortex, principally ACC, in selecting between choice alternatives and evaluating the outcome of that selection thereby ensuring that choices are optimal and adaptive. PMID:19453638

Commentary: physical approaches for the treatment of epilepsy: electrical and magnetic stimulation and cooling.

PubMed

Löscher, Wolfgang; Cole, Andrew J; McLean, Michael J

2009-04-01

Physical approaches for the treatment of epilepsy currently under study or development include electrical or magnetic brain stimulators and cooling devices, each of which may be implanted or applied externally. Some devices may stimulate peripheral structures, whereas others may be implanted directly into the brain. Stimulation may be delivered chronically, intermittently, or in response to either manual activation or computer-based detection of events of interest. Physical approaches may therefore ultimately be appropriate for seizure prophylaxis by causing a modification of the underlying substrate, presumably with a reduction in the intrinsic excitability of cerebral structures, or for seizure termination, by interfering with the spontaneous discharge of pathological neuronal networks. Clinical trials of device-based therapies are difficult due to ethical issues surrounding device implantation, problems with blinding, potential carryover effects that may occur in crossover designs if substrate modification occurs, and subject heterogeneity. Unresolved issues in the development of physical treatments include optimization of stimulation parameters, identification of the optimal volume of brain to be stimulated, development of adequate power supplies to stimulate the necessary areas, and a determination that stimulation itself does not promote epileptogenesis or adverse long-term effects on normal brain function.

Self-assembled nanoformulation of methylprednisolone succinate with carboxylated block copolymer for local glucocorticoid therapy.

PubMed

Kamalov, Marat I; Đặng, Trinh; Petrova, Natalia V; Laikov, Alexander V; Luong, Duong; Akhmadishina, Rezeda A; Lukashkin, Andrei N; Abdullin, Timur I

2018-04-01

A new self-assembled formulation of methylprednisolone succinate (MPS) based on a carboxylated trifunctional block copolymer of ethylene oxide and propylene oxide (TBC-COOH) was developed. TBC-COOH and MPS associated spontaneously at increased concentrations in aqueous solutions to form almost monodisperse mixed micelles (TBC-COOH/MPS) with a hydrodynamic diameter of 19.6 nm, zeta potential of -27.8 mV and optimal weight ratio ∼1:6.3. Conditions for the effective formation of TBC-COOH/MPS were elucidated by comparing copolymers and glucocorticoids with different structure. The micellar structure of TBC-COOH/MPS persisted upon dilution, temperature fluctuations and interaction with blood serum components. TBC-COOH increased antiradical activity of MPS and promoted its intrinsic cytotoxicity in vitro attributed to enhanced cellular availability of the mixed micelles. Intracellular transportation and hydrolysis of MPS were analyzed using optimized liquid chromatography tandem mass spectrometry with multiple reaction monitoring which showed increased level of both MPS and methylprednisolone in neuronal cells treated with the formulated glucocorticoid. Our results identify TBC-COOH/MPS as an advanced in situ prepared nanoformulation and encourage its further investigation for a potential local glucocorticoid therapy. Copyright © 2018 Elsevier B.V. All rights reserved.

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

Configurable hardware integrate and fire neurons for sparse approximation.

PubMed

Shapero, Samuel; Rozell, Christopher; Hasler, Paul

2013-09-01

Sparse approximation is an important optimization problem in signal and image processing applications. A Hopfield-Network-like system of integrate and fire (IF) neurons is proposed as a solution, using the Locally Competitive Algorithm (LCA) to solve an overcomplete L1 sparse approximation problem. A scalable system architecture is described, including IF neurons with a nonlinear firing function, and current-based synapses to provide linear computation. A network of 18 neurons with 12 inputs is implemented on the RASP 2.9v chip, a Field Programmable Analog Array (FPAA) with directly programmable floating gate elements. Said system uses over 1400 floating gates, the largest system programmed on a FPAA to date. The circuit successfully reproduced the outputs of a digital optimization program, converging to within 4.8% RMS, and an objective cost only 1.7% higher on average. The active circuit consumed 559 μA of current at 2.4 V and converges on solutions in 25 μs, with measurement of the converged spike rate taking an additional 1 ms. Extrapolating the scaling trends to a N=1000 node system, the spiking LCA compares favorably with state-of-the-art digital solutions, and analog solutions using a non-spiking approach. Copyright © 2013 Elsevier Ltd. All rights reserved.

Echo-level compensation and delay tuning in the auditory cortex of the mustached bat.

PubMed

Macías, Silvio; Mora, Emanuel C; Hechavarría, Julio C; Kössl, Manfred

2016-06-01

During echolocation, bats continuously perform audio-motor adjustments to optimize detection efficiency. It has been demonstrated that bats adjust the amplitude of their biosonar vocalizations (known as 'pulses') to stabilize the amplitude of the returning echo. Here, we investigated this echo-level compensation behaviour by swinging mustached bats on a pendulum towards a reflective surface. In such a situation, the bats lower the amplitude of their emitted pulses to maintain the amplitude of incoming echoes at a constant level as they approach a target. We report that cortical auditory neurons that encode target distance have receptive fields that are optimized for dealing with echo-level compensation. In most cortical delay-tuned neurons, the echo amplitude eliciting the maximum response matches the echo amplitudes measured from the bats' biosonar vocalizations while they are swung in a pendulum. In addition, neurons tuned to short target distances are maximally responsive to low pulse amplitudes while neurons tuned to long target distances respond maximally to high pulse amplitudes. Our results suggest that bats dynamically adjust biosonar pulse amplitude to match the encoding of target range and to keep the amplitude of the returning echo within the bounds of the cortical map of echo delays. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

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.

ANN-PSO Integrated Optimization Methodology for Intelligent Control of MMC Machining

NASA Astrophysics Data System (ADS)

Chandrasekaran, Muthumari; Tamang, Santosh

2017-08-01

Metal Matrix Composites (MMC) show improved properties in comparison with non-reinforced alloys and have found increased application in automotive and aerospace industries. The selection of optimum machining parameters to produce components of desired surface roughness is of great concern considering the quality and economy of manufacturing process. In this study, a surface roughness prediction model for turning Al-SiCp MMC is developed using Artificial Neural Network (ANN). Three turning parameters viz., spindle speed ( N), feed rate ( f) and depth of cut ( d) were considered as input neurons and surface roughness was an output neuron. ANN architecture having 3 -5 -1 is found to be optimum and the model predicts with an average percentage error of 7.72 %. Particle Swarm Optimization (PSO) technique is used for optimizing parameters to minimize machining time. The innovative aspect of this work is the development of an integrated ANN-PSO optimization method for intelligent control of MMC machining process applicable to manufacturing industries. The robustness of the method shows its superiority for obtaining optimum cutting parameters satisfying desired surface roughness. The method has better convergent capability with minimum number of iterations.

Mean-field equations for neuronal networks with arbitrary degree distributions.

PubMed

Nykamp, Duane Q; Friedman, Daniel; Shaker, Sammy; Shinn, Maxwell; Vella, Michael; Compte, Albert; Roxin, Alex

2017-04-01

The emergent dynamics in networks of recurrently coupled spiking neurons depends on the interplay between single-cell dynamics and network topology. Most theoretical studies on network dynamics have assumed simple topologies, such as connections that are made randomly and independently with a fixed probability (Erdös-Rényi network) (ER) or all-to-all connected networks. However, recent findings from slice experiments suggest that the actual patterns of connectivity between cortical neurons are more structured than in the ER random network. Here we explore how introducing additional higher-order statistical structure into the connectivity can affect the dynamics in neuronal networks. Specifically, we consider networks in which the number of presynaptic and postsynaptic contacts for each neuron, the degrees, are drawn from a joint degree distribution. We derive mean-field equations for a single population of homogeneous neurons and for a network of excitatory and inhibitory neurons, where the neurons can have arbitrary degree distributions. Through analysis of the mean-field equations and simulation of networks of integrate-and-fire neurons, we show that such networks have potentially much richer dynamics than an equivalent ER network. Finally, we relate the degree distributions to so-called cortical motifs.

Reduction in ins-7 gene expression in non-neuronal cells of high glucose exposed Caenorhabditis elegans protects from reactive metabolites, preserves neuronal structure and head motility, and prolongs lifespan.

PubMed

Mendler, Michael; Riedinger, Christin; Schlotterer, Andrea; Volk, Nadine; Fleming, Thomas; Herzig, Stephan; Nawroth, Peter P; Morcos, Michael

2017-02-01

Glucose derived metabolism generates reactive metabolites affecting the neuronal system and lifespan in C. elegans. Here, the role of the insulin homologue ins-7 and its downstream effectors in the generation of high glucose induced neuronal damage and shortening of lifespan was studied. In C. elegans high glucose conditions induced the expression of the insulin homologue ins-7. Abrogating ins-7 under high glucose conditions in non-neuronal cells decreased reactive oxygen species (ROS)-formation and accumulation of methylglyoxal derived advanced glycation endproducts (AGEs), prevented structural neuronal damage and normalised head motility and lifespan. The restoration of lifespan by decreased ins-7 expression was dependent on the concerted action of sod-3 and glod-4 coding for the homologues of iron-manganese superoxide dismutase and glyoxalase 1, respectively. Under high glucose conditions mitochondria-mediated oxidative stress and glycation are downstream targets of ins-7. This impairs the neuronal system and longevity via a non-neuronal/neuronal crosstalk by affecting sod-3 and glod-4, thus giving further insight into the pathophysiology of diabetic complications. Copyright © 2017 Elsevier Inc. All rights reserved.

Mean-field equations for neuronal networks with arbitrary degree distributions

NASA Astrophysics Data System (ADS)

Nykamp, Duane Q.; Friedman, Daniel; Shaker, Sammy; Shinn, Maxwell; Vella, Michael; Compte, Albert; Roxin, Alex

2017-04-01

The emergent dynamics in networks of recurrently coupled spiking neurons depends on the interplay between single-cell dynamics and network topology. Most theoretical studies on network dynamics have assumed simple topologies, such as connections that are made randomly and independently with a fixed probability (Erdös-Rényi network) (ER) or all-to-all connected networks. However, recent findings from slice experiments suggest that the actual patterns of connectivity between cortical neurons are more structured than in the ER random network. Here we explore how introducing additional higher-order statistical structure into the connectivity can affect the dynamics in neuronal networks. Specifically, we consider networks in which the number of presynaptic and postsynaptic contacts for each neuron, the degrees, are drawn from a joint degree distribution. We derive mean-field equations for a single population of homogeneous neurons and for a network of excitatory and inhibitory neurons, where the neurons can have arbitrary degree distributions. Through analysis of the mean-field equations and simulation of networks of integrate-and-fire neurons, we show that such networks have potentially much richer dynamics than an equivalent ER network. Finally, we relate the degree distributions to so-called cortical motifs.

Structural and Functional Alterations in Neocortical Circuits after Mild Traumatic Brain Injury

NASA Astrophysics Data System (ADS)

Vascak, Michal

National concern over traumatic brain injury (TBI) is growing rapidly. Recent focus is on mild TBI (mTBI), which is the most prevalent injury level in both civilian and military demographics. A preeminent sequelae of mTBI is cognitive network disruption. Advanced neuroimaging of mTBI victims supports this premise, revealing alterations in activation and structure-function of excitatory and inhibitory neuronal systems, which are essential for network processing. However, clinical neuroimaging cannot resolve the cellular and molecular substrates underlying such changes. Therefore, to understand the full scope of mTBI-induced alterations it is necessary to study cortical networks on the microscopic level, where neurons form local networks that are the fundamental computational modules supporting cognition. Recently, in a well-controlled animal model of mTBI, we demonstrated in the excitatory pyramidal neuron system, isolated diffuse axonal injury (DAI), in concert with electrophysiological abnormalities in nearby intact (non-DAI) neurons. These findings were consistent with altered axon initial segment (AIS) intrinsic activity functionally associated with structural plasticity, and/or disturbances in extrinsic systems related to parvalbumin (PV)-expressing interneurons that form GABAergic synapses along the pyramidal neuron perisomatic/AIS domains. The AIS and perisomatic GABAergic synapses are domains critical for regulating neuronal activity and E-I balance. In this dissertation, we focus on the neocortical excitatory pyramidal neuron/inhibitory PV+ interneuron local network following mTBI. Our central hypothesis is that mTBI disrupts neuronal network structure and function causing imbalance of excitatory and inhibitory systems. To address this hypothesis we exploited transgenic and cre/lox mouse models of mTBI, employing approaches that couple state-of-the-art bioimaging with electrophysiology to determine the structuralfunctional alterations of excitatory and inhibitory systems in the neocortex.

[Ultrastructural changes in the MP3 neuron of the mollusk Lymnaea stagnalis after cryopreservation of the isolated brain].

PubMed

Dmitrieva, E V; Moshkov, D A; Gakhova, E N

2006-01-01

Investigation of a possibility of long-term storage of frozen (-196 degrees C) viable neurons and nervous tissue is one of the central present day problems. In this study ultrastructural changes in neurons of frozen-thawed snail brain were examined as a function of time. We studied the influence of cryopreservation, cryoprotectant (Me2SO), cooling to 4-6 degrees C, and a prolonged incubation in physiological solution at 4-6 degrees C on dictyosomes of Golgi apparatus, endoplasmic reticulum (ER) cisternae and mitochondria. It has been found that responses of these intracellular structures of cryopreserved neurons to the above influences are similar: dissociation of Golgi dictyosomes, swelling of endoplasmic reticulum cisternae and mitochondrial cristae. Both freezing-thawing and cryoprotectant were seen to cause an increase in the number of lysosomes, liposomes, myelin-like structures, and to form large vacuoles. The structural changes in molluscan neurons caused by cryopreservation with Me2SO (2 M) were reversible.

Memory formation orchestrates the wiring of adult-born hippocampal neurons into brain circuits.

PubMed

Petsophonsakul, Petnoi; Richetin, Kevin; Andraini, Trinovita; Roybon, Laurent; Rampon, Claire

2017-08-01

During memory formation, structural rearrangements of dendritic spines provide a mean to durably modulate synaptic connectivity within neuronal networks. New neurons generated throughout the adult life in the dentate gyrus of the hippocampus contribute to learning and memory. As these neurons become incorporated into the network, they generate huge numbers of new connections that modify hippocampal circuitry and functioning. However, it is yet unclear as to how the dynamic process of memory formation influences their synaptic integration into neuronal circuits. New memories are established according to a multistep process during which new information is first acquired and then consolidated to form a stable memory trace. Upon recall, memory is transiently destabilized and vulnerable to modification. Using contextual fear conditioning, we found that learning was associated with an acceleration of dendritic spines formation of adult-born neurons, and that spine connectivity becomes strengthened after memory consolidation. Moreover, we observed that afferent connectivity onto adult-born neurons is enhanced after memory retrieval, while extinction training induces a change of spine shapes. Together, these findings reveal that the neuronal activity supporting memory processes strongly influences the structural dendritic integration of adult-born neurons into pre-existing neuronal circuits. Such change of afferent connectivity is likely to impact the overall wiring of hippocampal network, and consequently, to regulate hippocampal function.

GABA neurons are the major cell type of the nucleus reticularis thalami.

PubMed

Houser, C R; Vaughn, J E; Barber, R P; Roberts, E

1980-11-03

Glutamic acid decarboxylase (GAD), the synthesizing enzyme for the neurotransmitter gamma-aminobutyric acid (GABA), has been localized in a large number of neuronal somata within the nucleus reticularis thalami (NR) of rat brain by light microscopic immunocytochemical methods. GAD-positive staining of neuronal somata and proximal dendrites is observed in the NR of normal (untreated) rats, and this staining is substantially enhanced following colchicine injection into the lateral cerebral ventricle. GAD-positive neuronal cell bodies are prominent throughout the dorsoventral and rostrocaudal extents of the NR and, thus, form a band around the entire lateral aspect of the thalamus. In the lateral part of the NR, oval-shaped neurons with elongated GAD-positive dendritic processes are oriented parallel to the narrow axis of the NR and lie perpendicular to the penetrating fascicles of unstained thalamocortical and corticothalamic fibers. Semithin (2 micrometers) sections confirm that GAD-positive reaction product is contain within the cytoplasm of cell bodies and proximal dendrites. In addition, GAD-positive punctate structures, representing axon terminals, are present in the neuropil and, occasionally, are observed in close proximity to positively-stained neuronal somata. This finding suggests that GABA-mediated inhibition of GABA neurons may occur in the NR. The large number of GAD-positive cell bodies within the NR contrasts with a paucity of positively-stained somata in the more internally located thalamic nuclei. Within these nuclei, GAD-positive punctate structures that represent GABAergic synaptic sites are a characteristic feature. Since previous anatomical studies have demonstrated that a large proportion or reticularis neurons project into the thalamus, it is suggested that many of these GAD-positive punctate structures are the axon terminals of reticularis neurons. Through these projections, reticularis neurons may contribute to GABA-mediated inhibition within many of the thalamic nuclei.

The Adaptation of the Moth Pheromone Receptor Neuron to its Natural Stimulus

NASA Astrophysics Data System (ADS)

Kostal, Lubomir; Lansky, Petr; Rospars, Jean-Pierre

2008-07-01

We analyze the first phase of information transduction in the model of the olfactory receptor neuron of the male moth Antheraea polyphemus. We predict such stimulus characteristics that enable the system to perform optimally, i.e., to transfer as much information as possible. Few a priori constraints on the nature of stimulus and stimulus-to-signal transduction are assumed. The results are given in terms of stimulus distributions and intermittency factors which makes direct comparison with experimental data possible. Optimal stimulus is approximatelly described by exponential or log-normal probability density function which is in agreement with experiment and the predicted intermittency factors fall within the lowest range of observed values. The results are discussed with respect to electroantennogram measurements and behavioral observations.

Neurotrophins in healthy and diseased skin.

PubMed

Raap, U; Kapp, A

2010-04-01

Understanding the complex mechanism of allergic inflammatory skin diseases has been a main challenge of clinical and experimental research for years. It is well known that the inflammatory response is also controlled by tissue resident cells including neurons and structural cells. Thus, allergic inflammation triggers neuronal dysfunction and structural changes in diseased skin. Prime candidates for the interaction between immune, structural, and neuronal cells are presented by neurotrophins. Neurotrophins have initially been described for their neurotrophic capacity. However, recent evidence emerges that neurotrophins display bidirectional interaction pathways in activating structural cells, immune cells in addition to neurons. Neurotrophins including brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) are upregulated in allergic inflammatory skin diseases. Further, structural cells, neurons and tissue resident cells have not only been shown to be a target but also a source of neurotrophin. In this regard, eosinophil granulocytes which are key target effector cells in chronic inflammatory skin have been identified as a target of neurotrophins but are also capable of neurotrophin production. Thus, neuroimmune interaction mechanisms in allergic inflammatory skin display a novel pathophysiological aspect in which neurotrophins serve as prime candidates for bidirectional interaction mechanisms. In this review, we provide an actual overview of neurotrophins in healthy and diseased skin with special emphasis on atopic dermatitis and therapeutic implications.

Correlated neuronal discharges that increase coding efficiency during perceptual discrimination.

PubMed

Romo, Ranulfo; Hernández, Adrián; Zainos, Antonio; Salinas, Emilio

2003-05-22

During a sensory discrimination task, the responses of multiple sensory neurons must be combined to generate a choice. The optimal combination of responses is determined both by their dependence on the sensory stimulus and by their cofluctuations across trials-that is, the noise correlations. Positively correlated noise is considered deleterious, because it limits the coding accuracy of populations of similarly tuned neurons. However, positively correlated fluctuations between differently tuned neurons actually increase coding accuracy, because they allow the common noise to be subtracted without signal loss. This is demonstrated with data recorded from the secondary somatosensory cortex of monkeys performing a vibrotactile discrimination task. The results indicate that positive correlations are not always harmful and may be exploited by cortical networks to enhance the neural representation of features to be discriminated.

Behavioral and Single-Neuron Sensitivity to Millisecond Variations in Temporally Patterned Communication Signals

PubMed Central

Baker, Christa A.; Ma, Lisa; Casareale, Chelsea R.

2016-01-01

In many sensory pathways, central neurons serve as temporal filters for timing patterns in communication signals. However, how a population of neurons with diverse temporal filtering properties codes for natural variation in communication signals is unknown. Here we addressed this question in the weakly electric fish Brienomyrus brachyistius, which varies the time intervals between successive electric organ discharges to communicate. These fish produce an individually stereotyped signal called a scallop, which consists of a distinctive temporal pattern of ∼8–12 electric pulses. We manipulated the temporal structure of natural scallops during behavioral playback and in vivo electrophysiology experiments to probe the temporal sensitivity of scallop encoding and recognition. We found that presenting time-reversed, randomized, or jittered scallops increased behavioral response thresholds, demonstrating that fish's electric signaling behavior was sensitive to the precise temporal structure of scallops. Next, using in vivo intracellular recordings and discriminant function analysis, we found that the responses of interval-selective midbrain neurons were also sensitive to the precise temporal structure of scallops. Subthreshold changes in membrane potential recorded from single neurons discriminated natural scallops from time-reversed, randomized, and jittered sequences. Pooling the responses of multiple neurons improved the discriminability of natural sequences from temporally manipulated sequences. Finally, we found that single-neuron responses were sensitive to interindividual variation in scallop sequences, raising the question of whether fish may analyze scallop structure to gain information about the sender. Collectively, these results demonstrate that a population of interval-selective neurons can encode behaviorally relevant temporal patterns with millisecond precision. SIGNIFICANCE STATEMENT The timing patterns of action potentials, or spikes, play important roles in representing information in the nervous system. However, how these temporal patterns are recognized by downstream neurons is not well understood. Here we use the electrosensory system of mormyrid weakly electric fish to investigate how a population of neurons with diverse temporal filtering properties encodes behaviorally relevant input timing patterns, and how this relates to behavioral sensitivity. We show that fish are behaviorally sensitive to millisecond variations in natural, temporally patterned communication signals, and that the responses of individual midbrain neurons are also sensitive to variation in these patterns. In fact, the output of single neurons contains enough information to discriminate stereotyped communication signals produced by different individuals. PMID:27559179

Behavioral and Single-Neuron Sensitivity to Millisecond Variations in Temporally Patterned Communication Signals.

PubMed

Baker, Christa A; Ma, Lisa; Casareale, Chelsea R; Carlson, Bruce A

2016-08-24

In many sensory pathways, central neurons serve as temporal filters for timing patterns in communication signals. However, how a population of neurons with diverse temporal filtering properties codes for natural variation in communication signals is unknown. Here we addressed this question in the weakly electric fish Brienomyrus brachyistius, which varies the time intervals between successive electric organ discharges to communicate. These fish produce an individually stereotyped signal called a scallop, which consists of a distinctive temporal pattern of ∼8-12 electric pulses. We manipulated the temporal structure of natural scallops during behavioral playback and in vivo electrophysiology experiments to probe the temporal sensitivity of scallop encoding and recognition. We found that presenting time-reversed, randomized, or jittered scallops increased behavioral response thresholds, demonstrating that fish's electric signaling behavior was sensitive to the precise temporal structure of scallops. Next, using in vivo intracellular recordings and discriminant function analysis, we found that the responses of interval-selective midbrain neurons were also sensitive to the precise temporal structure of scallops. Subthreshold changes in membrane potential recorded from single neurons discriminated natural scallops from time-reversed, randomized, and jittered sequences. Pooling the responses of multiple neurons improved the discriminability of natural sequences from temporally manipulated sequences. Finally, we found that single-neuron responses were sensitive to interindividual variation in scallop sequences, raising the question of whether fish may analyze scallop structure to gain information about the sender. Collectively, these results demonstrate that a population of interval-selective neurons can encode behaviorally relevant temporal patterns with millisecond precision. The timing patterns of action potentials, or spikes, play important roles in representing information in the nervous system. However, how these temporal patterns are recognized by downstream neurons is not well understood. Here we use the electrosensory system of mormyrid weakly electric fish to investigate how a population of neurons with diverse temporal filtering properties encodes behaviorally relevant input timing patterns, and how this relates to behavioral sensitivity. We show that fish are behaviorally sensitive to millisecond variations in natural, temporally patterned communication signals, and that the responses of individual midbrain neurons are also sensitive to variation in these patterns. In fact, the output of single neurons contains enough information to discriminate stereotyped communication signals produced by different individuals. Copyright © 2016 the authors 0270-6474/16/368985-16$15.00/0.

Alzheimer's disease Braak Stage progressions: reexamined and redefined as Borrelia infection transmission through neural circuits.

PubMed

MacDonald, Alan B

2007-01-01

Brain structure in health is a dynamic energized equation incorporating chemistry, neuronal structure, and circuitry components. The chemistry "piece" is represented by multiple neurotransmitters such as Acetylcholine, Serotonin, and Dopamine. The neuronal structure "piece" incorporates synapses and their connections. And finally circuits of neurons establish "architectural blueprints" of anatomic wiring diagrams of the higher order of brain neuron organizations. In Alzheimer's disease, there are progressive losses in all of these components. Brain structure crumbles. The deterioration in Alzheimer's is ordered, reproducible, and stepwise. Drs. Braak and Braak have described stages in the Alzheimer disease continuum. "Progressions" through Braak Stages benchmark "Regressions" in Cognitive function. Under the microscope, the Stages of Braak commence in brain regions near to the hippocampus, and over time, like a tsunami wave of destruction, overturn healthy brain regions, with neurofibrillary tangle damaged neurons "marching" through the temporal lobe, neocortex and occipital cortex. In effect the destruction ascends from the limbic regions to progressively destroy the higher brain centers. Rabies infection also "begins low and finishes high" in its wave of destruction of brain tissue. Herpes Zoster infections offer the paradigm of clinical latency of infection inside of nerves before the "marching commences". Varicella Zoster virus enters neurons in the pediatric years. Dormant virus remains inside the neurons for 50-80 years, tissue damage late in life (shingles) demonstrates the "march of the infection" down neural pathways (dermatomes) as linear areas of painful blisters loaded with virus from a childhood infection. Amalgamation of Zoster with Rabies models produces a hybrid model to explain all of the Braak Stages of Alzheimer's disease under a new paradigm, namely "Alzheimer's neuroborreliosis" in which latent Borrelia infections ascend neural circuits through the hippocampus to the higher brain centers, creating a trail of neurofibrillary tangle injured neurons in neural circuits of cholinergic neurons by transsynaptic transmission of infection from nerve to nerve.

A proposed model for small-world structural organization of mission teams and tasks in order to optimize efficiency and minimize costs

NASA Astrophysics Data System (ADS)

Ribeiro, André S.; Almeida, Miguel

2003-11-01

We propose a model of structural organization and intercommunication between all elements of every team involved in the development of a space probe to improve efficiency. Such structure is built to minimize path between any two elements, allowing fast information flow in the structure. Structures are usually very clustered inside each task team but only the heads of departments, or occasional meetings, usually assure the links between team elements. This is responsible for a lack of information exchange between staff members of each team. We propose the establishment of permanent small working groups of staff elements from different teams, in a random but permanent basis. The elements chosen for such connections establishment can be chosen in a temporary basis, but the connections must exist permanently because only with permanent connections can information flow when needed. A few of such random connections between staff members will diminish the average path length, between any two elements of any team, for information exchange. A small world structure will emerge with low internal energy costs, which is the structure used by biological neuronal systems.

A proposed model for small-world structural organization of mission teams and tasks in order to optimize efficiency and minimize costs

NASA Astrophysics Data System (ADS)

Ribeiro, André S.; Almeida, Miguel

2006-10-01

We propose a model of structural organization and intercommunication between all elements of every team involved in the development of a space probe to improve efficiency. Such structure is built to minimize path between any two elements, allowing fast information flow in the structure. Structures are usually very clustered inside each task team but only the heads of departments, or occasional meetings, usually assure the links between team elements. This is responsible for a lack of information exchange between staff members of each team. We propose the establishment of permanent small working groups of staff elements from different teams, in a random but permanent basis. The elements chosen for such connections establishment can be chosen on a temporary basis, but the connections must exist permanently because only with permanent connections can information flow when needed. A few of such random connections between staff members will diminish the average path length, between any two elements of any team, for information exchange. A small world structure will emerge with low internal energy costs, which is the structure used by biological neuronal systems.

Characterizing Responses of Translation Invariant Neurons to Natural Stimuli: Maximally Informative Invariant Dimensions

PubMed Central

Eickenberg, Michael; Rowekamp, Ryan J.; Kouh, Minjoon; Sharpee, Tatyana O.

2012-01-01

Our visual system is capable of recognizing complex objects even when their appearances change drastically under various viewing conditions. Especially in the higher cortical areas, the sensory neurons reflect such functional capacity in their selectivity for complex visual features and invariance to certain object transformations, such as image translation. Due to the strong nonlinearities necessary to achieve both the selectivity and invariance, characterizing and predicting the response patterns of these neurons represents a formidable computational challenge. A related problem is that such neurons are poorly driven by randomized inputs, such as white noise, and respond strongly only to stimuli with complex high-order correlations, such as natural stimuli. Here we describe a novel two-step optimization technique that can characterize both the shape selectivity and the range and coarseness of position invariance from neural responses to natural stimuli. One step in the optimization involves finding the template as the maximally informative dimension given the estimated spatial location where the response could have been triggered within each image. The estimates of the locations that triggered the response are subsequently updated in the next step. Under the assumption of a monotonic relationship between the firing rate and stimulus projections on the template at a given position, the most likely location is the one that has the largest projection on the estimate of the template. The algorithm shows quick convergence during optimization, and the estimation results are reliable even in the regime of small signal-to-noise ratios. When we apply the algorithm to responses of complex cells in the primary visual cortex (V1) to natural movies, we find that responses of the majority of cells were significantly better described by translation invariant models based on one template compared with position-specific models with several relevant features. PMID:22734487

Influence of highly distinctive structural properties on the excitability of pyramidal neurons in monkey visual and prefrontal cortices

PubMed Central

Amatrudo, Joseph M.; Weaver, Christina M.; Crimins, Johanna L.; Hof, Patrick R.; Rosene, Douglas L.; Luebke, Jennifer I.

2012-01-01

Whole-cell patch-clamp recordings and high-resolution 3D morphometric analyses of layer 3 pyramidal neurons in in vitro slices of monkey primary visual cortex (V1) and dorsolateral granular prefrontal cortex (dlPFC) revealed that neurons in these two brain areas possess highly distinctive structural and functional properties. Area V1 pyramidal neurons are much smaller than dlPFC neurons, with significantly less extensive dendritic arbors and far fewer dendritic spines. Relative to dlPFC neurons, V1 neurons have a significantly higher input resistance, depolarized resting membrane potential and higher action potential (AP) firing rates. Most V1 neurons exhibit both phasic and regular-spiking tonic AP firing patterns, while dlPFC neurons exhibit only tonic firing. Spontaneous postsynaptic currents are lower in amplitude and have faster kinetics in V1 than in dlPFC neurons, but are no different in frequency. Three-dimensional reconstructions of V1 and dlPFC neurons were incorporated into computational models containing Hodgkin-Huxley and AMPA- and GABAA-receptor gated channels. Morphology alone largely accounted for observed passive physiological properties, but led to AP firing rates that differed more than observed empirically, and to synaptic responses that opposed empirical results. Accordingly, modeling predicts that active channel conductances differ between V1 and dlPFC neurons. The unique features of V1 and dlPFC neurons are likely fundamental determinants of area-specific network behavior. The compact electrotonic arbor and increased excitability of V1 neurons support the rapid signal integration required for early processing of visual information. The greater connectivity and dendritic complexity of dlPFC neurons likely support higher level cognitive functions including working memory and planning. PMID:23035077

The structure and function of serially homologous leg motor neurons in the locust. I. Anatomy.

PubMed

Wilson, J A

1979-01-01

Twenty-one prothoracic and 17 mesothoracic motor neurons innervating leg muscles have been identified physiologically and subsequently injected with dye from a microelectrode. A tract containing the primary neurites of motor neurons innervating the retractor unquis, levator and depressor tarsus, flexor tibiae, and reductor femora is described. All motor neurons studied have regions in which their dendritic branches overlap with those of other leg motor neurons. Identified, serially homologous motor neurons in the three thoracic ganglia were found to have: (1) cell bodies at similar locations and morphologically similar primary neurites (e.g., flexor tibiae motor neurons), (2) cell bodies at different locations in each ganglion and morphologically different primary neurites in each ganglion (e.g., fast retractor unguis motor neurons), or (3) cell bodies at similar locations and morphologically similar primary neurites but with a functional switch in one ganglion relative to the function of the neurons in the other two ganglia. As an example of the latter, the morphology of the metathoracic slow extensor tibiae (SETi) motor neurons was similar to that of pro- and mesothoracic fast extensor tibiae (FETi) motor neurons. Similarly the metathoracic FETi bears a striking resemblance to the pro- and the mesothoracic SETi. It is proposed that in the metathoracic ganglion the two extensor tibiae motor neurons have switched functions while retaining similar morphologies relative to the structure and function of their pro- and mesothoracic serial homologues.

A Simple Method to Simultaneously Detect and Identify Spikes from Raw Extracellular Recordings.

PubMed

Petrantonakis, Panagiotis C; Poirazi, Panayiota

2015-01-01

The ability to track when and which neurons fire in the vicinity of an electrode, in an efficient and reliable manner can revolutionize the neuroscience field. The current bottleneck lies in spike sorting algorithms; existing methods for detecting and discriminating the activity of multiple neurons rely on inefficient, multi-step processing of extracellular recordings. In this work, we show that a single-step processing of raw (unfiltered) extracellular signals is sufficient for both the detection and identification of active neurons, thus greatly simplifying and optimizing the spike sorting approach. The efficiency and reliability of our method is demonstrated in both real and simulated data.

Brain-derived neurotrophic factor/neurotrophin 3 regulate axon initial segment location and affect neuronal excitability in cultured hippocampal neurons.

PubMed

Guo, Yu; Su, Zi-Jun; Chen, Yi-Kun; Chai, Zhen

2017-07-01

Plasticity of the axon initial segment (AIS) has aroused great interest in recent years because it regulates action potential initiation and neuronal excitability. AIS plasticity manifests as modulation of ion channels or variation in AIS structure. However, the mechanisms underlying structural plasticity of the AIS are not well understood. Here, we combined immunofluorescence, patch-clamp recordings, and pharmacological methods in cultured hippocampal neurons to investigate the factors participating in AIS structural plasticity during development. With lowered neuronal density, the distance between the AIS and the soma increased, while neuronal excitability decreased, as shown by the increased action potential threshold and current threshold for firing an action potential. This variation in the location of the AIS was associated with cellular secretory substances, including brain-derived neurotrophic factor (BDNF) and neurotrophin 3 (NT3). Indeed, blocking BDNF and NT3 with TrkB-Fc eliminated the effect of conditioned medium collected from high-density cultures on AIS relocation. Elevating the extracellular concentration of BDNF or NT3 promoted movement of the AIS proximally to the soma and increased neuronal excitability. Furthermore, knockdown of neurotrophin receptors TrkB and TrkC caused distal movement of the AIS. Our results demonstrate that BDNF and NT3 regulate AIS location and neuronal excitability. These regulatory functions of neurotrophic factors provide insight into the molecular mechanisms underlying AIS biology. © 2017 International Society for Neurochemistry.

A neural network construction method for surrogate modeling of physics-based analysis

NASA Astrophysics Data System (ADS)

Sung, Woong Je

In this thesis existing methodologies related to the developmental methods of neural networks have been surveyed and their approaches to network sizing and structuring are carefully observed. This literature review covers the constructive methods, the pruning methods, and the evolutionary methods and questions about the basic assumption intrinsic to the conventional neural network learning paradigm, which is primarily devoted to optimization of connection weights (or synaptic strengths) for the pre-determined connection structure of the network. The main research hypothesis governing this thesis is that, without breaking a prevailing dichotomy between weights and connectivity of the network during learning phase, the efficient design of a task-specific neural network is hard to achieve because, as long as connectivity and weights are searched by separate means, a structural optimization of the neural network requires either repetitive re-training procedures or computationally expensive topological meta-search cycles. The main contribution of this thesis is designing and testing a novel learning mechanism which efficiently learns not only weight parameters but also connection structure from a given training data set, and positioning this learning mechanism within the surrogate modeling practice. In this work, a simple and straightforward extension to the conventional error Back-Propagation (BP) algorithm has been formulated to enable a simultaneous learning for both connectivity and weights of the Generalized Multilayer Perceptron (GMLP) in supervised learning tasks. A particular objective is to achieve a task-specific network having reasonable generalization performance with a minimal training time. The dichotomy between architectural design and weight optimization is reconciled by a mechanism establishing a new connection for a neuron pair which has potentially higher error-gradient than one of the existing connections. Interpreting an instance of the absence of connection as a zero-weight connection, the potential contribution to training error reduction of any present or absent connection can readily be evaluated using the BP algorithm. Instead of being broken, the connections that contribute less remain frozen with constant weight values optimized to that point but they are excluded from further weight optimization until reselected. In this way, a selective weight optimization is executed only for the dynamically maintained pool of high gradient connections. By searching the rapidly changing weights and concentrating optimization resources on them, the learning process is accelerated without either a significant increase in computational cost or a need for re-training. This results in a more task-adapted network connection structure. Combined with another important criterion for the division of a neuron which adds a new computational unit to a network, a highly fitted network can be grown out of the minimal random structure. This particular learning strategy can belong to a more broad class of the variable connectivity learning scheme and the devised algorithm has been named Optimal Brain Growth (OBG). The OBG algorithm has been tested on two canonical problems; a regression analysis using the Complicated Interaction Regression Function and a classification of the Two-Spiral Problem. A comparative study with conventional Multilayer Perceptrons (MLPs) consisting of single- and double-hidden layers shows that OBG is less sensitive to random initial conditions and generalizes better with only a minimal increase in computational time. This partially proves that a variable connectivity learning scheme has great potential to enhance computational efficiency and reduce efforts to select proper network architecture. To investigate the applicability of the OBG to more practical surrogate modeling tasks, the geometry-to-pressure mapping of a particular class of airfoils in the transonic flow regime has been sought using both the conventional MLP networks with pre-defined architecture and the OBG-developed networks started from the same initial MLP networks. Considering wide variety in airfoil geometry and diversity of flow conditions distributed over a range of flow Mach numbers and angles of attack, the new method shows a great potential to capture fundamentally nonlinear flow phenomena especially related to the occurrence of shock waves on airfoil surfaces in transonic flow regime. (Abstract shortened by UMI.).

Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces.

PubMed

Benyamini, Miri; Zacksenhouse, Miriam

2015-01-01

Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.

Optimal feedback control successfully explains changes in neural modulations during experiments with brain-machine interfaces

PubMed Central

Benyamini, Miri; Zacksenhouse, Miriam

2015-01-01

Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal. PMID:26042002

Neuroarchitecture and neuroanatomy of the Drosophila central complex: A GAL4-based dissection of protocerebral bridge neurons and circuits

PubMed Central

Wolff, Tanya; Iyer, Nirmala A; Rubin, Gerald M

2015-01-01

Insects exhibit an elaborate repertoire of behaviors in response to environmental stimuli. The central complex plays a key role in combining various modalities of sensory information with an insect's internal state and past experience to select appropriate responses. Progress has been made in understanding the broad spectrum of outputs from the central complex neuropils and circuits involved in numerous behaviors. Many resident neurons have also been identified. However, the specific roles of these intricate structures and the functional connections between them remain largely obscure. Significant gains rely on obtaining a comprehensive catalog of the neurons and associated GAL4 lines that arborize within these brain regions, and on mapping neuronal pathways connecting these structures. To this end, small populations of neurons in the Drosophila melanogaster central complex were stochastically labeled using the multicolor flip-out technique and a catalog was created of the neurons, their morphologies, trajectories, relative arrangements, and corresponding GAL4 lines. This report focuses on one structure of the central complex, the protocerebral bridge, and identifies just 17 morphologically distinct cell types that arborize in this structure. This work also provides new insights into the anatomical structure of the four components of the central complex and its accessory neuropils. Most strikingly, we found that the protocerebral bridge contains 18 glomeruli, not 16, as previously believed. Revised wiring diagrams that take into account this updated architectural design are presented. This updated map of the Drosophila central complex will facilitate a deeper behavioral and physiological dissection of this sophisticated set of structures. J. Comp. Neurol. 523:997–1037, 2015. © 2014 Wiley Periodicals, Inc. PMID:25380328

Neurotrophic requirements of human motor neurons defined using amplified and purified stem cell-derived cultures.

PubMed

Lamas, Nuno Jorge; Johnson-Kerner, Bethany; Roybon, Laurent; Kim, Yoon A; Garcia-Diaz, Alejandro; Wichterle, Hynek; Henderson, Christopher E

2014-01-01

Human motor neurons derived from embryonic and induced pluripotent stem cells (hESCs and hiPSCs) are a potentially important tool for studying motor neuron survival and pathological cell death. However, their basic survival requirements remain poorly characterized. Here, we sought to optimize a robust survival assay and characterize their response to different neurotrophic factors. First, to increase motor neuron yield, we screened a small-molecule collection and found that the Rho-associated kinase (ROCK) inhibitor Y-27632 enhances motor neuron progenitor proliferation up to 4-fold in hESC and hiPSC cultures. Next, we FACS-purified motor neurons expressing the Hb9::GFP reporter from Y-27632-amplified embryoid bodies and cultured them in the presence of mitotic inhibitors to eliminate dividing progenitors. Survival of these purified motor neurons in the absence of any other cell type was strongly dependent on neurotrophic support. GDNF, BDNF and CNTF all showed potent survival effects (EC(50) 1-2 pM). The number of surviving motor neurons was further enhanced in the presence of forskolin and IBMX, agents that increase endogenous cAMP levels. As a demonstration of the ability of the assay to detect novel neurotrophic agents, Y-27632 itself was found to support human motor neuron survival. Thus, purified human stem cell-derived motor neurons show survival requirements similar to those of primary rodent motor neurons and can be used for rigorous cell-based screening.

Neurotrophic Requirements of Human Motor Neurons Defined Using Amplified and Purified Stem Cell-Derived Cultures

PubMed Central

Lamas, Nuno Jorge; Johnson-Kerner, Bethany; Roybon, Laurent; Kim, Yoon A.; Garcia-Diaz, Alejandro; Wichterle, Hynek; Henderson, Christopher E.

2014-01-01

Human motor neurons derived from embryonic and induced pluripotent stem cells (hESCs and hiPSCs) are a potentially important tool for studying motor neuron survival and pathological cell death. However, their basic survival requirements remain poorly characterized. Here, we sought to optimize a robust survival assay and characterize their response to different neurotrophic factors. First, to increase motor neuron yield, we screened a small-molecule collection and found that the Rho-associated kinase (ROCK) inhibitor Y-27632 enhances motor neuron progenitor proliferation up to 4-fold in hESC and hiPSC cultures. Next, we FACS-purified motor neurons expressing the Hb9::GFP reporter from Y-27632-amplified embryoid bodies and cultured them in the presence of mitotic inhibitors to eliminate dividing progenitors. Survival of these purified motor neurons in the absence of any other cell type was strongly dependent on neurotrophic support. GDNF, BDNF and CNTF all showed potent survival effects (EC50 1–2 pM). The number of surviving motor neurons was further enhanced in the presence of forskolin and IBMX, agents that increase endogenous cAMP levels. As a demonstration of the ability of the assay to detect novel neurotrophic agents, Y-27632 itself was found to support human motor neuron survival. Thus, purified human stem cell-derived motor neurons show survival requirements similar to those of primary rodent motor neurons and can be used for rigorous cell-based screening. PMID:25337699

Effect of the heterogeneous neuron and information transmission delay on stochastic resonance of neuronal networks

NASA Astrophysics Data System (ADS)

Wang, Qingyun; Zhang, Honghui; Chen, Guanrong

2012-12-01

We study the effect of heterogeneous neuron and information transmission delay on stochastic resonance of scale-free neuronal networks. For this purpose, we introduce the heterogeneity to the specified neuron with the highest degree. It is shown that in the absence of delay, an intermediate noise level can optimally assist spike firings of collective neurons so as to achieve stochastic resonance on scale-free neuronal networks for small and intermediate αh, which plays a heterogeneous role. Maxima of stochastic resonance measure are enhanced as αh increases, which implies that the heterogeneity can improve stochastic resonance. However, as αh is beyond a certain large value, no obvious stochastic resonance can be observed. If the information transmission delay is introduced to neuronal networks, stochastic resonance is dramatically affected. In particular, the tuned information transmission delay can induce multiple stochastic resonance, which can be manifested as well-expressed maximum in the measure for stochastic resonance, appearing every multiple of one half of the subthreshold stimulus period. Furthermore, we can observe that stochastic resonance at odd multiple of one half of the subthreshold stimulus period is subharmonic, as opposed to the case of even multiple of one half of the subthreshold stimulus period. More interestingly, multiple stochastic resonance can also be improved by the suitable heterogeneous neuron. Presented results can provide good insights into the understanding of the heterogeneous neuron and information transmission delay on realistic neuronal networks.

Scaling of Brain Metabolism with a Fixed Energy Budget per Neuron: Implications for Neuronal Activity, Plasticity and Evolution

PubMed Central

Herculano-Houzel, Suzana

2011-01-01

It is usually considered that larger brains have larger neurons, which consume more energy individually, and are therefore accompanied by a larger number of glial cells per neuron. These notions, however, have never been tested. Based on glucose and oxygen metabolic rates in awake animals and their recently determined numbers of neurons, here I show that, contrary to the expected, the estimated glucose use per neuron is remarkably constant, varying only by 40% across the six species of rodents and primates (including humans). The estimated average glucose use per neuron does not correlate with neuronal density in any structure. This suggests that the energy budget of the whole brain per neuron is fixed across species and brain sizes, such that total glucose use by the brain as a whole, by the cerebral cortex and also by the cerebellum alone are linear functions of the number of neurons in the structures across the species (although the average glucose consumption per neuron is at least 10× higher in the cerebral cortex than in the cerebellum). These results indicate that the apparently remarkable use in humans of 20% of the whole body energy budget by a brain that represents only 2% of body mass is explained simply by its large number of neurons. Because synaptic activity is considered the major determinant of metabolic cost, a conserved energy budget per neuron has several profound implications for synaptic homeostasis and the regulation of firing rates, synaptic plasticity, brain imaging, pathologies, and for brain scaling in evolution. PMID:21390261

Scaling of brain metabolism with a fixed energy budget per neuron: implications for neuronal activity, plasticity and evolution.

PubMed

Herculano-Houzel, Suzana

2011-03-01

It is usually considered that larger brains have larger neurons, which consume more energy individually, and are therefore accompanied by a larger number of glial cells per neuron. These notions, however, have never been tested. Based on glucose and oxygen metabolic rates in awake animals and their recently determined numbers of neurons, here I show that, contrary to the expected, the estimated glucose use per neuron is remarkably constant, varying only by 40% across the six species of rodents and primates (including humans). The estimated average glucose use per neuron does not correlate with neuronal density in any structure. This suggests that the energy budget of the whole brain per neuron is fixed across species and brain sizes, such that total glucose use by the brain as a whole, by the cerebral cortex and also by the cerebellum alone are linear functions of the number of neurons in the structures across the species (although the average glucose consumption per neuron is at least 10× higher in the cerebral cortex than in the cerebellum). These results indicate that the apparently remarkable use in humans of 20% of the whole body energy budget by a brain that represents only 2% of body mass is explained simply by its large number of neurons. Because synaptic activity is considered the major determinant of metabolic cost, a conserved energy budget per neuron has several profound implications for synaptic homeostasis and the regulation of firing rates, synaptic plasticity, brain imaging, pathologies, and for brain scaling in evolution.

Development of serotonin-like immunoreactivity in the embryos and larvae of nudibranch mollusks with emphasis on the structure and possible function of the apical sensory organ.

PubMed

Kempf, S C; Page, L R; Pires, A

1997-09-29

This investigation provides a light and electron microscopic examination of the development of serotonin-like immunoreactivity and structure of the apical sensory organ (ASO) in embryos and/or larvae of four nudibranch species: Berghia verrucicornis, Phestilla sibogae, Melibe leonina, and Tritonia diomedea. Serotonin-like immunoreactivity is first expressed in somata, dendrites, and axons of a group of five distinct neurons within the ASO. These neurons extend axons into an apical neuropil, a structure that is situated centrally and immediately dorsal to the cerebral commissure. Three of these neurons possess sensory dendrites that extend through the pretrochal epithelium, each supporting two cilia at their distal ends. Later development of serotonin-like immunoreactivity includes 1) axons from the apical neuropil that extend into each of the velar lobes; 2) neuron perikarya in the cerebral and pedal ganglia; 3) axons that extend through the cerebral commissure, cerebral-pedal connectives, pedal commissure, and possibly the visceral loop connective; and 4) axons extending from each pedal ganglion into the larval foot. Ultrastructurally, the ASO can be seen to be composed of three lobes and an apical neuropil that is separately delineated from the cerebral commissure. Four cell types are present within the ASO: ciliary tuft cells, type I and type II parampullary neurons, and ampullary neurons. Immunofluorescence and 3,3' diaminobenzidine tetrahydrochloride (DAB) labeling verify that the serotonergic neurons of the ASO are type I and type II parampullary neurons. The ampullary and type I parampullary neurons possess dendrites that extend through the pretrochal epithelium. These dendrites are partitioned into three bundles, one on either side of the ciliary tuft cells and a third bundle penetrating the pretrochal epithelium centrally between the ciliary tuft cells. One serotonergic type I parampullary neuron is associated with each of these bundles. Two ampullary neurons are associated with each of the lateral dendritic bundles, while the central bundle includes only one. Ultrastructural analyses of serotonergic axonal innervation arising from the ASO agree with those determined from fluorescently labeled material. The structure of the ASO and its associated serotonergic axons suggest that the serotonergic component of this structure senses environmental stimuli affecting velar function, possibly the contractility of muscle fibers in the velar lobes. Similarities and differences among the ASOs of embryos and larvae from various invertebrate phyla may provide useful data that will assist in the reconstruction of phylogenetic relationships.

Structural connotations of bioactivity in a series of organophosphinates

NASA Astrophysics Data System (ADS)

King, James W.; Molnar, Stephen P.

Pretreatment before exposure is one of the options for temporarily protecting persons liable to exposure to toxic organophosphorus compounds in agricultural or warfare situations. It is known that organophosphinates interact with neuronal cholinesterases, but that the latter may spontaneously reactivate in time. Before that reactivation, the enzyme is protected against comlexation with organophosphates. In this study, geometrically optimized unitary molecular indices, i.e., the molecular transforms, FTm, FTe, and FTc, indicating general, electronic, and charge properties, respectively, and the analogous normalized molecular moments, Mn, Me, and Mc, were calculated for a number of phosphinates. These indices were subsequently used in correlation trials with spontaneous reactivation percentages at specific elapsed times, as well as in clustering procedures, to evaluate the effect of structure variations on the reactivation percentages. The results of these studies are discussed, as is the effect of the octanol/water partition coefficient on the noted bioactivity.

Protecting Neural Structures and Cognitive Function During Prolonged Space Flight by Targeting the Brain Derived Neurotrophic Factor Molecular Network

NASA Technical Reports Server (NTRS)

Schmidt, M. A.; Goodwin, T. J.

2014-01-01

Brain derived neurotrophic factor (BDNF) is the main activity-dependent neurotrophin in the human nervous system. BDNF is implicated in production of new neurons from dentate gyrus stem cells (hippocampal neurogenesis), synapse formation, sprouting of new axons, growth of new axons, sprouting of new dendrites, and neuron survival. Alterations in the amount or activity of BDNF can produce significant detrimental changes to cortical function and synaptic transmission in the human brain. This can result in glial and neuronal dysfunction, which may contribute to a range of clinical conditions, spanning a number of learning, behavioral, and neurological disorders. There is an extensive body of work surrounding the BDNF molecular network, including BDNF gene polymorphisms, methylated BDNF gene promoters, multiple gene transcripts, varied BDNF functional proteins, and different BDNF receptors (whose activation differentially drive the neuron to neurogenesis or apoptosis). BDNF is also closely linked to mitochondrial biogenesis through PGC-1alpha, which can influence brain and muscle metabolic efficiency. BDNF AS A HUMAN SPACE FLIGHT COUNTERMEASURE TARGET Earth-based studies reveal that BDNF is negatively impacted by many of the conditions encountered in the space environment, including oxidative stress, radiation, psychological stressors, sleep deprivation, and many others. A growing body of work suggests that the BDNF network is responsive to a range of diet, nutrition, exercise, drug, and other types of influences. This section explores the BDNF network in the context of 1) protecting the brain and nervous system in the space environment, 2) optimizing neurobehavioral performance in space, and 3) reducing the residual effects of space flight on the nervous system on return to Earth

Content-based retrieval using MPEG-7 visual descriptor and hippocampal neural network

NASA Astrophysics Data System (ADS)

Kim, Young Ho; Joung, Lyang-Jae; Kang, Dae-Seong

2005-12-01

As development of digital technology, many kinds of multimedia data are used variously and requirements for effective use by user are increasing. In order to transfer information fast and precisely what user wants, effective retrieval method is required. As existing multimedia data are impossible to apply the MPEG-1, MPEG-2 and MPEG-4 technologies which are aimed at compression, store and transmission. So MPEG-7 is introduced as a new technology for effective management and retrieval for multimedia data. In this paper, we extract content-based features using color descriptor among the MPEG-7 standardization visual descriptor, and reduce feature data applying PCA(Principal Components Analysis) technique. We remodel the cerebral cortex and hippocampal neural networks as a principle of a human's brain and it can label the features of the image-data which are inputted according to the order of hippocampal neuron structure to reaction-pattern according to the adjustment of a good impression in Dentate gyrus region and remove the noise through the auto-associate- memory step in the CA3 region. In the CA1 region receiving the information of the CA3, it can make long-term or short-term memory learned by neuron. Hippocampal neural network makes neuron of the neural network separate and combine dynamically, expand the neuron attaching additional information using the synapse and add new features according to the situation by user's demand. When user is querying, it compares feature value stored in long-term memory first and it learns feature vector fast and construct optimized feature. So the speed of index and retrieval is fast. Also, it uses MPEG-7 standard visual descriptors as content-based feature value, it improves retrieval efficiency.

Pyramidal Cells in Prefrontal Cortex of Primates: Marked Differences in Neuronal Structure Among Species

PubMed Central

Elston, Guy N.; Benavides-Piccione, Ruth; Elston, Alejandra; Manger, Paul R.; DeFelipe, Javier

2010-01-01

The most ubiquitous neuron in the cerebral cortex, the pyramidal cell, is characterized by markedly different dendritic structure among different cortical areas. The complex pyramidal cell phenotype in granular prefrontal cortex (gPFC) of higher primates endows specific biophysical properties and patterns of connectivity, which differ from those in other cortical regions. However, within the gPFC, data have been sampled from only a select few cortical areas. The gPFC of species such as human and macaque monkey includes more than 10 cortical areas. It remains unknown as to what degree pyramidal cell structure may vary among these cortical areas. Here we undertook a survey of pyramidal cells in the dorsolateral, medial, and orbital gPFC of cercopithecid primates. We found marked heterogeneity in pyramidal cell structure within and between these regions. Moreover, trends for gradients in neuronal complexity varied among species. As the structure of neurons determines their computational abilities, memory storage capacity and connectivity, we propose that these specializations in the pyramidal cell phenotype are an important determinant of species-specific executive cortical functions in primates. PMID:21347276

Harmonic template neurons in primate auditory cortex underlying complex sound processing

PubMed Central

Feng, Lei

2017-01-01

Harmonicity is a fundamental element of music, speech, and animal vocalizations. How the auditory system extracts harmonic structures embedded in complex sounds and uses them to form a coherent unitary entity is not fully understood. Despite the prevalence of sounds rich in harmonic structures in our everyday hearing environment, it has remained largely unknown what neural mechanisms are used by the primate auditory cortex to extract these biologically important acoustic structures. In this study, we discovered a unique class of harmonic template neurons in the core region of auditory cortex of a highly vocal New World primate, the common marmoset (Callithrix jacchus), across the entire hearing frequency range. Marmosets have a rich vocal repertoire and a similar hearing range to that of humans. Responses of these neurons show nonlinear facilitation to harmonic complex sounds over inharmonic sounds, selectivity for particular harmonic structures beyond two-tone combinations, and sensitivity to harmonic number and spectral regularity. Our findings suggest that the harmonic template neurons in auditory cortex may play an important role in processing sounds with harmonic structures, such as animal vocalizations, human speech, and music. PMID:28096341

The connection-set algebra--a novel formalism for the representation of connectivity structure in neuronal network models.

PubMed

Djurfeldt, Mikael

2012-07-01

The connection-set algebra (CSA) is a novel and general formalism for the description of connectivity in neuronal network models, from small-scale to large-scale structure. The algebra provides operators to form more complex sets of connections from simpler ones and also provides parameterization of such sets. CSA is expressive enough to describe a wide range of connection patterns, including multiple types of random and/or geometrically dependent connectivity, and can serve as a concise notation for network structure in scientific writing. CSA implementations allow for scalable and efficient representation of connectivity in parallel neuronal network simulators and could even allow for avoiding explicit representation of connections in computer memory. The expressiveness of CSA makes prototyping of network structure easy. A C+ + version of the algebra has been implemented and used in a large-scale neuronal network simulation (Djurfeldt et al., IBM J Res Dev 52(1/2):31-42, 2008b) and an implementation in Python has been publicly released.

Improved two-photon imaging of living neurons in brain tissue through temporal gating

PubMed Central

Gautam, Vini; Drury, Jack; Choy, Julian M. C.; Stricker, Christian; Bachor, Hans-A.; Daria, Vincent R.

2015-01-01

We optimize two-photon imaging of living neurons in brain tissue by temporally gating an incident laser to reduce the photon flux while optimizing the maximum fluorescence signal from the acquired images. Temporal gating produces a bunch of ~10 femtosecond pulses and the fluorescence signal is improved by increasing the bunch-pulse energy. Gating is achieved using an acousto-optic modulator with a variable gating frequency determined as integral multiples of the imaging sampling frequency. We hypothesize that reducing the photon flux minimizes the photo-damage to the cells. Our results, however, show that despite producing a high fluorescence signal, cell viability is compromised when the gating and sampling frequencies are equal (or effectively one bunch-pulse per pixel). We found an optimum gating frequency range that maintains the viability of the cells while preserving a pre-set fluorescence signal of the acquired two-photon images. The neurons are imaged while under whole-cell patch, and the cell viability is monitored as a change in the membrane’s input resistance. PMID:26504651

Preparation of Acute Brain Slices Using an Optimized N-Methyl-D-glucamine Protective Recovery Method.

PubMed

Ting, Jonathan T; Lee, Brian R; Chong, Peter; Soler-Llavina, Gilberto; Cobbs, Charles; Koch, Christof; Zeng, Hongkui; Lein, Ed

2018-02-26

This protocol is a practical guide to the N-methyl-D-glucamine (NMDG) protective recovery method of brain slice preparation. Numerous recent studies have validated the utility of this method for enhancing neuronal preservation and overall brain slice viability. The implementation of this technique by early adopters has facilitated detailed investigations into brain function using diverse experimental applications and spanning a wide range of animal ages, brain regions, and cell types. Steps are outlined for carrying out the protective recovery brain slice technique using an optimized NMDG artificial cerebrospinal fluid (aCSF) media formulation and enhanced procedure to reliably obtain healthy brain slices for patch clamp electrophysiology. With this updated approach, a substantial improvement is observed in the speed and reliability of gigaohm seal formation during targeted patch clamp recording experiments while maintaining excellent neuronal preservation, thereby facilitating challenging experimental applications. Representative results are provided from multi-neuron patch clamp recording experiments to assay synaptic connectivity in neocortical brain slices prepared from young adult transgenic mice and mature adult human neurosurgical specimens. Furthermore, the optimized NMDG protective recovery method of brain slicing is compatible with both juvenile and adult animals, thus resolving a limitation of the original methodology. In summary, a single media formulation and brain slicing procedure can be implemented across various species and ages to achieve excellent viability and tissue preservation.

Preparation of Acute Brain Slices Using an Optimized N-Methyl-D-glucamine Protective Recovery Method

PubMed Central

Chong, Peter; Soler-Llavina, Gilberto; Cobbs, Charles; Koch, Christof; Zeng, Hongkui; Lein, Ed

2018-01-01

This protocol is a practical guide to the N-methyl-D-glucamine (NMDG) protective recovery method of brain slice preparation. Numerous recent studies have validated the utility of this method for enhancing neuronal preservation and overall brain slice viability. The implementation of this technique by early adopters has facilitated detailed investigations into brain function using diverse experimental applications and spanning a wide range of animal ages, brain regions, and cell types. Steps are outlined for carrying out the protective recovery brain slice technique using an optimized NMDG artificial cerebrospinal fluid (aCSF) media formulation and enhanced procedure to reliably obtain healthy brain slices for patch clamp electrophysiology. With this updated approach, a substantial improvement is observed in the speed and reliability of gigaohm seal formation during targeted patch clamp recording experiments while maintaining excellent neuronal preservation, thereby facilitating challenging experimental applications. Representative results are provided from multi-neuron patch clamp recording experiments to assay synaptic connectivity in neocortical brain slices prepared from young adult transgenic mice and mature adult human neurosurgical specimens. Furthermore, the optimized NMDG protective recovery method of brain slicing is compatible with both juvenile and adult animals, thus resolving a limitation of the original methodology. In summary, a single media formulation and brain slicing procedure can be implemented across various species and ages to achieve excellent viability and tissue preservation. PMID:29553547

Emotions and motivated behavior converge on an amygdala-like structure in the zebrafish

PubMed Central

von Trotha, Jakob William; Vernier, Philippe; Bally-Cuif, Laure

2014-01-01

The brain reward circuitry plays a key role in emotional and motivational behaviors, and its dysfunction underlies neuropsychiatric disorders such as schizophrenia, depression and drug addiction. Here, we characterized the neuronal activity pattern induced by acute amphetamine administration and during drug-seeking behavior in the zebrafish, and demonstrate the existence of conserved underlying brain circuitry. Combining quantitative analyses of cfos expression with neuronal subtype-specific markers at single-cell resolution, we show that acute d-amphetamine administration leads to both increased neuronal activation and the recruitment of neurons in the medial (Dm) and the lateral (Dl) domains of the adult zebrafish pallium, which contain homologous structures to the mammalian amygdala and hippocampus, respectively. Calbindin-positive and glutamatergic neurons are recruited in Dm, and glutamatergic and γ-aminobutyric acid (GABAergic) neurons in Dl. The drug-activated neurons in Dm and Dl are born at juvenile stage rather than in the embryo or during adulthood. Furthermore, the same territory in Dm is activated during both drug-seeking approach and light avoidance behavior, while these behaviors do not elicit activation in Dl. These data identify the pallial territories involved in acute psychostimulant response and reward formation in the adult zebrafish. They further suggest an evolutionarily conserved function of amygdala-like structures in positive emotions and motivated behavior in zebrafish and mammals. PMID:25145867

Schaffer Collateral Inputs to CA1 Excitatory and Inhibitory Neurons Follow Different Connectivity Rules.

PubMed

Kwon, Osung; Feng, Linqing; Druckmann, Shaul; Kim, Jinhyun

2018-05-30

Neural circuits, governed by a complex interplay between excitatory and inhibitory neurons, are the substrate for information processing, and the organization of synaptic connectivity in neural network is an important determinant of circuit function. Here, we analyzed the fine structure of connectivity in hippocampal CA1 excitatory and inhibitory neurons innervated by Schaffer collaterals (SCs) using mGRASP in male mice. Our previous study revealed spatially structured synaptic connectivity between CA3 and CA1 pyramidal cells (PCs). Surprisingly, parvalbumin-positive interneurons (PVs) showed a significantly more random pattern spatial structure. Notably, application of Peters' rule for synapse prediction by random overlap between axons and dendrites enhanced structured connectivity in PCs, but, by contrast, made the connectivity pattern in PVs more random. In addition, PCs in a deep sublayer of striatum pyramidale appeared more highly structured than PCs in superficial layers, and little or no sublayer specificity was found in PVs. Our results show that CA1 excitatory PCs and inhibitory PVs innervated by the same SC inputs follow different connectivity rules. The different organizations of fine scale structured connectivity in hippocampal excitatory and inhibitory neurons provide important insights into the development and functions of neural networks. SIGNIFICANCE STATEMENT Understanding how neural circuits generate behavior is one of the central goals of neuroscience. An important component of this endeavor is the mapping of fine-scale connection patterns that underlie, and help us infer, signal processing in the brain. Here, using our recently developed synapse detection technology (mGRASP and neuTube), we provide detailed profiles of synaptic connectivity in excitatory (CA1 pyramidal) and inhibitory (CA1 parvalbumin-positive) neurons innervated by the same presynaptic inputs (CA3 Schaffer collaterals). Our results reveal that these two types of CA1 neurons follow different connectivity patterns. Our new evidence for differently structured connectivity at a fine scale in hippocampal excitatory and inhibitory neurons provides a better understanding of hippocampal networks and will guide theoretical and experimental studies. Copyright © 2018 the authors 0270-6474/18/385140-13$15.00/0.

Economy of scale: a motion sensor with variable speed tuning.

PubMed

Perrone, John A

2005-01-26

We have previously presented a model of how neurons in the primate middle temporal (MT/V5) area can develop selectivity for image speed by using common properties of the V1 neurons that precede them in the visual motion pathway (J. A. Perrone & A. Thiele, 2002). The motion sensor developed in this model is based on two broad classes of V1 complex neurons (sustained and transient). The S-type neuron has low-pass temporal frequency tuning, p(omega), and the T-type has band-pass temporal frequency tuning, m(omega). The outputs from the S and T neurons are combined in a special way (weighted intersection mechanism [WIM]) to generate a sensor tuned to a particular speed, v. Here I go on to show that if the S and T temporal frequency tuning functions have a particular form (i.e., p(omega)/(m(omega) = k/omega), then a motion sensor with variable speed tuning can be generated from just two V1 neurons. A simple scaling of the S- or T-type neuron output before it is incorporated into the WIM model produces a motion sensor that can be tuned to a wide continuous range of optimal speeds.

Enhancement of neuronal differentiation by using small molecules modulating Nodal/Smad, Wnt/β-catenin, and FGF signaling.

PubMed

Song, Yonghee; Lee, Somyung; Jho, Eek-Hoon

2018-06-08

Pluripotent embryonic stem cells are one of the best modalities for the disease treatment due to their potential for self-renewal and differentiation into various cell types. Induction of stem cell differentiation into specific cell lineages has been investigated for decades, especially in vitro neuronal differentiation of embryonic stem cells. However, in vitro differentiation methods do not yield sufficient amounts of neurons for use in the therapeutic treatment of neurological disorders. Here, we provide an improved neuronal differentiation method based on a combination of small regulatory molecules for specific signaling pathways (FGF4 for FGF signaling, SB431542 for Nodal/Smad signaling, and XAV939 and BIO for Wnt signaling) in N2B27 media. We found that FGF4 was required for neural induction, SB431542 accelerated neural precursor differentiation, and treatment with XAV939 and BIO at different periods enhanced neuronal differentiation. These optimized neuronal differentiation conditions may allow a greater neuron cell yield within a shorter time than current methods and be the basis for treatment of neurological dysfunction using stem cells. Copyright © 2018. Published by Elsevier Inc.

Parameter Estimation of a Spiking Silicon Neuron

PubMed Central

Russell, Alexander; Mazurek, Kevin; Mihalaş, Stefan; Niebur, Ernst; Etienne-Cummings, Ralph

2012-01-01

Spiking neuron models are used in a multitude of tasks ranging from understanding neural behavior at its most basic level to neuroprosthetics. Parameter estimation of a single neuron model, such that the model’s output matches that of a biological neuron is an extremely important task. Hand tuning of parameters to obtain such behaviors is a difficult and time consuming process. This is further complicated when the neuron is instantiated in silicon (an attractive medium in which to implement these models) as fabrication imperfections make the task of parameter configuration more complex. In this paper we show two methods to automate the configuration of a silicon (hardware) neuron’s parameters. First, we show how a Maximum Likelihood method can be applied to a leaky integrate and fire silicon neuron with spike induced currents to fit the neuron’s output to desired spike times. We then show how a distance based method which approximates the negative log likelihood of the lognormal distribution can also be used to tune the neuron’s parameters. We conclude that the distance based method is better suited for parameter configuration of silicon neurons due to its superior optimization speed. PMID:23852978

Histone Methylation Restrains the Expression of Subtype-Specific Genes during Terminal Neuronal Differentiation in Caenorhabditis elegans

PubMed Central

Chiang, Victor; Chalfie, Martin

2013-01-01

Although epigenetic control of stem cell fate choice is well established, little is known about epigenetic regulation of terminal neuronal differentiation. We found that some differences among the subtypes of Caenorhabditis elegans VC neurons, particularly the expression of the transcription factor gene unc-4, require histone modification, most likely H3K9 methylation. An EGF signal from the vulva alleviated the epigenetic repression of unc-4 in vulval VC neurons but not the more distant nonvulval VC cells, which kept unc-4 silenced. Loss of the H3K9 methyltransferase MET-2 or H3K9me2/3 binding proteins HPL-2 and LIN-61 or a novel chromodomain protein CEC-3 caused ectopic unc-4 expression in all VC neurons. Downstream of the EGF signaling in vulval VC neurons, the transcription factor LIN-11 and histone demethylases removed the suppressive histone marks and derepressed unc-4. Behaviorally, expression of UNC-4 in all the VC neurons caused an imbalance in the egg-laying circuit. Thus, epigenetic mechanisms help establish subtype-specific gene expression, which are needed for optimal activity of a neural circuit. PMID:24348272

The Topographical Mapping in Drosophila Central Complex Network and Its Signal Routing

PubMed Central

Chang, Po-Yen; Su, Ta-Shun; Shih, Chi-Tin; Lo, Chung-Chuan

2017-01-01

Neural networks regulate brain functions by routing signals. Therefore, investigating the detailed organization of a neural circuit at the cellular levels is a crucial step toward understanding the neural mechanisms of brain functions. To study how a complicated neural circuit is organized, we analyzed recently published data on the neural circuit of the Drosophila central complex, a brain structure associated with a variety of functions including sensory integration and coordination of locomotion. We discovered that, except for a small number of “atypical” neuron types, the network structure formed by the identified 194 neuron types can be described by only a few simple mathematical rules. Specifically, the topological mapping formed by these neurons can be reconstructed by applying a generation matrix on a small set of initial neurons. By analyzing how information flows propagate with or without the atypical neurons, we found that while the general pattern of signal propagation in the central complex follows the simple topological mapping formed by the “typical” neurons, some atypical neurons can substantially re-route the signal pathways, implying specific roles of these neurons in sensory signal integration. The present study provides insights into the organization principle and signal integration in the central complex. PMID:28443014

An FPGA-Based Silicon Neuronal Network with Selectable Excitability Silicon Neurons

PubMed Central

Li, Jing; Katori, Yuichi; Kohno, Takashi

2012-01-01

This paper presents a digital silicon neuronal network which simulates the nerve system in creatures and has the ability to execute intelligent tasks, such as associative memory. Two essential elements, the mathematical-structure-based digital spiking silicon neuron (DSSN) and the transmitter release based silicon synapse, allow us to tune the excitability of silicon neurons and are computationally efficient for hardware implementation. We adopt mixed pipeline and parallel structure and shift operations to design a sufficient large and complex network without excessive hardware resource cost. The network with 256 full-connected neurons is built on a Digilent Atlys board equipped with a Xilinx Spartan-6 LX45 FPGA. Besides, a memory control block and USB control block are designed to accomplish the task of data communication between the network and the host PC. This paper also describes the mechanism of associative memory performed in the silicon neuronal network. The network is capable of retrieving stored patterns if the inputs contain enough information of them. The retrieving probability increases with the similarity between the input and the stored pattern increasing. Synchronization of neurons is observed when the successful stored pattern retrieval occurs. PMID:23269911

A comparison of optimal MIMO linear and nonlinear models for brain machine interfaces

NASA Astrophysics Data System (ADS)

Kim, S.-P.; Sanchez, J. C.; Rao, Y. N.; Erdogmus, D.; Carmena, J. M.; Lebedev, M. A.; Nicolelis, M. A. L.; Principe, J. C.

2006-06-01

The field of brain-machine interfaces requires the estimation of a mapping from spike trains collected in motor cortex areas to the hand kinematics of the behaving animal. This paper presents a systematic investigation of several linear (Wiener filter, LMS adaptive filters, gamma filter, subspace Wiener filters) and nonlinear models (time-delay neural network and local linear switching models) applied to datasets from two experiments in monkeys performing motor tasks (reaching for food and target hitting). Ensembles of 100-200 cortical neurons were simultaneously recorded in these experiments, and even larger neuronal samples are anticipated in the future. Due to the large size of the models (thousands of parameters), the major issue studied was the generalization performance. Every parameter of the models (not only the weights) was selected optimally using signal processing and machine learning techniques. The models were also compared statistically with respect to the Wiener filter as the baseline. Each of the optimization procedures produced improvements over that baseline for either one of the two datasets or both.

A comparison of optimal MIMO linear and nonlinear models for brain-machine interfaces.

PubMed

Kim, S-P; Sanchez, J C; Rao, Y N; Erdogmus, D; Carmena, J M; Lebedev, M A; Nicolelis, M A L; Principe, J C

2006-06-01

The field of brain-machine interfaces requires the estimation of a mapping from spike trains collected in motor cortex areas to the hand kinematics of the behaving animal. This paper presents a systematic investigation of several linear (Wiener filter, LMS adaptive filters, gamma filter, subspace Wiener filters) and nonlinear models (time-delay neural network and local linear switching models) applied to datasets from two experiments in monkeys performing motor tasks (reaching for food and target hitting). Ensembles of 100-200 cortical neurons were simultaneously recorded in these experiments, and even larger neuronal samples are anticipated in the future. Due to the large size of the models (thousands of parameters), the major issue studied was the generalization performance. Every parameter of the models (not only the weights) was selected optimally using signal processing and machine learning techniques. The models were also compared statistically with respect to the Wiener filter as the baseline. Each of the optimization procedures produced improvements over that baseline for either one of the two datasets or both.

The Importance of the Numerical Resolution of the Laplace Equation in the optimization of a Neuronal Stimulation Technique

NASA Astrophysics Data System (ADS)

Faria, Paula

2010-09-01

For the past few years, the potential of transcranial direct current stimulation (tDCS) for the treatment of several pathologies has been investigated. Knowledge of the current density distribution is an important factor in optimizing such applications of tDCS. For this goal, we used the finite element method to solve the Laplace equation in a spherical head model in order to investigate the three dimensional distribution of the current density and the variation of its intensity with depth using different electrodes montages: the traditional one with two sponge electrodes and new electrode montages: with sponge and EEG electrodes and with EEG electrodes varying the numbers of electrodes. The simulation results confirm the effectiveness of the mixed system which may allow the use of tDCS and EEG recording concomitantly and may help to optimize this neuronal stimulation technique. The numerical results were used in a promising application of tDCS in epilepsy.

Electronic neural networks for global optimization

NASA Technical Reports Server (NTRS)

Thakoor, A. P.; Moopenn, A. W.; Eberhardt, S.

1990-01-01

An electronic neural network with feedback architecture, implemented in analog custom VLSI is described. Its application to problems of global optimization for dynamic assignment is discussed. The convergence properties of the neural network hardware are compared with computer simulation results. The neural network's ability to provide optimal or near optimal solutions within only a few neuron time constants, a speed enhancement of several orders of magnitude over conventional search methods, is demonstrated. The effect of noise on the circuit dynamics and the convergence behavior of the neural network hardware is also examined.

Energy-efficient neural information processing in individual neurons and neuronal networks.

PubMed

Yu, Lianchun; Yu, Yuguo

2017-11-01

Brains are composed of networks of an enormous number of neurons interconnected with synapses. Neural information is carried by the electrical signals within neurons and the chemical signals among neurons. Generating these electrical and chemical signals is metabolically expensive. The fundamental issue raised here is whether brains have evolved efficient ways of developing an energy-efficient neural code from the molecular level to the circuit level. Here, we summarize the factors and biophysical mechanisms that could contribute to the energy-efficient neural code for processing input signals. The factors range from ion channel kinetics, body temperature, axonal propagation of action potentials, low-probability release of synaptic neurotransmitters, optimal input and noise, the size of neurons and neuronal clusters, excitation/inhibition balance, coding strategy, cortical wiring, and the organization of functional connectivity. Both experimental and computational evidence suggests that neural systems may use these factors to maximize the efficiency of energy consumption in processing neural signals. Studies indicate that efficient energy utilization may be universal in neuronal systems as an evolutionary consequence of the pressure of limited energy. As a result, neuronal connections may be wired in a highly economical manner to lower energy costs and space. Individual neurons within a network may encode independent stimulus components to allow a minimal number of neurons to represent whole stimulus characteristics efficiently. This basic principle may fundamentally change our view of how billions of neurons organize themselves into complex circuits to operate and generate the most powerful intelligent cognition in nature. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Influence of Pyrethroid Insecticides on Sodium and Calcium Influx in Neocortical Neurons

EPA Science Inventory

Pyrethroid insecticides bind to voltage-gated sodium channels and modify their gating kinetics, thereby disrupting neuronal function. Using murine neocortical neurons in primary culture, we have compared the ability of 11 structurally diverse pyrethroid insecticides to evoke Na+ ...

Sustained synchronized neuronal network activity in a human astrocyte co-culture system

PubMed Central

Kuijlaars, Jacobine; Oyelami, Tutu; Diels, Annick; Rohrbacher, Jutta; Versweyveld, Sofie; Meneghello, Giulia; Tuefferd, Marianne; Verstraelen, Peter; Detrez, Jan R.; Verschuuren, Marlies; De Vos, Winnok H.; Meert, Theo; Peeters, Pieter J.; Cik, Miroslav; Nuydens, Rony; Brône, Bert; Verheyen, An

2016-01-01

Impaired neuronal network function is a hallmark of neurodevelopmental and neurodegenerative disorders such as autism, schizophrenia, and Alzheimer’s disease and is typically studied using genetically modified cellular and animal models. Weak predictive capacity and poor translational value of these models urge for better human derived in vitro models. The implementation of human induced pluripotent stem cells (hiPSCs) allows studying pathologies in differentiated disease-relevant and patient-derived neuronal cells. However, the differentiation process and growth conditions of hiPSC-derived neurons are non-trivial. In order to study neuronal network formation and (mal)function in a fully humanized system, we have established an in vitro co-culture model of hiPSC-derived cortical neurons and human primary astrocytes that recapitulates neuronal network synchronization and connectivity within three to four weeks after final plating. Live cell calcium imaging, electrophysiology and high content image analyses revealed an increased maturation of network functionality and synchronicity over time for co-cultures compared to neuronal monocultures. The cells express GABAergic and glutamatergic markers and respond to inhibitors of both neurotransmitter pathways in a functional assay. The combination of this co-culture model with quantitative imaging of network morphofunction is amenable to high throughput screening for lead discovery and drug optimization for neurological diseases. PMID:27819315

Solving Constraint Satisfaction Problems with Networks of Spiking Neurons

PubMed Central

Jonke, Zeno; Habenschuss, Stefan; Maass, Wolfgang

2016-01-01

Network of neurons in the brain apply—unlike processors in our current generation of computer hardware—an event-based processing strategy, where short pulses (spikes) are emitted sparsely by neurons to signal the occurrence of an event at a particular point in time. Such spike-based computations promise to be substantially more power-efficient than traditional clocked processing schemes. However, it turns out to be surprisingly difficult to design networks of spiking neurons that can solve difficult computational problems on the level of single spikes, rather than rates of spikes. We present here a new method for designing networks of spiking neurons via an energy function. Furthermore, we show how the energy function of a network of stochastically firing neurons can be shaped in a transparent manner by composing the networks of simple stereotypical network motifs. We show that this design approach enables networks of spiking neurons to produce approximate solutions to difficult (NP-hard) constraint satisfaction problems from the domains of planning/optimization and verification/logical inference. The resulting networks employ noise as a computational resource. Nevertheless, the timing of spikes plays an essential role in their computations. Furthermore, networks of spiking neurons carry out for the Traveling Salesman Problem a more efficient stochastic search for good solutions compared with stochastic artificial neural networks (Boltzmann machines) and Gibbs sampling. PMID:27065785

In Vitro Studies of Neuronal Networks and Synaptic Plasticity in Invertebrates and in Mammals Using Multielectrode Arrays

PubMed Central

Tessadori, Jacopo; Ghirardi, Mirella

2015-01-01

Brain functions are strictly dependent on neural connections formed during development and modified during life. The cellular and molecular mechanisms underlying synaptogenesis and plastic changes involved in learning and memory have been analyzed in detail in simple animals such as invertebrates and in circuits of mammalian brains mainly by intracellular recordings of neuronal activity. In the last decades, the evolution of techniques such as microelectrode arrays (MEAs) that allow simultaneous, long-lasting, noninvasive, extracellular recordings from a large number of neurons has proven very useful to study long-term processes in neuronal networks in vivo and in vitro. In this work, we start off by briefly reviewing the microelectrode array technology and the optimization of the coupling between neurons and microtransducers to detect subthreshold synaptic signals. Then, we report MEA studies of circuit formation and activity in invertebrate models such as Lymnaea, Aplysia, and Helix. In the following sections, we analyze plasticity and connectivity in cultures of mammalian dissociated neurons, focusing on spontaneous activity and electrical stimulation. We conclude by discussing plasticity in closed-loop experiments. PMID:25866681

An optogenetics- and imaging-assisted simultaneous multiple patch-clamp recording system for decoding complex neural circuits

PubMed Central

Wang, Guangfu; Wyskiel, Daniel R; Yang, Weiguo; Wang, Yiqing; Milbern, Lana C; Lalanne, Txomin; Jiang, Xiaolong; Shen, Ying; Sun, Qian-Quan; Zhu, J Julius

2015-01-01

Deciphering neuronal circuitry is central to understanding brain function and dysfunction, yet it remains a daunting task. To facilitate the dissection of neuronal circuits, a process requiring functional analysis of synaptic connections and morphological identification of interconnected neurons, we present here a method for stable simultaneous octuple patch-clamp recordings. This method allows physiological analysis of synaptic interconnections among 4–8 simultaneously recorded neurons and/or 10–30 sequentially recorded neurons, and it allows anatomical identification of >85% of recorded interneurons and >99% of recorded principal neurons. We describe how to apply the method to rodent tissue slices; however, it can be used on other model organisms. We also describe the latest refinements and optimizations of mechanics, electronics, optics and software programs that are central to the realization of a combined single- and two-photon microscopy–based, optogenetics- and imaging-assisted, stable, simultaneous quadruple–viguple patch-clamp recording system. Setting up the system, from the beginning of instrument assembly and software installation to full operation, can be completed in 3–4 d. PMID:25654757

Automatic Fitting of Spiking Neuron Models to Electrophysiological Recordings

PubMed Central

Rossant, Cyrille; Goodman, Dan F. M.; Platkiewicz, Jonathan; Brette, Romain

2010-01-01

Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to electrophysiological recordings. The fitting procedure can be very time consuming both in terms of computer simulations and in terms of code writing. We present algorithms to fit spiking models to electrophysiological data (time-varying input and spike trains) that can run in parallel on graphics processing units (GPUs). The model fitting library is interfaced with Brian, a neural network simulator in Python. If a GPU is present it uses just-in-time compilation to translate model equations into optimized code. Arbitrary models can then be defined at script level and run on the graphics card. This tool can be used to obtain empirically validated spiking models of neurons in various systems. We demonstrate its use on public data from the INCF Quantitative Single-Neuron Modeling 2009 competition by comparing the performance of a number of neuron spiking models. PMID:20224819

Atlas-Independent, Electrophysiological Mapping of the Optimal Locus of Subthalamic Deep Brain Stimulation for the Motor Symptoms of Parkinson Disease.

PubMed

Conrad, Erin C; Mossner, James M; Chou, Kelvin L; Patil, Parag G

2018-05-23

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) improves motor symptoms of Parkinson disease (PD). However, motor outcomes can be variable, perhaps due to inconsistent positioning of the active contact relative to an unknown optimal locus of stimulation. Here, we determine the optimal locus of STN stimulation in a geometrically unconstrained, mathematically precise, and atlas-independent manner, using Unified Parkinson Disease Rating Scale (UPDRS) motor outcomes and an electrophysiological neuronal stimulation model. In 20 patients with PD, we mapped motor improvement to active electrode location, relative to the individual, directly MRI-visualized STN. Our analysis included a novel, unconstrained and computational electrical-field model of neuronal activation to estimate the optimal locus of DBS. We mapped the optimal locus to a tightly defined ovoid region 0.49 mm lateral, 0.88 mm posterior, and 2.63 mm dorsal to the anatomical midpoint of the STN. On average, this locus is 11.75 lateral, 1.84 mm posterior, and 1.08 mm ventral to the mid-commissural point. Our novel, atlas-independent method reveals a single, ovoid optimal locus of stimulation in STN DBS for PD. The methodology, here applied to UPDRS and PD, is generalizable to atlas-independent mapping of other motor and non-motor effects of DBS. © 2018 S. Karger AG, Basel.

Visible rodent brain-wide networks at single-neuron resolution

PubMed Central

Yuan, Jing; Gong, Hui; Li, Anan; Li, Xiangning; Chen, Shangbin; Zeng, Shaoqun; Luo, Qingming

2015-01-01

There are some unsolvable fundamental questions, such as cell type classification, neural circuit tracing and neurovascular coupling, though great progresses are being made in neuroscience. Because of the structural features of neurons and neural circuits, the solution of these questions needs us to break through the current technology of neuroanatomy for acquiring the exactly fine morphology of neuron and vessels and tracing long-distant circuit at axonal resolution in the whole brain of mammals. Combined with fast-developing labeling techniques, efficient whole-brain optical imaging technology emerging at the right moment presents a huge potential in the structure and function research of specific-function neuron and neural circuit. In this review, we summarize brain-wide optical tomography techniques, review the progress on visible brain neuronal/vascular networks benefit from these novel techniques, and prospect the future technical development. PMID:26074784

Neuronal Type-Specific Gene Expression Profiling and Laser-Capture Microdissection

PubMed Central

Pietersen, Charmaine Y.; Lim, Maribel P.; Macey, Laurel; Woo, Tsung-Ung W.; Sonntag, Kai C.

2014-01-01

The human brain is an exceptionally heterogeneous structure. In order to gain insight into the neurobiological basis of neural circuit disturbances in various neurologic or psychiatric diseases, it is often important to define the molecular cascades that are associated with these disturbances in a neuronal type-specific manner. This can be achieved by the use of laser microdissection, in combination with molecular techniques such as gene expression profiling. To identify neurons in human postmortem brain tissue, one can use the inherent properties of the neuron, such as pigmentation and morphology or its structural composition through immunohistochemistry (IHC). Here, we describe the isolation of homogeneous neuronal cells and high-quality RNA from human postmortem brain material using a combination of rapid IHC, Nissl staining, or simple morphology with Laser-Capture Microdissection (LCM) or Laser Microdissection (LMD). PMID:21761317

Neuronal type-specific gene expression profiling and laser-capture microdissection.

PubMed

Pietersen, Charmaine Y; Lim, Maribel P; Macey, Laurel; Woo, Tsung-Ung W; Sonntag, Kai C

2011-01-01

The human brain is an exceptionally heterogeneous structure. In order to gain insight into the neurobiological basis of neural circuit disturbances in various neurologic or psychiatric diseases, it is often important to define the molecular cascades that are associated with these disturbances in a neuronal type-specific manner. This can be achieved by the use of laser microdissection, in combination with molecular techniques such as gene expression profiling. To identify neurons in human postmortem brain tissue, one can use the inherent properties of the neuron, such as pigmentation and morphology or its structural composition through immunohistochemistry (IHC). Here, we describe the isolation of homogeneous neuronal cells and high-quality RNA from human postmortem brain material using a combination of rapid IHC, Nissl staining, or simple morphology with Laser-Capture Microdissection (LCM) or Laser Microdissection (LMD).

Modelling the formation of working memory with networks of integrate-and-fire neurons connected by plastic synapses.

PubMed

Del Giudice, Paolo; Fusi, Stefano; Mattia, Maurizio

2003-01-01

In this paper we review a series of works concerning models of spiking neurons interacting via spike-driven, plastic, Hebbian synapses, meant to implement stimulus driven, unsupervised formation of working memory (WM) states. Starting from a summary of the experimental evidence emerging from delayed matching to sample (DMS) experiments, we briefly review the attractor picture proposed to underlie WM states. We then describe a general framework for a theoretical approach to learning with synapses subject to realistic constraints and outline some general requirements to be met by a mechanism of Hebbian synaptic structuring. We argue that a stochastic selection of the synapses to be updated allows for optimal memory storage, even if the number of stable synaptic states is reduced to the extreme (bistable synapses). A description follows of models of spike-driven synapses that implement the stochastic selection by exploiting the high irregularity in the pre- and post-synaptic activity. Reasons are listed why dynamic learning, that is the process by which the synaptic structure develops under the only guidance of neural activities, driven in turn by stimuli, is hard to accomplish. We provide a 'feasibility proof' of dynamic formation of WM states in this context the beneficial role of short-term depression (STD) is illustrated. by showing how an initially unstructured network autonomously develops a synaptic structure supporting simultaneously stable spontaneous and WM states in this context the beneficial role of short-term depression (STD) is illustrated. After summarizing heuristic indications emerging from the study performed, we conclude by briefly discussing open problems and critical issues still to be clarified.

Characterization of Differentiated SH-SY5Y as Neuronal Screening Model Reveals Increased Oxidative Vulnerability

PubMed Central

Forster, J. I.; Köglsberger, S.; Trefois, C.; Boyd, O.; Baumuratov, A. S.; Buck, L.; Balling, R.; Antony, P. M. A.

2016-01-01

The immortalized and proliferative cell line SH-SY5Y is one of the most commonly used cell lines in neuroscience and neuroblastoma research. However, undifferentiated SH-SY5Y cells share few properties with mature neurons. In this study, we present an optimized neuronal differentiation protocol for SH-SY5Y that requires only two work steps and 6 days. After differentiation, the cells present increased levels of ATP and plasma membrane activity but reduced expression of energetic stress response genes. Differentiation results in reduced mitochondrial membrane potential and decreased robustness toward perturbations with 6-hydroxydopamine. We are convinced that the presented differentiation method will leverage genetic and chemical high-throughput screening projects targeting pathways that are involved in the selective vulnerability of neurons with high energetic stress levels. PMID:26738520

Channel noise-induced temporal coherence transitions and synchronization transitions in adaptive neuronal networks with time delay

NASA Astrophysics Data System (ADS)

Gong, Yubing; Xie, Huijuan

2017-09-01

Using spike-timing-dependent plasticity (STDP), we study the effect of channel noise on temporal coherence and synchronization of adaptive scale-free Hodgkin-Huxley neuronal networks with time delay. It is found that the spiking regularity and spatial synchronization of the neurons intermittently increase and decrease as channel noise intensity is varied, exhibiting transitions of temporal coherence and synchronization. Moreover, this phenomenon depends on time delay, STDP, and network average degree. As time delay increases, the phenomenon is weakened, however, there are optimal STDP and network average degree by which the phenomenon becomes strongest. These results show that channel noise can intermittently enhance the temporal coherence and synchronization of the delayed adaptive neuronal networks. These findings provide a new insight into channel noise for the information processing and transmission in neural systems.

A plastic corticostriatal circuit model of adaptation in perceptual decision making

PubMed Central

Hsiao, Pao-Yueh; Lo, Chung-Chuan

2013-01-01

The ability to optimize decisions and adapt them to changing environments is a crucial brain function that increase survivability. Although much has been learned about the neuronal activity in various brain regions that are associated with decision making, and about how the nervous systems may learn to achieve optimization, the underlying neuronal mechanisms of how the nervous systems optimize decision strategies with preference given to speed or accuracy, and how the systems adapt to changes in the environment, remain unclear. Based on extensive empirical observations, we addressed the question by extending a previously described cortico-basal ganglia circuit model of perceptual decisions with the inclusion of a dynamic dopamine (DA) system that modulates spike-timing dependent plasticity (STDP). We found that, once an optimal model setting that maximized the reward rate was selected, the same setting automatically optimized decisions across different task environments through dynamic balancing between the facilitating and depressing components of the DA dynamics. Interestingly, other model parameters were also optimal if we considered the reward rate that was weighted by the subject's preferences for speed or accuracy. Specifically, the circuit model favored speed if we increased the phasic DA response to the reward prediction error, whereas the model favored accuracy if we reduced the tonic DA activity or the phasic DA responses to the estimated reward probability. The proposed model provides insight into the roles of different components of DA responses in decision adaptation and optimization in a changing environment. PMID:24339814

[A modified intracellular labelling technique for high-resolution staining of neuron in 500 microm-thickness brain slice].

PubMed

Zhao, Ming-liang; Liu, Guo-long; Sui, Jian-feng; Ruan, Huai-zhen; Xiong, Ying

2007-05-01

To develop simple but reliable intracellular labelling method for high-resolution visualization of the fine structure of single neurons in brain slice with thickness of 500 microm. Biocytin was introduced into neurons in 500 microm-thickness brain slices while blind whole cell recording. Following processed for histochemistry using the avidin-biotin-complex method, stained slices were mounted in glycerol on special glass slides. Labelled cells were digital photomicrographed every 30 microm and reconstructed with Adobe Photoshop software. After histochemistry, limited background staining was produced. The resolution was so high that fine structure, including branching, termination of individual axons and even spines of neurons could be identified in exquisite detail with optic microscope. With the help of software, the neurons of interest could be reconstructed from a stack of photomicrographs. The modified method provides an easy and reliable approach to revealing the detailed morphological properties of single neurons in 500 microm-thickness brain slice. Without requisition of special equipment, it is suited to be broadly applied.

Long-term, high-resolution imaging in the mouse neocortex through a chronic cranial window

PubMed Central

Holtmaat, Anthony; Bonhoeffer, Tobias; Chow, David K; Chuckowree, Jyoti; De Paola, Vincenzo; Hofer, Sonja B; Hübener, Mark; Keck, Tara; Knott, Graham; Lee, Wei-Chung A; Mostany, Ricardo; Mrsic-Flogel, Tom D; Nedivi, Elly; Portera-Cailliau, Carlos; Svoboda, Karel; Trachtenberg, Joshua T; Wilbrecht, Linda

2011-01-01

To understand the cellular and circuit mechanisms of experience-dependent plasticity, neurons and their synapses need to be studied in the intact brain over extended periods of time. Two-photon excitation laser scanning microscopy (2PLSM), together with expression of fluorescent proteins, enables high-resolution imaging of neuronal structure in vivo. In this protocol we describe a chronic cranial window to obtain optical access to the mouse cerebral cortex for long-term imaging. A small bone flap is replaced with a coverglass, which is permanently sealed in place with dental acrylic, providing a clear imaging window with a large field of view (∼0.8–12 mm2). The surgical procedure can be completed within ∼1 h. The preparation allows imaging over time periods of months with arbitrary imaging intervals. The large size of the imaging window facilitates imaging of ongoing structural plasticity of small neuronal structures in mice, with low densities of labeled neurons. The entire dendritic and axonal arbor of individual neurons can be reconstructed. PMID:19617885

Establishment of a long-term primary culture of striatal neurons.

PubMed

Sebben, M; Gabrion, J; Manzoni, O; Sladeczek, F; Gril, C; Bockaert, J; Dumuis, A

1990-03-01

A new method of obtaining long-term primary cultures (lasting more than 8 weeks) of striatal neurons is described in this paper. The originality of the method consists of: (1) starting the culture for 3 days in a serum-free medium which allows attachment and neurite proliferation of neurons as well as the death of non-neuronal cells (mainly consisting of astrocytes); (2) introducing a limited amount of fetal calf serum (FCS) (2-5%) after 3 days in vitro (3 DIV), which likely provides optimal neuronal survival and attachment factors, and a limited amount of astrocyte proliferating factors. The period of introduction of serum, as well as the amount of serum introduced are critical factors. By phase contrast and transmission electron microscopy, we observed that neurons continued to develop neurite extensions, synaptic vesicles and synapse formations up to 50 DIV. Neuronal membranes, and synaptic contacts were particularly healthy up to 50 DIV. Interestingly, the number of astrocytes was constant between 30-50 DIV and limited to about 10%. We therefore obtained an equilibrium between neuronal and astrocyte differentiation and proliferation. It is likely that the small population of astrocytes, plus the low percentage of FCS added, provide essential factors for neuronal survival and differentiation, whereas a high density of differentiated neurons inhibited astrocyte cell proliferation. The clear-cut stability of these neuronal cultures goes in parallel with the stability of the pharmacological responses studied here: the coupling of carbachol and quisqualate receptors with the inositol phosphate production system. The culture method described here could be of particular interest to pursue biochemical, pharmacological and biological studies on neurons as well as on reciprocal interactions between neurons and astrocytes.

Neuronal prediction of opponent's behavior during cooperative social interchange in primates.

PubMed

Haroush, Keren; Williams, Ziv M

2015-03-12

A cornerstone of successful social interchange is the ability to anticipate each other's intentions or actions. While generating these internal predictions is essential for constructive social behavior, their single neuronal basis and causal underpinnings are unknown. Here, we discover specific neurons in the primate dorsal anterior cingulate that selectively predict an opponent's yet unknown decision to invest in their common good or defect and distinct neurons that encode the monkey's own current decision based on prior outcomes. Mixed population predictions of the other was remarkably near optimal compared to behavioral decoders. Moreover, disrupting cingulate activity selectively biased mutually beneficial interactions between the monkeys but, surprisingly, had no influence on their decisions when no net-positive outcome was possible. These findings identify a group of other-predictive neurons in the primate anterior cingulate essential for enacting cooperative interactions and may pave a way toward the targeted treatment of social behavioral disorders. Copyright © 2015 Elsevier Inc. All rights reserved.

Magnetosensitive neurons mediate geomagnetic orientation in Caenorhabditis elegans

PubMed Central

Vidal-Gadea, Andrés; Ward, Kristi; Beron, Celia; Ghorashian, Navid; Gokce, Sertan; Russell, Joshua; Truong, Nicholas; Parikh, Adhishri; Gadea, Otilia; Ben-Yakar, Adela; Pierce-Shimomura, Jonathan

2015-01-01

Many organisms spanning from bacteria to mammals orient to the earth's magnetic field. For a few animals, central neurons responsive to earth-strength magnetic fields have been identified; however, magnetosensory neurons have yet to be identified in any animal. We show that the nematode Caenorhabditis elegans orients to the earth's magnetic field during vertical burrowing migrations. Well-fed worms migrated up, while starved worms migrated down. Populations isolated from around the world, migrated at angles to the magnetic vector that would optimize vertical translation in their native soil, with northern- and southern-hemisphere worms displaying opposite migratory preferences. Magnetic orientation and vertical migrations required the TAX-4 cyclic nucleotide-gated ion channel in the AFD sensory neuron pair. Calcium imaging showed that these neurons respond to magnetic fields even without synaptic input. C. elegans may have adapted magnetic orientation to simplify their vertical burrowing migration by reducing the orientation task from three dimensions to one. DOI: http://dx.doi.org/10.7554/eLife.07493.001 PMID:26083711

Cell-intrinsic mechanisms of temperature compensation in a grasshopper sensory receptor neuron

PubMed Central

Roemschied, Frederic A; Eberhard, Monika JB; Schleimer, Jan-Hendrik; Ronacher, Bernhard; Schreiber, Susanne

2014-01-01

Changes in temperature affect biochemical reaction rates and, consequently, neural processing. The nervous systems of poikilothermic animals must have evolved mechanisms enabling them to retain their functionality under varying temperatures. Auditory receptor neurons of grasshoppers respond to sound in a surprisingly temperature-compensated manner: firing rates depend moderately on temperature, with average Q10 values around 1.5. Analysis of conductance-based neuron models reveals that temperature compensation of spike generation can be achieved solely relying on cell-intrinsic processes and despite a strong dependence of ion conductances on temperature. Remarkably, this type of temperature compensation need not come at an additional metabolic cost of spike generation. Firing rate-based information transfer is likely to increase with temperature and we derive predictions for an optimal temperature dependence of the tympanal transduction process fostering temperature compensation. The example of auditory receptor neurons demonstrates how neurons may exploit single-cell mechanisms to cope with multiple constraints in parallel. DOI: http://dx.doi.org/10.7554/eLife.02078.001 PMID:24843016

Fast targeted gene transfection and optogenetic modification of single neurons using femtosecond laser irradiation

PubMed Central

Antkowiak, Maciej; Torres-Mapa, Maria Leilani; Witts, Emily C.; Miles, Gareth B.; Dholakia, Kishan; Gunn-Moore, Frank J.

2013-01-01

A prevailing problem in neuroscience is the fast and targeted delivery of DNA into selected neurons. The development of an appropriate methodology would enable the transfection of multiple genes into the same cell or different genes into different neighboring cells as well as rapid cell selective functionalization of neurons. Here, we show that optimized femtosecond optical transfection fulfills these requirements. We also demonstrate successful optical transfection of channelrhodopsin-2 in single selected neurons. We extend the functionality of this technique for wider uptake by neuroscientists by using fast three-dimensional laser beam steering enabling an image-guided “point-and-transfect” user-friendly transfection of selected cells. A sub-second transfection timescale per cell makes this method more rapid by at least two orders of magnitude when compared to alternative single-cell transfection techniques. This novel technology provides the ability to carry out large-scale cell selective genetic studies on neuronal ensembles and perform rapid genetic programming of neural circuits. PMID:24257461

Fast targeted gene transfection and optogenetic modification of single neurons using femtosecond laser irradiation.

PubMed

Antkowiak, Maciej; Torres-Mapa, Maria Leilani; Witts, Emily C; Miles, Gareth B; Dholakia, Kishan; Gunn-Moore, Frank J

2013-11-21

A prevailing problem in neuroscience is the fast and targeted delivery of DNA into selected neurons. The development of an appropriate methodology would enable the transfection of multiple genes into the same cell or different genes into different neighboring cells as well as rapid cell selective functionalization of neurons. Here, we show that optimized femtosecond optical transfection fulfills these requirements. We also demonstrate successful optical transfection of channelrhodopsin-2 in single selected neurons. We extend the functionality of this technique for wider uptake by neuroscientists by using fast three-dimensional laser beam steering enabling an image-guided "point-and-transfect" user-friendly transfection of selected cells. A sub-second transfection timescale per cell makes this method more rapid by at least two orders of magnitude when compared to alternative single-cell transfection techniques. This novel technology provides the ability to carry out large-scale cell selective genetic studies on neuronal ensembles and perform rapid genetic programming of neural circuits.

Assessment of the upper motor neuron in amyotrophic lateral sclerosis.

PubMed

Huynh, William; Simon, Neil G; Grosskreutz, Julian; Turner, Martin R; Vucic, Steve; Kiernan, Matthew C

2016-07-01

Clinical signs of upper motor neuron (UMN) involvement are an important component in supporting the diagnosis of amyotrophic lateral sclerosis (ALS), but are often not easily appreciated in a limb that is concurrently affected by muscle wasting and lower motor neuron degeneration, particularly in the early symptomatic stages of ALS. Whilst recent criteria have been proposed to facilitate improved detection of lower motor neuron impairment through electrophysiological features that have improved diagnostic sensitivity, assessment of upper motor neuron involvement remains essentially clinical. As a result, there is often a significant diagnostic delay that in turn may impact institution of disease-modifying therapy and access to other optimal patient management. Biomarkers of pathological UMN involvement are also required to ensure patients with suspected ALS have timely access to appropriate therapeutic trials. The present review provides an analysis of current and recently developed assessment techniques, including novel imaging and electrophysiological approaches used to study corticomotoneuronal pathology in ALS. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Reprogramming of orientation columns in visual cortex: a domino effect

PubMed Central

Bachatene, Lyes; Bharmauria, Vishal; Cattan, Sarah; Rouat, Jean; Molotchnikoff, Stéphane

2015-01-01

Cortical organization rests upon the fundamental principle that neurons sharing similar properties are co-located. In the visual cortex, neurons are organized into orientation columns. In a column, most neurons respond optimally to the same axis of an oriented edge, that is, the preferred orientation. This orientation selectivity is believed to be absolute in adulthood. However, in a fully mature brain, it has been established that neurons change their selectivity following sensory experience or visual adaptation. Here, we show that after applying an adapter away from the tested cells, neurons whose receptive fields were located remotely from the adapted site also exhibit a novel selectivity in spite of the fact that they were not adapted. These results indicate a robust reconfiguration and remapping of the orientation domains with respect to each other thus removing the possibility of an orientation hole in the new hypercolumn. These data suggest that orientation columns transcend anatomy, and are almost strictly functionally dynamic. PMID:25801392

Fragile X Mental Retardation Protein and Dendritic Local Translation of the Alpha Subunit of the Calcium/Calmodulin-Dependent Kinase II Messenger RNA Are Required for the Structural Plasticity Underlying Olfactory Learning.

PubMed

Daroles, Laura; Gribaudo, Simona; Doulazmi, Mohamed; Scotto-Lomassese, Sophie; Dubacq, Caroline; Mandairon, Nathalie; Greer, Charles August; Didier, Anne; Trembleau, Alain; Caillé, Isabelle

2016-07-15

In the adult brain, structural plasticity allowing gain or loss of synapses remodels circuits to support learning. In fragile X syndrome, the absence of fragile X mental retardation protein (FMRP) leads to defects in plasticity and learning deficits. FMRP is a master regulator of local translation but its implication in learning-induced structural plasticity is unknown. Using an olfactory learning task requiring adult-born olfactory bulb neurons and cell-specific ablation of FMRP, we investigated whether learning shapes adult-born neuron morphology during their synaptic integration and its dependence on FMRP. We used alpha subunit of the calcium/calmodulin-dependent kinase II (αCaMKII) mutant mice with altered dendritic localization of αCaMKII messenger RNA, as well as a reporter of αCaMKII local translation to investigate the role of this FMRP messenger RNA target in learning-dependent structural plasticity. Learning induces profound changes in dendritic architecture and spine morphology of adult-born neurons that are prevented by ablation of FMRP in adult-born neurons and rescued by an metabotropic glutamate receptor 5 antagonist. Moreover, dendritically translated αCaMKII is necessary for learning and associated structural modifications and learning triggers an FMRP-dependent increase of αCaMKII dendritic translation in adult-born neurons. Our results strongly suggest that FMRP mediates structural plasticity of olfactory bulb adult-born neurons to support olfactory learning through αCaMKII local translation. This reveals a new role for FMRP-regulated dendritic local translation in learning-induced structural plasticity. This might be of clinical relevance for the understanding of critical periods disruption in autism spectrum disorder patients, among which fragile X syndrome is the primary monogenic cause. Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Behavioral plasticity through the modulation of switch neurons.

PubMed

Vassiliades, Vassilis; Christodoulou, Chris

2016-02-01

A central question in artificial intelligence is how to design agents capable of switching between different behaviors in response to environmental changes. Taking inspiration from neuroscience, we address this problem by utilizing artificial neural networks (NNs) as agent controllers, and mechanisms such as neuromodulation and synaptic gating. The novel aspect of this work is the introduction of a type of artificial neuron we call "switch neuron". A switch neuron regulates the flow of information in NNs by selectively gating all but one of its incoming synaptic connections, effectively allowing only one signal to propagate forward. The allowed connection is determined by the switch neuron's level of modulatory activation which is affected by modulatory signals, such as signals that encode some information about the reward received by the agent. An important aspect of the switch neuron is that it can be used in appropriate "switch modules" in order to modulate other switch neurons. As we show, the introduction of the switch modules enables the creation of sequences of gating events. This is achieved through the design of a modulatory pathway capable of exploring in a principled manner all permutations of the connections arriving on the switch neurons. We test the model by presenting appropriate architectures in nonstationary binary association problems and T-maze tasks. The results show that for all tasks, the switch neuron architectures generate optimal adaptive behaviors, providing evidence that the switch neuron model could be a valuable tool in simulations where behavioral plasticity is required. Copyright © 2015 Elsevier Ltd. All rights reserved.

Structural basis for serotonergic regulation of neural circuits in the mouse olfactory bulb.

PubMed

Suzuki, Yoshinori; Kiyokage, Emi; Sohn, Jaerin; Hioki, Hiroyuki; Toida, Kazunori

2015-02-01

Olfactory processing is well known to be regulated by centrifugal afferents from other brain regions, such as noradrenergic, acetylcholinergic, and serotonergic neurons. Serotonergic neurons widely innervate and regulate the functions of various brain regions. In the present study, we focused on serotonergic regulation of the olfactory bulb (OB), one of the most structurally and functionally well-defined brain regions. Visualization of a single neuron among abundant and dense fibers is essential to characterize and understand neuronal circuits. We accomplished this visualization by successfully labeling and reconstructing serotonin (5-hydroxytryptamine: 5-HT) neurons by infection with sindbis and adeno-associated virus into dorsal raphe nuclei (DRN) of mice. 5-HT synapses were analyzed by correlative confocal laser microscopy and serial-electron microscopy (EM) study. To further characterize 5-HT neuronal and network function, we analyzed whether glutamate was released from 5-HT synaptic terminals using immuno-EM. Our results are the first visualizations of complete 5-HT neurons and fibers projecting from DRN to the OB with bifurcations. We found that a single 5-HT axon can form synaptic contacts to both type 1 and 2 periglomerular cells within a single glomerulus. Through immunolabeling, we also identified vesicular glutamate transporter 3 in 5-HT neurons terminals, indicating possible glutamatergic transmission. Our present study strongly implicates the involvement of brain regions such as the DRN in regulation of the elaborate mechanisms of olfactory processing. We further provide a structure basis of the network for coordinating or linking olfactory encoding with other neural systems, with special attention to serotonergic regulation. © 2014 Wiley Periodicals, Inc.

Iterative free-energy optimization for recurrent neural networks (INFERNO).

PubMed

Pitti, Alexandre; Gaussier, Philippe; Quoy, Mathias

2017-01-01

The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes' synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle.

Evolution of the cerebellum as a neuronal machine for Bayesian state estimation

NASA Astrophysics Data System (ADS)

Paulin, M. G.

2005-09-01

The cerebellum evolved in association with the electric sense and vestibular sense of the earliest vertebrates. Accurate information provided by these sensory systems would have been essential for precise control of orienting behavior in predation. A simple model shows that individual spikes in electrosensory primary afferent neurons can be interpreted as measurements of prey location. Using this result, I construct a computational neural model in which the spatial distribution of spikes in a secondary electrosensory map forms a Monte Carlo approximation to the Bayesian posterior distribution of prey locations given the sense data. The neural circuit that emerges naturally to perform this task resembles the cerebellar-like hindbrain electrosensory filtering circuitry of sharks and other electrosensory vertebrates. The optimal filtering mechanism can be extended to handle dynamical targets observed from a dynamical platform; that is, to construct an optimal dynamical state estimator using spiking neurons. This may provide a generic model of cerebellar computation. Vertebrate motion-sensing neurons have specific fractional-order dynamical characteristics that allow Bayesian state estimators to be implemented elegantly and efficiently, using simple operations with asynchronous pulses, i.e. spikes. The computational neural models described in this paper represent a novel kind of particle filter, using spikes as particles. The models are specific and make testable predictions about computational mechanisms in cerebellar circuitry, while providing a plausible explanation of cerebellar contributions to aspects of motor control, perception and cognition.

Coding of time-dependent stimuli in homogeneous and heterogeneous neural populations.

PubMed

Beiran, Manuel; Kruscha, Alexandra; Benda, Jan; Lindner, Benjamin

2018-04-01

We compare the information transmission of a time-dependent signal by two types of uncoupled neuron populations that differ in their sources of variability: i) a homogeneous population whose units receive independent noise and ii) a deterministic heterogeneous population, where each unit exhibits a different baseline firing rate ('disorder'). Our criterion for making both sources of variability quantitatively comparable is that the interspike-interval distributions are identical for both systems. Numerical simulations using leaky integrate-and-fire neurons unveil that a non-zero amount of both noise or disorder maximizes the encoding efficiency of the homogeneous and heterogeneous system, respectively, as a particular case of suprathreshold stochastic resonance. Our findings thus illustrate that heterogeneity can render similarly profitable effects for neuronal populations as dynamic noise. The optimal noise/disorder depends on the system size and the properties of the stimulus such as its intensity or cutoff frequency. We find that weak stimuli are better encoded by a noiseless heterogeneous population, whereas for strong stimuli a homogeneous population outperforms an equivalent heterogeneous system up to a moderate noise level. Furthermore, we derive analytical expressions of the coherence function for the cases of very strong noise and of vanishing intrinsic noise or heterogeneity, which predict the existence of an optimal noise intensity. Our results show that, depending on the type of signal, noise as well as heterogeneity can enhance the encoding performance of neuronal populations.

The TrkB agonist 7,8-dihydroxyflavone changes the structural dynamics of neocortical pyramidal neurons and improves object recognition in mice.

PubMed

Perez-Rando, Marta; Castillo-Gomez, Esther; Bueno-Fernandez, Clara; Nacher, Juan

2018-06-01

BDNF and its receptor TrkB have important roles in neurodevelopment, neural plasticity, learning, and memory. Alterations in TrkB expression have been described in different CNS disorders. Therefore, drugs interacting with TrkB, specially agonists, are promising therapeutic tools. Among them, the recently described 7,8-dihydroxyflavone (DHF), an orally bioactive compound, has been successfully tested in animal models of these diseases. Recent studies have shown the influence of this drug on the structure of pyramidal neurons, specifically on dendritic spine density. However, there is no information yet on how DHF may alter the structural dynamics of these neurons (i.e., real-time study of the addition/elimination of dendritic spines and axonal boutons). To gain knowledge on these effects of DHF, we have performed a real-time analysis of spine and axonal dynamics in pyramidal neurons of barrel cortex, using cranial windows and 2-photon microscopy during a chronic oral treatment with this drug. After confirming TrkB expression in these neurons, we found that DHF increased the gain rates of spines and axonal boutons, as well as improved object recognition memory. These results help to understand how the activation of the BDNF-TrkB system can improve basic behavioral tasks through changes in the structural dynamics of pyramidal neurons. Moreover, they highlight DHF as a promising therapeutic vector for certain brain disorders in which this system is altered.

A-type potassium channels differentially tune afferent pathways from rat solitary tract nucleus to caudal ventrolateral medulla or paraventricular hypothalamus

PubMed Central

Bailey, T W; Hermes, S M; Whittier, K L; Aicher, S A; Andresen, M C

2007-01-01

The solitary tract nucleus (NTS) conveys visceral information to diverse central networks involved in homeostatic regulation. Although afferent information content arriving at various CNS sites varies substantially, little is known about the contribution of processing within the NTS to these differences. Using retrograde dyes to identify specific NTS projection neurons, we recently reported that solitary tract (ST) afferents directly contact NTS neurons projecting to caudal ventrolateral medulla (CVLM) but largely only indirectly contact neurons projecting to the hypothalamic paraventricular nucleus (PVN). Since intrinsic properties impact information transmission, here we evaluated potassium channel expression and somatodendritic morphology of projection neurons and their relation to afferent information output directed to PVN or CVLM pathways. In slices, tracer-identified projection neurons were classified as directly or indirectly (polysynaptically) coupled to ST afferents by EPSC latency characteristics (directly coupled, jitter < 200 μs). In each neuron, voltage-dependent potassium currents (IK) were evaluated and, in representative neurons, biocytin-filled structures were quantified. Both CVLM- and PVN-projecting neurons had similar, tetraethylammonium-sensitive IK. However, only PVN-projecting NTS neurons displayed large transient, 4aminopyridine-sensitive, A-type currents (IKA). PVN-projecting neurons had larger cell bodies with more elaborate dendritic morphology than CVLM-projecting neurons. ST shocks faithfully (> 75%) triggered action potentials in CVLM-projecting neurons but spike output was uniformly low (< 20%) in PVN-projecting neurons. Pre-conditioning hyperpolarization removed IKA inactivation and attenuated ST-evoked spike generation along PVN but not CVLM pathways. Thus, multiple differences in structure, organization, synaptic transmission and ion channel expression tune the overall fidelity of afferent signals that reach these destinations. PMID:17510187

Synaptic Impairment and Robustness of Excitatory Neuronal Networks with Different Topologies

PubMed Central

Mirzakhalili, Ehsan; Gourgou, Eleni; Booth, Victoria; Epureanu, Bogdan

2017-01-01

Synaptic deficiencies are a known hallmark of neurodegenerative diseases, but the diagnosis of impaired synapses on the cellular level is not an easy task. Nonetheless, changes in the system-level dynamics of neuronal networks with damaged synapses can be detected using techniques that do not require high spatial resolution. This paper investigates how the structure/topology of neuronal networks influences their dynamics when they suffer from synaptic loss. We study different neuronal network structures/topologies by specifying their degree distributions. The modes of the degree distribution can be used to construct networks that consist of rich clubs and resemble small world networks, as well. We define two dynamical metrics to compare the activity of networks with different structures: persistent activity (namely, the self-sustained activity of the network upon removal of the initial stimulus) and quality of activity (namely, percentage of neurons that participate in the persistent activity of the network). Our results show that synaptic loss affects the persistent activity of networks with bimodal degree distributions less than it affects random networks. The robustness of neuronal networks enhances when the distance between the modes of the degree distribution increases, suggesting that the rich clubs of networks with distinct modes keep the whole network active. In addition, a tradeoff is observed between the quality of activity and the persistent activity. For a range of distributions, both of these dynamical metrics are considerably high for networks with bimodal degree distribution compared to random networks. We also propose three different scenarios of synaptic impairment, which may correspond to different pathological or biological conditions. Regardless of the network structure/topology, results demonstrate that synaptic loss has more severe effects on the activity of the network when impairments are correlated with the activity of the neurons. PMID:28659765

Organization of monosynaptic inputs to the serotonin and dopamine neuromodulatorysystems

PubMed Central

Ogawa, Sachie K.; Cohen, Jeremiah Y.; Hwang, Dabin; Uchida, Naoshige; Watabe-Uchida, Mitsuko

2014-01-01

SUMMARY Serotonin and dopamine are major neuromodulators. Here we used a modified rabies virus to identify monosynaptic inputs to serotonin neurons in the dorsal and median raphe (DR and MR). We found that inputs to DR and MR serotonin neurons are spatially shiftedin the forebrain, with MRserotonin neurons receiving inputs from more medial structures. We then compared these data with inputs to dopamine neurons in the ventral tegmental area (VTA) and substantianigra pars compacta (SNc). We found that DR serotonin neurons receive inputs from a remarkably similar set of areas as VTA dopamine neurons, apart from the striatum, which preferentially targets dopamine neurons. Ourresults suggest three majorinput streams: amedial stream regulates MR serotonin neurons, anintermediate stream regulatesDR serotonin and VTA dopamine neurons, and alateral stream regulatesSNc dopamine neurons. These results providefundamental organizational principlesofafferent control forserotonin and dopamine. PMID:25108805

Theory of optimal balance predicts and explains the amplitude and decay time of synaptic inhibition

PubMed Central

Kim, Jaekyung K.; Fiorillo, Christopher D.

2017-01-01

Synaptic inhibition counterbalances excitation, but it is not known what constitutes optimal inhibition. We previously proposed that perfect balance is achieved when the peak of an excitatory postsynaptic potential (EPSP) is exactly at spike threshold, so that the slightest variation in excitation determines whether a spike is generated. Using simulations, we show that the optimal inhibitory postsynaptic conductance (IPSG) increases in amplitude and decay rate as synaptic excitation increases from 1 to 800 Hz. As further proposed by theory, we show that optimal IPSG parameters can be learned through anti-Hebbian rules. Finally, we compare our theoretical optima to published experimental data from 21 types of neurons, in which rates of synaptic excitation and IPSG decay times vary by factors of about 100 (5–600 Hz) and 50 (1–50 ms), respectively. From an infinite range of possible decay times, theory predicted experimental decay times within less than a factor of 2. Across a distinct set of 15 types of neuron recorded in vivo, theory predicted the amplitude of synaptic inhibition within a factor of 1.7. Thus, the theory can explain biophysical quantities from first principles. PMID:28281523

Theory of optimal balance predicts and explains the amplitude and decay time of synaptic inhibition

NASA Astrophysics Data System (ADS)

Kim, Jaekyung K.; Fiorillo, Christopher D.

2017-03-01

Synaptic inhibition counterbalances excitation, but it is not known what constitutes optimal inhibition. We previously proposed that perfect balance is achieved when the peak of an excitatory postsynaptic potential (EPSP) is exactly at spike threshold, so that the slightest variation in excitation determines whether a spike is generated. Using simulations, we show that the optimal inhibitory postsynaptic conductance (IPSG) increases in amplitude and decay rate as synaptic excitation increases from 1 to 800 Hz. As further proposed by theory, we show that optimal IPSG parameters can be learned through anti-Hebbian rules. Finally, we compare our theoretical optima to published experimental data from 21 types of neurons, in which rates of synaptic excitation and IPSG decay times vary by factors of about 100 (5-600 Hz) and 50 (1-50 ms), respectively. From an infinite range of possible decay times, theory predicted experimental decay times within less than a factor of 2. Across a distinct set of 15 types of neuron recorded in vivo, theory predicted the amplitude of synaptic inhibition within a factor of 1.7. Thus, the theory can explain biophysical quantities from first principles.

Reconstruction of Sensory Stimuli Encoded with Integrate-and-Fire Neurons with Random Thresholds

PubMed Central

Lazar, Aurel A.; Pnevmatikakis, Eftychios A.

2013-01-01

We present a general approach to the reconstruction of sensory stimuli encoded with leaky integrate-and-fire neurons with random thresholds. The stimuli are modeled as elements of a Reproducing Kernel Hilbert Space. The reconstruction is based on finding a stimulus that minimizes a regularized quadratic optimality criterion. We discuss in detail the reconstruction of sensory stimuli modeled as absolutely continuous functions as well as stimuli with absolutely continuous first-order derivatives. Reconstruction results are presented for stimuli encoded with single as well as a population of neurons. Examples are given that demonstrate the performance of the reconstruction algorithms as a function of threshold variability. PMID:24077610

Degree Correlations Optimize Neuronal Network Sensitivity to Sub-Threshold Stimuli

PubMed Central

Schmeltzer, Christian; Kihara, Alexandre Hiroaki; Sokolov, Igor Michailovitsch; Rüdiger, Sten

2015-01-01

Information processing in the brain crucially depends on the topology of the neuronal connections. We investigate how the topology influences the response of a population of leaky integrate-and-fire neurons to a stimulus. We devise a method to calculate firing rates from a self-consistent system of equations taking into account the degree distribution and degree correlations in the network. We show that assortative degree correlations strongly improve the sensitivity for weak stimuli and propose that such networks possess an advantage in signal processing. We moreover find that there exists an optimum in assortativity at an intermediate level leading to a maximum in input/output mutual information. PMID:26115374

Fluoxetine induces input-specific hippocampal dendritic spine remodeling along the septotemporal axis in adulthood and middle age.

PubMed

McAvoy, Kathleen; Russo, Craig; Kim, Shannen; Rankin, Genelle; Sahay, Amar

2015-11-01

Fluoxetine, a selective serotonin-reuptake inhibitor (SSRI), is known to induce structural rearrangements and changes in synaptic transmission in hippocampal circuitry. In the adult hippocampus, structural changes include neurogenesis, dendritic, and axonal plasticity of pyramidal and dentate granule neurons, and dedifferentiation of dentate granule neurons. However, much less is known about how chronic fluoxetine affects these processes along the septotemporal axis and during the aging process. Importantly, studies documenting the effects of fluoxetine on density and distribution of spines along different dendritic segments of dentate granule neurons and CA1 pyramidal neurons along the septotemporal axis of hippocampus in adulthood and during aging are conspicuously absent. Here, we use a transgenic mouse line in which mature dentate granule neurons and CA1 pyramidal neurons are genetically labeled with green fluorescent protein (GFP) to investigate the effects of chronic fluoxetine treatment (18 mg/kg/day) on input-specific spine remodeling and mossy fiber structural plasticity in the dorsal and ventral hippocampus in adulthood and middle age. In addition, we examine levels of adult hippocampal neurogenesis, maturation state of dentate granule neurons, neuronal activity, and glutamic acid decarboxylase-67 expression in response to chronic fluoxetine in adulthood and middle age. Our studies reveal that while chronic fluoxetine fails to augment adult hippocampal neurogenesis in middle age, the middle-aged hippocampus retains high sensitivity to changes in the dentate gyrus (DG) such as dematuration, hypoactivation, and increased glutamic acid decarboxylase 67 (GAD67) expression. Interestingly, the middle-aged hippocampus shows greater sensitivity to fluoxetine-induced input-specific synaptic remodeling than the hippocampus in adulthood with the stratum-oriens of CA1 exhibiting heightened structural plasticity. The input-specific changes and circuit-level modifications in middle-age were associated with modest enhancement in contextual fear memory precision, anxiety-like behavior and antidepressant-like behavioral responses. © 2015 Wiley Periodicals, Inc.

Fluoxetine induces input-specific hippocampal dendritic spine remodeling along the septo-temporal axis in adulthood and middle age

PubMed Central

McAvoy, Kathleen; Russo, Craig; Kim, Shannen; Rankin, Genelle; Sahay, Amar

2015-01-01

Fluoxetine, a selective serotonin-reuptake inhibitor (SSRI), is known to induce structural rearrangements and changes in synaptic transmission in hippocampal circuitry. In the adult hippocampus, structural changes include neurogenesis, dendritic and axonal plasticity of pyramidal and dentate granule neurons, and dedifferentiation of dentate granule neurons. However, much less is known about how chronic fluoxetine affects these processes along the septo-temporal axis and during the aging process. Importantly, studies documenting the effects of fluoxetine on density and distribution of spines along different dendritic segments of dentate granule neurons and CA1 pyramidal neurons along the septo-temporal axis of hippocampus in adulthood and during aging are conspicuously absent. Here, we use a transgenic mouse line in which mature dentate granule neurons and CA1 pyramidal neurons are genetically labeled with green fluorescent protein (GFP) to investigate the effects of chronic fluoxetine treatment (18mg/kg/day) on input-specific spine remodeling and mossy fiber structural plasticity in the dorsal and ventral hippocampus in adulthood and middle age. In addition, we examine levels of adult hippocampal neurogenesis, maturation state of dentate granule neurons, neuronal activity and glutamic acid decarboxylase-67 expression in response to chronic fluoxetine in adulthood and middle age. Our studies reveal that while chronic fluoxetine fails to augment adult hippocampal neurogenesis in middle age, the middle-aged hippocampus retains high sensitivity to changes in the dentate gyrus (DG) such as dematuration, hypoactivation, and increased glutamic acid decarboxylase 67 (GAD67) expression. Interestingly, the middle-aged hippocampus shows greater sensitivity to fluoxetine-induced input-specific synaptic remodeling than the hippocampus in adulthood with the stratum-oriens of CA1 exhibiting heightened structural plasticity. The input-specific changes and circuit-level modifications in middle-age were associated with modest enhancement in contextual fear memory precision, anxiety-like behavior and antidepressant-like behavioral responses. PMID:25850664

Ultrasensitive dual probe immunosensor for the monitoring of nicotine induced-brain derived neurotrophic factor released from cancer cells.

PubMed

Akhtar, Mahmood H; Hussain, Khalil K; Gurudatt, N G; Chandra, Pranjal; Shim, Yoon-Bo

2018-09-30

Brain-derived neurotrophic factor (BDNF) was detected in the extracellular matrix of neuronal cells using a dual probe immunosensor (DPI), where one of them was used as a working and another bioconjugate loading probe. The working probe was fabricated by covalently immobilizing capture anti-BDNF (Cap Ab) on the gold nanoparticles (AuNPs)/conducting polymer composite layer. The bioconjugate probe was modified by drop casting a bioconjugate particles composed of conducting polymer self-assembled AuNPs, immobilized with detection anti-BDNF (Det Ab) and toluidine blue O (TBO). Each sensor layer was characterized using the surface analysis and electrochemical methods. Two modified probes were precisely faced each other to form a microfluidic channel structure and the gap between inside modified surfaces was about 19 µm. At optimized conditions, the DPI showed a linear dynamic range from 4.0 to 600.0 pg/ml with a detection limit of 1.5 ± 0.012 pg/ml. Interference effect of IgG, arginine, glutamine, serine, albumin, and fibrinogene were examined and stability of the developed biosensor was also investigated. The reliability of the DPI sensor was evaluated by monitoring the extracellular release of BDNF using exogenic activators (ethanol, K + , and nicotine) in neuronal and non-neuronal cells. In addition, the effect of nicotine onto neuroblastoma cancer cells (SH-SY5Y) was studied in detail. Copyright © 2018 Elsevier B.V. All rights reserved.

Neural Correlates of Temporal Credit Assignment in the Parietal Lobe

PubMed Central

Eisenberg, Ian; Gottlieb, Jacqueline

2014-01-01

Empirical studies of decision making have typically assumed that value learning is governed by time, such that a reward prediction error arising at a specific time triggers temporally-discounted learning for all preceding actions. However, in natural behavior, goals must be acquired through multiple actions, and each action can have different significance for the final outcome. As is recognized in computational research, carrying out multi-step actions requires the use of credit assignment mechanisms that focus learning on specific steps, but little is known about the neural correlates of these mechanisms. To investigate this question we recorded neurons in the monkey lateral intraparietal area (LIP) during a serial decision task where two consecutive eye movement decisions led to a final reward. The underlying decision trees were structured such that the two decisions had different relationships with the final reward, and the optimal strategy was to learn based on the final reward at one of the steps (the “F” step) but ignore changes in this reward at the remaining step (the “I” step). In two distinct contexts, the F step was either the first or the second in the sequence, controlling for effects of temporal discounting. We show that LIP neurons had the strongest value learning and strongest post-decision responses during the transition after the F step regardless of the serial position of this step. Thus, the neurons encode correlates of temporal credit assignment mechanisms that allocate learning to specific steps independently of temporal discounting. PMID:24523935

Fidelity of the ensemble code for visual motion in primate retina.

PubMed

Frechette, E S; Sher, A; Grivich, M I; Petrusca, D; Litke, A M; Chichilnisky, E J

2005-07-01

Sensory experience typically depends on the ensemble activity of hundreds or thousands of neurons, but little is known about how populations of neurons faithfully encode behaviorally important sensory information. We examined how precisely speed of movement is encoded in the population activity of magnocellular-projecting parasol retinal ganglion cells (RGCs) in macaque monkey retina. Multi-electrode recordings were used to measure the activity of approximately 100 parasol RGCs simultaneously in isolated retinas stimulated with moving bars. To examine how faithfully the retina signals motion, stimulus speed was estimated directly from recorded RGC responses using an optimized algorithm that resembles models of motion sensing in the brain. RGC population activity encoded speed with a precision of approximately 1%. The elementary motion signal was conveyed in approximately 10 ms, comparable to the interspike interval. Temporal structure in spike trains provided more precise speed estimates than time-varying firing rates. Correlated activity between RGCs had little effect on speed estimates. The spatial dispersion of RGC receptive fields along the axis of motion influenced speed estimates more strongly than along the orthogonal direction, as predicted by a simple model based on RGC response time variability and optimal pooling. on and off cells encoded speed with similar and statistically independent variability. Simulation of downstream speed estimation using populations of speed-tuned units showed that peak (winner take all) readout provided more precise speed estimates than centroid (vector average) readout. These findings reveal how faithfully the retinal population code conveys information about stimulus speed and the consequences for motion sensing in the brain.

Central Chemoreceptors: Locations and Functions

PubMed Central

Nattie, Eugene; Li, Aihua

2016-01-01

Central chemoreception traditionally refers to a change in ventilation attributable to changes in CO2/H+ detected within the brain. Interest in central chemoreception has grown substantially since the previous Handbook of Physiology published in 1986. Initially, central chemoreception was localized to areas on the ventral medullary surface, a hypothesis complemented by the recent identification of neurons with specific phenotypes near one of these areas as putative chemoreceptor cells. However, there is substantial evidence that many sites participate in central chemoreception some located at a distance from the ventral medulla. Functionally, central chemoreception, via the sensing of brain interstitial fluid H+, serves to detect and integrate information on 1) alveolar ventilation (arterial PCO2), 2) brain blood flow and metabolism and 3) acid-base balance, and, in response, can affect breathing, airway resistance, blood pressure (sympathetic tone) and arousal. In addition, central chemoreception provides a tonic ‘drive’ (source of excitation) at the normal, baseline PCO2 level that maintains a degree of functional connectivity among brainstem respiratory neurons necessary to produce eupneic breathing. Central chemoreception responds to small variations in PCO2 to regulate normal gas exchange and to large changes in PCO2 to minimize acid-base changes. Central chemoreceptor sites vary in function with sex and with development. From an evolutionary perspective, central chemoreception grew out of the demands posed by air vs. water breathing, homeothermy, sleep, optimization of the work of breathing with the ‘ideal’ arterial PCO2, and the maintenance of the appropriate pH at 37°C for optimal protein structure and function. PMID:23728974

Olfactory receptor neuron profiling using sandalwood odorants.

PubMed

Bieri, Stephan; Monastyrskaia, Katherine; Schilling, Boris

2004-07-01

The mammalian olfactory system can discriminate between volatile molecules with subtle differences in their molecular structures. Efforts in synthetic chemistry have delivered a myriad of smelling compounds of different qualities as well as many molecules with very similar olfactive properties. One important class of molecules in the fragrance industry are sandalwood odorants. Sandalwood oil and four synthetic sandalwood molecules were selected to study the activation profile of endogenous olfactory receptors when exposed to compounds from the same odorant family. Dissociated rat olfactory receptor neurons were exposed to the sandalwood molecules and the receptor activation studied by monitoring fluxes in the internal calcium concentration. Olfactory receptor neurons were identified that were specifically stimulated by sandalwood compounds. These neurons expressed olfactory receptors that can discriminate between sandalwood odorants with slight differences in their molecular structures. This is the first study in which an important class of perfume compounds was analyzed for its ability to activate endogenous olfactory receptors in olfactory receptor neurons.

Stress Increases Peripheral Axon Growth and Regeneration through Glucocorticoid Receptor-Dependent Transcriptional Programs

PubMed Central

Alexander, Jessica K.; Madalena, Kathryn M.; Motti, Dario; Quach, Tam; Zha, Alicia; Webster Marketon, Jeanette

2017-01-01

Abstract Stress and glucocorticoid (GC) release are common behavioral and hormonal responses to injury or disease. In the brain, stress/GCs can alter neuron structure and function leading to cognitive impairment. Stress and GCs also exacerbate pain, but whether a corresponding change occurs in structural plasticity of sensory neurons is unknown. Here, we show that in female mice (Mus musculus) basal GC receptor (Nr3c1, also known as GR) expression in dorsal root ganglion (DRG) sensory neurons is 15-fold higher than in neurons in canonical stress-responsive brain regions (M. musculus). In response to stress or GCs, adult DRG neurite growth increases through mechanisms involving GR-dependent gene transcription. In vivo, prior exposure to an acute systemic stress increases peripheral nerve regeneration. These data have broad clinical implications and highlight the importance of stress and GCs as novel behavioral and circulating modifiers of neuronal plasticity. PMID:28828403

Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols.

PubMed

Xi, Jun; Xue, Yujing; Xu, Yinxiang; Shen, Yuhong

2013-11-01

In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

3D quantitative phase imaging of neural networks using WDT

NASA Astrophysics Data System (ADS)

Kim, Taewoo; Liu, S. C.; Iyer, Raj; Gillette, Martha U.; Popescu, Gabriel

2015-03-01

White-light diffraction tomography (WDT) is a recently developed 3D imaging technique based on a quantitative phase imaging system called spatial light interference microscopy (SLIM). The technique has achieved a sub-micron resolution in all three directions with high sensitivity granted by the low-coherence of a white-light source. Demonstrations of the technique on single cell imaging have been presented previously; however, imaging on any larger sample, including a cluster of cells, has not been demonstrated using the technique. Neurons in an animal body form a highly complex and spatially organized 3D structure, which can be characterized by neuronal networks or circuits. Currently, the most common method of studying the 3D structure of neuron networks is by using a confocal fluorescence microscope, which requires fluorescence tagging with either transient membrane dyes or after fixation of the cells. Therefore, studies on neurons are often limited to samples that are chemically treated and/or dead. WDT presents a solution for imaging live neuron networks with a high spatial and temporal resolution, because it is a 3D imaging method that is label-free and non-invasive. Using this method, a mouse or rat hippocampal neuron culture and a mouse dorsal root ganglion (DRG) neuron culture have been imaged in order to see the extension of processes between the cells in 3D. Furthermore, the tomogram is compared with a confocal fluorescence image in order to investigate the 3D structure at synapses.

Structure-activity relationship of sulfated hetero/galactofucan polysaccharides on dopaminergic neuron.

PubMed

Wang, Jing; Liu, Huaide; Jin, Weihua; Zhang, Hong; Zhang, Quanbin

2016-01-01

Parkinson's disease (PD) is associated with progressive loss of dopaminergic neurons and more-widespread neuronal changes that cause complex symptoms. The aim of this study was to investigate the structure-activity relationship of sulfated hetero-polysaccharides (DF1) and sulfated galactofucan polysaccharides (DF2) on dopaminergic neuron in vivo and in vitro. Treatment with samples significantly ameliorated the depletion of both DA and TH-, Bcl-2- and Bax-positive neurons in MPTP-induced PD mice, DF1 showed the highest activity. The in vitro results found that DF1 and DF2 could reverse the decreased mitochondrial activity and the increased LDL release induced by MPP(+) (P<0.01 or P<0.001) which provides further evidence that DF1 and DF2 also exerts a direct protection against the neuronal injury caused by MPP(+). Furthermore, the administration of samples effectively decreased lipid peroxidation and increased the level/activities of GSH, GSH-PX, MDA and CAT in MPTP mice. Thus, the neuron protective effect may be mediated, in part, through antioxidant activity and the prevention of cell apoptosis. The chemical composition of DF1, DF2 and DF differed markedly, the DF1 fraction had the most complex chemical composition and showed the highest neuron protective activity. These results suggest that diverse monosaccharides and uronic acid might contribute to neuron protective activity. Copyright © 2015 Elsevier B.V. All rights reserved.

Visual Receptive Field Structure of Cortical Inhibitory Neurons Revealed by Two-Photon Imaging Guided Recording

PubMed Central

Liu, Bao-hua; Li, Pingyang; Li, Ya-tang; Sun, Yujiao J.; Yanagawa, Yuchio; Obata, Kunihiko; Zhang, Li I.; Tao, Huizhong W.

2009-01-01

Synaptic inhibition plays an important role in shaping receptive field (RF) properties in the visual cortex. However, the underlying mechanisms remain not well understood, partly due to difficulties in systematically studying functional properties of cortical inhibitory neurons in vivo. Here, we established two-photon imaging guided cell-attached recordings from genetically labelled inhibitory neurons and nearby “shadowed” excitatory neurons in the primary visual cortex of adult mice. Our results revealed that in layer 2/3, the majority of excitatory neurons exhibited both On and Off spike subfields, with their spatial arrangement varying from being completely segregated to overlapped. On the other hand, most layer 4 excitatory neurons exhibited only one discernable subfield. Interestingly, no RF structure with significantly segregated On and Off subfields was observed for layer 2/3 inhibitory neurons of either the fast-spike or regular-spike type. They predominantly possessed overlapped On and Off subfields with a significantly larger size than the excitatory neurons, and exhibited much weaker orientation tuning. These results from the mouse visual cortex suggest that different from the push-pull model proposed for simple cells, layer 2/3 simple-type neurons with segregated spike On and Off subfields likely receive spatially overlapped inhibitory On and Off inputs. We propose that the phase-insensitive inhibition can enhance the spatial distinctiveness of On and Off subfields through a gain control mechanism. PMID:19710305

The basic nonuniformity of the cerebral cortex

PubMed Central

Herculano-Houzel, Suzana; Collins, Christine E.; Wong, Peiyan; Kaas, Jon H.; Lent, Roberto

2008-01-01

Evolutionary changes in the size of the cerebral cortex, a columnar structure, often occur through the addition or subtraction of columnar modules with the same number of neurons underneath a unit area of cortical surface. This view is based on the work of Rockel et al. [Rockel AJ, Hiorns RW, Powell TP (1980) The basic uniformity in structure of the neocortex. Brain 103:221–244], who found a steady number of approximately 110 neurons underneath a surface area of 750 μm2 (147,000 underneath 1 mm2) of the cerebral cortex of five species from different mammalian orders. These results have since been either corroborated or disputed by different groups. Here, we show that the number of neurons underneath 1 mm2 of the cerebral cortical surface of nine primate species and the closely related Tupaia sp. is not constant and varies by three times across species. We found that cortical thickness is not inversely proportional to neuronal density across species and that total cortical surface area increases more slowly than, rather than linearly with, the number of neurons underneath it. The number of neurons beneath a unit area of cortical surface varies linearly with neuronal density, a parameter that is neither related to cortical size nor total number of neurons. Our finding of a variable number of neurons underneath a unit area of the cerebral cortex across primate species indicates that models of cortical organization cannot assume that cortical columns in different primates consist of invariant numbers of neurons. PMID:18689685

The basic nonuniformity of the cerebral cortex.

PubMed

Herculano-Houzel, Suzana; Collins, Christine E; Wong, Peiyan; Kaas, Jon H; Lent, Roberto

2008-08-26

Evolutionary changes in the size of the cerebral cortex, a columnar structure, often occur through the addition or subtraction of columnar modules with the same number of neurons underneath a unit area of cortical surface. This view is based on the work of Rockel et al. [Rockel AJ, Hiorns RW, Powell TP (1980) The basic uniformity in structure of the neocortex. Brain 103:221-244], who found a steady number of approximately 110 neurons underneath a surface area of 750 microm(2) (147,000 underneath 1 mm(2)) of the cerebral cortex of five species from different mammalian orders. These results have since been either corroborated or disputed by different groups. Here, we show that the number of neurons underneath 1 mm(2) of the cerebral cortical surface of nine primate species and the closely related Tupaia sp. is not constant and varies by three times across species. We found that cortical thickness is not inversely proportional to neuronal density across species and that total cortical surface area increases more slowly than, rather than linearly with, the number of neurons underneath it. The number of neurons beneath a unit area of cortical surface varies linearly with neuronal density, a parameter that is neither related to cortical size nor total number of neurons. Our finding of a variable number of neurons underneath a unit area of the cerebral cortex across primate species indicates that models of cortical organization cannot assume that cortical columns in different primates consist of invariant numbers of neurons.

Comparisons of Neuronal and Excitatory Network Properties between the Rat Brainstem Nuclei that Participate in Vertical and Horizontal Gaze Holding

PubMed Central

Sugimura, Taketoshi; Yanagawa, Yuchio

2017-01-01

Gaze holding is primarily controlled by neural structures including the prepositus hypoglossi nucleus (PHN) for horizontal gaze and the interstitial nucleus of Cajal (INC) for vertical and torsional gaze. In contrast to the accumulating findings of the PHN, there is no report regarding the membrane properties of INC neurons or the local networks in the INC. In this study, to verify whether the neural structure of the INC is similar to that of the PHN, we investigated the neuronal and network properties of the INC using whole-cell recordings in rat brainstem slices. Three types of afterhyperpolarization (AHP) profiles and five firing patterns observed in PHN neurons were also observed in INC neurons. However, the overall distributions based on the AHP profile and the firing patterns of INC neurons were different from those of PHN neurons. The application of burst stimulation to a nearby site of a recorded INC neuron induced an increase in the frequency of spontaneous EPSCs. The duration of the increased EPSC frequency of INC neurons was not significantly different from that of PHN neurons. The percent of duration reduction induced by a Ca2+-permeable AMPA (CP-AMPA) receptor antagonist was significantly smaller in the INC than in the PHN. These findings suggest that local excitatory networks that activate sustained EPSC responses also exist in the INC, but their activation mechanisms including the contribution of CP-AMPA receptors differ between the INC and the PHN. PMID:28966973

Theoretical Limitations on Functional Imaging Resolution in Auditory Cortex

PubMed Central

Chen, Thomas L.; Watkins, Paul V.; Barbour, Dennis L.

2010-01-01

Functional imaging can reveal detailed organizational structure in cerebral cortical areas, but neuronal response features and local neural interconnectivity can influence the resulting images, possibly limiting the inferences that can be drawn about neural function. Discerning the fundamental principles of organizational structure in the auditory cortex of multiple species has been somewhat challenging historically both with functional imaging and with electrophysiology. A possible limitation affecting any methodology using pooled neuronal measures may be the relative distribution of response selectivity throughout the population of auditory cortex neurons. One neuronal response type inherited from the cochlea, for example, exhibits a receptive field that increases in size (i.e., decreases in selectivity) at higher stimulus intensities. Even though these neurons appear to represent a minority of auditory cortex neurons, they are likely to contribute disproportionately to the activity detected in functional images, especially if intense sounds are used for stimulation. To evaluate the potential influence of neuronal subpopulations upon functional images of primary auditory cortex, a model array representing cortical neurons was probed with virtual imaging experiments under various assumptions about the local circuit organization. As expected, different neuronal subpopulations were activated preferentially under different stimulus conditions. In fact, stimulus protocols that can preferentially excite selective neurons, resulting in a relatively sparse activation map, have the potential to improve the effective resolution of functional auditory cortical images. These experimental results also make predictions about auditory cortex organization that can be tested with refined functional imaging experiments. PMID:20079343

Dendrites are dispensable for basic motoneuron function but essential for fine tuning of behavior.

PubMed

Ryglewski, Stefanie; Kadas, Dimitrios; Hutchinson, Katie; Schuetzler, Natalie; Vonhoff, Fernando; Duch, Carsten

2014-12-16

Dendrites are highly complex 3D structures that define neuronal morphology and connectivity and are the predominant sites for synaptic input. Defects in dendritic structure are highly consistent correlates of brain diseases. However, the precise consequences of dendritic structure defects for neuronal function and behavioral performance remain unknown. Here we probe dendritic function by using genetic tools to selectively abolish dendrites in identified Drosophila wing motoneurons without affecting other neuronal properties. We find that these motoneuron dendrites are unexpectedly dispensable for synaptic targeting, qualitatively normal neuronal activity patterns during behavior, and basic behavioral performance. However, significant performance deficits in sophisticated motor behaviors, such as flight altitude control and switching between discrete courtship song elements, scale with the degree of dendritic defect. To our knowledge, our observations provide the first direct evidence that complex dendrite architecture is critically required for fine-tuning and adaptability within robust, evolutionarily constrained behavioral programs that are vital for mating success and survival. We speculate that the observed scaling of performance deficits with the degree of structural defect is consistent with gradual increases in intellectual disability during continuously advancing structural deficiencies in progressive neurological disorders.

High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons.

PubMed

Tyukin, Ivan; Gorban, Alexander N; Calvo, Carlos; Makarova, Julia; Makarov, Valeri A

2018-03-19

Codifying memories is one of the fundamental problems of modern Neuroscience. The functional mechanisms behind this phenomenon remain largely unknown. Experimental evidence suggests that some of the memory functions are performed by stratified brain structures such as the hippocampus. In this particular case, single neurons in the CA1 region receive a highly multidimensional input from the CA3 area, which is a hub for information processing. We thus assess the implication of the abundance of neuronal signalling routes converging onto single cells on the information processing. We show that single neurons can selectively detect and learn arbitrary information items, given that they operate in high dimensions. The argument is based on stochastic separation theorems and the concentration of measure phenomena. We demonstrate that a simple enough functional neuronal model is capable of explaining: (i) the extreme selectivity of single neurons to the information content, (ii) simultaneous separation of several uncorrelated stimuli or informational items from a large set, and (iii) dynamic learning of new items by associating them with already "known" ones. These results constitute a basis for organization of complex memories in ensembles of single neurons. Moreover, they show that no a priori assumptions on the structural organization of neuronal ensembles are necessary for explaining basic concepts of static and dynamic memories.

Synthesis, analgesic activity, and binding properties of some epibatidine analogs with a tropine skeleton.

PubMed

Rádl, S; Hafner, W; Budesínsky, M; Hejnová, L; Krejcí, I

2000-06-01

A series of epibatidine analogs and their positional isomers bearing an 8-azabicyclo[3.2.1]octane moiety is described. Also some of their simplified analogs bearing a 3-piperidine moiety are reported. Their receptor binding profiles (5-HT1A, 5-HT1B, M1, M2, neuronal nicotinic receptor) and analgesic activity (hot plate, acetic acid induced writhing) have been studied. Some of the compounds, especially those containing an 8-azabicyclo[3.2.1]oct-2-ene moiety possess high afinity for the nicotinic cholinergic receptor. The most analgesically active compounds are also highly toxic. Optimized structures (PM3-MOPAC, Alchemy 2000, Tripos Inc.) of compounds 1-9 were compared with that of epibatidine.

Keeping time: could quantum beating in microtubules be the basis for the neural synchrony related to consciousness?

PubMed

Craddock, Travis J A; Priel, Avner; Tuszynski, Jack A

2014-06-01

This paper discusses the possibility of quantum coherent oscillations playing a role in neuronal signaling. Consciousness correlates strongly with coherent neural oscillations, however the mechanisms by which neurons synchronize are not fully elucidated. Recent experimental evidence of quantum beats in light-harvesting complexes of plants (LHCII) and bacteria provided a stimulus for seeking similar effects in important structures found in animal cells, especially in neurons. We argue that microtubules (MTs), which play critical roles in all eukaryotic cells, possess structural and functional characteristics that are consistent with quantum coherent excitations in the aromatic groups of their tryptophan residues. Furthermore we outline the consequences of these findings on neuronal processes including the emergence of consciousness.

Optimal balance of the striatal medium spiny neuron network.

PubMed

Ponzi, Adam; Wickens, Jeffery R

2013-04-01

Slowly varying activity in the striatum, the main Basal Ganglia input structure, is important for the learning and execution of movement sequences. Striatal medium spiny neurons (MSNs) form cell assemblies whose population firing rates vary coherently on slow behaviourally relevant timescales. It has been shown that such activity emerges in a model of a local MSN network but only at realistic connectivities of 10 ~ 20% and only when MSN generated inhibitory post-synaptic potentials (IPSPs) are realistically sized. Here we suggest a reason for this. We investigate how MSN network generated population activity interacts with temporally varying cortical driving activity, as would occur in a behavioural task. We find that at unrealistically high connectivity a stable winners-take-all type regime is found where network activity separates into fixed stimulus dependent regularly firing and quiescent components. In this regime only a small number of population firing rate components interact with cortical stimulus variations. Around 15% connectivity a transition to a more dynamically active regime occurs where all cells constantly switch between activity and quiescence. In this low connectivity regime, MSN population components wander randomly and here too are independent of variations in cortical driving. Only in the transition regime do weak changes in cortical driving interact with many population components so that sequential cell assemblies are reproducibly activated for many hundreds of milliseconds after stimulus onset and peri-stimulus time histograms display strong stimulus and temporal specificity. We show that, remarkably, this activity is maximized at striatally realistic connectivities and IPSP sizes. Thus, we suggest the local MSN network has optimal characteristics - it is neither too stable to respond in a dynamically complex temporally extended way to cortical variations, nor is it too unstable to respond in a consistent repeatable way. Rather, it is optimized to generate stimulus dependent activity patterns for long periods after variations in cortical excitation.

Optimal Balance of the Striatal Medium Spiny Neuron Network

PubMed Central

Ponzi, Adam; Wickens, Jeffery R.

2013-01-01

Slowly varying activity in the striatum, the main Basal Ganglia input structure, is important for the learning and execution of movement sequences. Striatal medium spiny neurons (MSNs) form cell assemblies whose population firing rates vary coherently on slow behaviourally relevant timescales. It has been shown that such activity emerges in a model of a local MSN network but only at realistic connectivities of and only when MSN generated inhibitory post-synaptic potentials (IPSPs) are realistically sized. Here we suggest a reason for this. We investigate how MSN network generated population activity interacts with temporally varying cortical driving activity, as would occur in a behavioural task. We find that at unrealistically high connectivity a stable winners-take-all type regime is found where network activity separates into fixed stimulus dependent regularly firing and quiescent components. In this regime only a small number of population firing rate components interact with cortical stimulus variations. Around connectivity a transition to a more dynamically active regime occurs where all cells constantly switch between activity and quiescence. In this low connectivity regime, MSN population components wander randomly and here too are independent of variations in cortical driving. Only in the transition regime do weak changes in cortical driving interact with many population components so that sequential cell assemblies are reproducibly activated for many hundreds of milliseconds after stimulus onset and peri-stimulus time histograms display strong stimulus and temporal specificity. We show that, remarkably, this activity is maximized at striatally realistic connectivities and IPSP sizes. Thus, we suggest the local MSN network has optimal characteristics – it is neither too stable to respond in a dynamically complex temporally extended way to cortical variations, nor is it too unstable to respond in a consistent repeatable way. Rather, it is optimized to generate stimulus dependent activity patterns for long periods after variations in cortical excitation. PMID:23592954

Visual Input to the Drosophila Central Complex by Developmentally and Functionally Distinct Neuronal Populations.

PubMed

Omoto, Jaison Jiro; Keleş, Mehmet Fatih; Nguyen, Bao-Chau Minh; Bolanos, Cheyenne; Lovick, Jennifer Kelly; Frye, Mark Arthur; Hartenstein, Volker

2017-04-24

The Drosophila central brain consists of stereotyped neural lineages, developmental-structural units of macrocircuitry formed by the sibling neurons of single progenitors called neuroblasts. We demonstrate that the lineage principle guides the connectivity and function of neurons, providing input to the central complex, a collection of neuropil compartments important for visually guided behaviors. One of these compartments is the ellipsoid body (EB), a structure formed largely by the axons of ring (R) neurons, all of which are generated by a single lineage, DALv2. Two further lineages, DALcl1 and DALcl2, produce neurons that connect the anterior optic tubercle, a central brain visual center, with R neurons. Finally, DALcl1/2 receive input from visual projection neurons of the optic lobe medulla, completing a three-legged circuit that we call the anterior visual pathway (AVP). The AVP bears a fundamental resemblance to the sky-compass pathway, a visual navigation circuit described in other insects. Neuroanatomical analysis and two-photon calcium imaging demonstrate that DALcl1 and DALcl2 form two parallel channels, establishing connections with R neurons located in the peripheral and central domains of the EB, respectively. Although neurons of both lineages preferentially respond to bright objects, DALcl1 neurons have small ipsilateral, retinotopically ordered receptive fields, whereas DALcl2 neurons share a large excitatory receptive field in the contralateral hemifield. DALcl2 neurons become inhibited when the object enters the ipsilateral hemifield and display an additional excitation after the object leaves the field of view. Thus, the spatial position of a bright feature, such as a celestial body, may be encoded within this pathway. Copyright © 2017 Elsevier Ltd. All rights reserved.

The neuronal porosome complex in health and disease

PubMed Central

Naik, Akshata R; Lewis, Kenneth T

2015-01-01

Cup-shaped secretory portals at the cell plasma membrane called porosomes mediate the precision release of intravesicular material from cells. Membrane-bound secretory vesicles transiently dock and fuse at the base of porosomes facing the cytosol to expel pressurized intravesicular contents from the cell during secretion. The structure, isolation, composition, and functional reconstitution of the neuronal porosome complex have greatly progressed, providing a molecular understanding of its function in health and disease. Neuronal porosomes are 15 nm cup-shaped lipoprotein structures composed of nearly 40 proteins, compared to the 120 nm nuclear pore complex composed of >500 protein molecules. Membrane proteins compose the porosome complex, making it practically impossible to solve its atomic structure. However, atomic force microscopy and small-angle X-ray solution scattering studies have provided three-dimensional structural details of the native neuronal porosome at sub-nanometer resolution, providing insights into the molecular mechanism of its function. The participation of several porosome proteins previously implicated in neurotransmission and neurological disorders, further attest to the crosstalk between porosome proteins and their coordinated involvement in release of neurotransmitter at the synapse. PMID:26264442

A mean field neural network for hierarchical module placement

NASA Technical Reports Server (NTRS)

Unaltuna, M. Kemal; Pitchumani, Vijay

1992-01-01

This paper proposes a mean field neural network for the two-dimensional module placement problem. An efficient coding scheme with only O(N log N) neurons is employed where N is the number of modules. The neurons are evolved in groups of N in log N iteration steps such that the circuit is recursively partitioned in alternating vertical and horizontal directions. In our simulations, the network was able to find optimal solutions to all test problems with up to 128 modules.

Cooperation-Controlled Learning for Explicit Class Structure in Self-Organizing Maps

PubMed Central

Kamimura, Ryotaro

2014-01-01

We attempt to demonstrate the effectiveness of multiple points of view toward neural networks. By restricting ourselves to two points of view of a neuron, we propose a new type of information-theoretic method called “cooperation-controlled learning.” In this method, individual and collective neurons are distinguished from one another, and we suppose that the characteristics of individual and collective neurons are different. To implement individual and collective neurons, we prepare two networks, namely, cooperative and uncooperative networks. The roles of these networks and the roles of individual and collective neurons are controlled by the cooperation parameter. As the parameter is increased, the role of cooperative networks becomes more important in learning, and the characteristics of collective neurons become more dominant. On the other hand, when the parameter is small, individual neurons play a more important role. We applied the method to the automobile and housing data from the machine learning database and examined whether explicit class boundaries could be obtained. Experimental results showed that cooperation-controlled learning, in particular taking into account information on input units, could be used to produce clearer class structure than conventional self-organizing maps. PMID:25309950

Overlapping Role of Dynamin Isoforms in Synaptic Vesicle Endocytosis

PubMed Central

Raimondi, Andrea; Ferguson, Shawn M.; Lou, Xuelin; Armbruster, Moritz; Paradise, Summer; Giovedi, Silvia; Messa, Mirko; Kono, Nao; Takasaki, Junko; Cappello, Valentina; O’Toole, Eileen; Ryan, Timothy A.; De Camilli, Pietro

2011-01-01

The existence of neuron specific endocytic protein isoforms raises questions about their importance for specialized neuronal functions. Dynamin, a GTPase implicated in the fission reaction of endocytosis, is encoded by three genes, two of which, dynamin 1 and 3, are highly expressed in neurons. We show that dynamin 3, thought to play a predominantly postsynaptic role, has a major presynaptic function. While lack of dynamin 3 does not produce an overt phenotype in mice, it worsens the dynamin 1 KO phenotype, leading to perinatal lethality and a more severe defect in activity-dependent synaptic vesicle endocytosis. Thus, dynamin 1 and 3, which together account for the overwhelming majority of brain dynamin, cooperate in supporting optimal rates of synaptic vesicle endocytosis. Persistence of synaptic transmission in their absence indicates that if dynamin plays essential functions in neurons, such functions can be achieved by the very low levels of dynamin 2. PMID:21689597

Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits.

PubMed

Ujfalussy, Balázs B; Makara, Judit K; Branco, Tiago; Lengyel, Máté

2015-12-24

Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways. It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits. Here, we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons. We developed a theory that formalizes how a neuron's dendritic nonlinearity that is optimal for integrating synaptic inputs depends on the statistics of its presynaptic activity patterns. Based on their in vivo preynaptic population statistics (firing rates, membrane potential fluctuations, and correlations due to ensemble dynamics), our theory accurately predicted the responses of two different types of cortical pyramidal cells to patterned stimulation by two-photon glutamate uncaging. These results reveal a new computational principle underlying dendritic integration in cortical neurons by suggesting a functional link between cellular and systems--level properties of cortical circuits.

Enhanced conversion of induced neuronal cells (iN cells) from human fibroblasts: utility in uncovering cellular deficits in mental illness-associated chromosomal abnormalities

PubMed Central

Passeri, Eleonora; Wilson, Ashley M.; Primerano, Amedeo; Kondo, Mari A.; Sengupta, Srona; Srivastava, Rupali; Koga, Minori; Obie, Cassandra; Zandi, Peter P.; Goes, Fernando S.; Valle, David; Rapoport, Judith L.; Sawa, Akira; Kano, Shin-ichi; Ishizuka, Koko

2016-01-01

The novel technology of induced neuronal cells (iN cells) is promising for translational neuroscience, as it allows the conversion of human fibroblasts into cells with postmitotic neuronal traits. However, a major technical barrier is the low conversion rate. To overcome this problem, we optimized the conversion media. Using our improved formulation, we studied how major mental illness-associated chromosomal abnormalities may impact the characteristics of iN cells. We demonstrated that our new iN cell culture protocol enabled us to obtain more precise measurement of neuronal cellular phenotypes than previous iN cell methods. Thus, this iN cell culture provides a platform to efficiently obtain possible cellular phenotypes caused by genetic differences, which can be more thoroughly studied in research using other human cell models such as induced pluripotent stem cells. PMID:26260244

Efficient implementation of a real-time estimation system for thalamocortical hidden Parkinsonian properties

NASA Astrophysics Data System (ADS)

Yang, Shuangming; Deng, Bin; Wang, Jiang; Li, Huiyan; Liu, Chen; Fietkiewicz, Chris; Loparo, Kenneth A.

2017-01-01

Real-time estimation of dynamical characteristics of thalamocortical cells, such as dynamics of ion channels and membrane potentials, is useful and essential in the study of the thalamus in Parkinsonian state. However, measuring the dynamical properties of ion channels is extremely challenging experimentally and even impossible in clinical applications. This paper presents and evaluates a real-time estimation system for thalamocortical hidden properties. For the sake of efficiency, we use a field programmable gate array for strictly hardware-based computation and algorithm optimization. In the proposed system, the FPGA-based unscented Kalman filter is implemented into a conductance-based TC neuron model. Since the complexity of TC neuron model restrains its hardware implementation in parallel structure, a cost efficient model is proposed to reduce the resource cost while retaining the relevant ionic dynamics. Experimental results demonstrate the real-time capability to estimate thalamocortical hidden properties with high precision under both normal and Parkinsonian states. While it is applied to estimate the hidden properties of the thalamus and explore the mechanism of the Parkinsonian state, the proposed method can be useful in the dynamic clamp technique of the electrophysiological experiments, the neural control engineering and brain-machine interface studies.

Synchronisation hubs in the visual cortex may arise from strong rhythmic inhibition during gamma oscillations.

PubMed

Folias, Stefanos E; Yu, Shan; Snyder, Abigail; Nikolić, Danko; Rubin, Jonathan E

2013-09-01

Neurons in the visual cortex exhibit heterogeneity in feature selectivity and the tendency to generate action potentials synchronously with other nearby neurons. By examining visual responses from cat area 17 we found that, during gamma oscillations, there was a positive correlation between each unit's sharpness of orientation tuning, strength of oscillations, and propensity towards synchronisation with other units. Using a computational model, we demonstrated that heterogeneity in the strength of rhythmic inhibitory inputs can account for the correlations between these three properties. Neurons subject to strong inhibition tend to oscillate strongly in response to both optimal and suboptimal stimuli and synchronise promiscuously with other neurons, even if they have different orientation preferences. Moreover, these strongly inhibited neurons can exhibit sharp orientation selectivity provided that the inhibition they receive is broadly tuned relative to their excitatory inputs. These results predict that the strength and orientation tuning of synaptic inhibition are heterogeneous across area 17 neurons, which could have important implications for these neurons' sensory processing capabilities. Furthermore, although our experimental recordings were conducted in the visual cortex, our model and simulation results can apply more generally to any brain region with analogous neuron types in which heterogeneity in the strength of rhythmic inhibition can arise during gamma oscillations. © 2013 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

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

Hydrogel limits stem cell dispersal in the deaf cochlea: implications for cochlear implants

NASA Astrophysics Data System (ADS)

Nayagam, Bryony A.; Backhouse, Steven S.; Cimenkaya, Cengiz; Shepherd, Robert K.

2012-12-01

Auditory neurons provide the critical link between a cochlear implant and the brain in deaf individuals, therefore their preservation and/or regeneration is important for optimal performance of this neural prosthesis. In cases where auditory neurons are significantly depleted, stem cells (SCs) may be used to replace the lost population of neurons, thereby re-establishing the critical link between the periphery (implant) and the brain. For such a therapy to be therapeutically viable, SCs must be differentiated into neurons, retained at their delivery site and damage caused to the residual auditory neurons minimized. Here we describe the transplantation of SC-derived neurons into the deaf cochlea, using a peptide hydrogel to limit their dispersal. The described approach illustrates that SCs can be delivered to and are retained within the basal turn of the cochlea, without a significant loss of endogenous auditory neurons. In addition, the tissue response elicited from this surgical approach was restricted to the surgical site and did not extend beyond the cochlear basal turn. Overall, this approach illustrates the feasibility of targeted cell delivery into the mammalian cochlea using hydrogel, which may be useful for future cell-based transplantation strategies, for combined treatment with a cochlear implant to restore function.

Capsaicin-induced reactivation of latent herpes simplex virus type 1 in sensory neurons in culture.

PubMed

Hunsperger, Elizabeth A; Wilcox, Christine L

2003-05-01

Herpes simplex virus type 1 (HSV-1) produces a life-long latent infection in neurons of the peripheral nervous system, primarily in the trigeminal and dorsal root ganglia. Neurons of these ganglia express high levels of the capsaicin receptor, also known as the vanilloid receptor-1 (VR-1). VR-1 is a non-selective ion channel, found on sensory neurons, that primarily fluxes Ca(2+) ions in response to various stimuli, including physiologically acidic conditions, heat greater than 45 degrees C and noxious compounds such as capsaicin. Using an in vitro neuronal model to study HSV-1 latency and reactivation, we found that agonists of the VR-1 channel - capsaicin and heat - resulted in reactivation of latent HSV-1. Capsaicin-induced reactivation of HSV-1 latently infected neurons was dose-dependent. Additionally, activation of VR-1 at its optimal temperature of 46 degrees C caused a significant increase in virus titres, which could be attenuated with the VR-1 antagonist, capsazepine. VR-1 activation that resulted in HSV-1 reactivation was calcium-dependent, since the calcium chelator BAPTA significantly reduced reactivation following treatment with caspsaicin and forskolin. Taken together, these results suggest that activation of the VR-1 channel, often associated with increases in intracellular calcium, results in HSV-1 reactivation in sensory neurons.

Calcium signaling, excitability, and synaptic plasticity defects in a mouse model of Alzheimer's disease.

PubMed

Zhang, Hua; Liu, Jie; Sun, Suya; Pchitskaya, Ekaterina; Popugaeva, Elena; Bezprozvanny, Ilya

2015-01-01

Alzheimer's disease (AD) and aging result in impaired ability to store memories, but the cellular mechanisms responsible for these defects are poorly understood. Presenilin 1 (PS1) mutations are responsible for many early-onset familial AD (FAD) cases. The phenomenon of hippocampal long-term potentiation (LTP) is widely used in studies of memory formation and storage. Recent data revealed long-term LTP maintenance (L-LTP) is impaired in PS1-M146V knock-in (KI) FAD mice. To understand the basis for this phenomenon, in the present study we analyzed structural synaptic plasticity in hippocampal cultures from wild type (WT) and KI mice. We discovered that exposure to picrotoxin induces formation of mushroom spines in both WT and KI cultures, but the maintenance of mushroom spines is impaired in KI neurons. This maintenance defect can be explained by an abnormal firing pattern during the consolidation phase of structural plasticity in KI neurons. Reduced frequency of neuronal firing in KI neurons is caused by enhanced calcium-induced calcium release (CICR), enhanced activity of calcium-activated potassium channels, and increased afterhyperpolarization. As a result, "consolidation" pattern of neuronal activity converted to "depotentiation" pattern of neuronal activity in KI neurons. Consistent with this model, we demonstrated that pharmacological inhibitors of CICR (dantrolene), of calcium-activated potassium channels (apamin), and of calcium-dependent phosphatase calcineurin (FK506) are able to rescue structural plasticity defects in KI neurons. Furthermore, we demonstrate that incubation with dantrolene or apamin also rescued L-LTP defects in KI hippocampal slices, suggesting a role for a similar mechanism. This proposed mechanism may be responsible for memory defects in AD but also for age-related memory decline.

Axonal Conduction Delays, Brain State, and Corticogeniculate Communication

PubMed Central

2017-01-01

Thalamocortical conduction times are short, but layer 6 corticothalamic axons display an enormous range of conduction times, some exceeding 40–50 ms. Here, we investigate (1) how axonal conduction times of corticogeniculate (CG) neurons are related to the visual information conveyed to the thalamus, and (2) how alert versus nonalert awake brain states affect visual processing across the spectrum of CG conduction times. In awake female Dutch-Belted rabbits, we found 58% of CG neurons to be visually responsive, and 42% to be unresponsive. All responsive CG neurons had simple, orientation-selective receptive fields, and generated sustained responses to stationary stimuli. CG axonal conduction times were strongly related to modulated firing rates (F1 values) generated by drifting grating stimuli, and their associated interspike interval distributions, suggesting a continuum of visual responsiveness spanning the spectrum of axonal conduction times. CG conduction times were also significantly related to visual response latency, contrast sensitivity (C-50 values), directional selectivity, and optimal stimulus velocity. Increasing alertness did not cause visually unresponsive CG neurons to become responsive and did not change the response linearity (F1/F0 ratios) of visually responsive CG neurons. However, for visually responsive CG neurons, increased alertness nearly doubled the modulated response amplitude to optimal visual stimulation (F1 values), significantly shortened response latency, and dramatically increased response reliability. These effects of alertness were uniform across the broad spectrum of CG axonal conduction times. SIGNIFICANCE STATEMENT Corticothalamic neurons of layer 6 send a dense feedback projection to thalamic nuclei that provide input to sensory neocortex. While sensory information reaches the cortex after brief thalamocortical axonal delays, corticothalamic axons can exhibit conduction delays of <2 ms to 40–50 ms. Here, in the corticogeniculate visual system of awake rabbits, we investigate the functional significance of this axonal diversity, and the effects of shifting alert/nonalert brain states on corticogeniculate processing. We show that axonal conduction times are strongly related to multiple visual response properties, suggesting a continuum of visual responsiveness spanning the spectrum of corticogeniculate axonal conduction times. We also show that transitions between awake brain states powerfully affect corticogeniculate processing, in some ways more strongly than in layer 4. PMID:28559382

Schedule-induced polydipsia is associated with increased spine density in dorsolateral striatum neurons.

PubMed

Íbias, J; Soria-Molinillo, E; Kastanauskaite, A; Orgaz, C; DeFelipe, J; Pellón, R; Miguéns, M

2015-08-06

Schedule-induced polydipsia (SIP) is an adjunctive behavior in which rats exhibit excessive drinking as a consequence of intermittent feeding, and it has been proposed as a candidate model to study the development of compulsive and repetitive behavior. Although several brain structures are involved in compulsive behavior, it has been suggested that alterations in fronto-striatal circuits may underlie compulsive spectrum disorders. In the present work, we examined whether SIP would induce modifications in dorsolateral striatum (DLS) and anterior prefrontal cortex (aPFC) neurons. Specifically, the effects of 20 sessions of SIP were determined in the dendrites of DLS medium spiny neurons and in the basal dendritic arbors of layer V pyramidal cells in the aPFC. The structure, size and branching complexity in aPFC neurons were also studied. Results showed that SIP resulted in an increase in dendritic spine density in DLS neurons. Moreover, dendritic spine density was highly correlated with the level of drinking in animals subjected to SIP. By contrast, we observed no differences either in dendritic spine density or in the morphological structure of the dendrites of the aPFC in SIP rats compared to their control counterparts. We hypothesize that SIP-induced structural plasticity in DLS neurons could be related to inflexible response in compulsive behavior. The findings of this study could provide new insights into the involvement of particular cell populations of the dorsolateral striatum and anterior prefrontal cortex regions in compulsive spectrum disorders. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

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

Changes in the Golgi Apparatus of Neocortical and Hippocampal Neurons in the Hibernating Hamster.

PubMed

Antón-Fernández, Alejandro; León-Espinosa, Gonzalo; DeFelipe, Javier; Muñoz, Alberto

2015-01-01

Hibernating animals have been used as models to study several aspects of the plastic changes that occur in the metabolism and physiology of neurons. These models are also of interest in the study of Alzheimer's disease because the microtubule-associated protein tau is hyperphosphorylated during the hibernation state known as torpor, similar to the pretangle stage of Alzheimer's disease. Hibernating animals undergo torpor periods with drops in body temperature and metabolic rate, and a virtual cessation of neural activity. These processes are accompanied by morphological and neurochemical changes in neurons, which reverse a few hours after coming out of the torpor state. Since tau has been implicated in the structural regulation of the neuronal Golgi apparatus (GA) we have used Western Blot and immunocytochemistry to analyze whether the GA is modified in cortical neurons of the Syrian hamster at different hibernation stages. The results show that, during the hibernation cycle, the GA undergo important structural changes along with differential modifications in expression levels and distribution patterns of Golgi structural proteins. These changes were accompanied by significant transitory reductions in the volume and surface area of the GA elements during torpor and arousal stages as compared with euthermic animals.

Changes in the Golgi Apparatus of Neocortical and Hippocampal Neurons in the Hibernating Hamster

PubMed Central

Antón-Fernández, Alejandro; León-Espinosa, Gonzalo; DeFelipe, Javier; Muñoz, Alberto

2015-01-01

Hibernating animals have been used as models to study several aspects of the plastic changes that occur in the metabolism and physiology of neurons. These models are also of interest in the study of Alzheimer's disease because the microtubule-associated protein tau is hyperphosphorylated during the hibernation state known as torpor, similar to the pretangle stage of Alzheimer's disease. Hibernating animals undergo torpor periods with drops in body temperature and metabolic rate, and a virtual cessation of neural activity. These processes are accompanied by morphological and neurochemical changes in neurons, which reverse a few hours after coming out of the torpor state. Since tau has been implicated in the structural regulation of the neuronal Golgi apparatus (GA) we have used Western Blot and immunocytochemistry to analyze whether the GA is modified in cortical neurons of the Syrian hamster at different hibernation stages. The results show that, during the hibernation cycle, the GA undergo important structural changes along with differential modifications in expression levels and distribution patterns of Golgi structural proteins. These changes were accompanied by significant transitory reductions in the volume and surface area of the GA elements during torpor and arousal stages as compared with euthermic animals. PMID:26696838

Neocortical Maturation during Adolescence: Change in Neuronal Soma Dimension

ERIC Educational Resources Information Center

Rabinowicz, Theodore; Petetot, Jean MacDonald-Comber; Khoury, Jane C.; de Courten-Myers, Gabrielle M.

2009-01-01

During adolescence, cognitive abilities increase robustly. To search for possible related structural alterations of the cerebral cortex, we measured neuronal soma dimension (NSD = width times height), cortical thickness and neuronal densities in different types of neocortex in post-mortem brains of five 12-16 and five 17-24 year-olds (each 2F,…

An optimally evolved connective ratio of neural networks that maximizes the occurrence of synchronized bursting behavior

PubMed Central

2012-01-01

Background Synchronized bursting activity (SBA) is a remarkable dynamical behavior in both ex vivo and in vivo neural networks. Investigations of the underlying structural characteristics associated with SBA are crucial to understanding the system-level regulatory mechanism of neural network behaviors. Results In this study, artificial pulsed neural networks were established using spike response models to capture fundamental dynamics of large scale ex vivo cortical networks. Network simulations with synaptic parameter perturbations showed the following two findings. (i) In a network with an excitatory ratio (ER) of 80-90%, its connective ratio (CR) was within a range of 10-30% when the occurrence of SBA reached the highest expectation. This result was consistent with the experimental observation in ex vivo neuronal networks, which were reported to possess a matured inhibitory synaptic ratio of 10-20% and a CR of 10-30%. (ii) No SBA occurred when a network does not contain any all-positive-interaction feedback loop (APFL) motif. In a neural network containing APFLs, the number of APFLs presented an optimal range corresponding to the maximal occurrence of SBA, which was very similar to the optimal CR. Conclusions In a neural network, the evolutionarily selected CR (10-30%) optimizes the occurrence of SBA, and APFL serves a pivotal network motif required to maximize the occurrence of SBA. PMID:22462685

Observing complex action sequences: The role of the fronto-parietal mirror neuron system.

PubMed

Molnar-Szakacs, Istvan; Kaplan, Jonas; Greenfield, Patricia M; Iacoboni, Marco

2006-11-15

A fronto-parietal mirror neuron network in the human brain supports the ability to represent and understand observed actions allowing us to successfully interact with others and our environment. Using functional magnetic resonance imaging (fMRI), we wanted to investigate the response of this network in adults during observation of hierarchically organized action sequences of varying complexity that emerge at different developmental stages. We hypothesized that fronto-parietal systems may play a role in coding the hierarchical structure of object-directed actions. The observation of all action sequences recruited a common bilateral network including the fronto-parietal mirror neuron system and occipito-temporal visual motion areas. Activity in mirror neuron areas varied according to the motoric complexity of the observed actions, but not according to the developmental sequence of action structures, possibly due to the fact that our subjects were all adults. These results suggest that the mirror neuron system provides a fairly accurate simulation process of observed actions, mimicking internally the level of motoric complexity. We also discuss the results in terms of the links between mirror neurons, language development and evolution.

Human Subthalamic Nucleus Theta and Beta Oscillations Entrain Neuronal Firing During Sensorimotor Conflict

PubMed Central

Zavala, Baltazar; Damera, Srikanth; Dong, Jian Wilson; Lungu, Codrin; Brown, Peter; Zaghloul, Kareem A.

2017-01-01

Recent evidence has suggested that prefrontal cortical structures may inhibit impulsive actions during conflict through activation of the subthalamic nucleus (STN). Consistent with this hypothesis, deep brain stimulation to the STN has been associated with altered prefrontal cortical activity and impaired response inhibition. The interactions between oscillatory activity in the STN and its presumably antikinetic neuronal spiking, however, remain poorly understood. Here, we simultaneously recorded intraoperative local field potential and spiking activity from the human STN as participants performed a sensorimotor action selection task involving conflict. We identified several STN neuronal response types that exhibited different temporal dynamics during the task. Some neurons showed early, cue-related firing rate increases that remained elevated longer during high conflict trials, whereas other neurons showed late, movement-related firing rate increases. Notably, the high conflict trials were associated with an entrainment of individual neurons by theta- and beta-band oscillations, both of which have been observed in cortical structures involved in response inhibition. Our data suggest that frequency-specific activity in the beta and theta bands influence STN firing to inhibit impulsivity during conflict. PMID:26494798

Graph-based unsupervised segmentation algorithm for cultured neuronal networks' structure characterization and modeling.

PubMed

de Santos-Sierra, Daniel; Sendiña-Nadal, Irene; Leyva, Inmaculada; Almendral, Juan A; Ayali, Amir; Anava, Sarit; Sánchez-Ávila, Carmen; Boccaletti, Stefano

2015-06-01

Large scale phase-contrast images taken at high resolution through the life of a cultured neuronal network are analyzed by a graph-based unsupervised segmentation algorithm with a very low computational cost, scaling linearly with the image size. The processing automatically retrieves the whole network structure, an object whose mathematical representation is a matrix in which nodes are identified neurons or neurons' clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocytochemistry techniques, our non invasive measures entitle us to perform a longitudinal analysis during the maturation of a single culture. Such an analysis furnishes the way of individuating the main physical processes underlying the self-organization of the neurons' ensemble into a complex network, and drives the formulation of a phenomenological model yet able to describe qualitatively the overall scenario observed during the culture growth. © 2014 International Society for Advancement of Cytometry.

Segmentation of neuronal structures using SARSA (λ)-based boundary amendment with reinforced gradient-descent curve shape fitting.

PubMed

Zhu, Fei; Liu, Quan; Fu, Yuchen; Shen, Bairong

2014-01-01

The segmentation of structures in electron microscopy (EM) images is very important for neurobiological research. The low resolution neuronal EM images contain noise and generally few features are available for segmentation, therefore application of the conventional approaches to identify the neuron structure from EM images is not successful. We therefore present a multi-scale fused structure boundary detection algorithm in this study. In the algorithm, we generate an EM image Gaussian pyramid first, then at each level of the pyramid, we utilize Laplacian of Gaussian function (LoG) to attain structure boundary, we finally assemble the detected boundaries by using fusion algorithm to attain a combined neuron structure image. Since the obtained neuron structures usually have gaps, we put forward a reinforcement learning-based boundary amendment method to connect the gaps in the detected boundaries. We use a SARSA (λ)-based curve traveling and amendment approach derived from reinforcement learning to repair the incomplete curves. Using this algorithm, a moving point starts from one end of the incomplete curve and walks through the image where the decisions are supervised by the approximated curve model, with the aim of minimizing the connection cost until the gap is closed. Our approach provided stable and efficient structure segmentation. The test results using 30 EM images from ISBI 2012 indicated that both of our approaches, i.e., with or without boundary amendment, performed better than six conventional boundary detection approaches. In particular, after amendment, the Rand error and warping error, which are the most important performance measurements during structure segmentation, were reduced to very low values. The comparison with the benchmark method of ISBI 2012 and the recent developed methods also indicates that our method performs better for the accurate identification of substructures in EM images and therefore useful for the identification of imaging features related to brain diseases.

Segmentation of Neuronal Structures Using SARSA (λ)-Based Boundary Amendment with Reinforced Gradient-Descent Curve Shape Fitting

PubMed Central

Zhu, Fei; Liu, Quan; Fu, Yuchen; Shen, Bairong

2014-01-01

The segmentation of structures in electron microscopy (EM) images is very important for neurobiological research. The low resolution neuronal EM images contain noise and generally few features are available for segmentation, therefore application of the conventional approaches to identify the neuron structure from EM images is not successful. We therefore present a multi-scale fused structure boundary detection algorithm in this study. In the algorithm, we generate an EM image Gaussian pyramid first, then at each level of the pyramid, we utilize Laplacian of Gaussian function (LoG) to attain structure boundary, we finally assemble the detected boundaries by using fusion algorithm to attain a combined neuron structure image. Since the obtained neuron structures usually have gaps, we put forward a reinforcement learning-based boundary amendment method to connect the gaps in the detected boundaries. We use a SARSA (λ)-based curve traveling and amendment approach derived from reinforcement learning to repair the incomplete curves. Using this algorithm, a moving point starts from one end of the incomplete curve and walks through the image where the decisions are supervised by the approximated curve model, with the aim of minimizing the connection cost until the gap is closed. Our approach provided stable and efficient structure segmentation. The test results using 30 EM images from ISBI 2012 indicated that both of our approaches, i.e., with or without boundary amendment, performed better than six conventional boundary detection approaches. In particular, after amendment, the Rand error and warping error, which are the most important performance measurements during structure segmentation, were reduced to very low values. The comparison with the benchmark method of ISBI 2012 and the recent developed methods also indicates that our method performs better for the accurate identification of substructures in EM images and therefore useful for the identification of imaging features related to brain diseases. PMID:24625699

Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.

PubMed

Nitta, Tohru

2017-10-01

We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).

Hand placement near the visual stimulus improves orientation selectivity in V2 neurons

PubMed Central

Sergio, Lauren E.; Crawford, J. Douglas; Fallah, Mazyar

2015-01-01

Often, the brain receives more sensory input than it can process simultaneously. Spatial attention helps overcome this limitation by preferentially processing input from a behaviorally-relevant location. Recent neuropsychological and psychophysical studies suggest that attention is deployed to near-hand space much like how the oculomotor system can deploy attention to an upcoming gaze position. Here we provide the first neuronal evidence that the presence of a nearby hand enhances orientation selectivity in early visual processing area V2. When the hand was placed outside the receptive field, responses to the preferred orientation were significantly enhanced without a corresponding significant increase at the orthogonal orientation. Consequently, there was also a significant sharpening of orientation tuning. In addition, the presence of the hand reduced neuronal response variability. These results indicate that attention is automatically deployed to the space around a hand, improving orientation selectivity. Importantly, this appears to be optimal for motor control of the hand, as opposed to oculomotor mechanisms which enhance responses without sharpening orientation selectivity. Effector-based mechanisms for visual enhancement thus support not only the spatiotemporal dissociation of gaze and reach, but also the optimization of vision for their separate requirements for guiding movements. PMID:25717165

The most likely voltage path and large deviations approximations for integrate-and-fire neurons.

PubMed

Paninski, Liam

2006-08-01

We develop theory and numerical methods for computing the most likely subthreshold voltage path of a noisy integrate-and-fire (IF) neuron, given observations of the neuron's superthreshold spiking activity. This optimal voltage path satisfies a second-order ordinary differential (Euler-Lagrange) equation which may be solved analytically in a number of special cases, and which may be solved numerically in general via a simple "shooting" algorithm. Our results are applicable for both linear and nonlinear subthreshold dynamics, and in certain cases may be extended to correlated subthreshold noise sources. We also show how this optimal voltage may be used to obtain approximations to (1) the likelihood that an IF cell with a given set of parameters was responsible for the observed spike train; and (2) the instantaneous firing rate and interspike interval distribution of a given noisy IF cell. The latter probability approximations are based on the classical Freidlin-Wentzell theory of large deviations principles for stochastic differential equations. We close by comparing this most likely voltage path to the true observed subthreshold voltage trace in a case when intracellular voltage recordings are available in vitro.

A spiking neural network model of the midbrain superior colliculus that generates saccadic motor commands.

PubMed

Kasap, Bahadir; van Opstal, A John

2017-08-01

Single-unit recordings suggest that the midbrain superior colliculus (SC) acts as an optimal controller for saccadic gaze shifts. The SC is proposed to be the site within the visuomotor system where the nonlinear spatial-to-temporal transformation is carried out: the population encodes the intended saccade vector by its location in the motor map (spatial), and its trajectory and velocity by the distribution of firing rates (temporal). The neurons' burst profiles vary systematically with their anatomical positions and intended saccade vectors, to account for the nonlinear main-sequence kinematics of saccades. Yet, the underlying collicular mechanisms that could result in these firing patterns are inaccessible to current neurobiological techniques. Here, we propose a simple spiking neural network model that reproduces the spike trains of saccade-related cells in the intermediate and deep SC layers during saccades. The model assumes that SC neurons have distinct biophysical properties for spike generation that depend on their anatomical position in combination with a center-surround lateral connectivity. Both factors are needed to account for the observed firing patterns. Our model offers a basis for neuronal algorithms for spatiotemporal transformations and bio-inspired optimal controllers.

Temporal characteristics of gustatory responses in rat parabrachial neurons vary by stimulus and chemosensitive neuron type.

PubMed

Geran, Laura; Travers, Susan

2013-01-01

It has been demonstrated that temporal features of spike trains can increase the amount of information available for gustatory processing. However, the nature of these temporal characteristics and their relationship to different taste qualities and neuron types are not well-defined. The present study analyzed the time course of taste responses from parabrachial (PBN) neurons elicited by multiple applications of "sweet" (sucrose), "salty" (NaCl), "sour" (citric acid), and "bitter" (quinine and cycloheximide) stimuli in an acute preparation. Time course varied significantly by taste stimulus and best-stimulus classification. Across neurons, the ensemble code for the three electrolytes was similar initially but quinine diverged from NaCl and acid during the second 500 ms of stimulation and all four qualities became distinct just after 1s. This temporal evolution was reflected in significantly broader tuning during the initial response. Metric space analyses of quality discrimination by individual neurons showed that increases in information (H) afforded by temporal factors was usually explained by differences in rate envelope, which had a greater impact during the initial 2s (22.5% increase in H) compared to the later response (9.5%). Moreover, timing had a differential impact according to cell type, with between-quality discrimination in neurons activated maximally by NaCl or citric acid most affected. Timing was also found to dramatically improve within-quality discrimination (80% increase in H) in neurons that responded optimally to bitter stimuli (B-best). Spikes from B-best neurons were also more likely to occur in bursts. These findings suggest that among PBN taste neurons, time-dependent increases in mutual information can arise from stimulus- and neuron-specific differences in response envelope during the initial dynamic period. A stable rate code predominates in later epochs.

Temporal Characteristics of Gustatory Responses in Rat Parabrachial Neurons Vary by Stimulus and Chemosensitive Neuron Type

PubMed Central

Geran, Laura; Travers, Susan

2013-01-01

It has been demonstrated that temporal features of spike trains can increase the amount of information available for gustatory processing. However, the nature of these temporal characteristics and their relationship to different taste qualities and neuron types are not well-defined. The present study analyzed the time course of taste responses from parabrachial (PBN) neurons elicited by multiple applications of “sweet” (sucrose), “salty” (NaCl), “sour” (citric acid), and “bitter” (quinine and cycloheximide) stimuli in an acute preparation. Time course varied significantly by taste stimulus and best-stimulus classification. Across neurons, the ensemble code for the three electrolytes was similar initially but quinine diverged from NaCl and acid during the second 500ms of stimulation and all four qualities became distinct just after 1s. This temporal evolution was reflected in significantly broader tuning during the initial response. Metric space analyses of quality discrimination by individual neurons showed that increases in information (H) afforded by temporal factors was usually explained by differences in rate envelope, which had a greater impact during the initial 2s (22.5% increase in H) compared to the later response (9.5%). Moreover, timing had a differential impact according to cell type, with between-quality discrimination in neurons activated maximally by NaCl or citric acid most affected. Timing was also found to dramatically improve within-quality discrimination (80% increase in H) in neurons that responded optimally to bitter stimuli (B-best). Spikes from B-best neurons were also more likely to occur in bursts. These findings suggest that among PBN taste neurons, time-dependent increases in mutual information can arise from stimulus- and neuron-specific differences in response envelope during the initial dynamic period. A stable rate code predominates in later epochs. PMID:24124597

Fixative Composition Alters Distributions of Immunoreactivity for Glutaminase and Two Markers of Nociceptive Neurons, Nav1.8 and TRPV1, in the Rat Dorsal Root Ganglion

PubMed Central

Hoffman, E. Matthew; Schechter, Ruben; Miller, Kenneth E.

2010-01-01

Most, if not all, dorsal root ganglion (DRG) neurons use the neurotransmitter glutamate. There are, however, conflicting reports of the percentages of DRG neurons that express glutaminase (GLS), the enzyme that synthesizes glutamate, ranging from 30% to 100% of DRG neurons. Defining DRG neuron populations by the expression of proteins like GLS, which indicates function, is routinely accomplished with immunolabeling techniques. Proper characterization of DRG neuron populations relies on accurate detection of such antigens. It is known intuitively that fixation can alter immunoreactivity (IR). In this study, we compared the effects of five formaldehyde concentrations between 0.25% and 4.0% (w/v) and five picric acid concentrations between 0.0% and 0.8% (w/v) on the IR of GLS, the voltage-gated sodium channel 1.8 (Nav1.8), and the capsaicin receptor TRPV1. We also compared the effects of five incubation time lengths from 2 to 192 hr, in primary antiserum on IR. Lowering formaldehyde concentration elevated IR for all three antigens, while raising picric acid concentration increased Nav1.8 and TRPV1 IR. Increasing IR improved detection sensitivity, which led to higher percentages of labeled DRG neurons. By selecting fixation conditions that optimized IR, we found that all DRG neurons express GLS, 69% of neurons express Nav1.8, and 77% of neurons express TRPV1, indicating that some previous studies may have underestimated the percentages of DRG neurons expressing these proteins. This manuscript contains online supplemental material at http://www.jhc.org. Please visit this article online to view these materials. (J Histochem Cytochem 58:329–344, 2010) PMID:20026672

M-AMST: an automatic 3D neuron tracing method based on mean shift and adapted minimum spanning tree.

PubMed

Wan, Zhijiang; He, Yishan; Hao, Ming; Yang, Jian; Zhong, Ning

2017-03-29

Understanding the working mechanism of the brain is one of the grandest challenges for modern science. Toward this end, the BigNeuron project was launched to gather a worldwide community to establish a big data resource and a set of the state-of-the-art of single neuron reconstruction algorithms. Many groups contributed their own algorithms for the project, including our mean shift and minimum spanning tree (M-MST). Although M-MST is intuitive and easy to implement, the MST just considers spatial information of single neuron and ignores the shape information, which might lead to less precise connections between some neuron segments. In this paper, we propose an improved algorithm, namely M-AMST, in which a rotating sphere model based on coordinate transformation is used to improve the weight calculation method in M-MST. Two experiments are designed to illustrate the effect of adapted minimum spanning tree algorithm and the adoptability of M-AMST in reconstructing variety of neuron image datasets respectively. In the experiment 1, taking the reconstruction of APP2 as reference, we produce the four difference scores (entire structure average (ESA), different structure average (DSA), percentage of different structure (PDS) and max distance of neurons' nodes (MDNN)) by comparing the neuron reconstruction of the APP2 and the other 5 competing algorithm. The result shows that M-AMST gets lower difference scores than M-MST in ESA, PDS and MDNN. Meanwhile, M-AMST is better than N-MST in ESA and MDNN. It indicates that utilizing the adapted minimum spanning tree algorithm which took the shape information of neuron into account can achieve better neuron reconstructions. In the experiment 2, 7 neuron image datasets are reconstructed and the four difference scores are calculated by comparing the gold standard reconstruction and the reconstructions produced by 6 competing algorithms. Comparing the four difference scores of M-AMST and the other 5 algorithm, we can conclude that M-AMST is able to achieve the best difference score in 3 datasets and get the second-best difference score in the other 2 datasets. We develop a pathway extraction method using a rotating sphere model based on coordinate transformation to improve the weight calculation approach in MST. The experimental results show that M-AMST utilizes the adapted minimum spanning tree algorithm which takes the shape information of neuron into account can achieve better neuron reconstructions. Moreover, M-AMST is able to get good neuron reconstruction in variety of image datasets.

Architecture of the Synaptotagmin-SNARE Machinery for Neuronal Exocytosis

PubMed Central

Zhou, Qiangjun; Lai, Ying; Bacaj, Taulant; Zhao, Minglei; Lyubimov, Artem Y.; Uervirojnangkoorn, Monarin; Zeldin, Oliver B.; Brewster, Aaron S.; Sauter, Nicholas K.; Cohen, Aina E.; Soltis, S. Michael; Alonso-Mori, Roberto; Chollet, Matthieu; Lemke, Henrik T.; Pfuetzner, Richard A.; Choi, Ucheor B.; Weis, William I.; Diao, Jiajie; Südhof, Thomas C.; Brunger, Axel T.

2015-01-01

Summary Synaptotagmin-1 and neuronal SNARE proteins play key roles in evoked synchronous neurotransmitter release. However, it is unknown how they cooperate to trigger synaptic vesicle fusion. Here we report atomic-resolution crystal structures of Ca2+- and Mg2+-bound complexes between synaptotagmin-1 and the neuronal SNARE complex, one of which was determined with diffraction data from an X-ray free electron laser, leading to an atomic-resolution structure with accurate rotamer assignments for many sidechains. The structures revealed several interfaces, including a large, specific, Ca2+-independent, and conserved interface. Tests of this interface by mutagenesis suggest that it is essential for Ca2+-triggered neurotransmitter release in neuronal synapses and for Ca2+-triggered vesicle fusion in a reconstituted system. We propose that this interface forms prior to Ca2+-triggering, and moves en bloc as Ca2+ influx promotes the interactions between synaptotagmin-1 and the plasma membrane, and consequently remodels the membrane to promote fusion, possibly in conjunction with other interfaces. PMID:26280336

Architecture of the synaptotagmin–SNARE machinery for neuronal exocytosis

DOE PAGES

Zhou, Qiangjun; Lai, Ying; Bacaj, Taulant; ...

2015-08-17

Synaptotagmin-1 and neuronal SNARE proteins have central roles in evoked synchronous neurotransmitter release; however, it is unknown how they cooperate to trigger synaptic vesicle fusion. We report atomic-resolution crystal structures of Ca 2+- and Mg 2+-bound complexes between synaptotagmin-1 and the neuronal SNARE complex, one of which was determined with diffraction data from an X-ray free-electron laser, leading to an atomic-resolution structure with accurate rotamer assignments for many side chains. The structures reveal several interfaces, including a large, specific, Ca 2+-independent and conserved interface. Tests of this interface by mutagenesis suggest that it is essential for Ca 2+-triggered neurotransmitter releasemore » in mouse hippocampal neuronal synapses and for Ca 2+-triggered vesicle fusion in a reconstituted system. Lastly, we propose that this interface forms before Ca 2+ triggering, moves en bloc as Ca 2+ influx promotes the interactions between synaptotagmin-1 and the plasma membrane, and consequently remodels the membrane to promote fusion, possibly in conjunction with other interfaces.« less

Populations of subplate and interstitial neurons in fetal and adult human telencephalon.

PubMed

Judaš, Miloš; Sedmak, Goran; Pletikos, Mihovil; Jovanov-Milošević, Nataša

2010-10-01

In the adult human telencephalon, subcortical (gyral) white matter contains a special population of interstitial neurons considered to be surviving descendants of fetal subplate neurons [Kostovic & Rakic (1980) Cytology and the time of origin of interstitial neurons in the white matter in infant and adult human and monkey telencephalon. J Neurocytol9, 219]. We designate this population of cells as superficial (gyral) interstitial neurons and describe their morphology and distribution in the postnatal and adult human cerebrum. Human fetal subplate neurons cannot be regarded as interstitial, because the subplate zone is an essential part of the fetal cortex, the major site of synaptogenesis and the 'waiting' compartment for growing cortical afferents, and contains both projection neurons and interneurons with distinct input-output connectivity. However, although the subplate zone is a transient fetal structure, many subplate neurons survive postnatally as superficial (gyral) interstitial neurons. The fetal white matter is represented by the intermediate zone and well-defined deep periventricular tracts of growing axons, such as the corpus callosum, anterior commissure, internal and external capsule, and the fountainhead of the corona radiata. These tracts gradually occupy the territory of transient fetal subventricular and ventricular zones.The human fetal white matter also contains distinct populations of deep fetal interstitial neurons, which, by virtue of their location, morphology, molecular phenotypes and advanced level of dendritic maturation, remain distinct from subplate neurons and neurons in adjacent structures (e.g. basal ganglia, basal forebrain). We describe the morphological, histochemical (nicotinamide-adenine dinucleotide phosphate-diaphorase) and immunocytochemical (neuron-specific nuclear protein, microtubule-associated protein-2, calbindin, calretinin, neuropeptide Y) features of both deep fetal interstitial neurons and deep (periventricular) interstitial neurons in the postnatal and adult deep cerebral white matter (i.e. corpus callosum, anterior commissure, internal and external capsule and the corona radiata/centrum semiovale). Although these deep interstitial neurons are poorly developed or absent in the brains of rodents, they represent a prominent feature of the significantly enlarged white matter of human and non-human primate brains. © 2010 The Authors. Journal of Anatomy © 2010 Anatomical Society of Great Britain and Ireland.

Decoding emotional valence from electroencephalographic rhythmic activity.

PubMed

Celikkanat, Hande; Moriya, Hiroki; Ogawa, Takeshi; Kauppi, Jukka-Pekka; Kawanabe, Motoaki; Hyvarinen, Aapo

2017-07-01

We attempt to decode emotional valence from electroencephalographic rhythmic activity in a naturalistic setting. We employ a data-driven method developed in a previous study, Spectral Linear Discriminant Analysis, to discover the relationships between the classification task and independent neuronal sources, optimally utilizing multiple frequency bands. A detailed investigation of the classifier provides insight into the neuronal sources related with emotional valence, and the individual differences of the subjects in processing emotions. Our findings show: (1) sources whose locations are similar across subjects are consistently involved in emotional responses, with the involvement of parietal sources being especially significant, and (2) even though the locations of the involved neuronal sources are consistent, subjects can display highly varying degrees of valence-related EEG activity in the sources.

Network Modeling of Adult Neurogenesis: Shifting Rates of Neuronal Turnover Optimally Gears Network Learning according to Novelty Gradient

PubMed Central

Chambers, R. Andrew; Conroy, Susan K.

2010-01-01

Apoptotic and neurogenic events in the adult hippocampus are hypothesized to play a role in cognitive responses to new contexts. Corticosteroid-mediated stress responses and other neural processes invoked by substantially novel contextual changes may regulate these processes. Using elementary three-layer neural networks that learn by incremental synaptic plasticity, we explored whether the cognitive effects of differential regimens of neuronal turnover depend on the environmental context in terms of the degree of novelty in the new information to be learned. In “adult” networks that had achieved mature synaptic connectivity upon prior learning of the Roman alphabet, imposition of apoptosis/neurogenesis before learning increasingly novel information (alternate Roman < Russian < Hebrew) reveals optimality of informatic cost benefits when rates of turnover are geared in proportion to the degree of novelty. These findings predict that flexible control of rates of apoptosis–neurogenesis within plastic, mature neural systems optimizes learning attributes under varying degrees of contextual change, and that failures in this regulation may define a role for adult hippocampal neurogenesis in novelty- and stress-responsive psychiatric disorders. PMID:17214558

Network modeling of adult neurogenesis: shifting rates of neuronal turnover optimally gears network learning according to novelty gradient.

PubMed

Chambers, R Andrew; Conroy, Susan K

2007-01-01

Apoptotic and neurogenic events in the adult hippocampus are hypothesized to play a role in cognitive responses to new contexts. Corticosteroid-mediated stress responses and other neural processes invoked by substantially novel contextual changes may regulate these processes. Using elementary three-layer neural networks that learn by incremental synaptic plasticity, we explored whether the cognitive effects of differential regimens of neuronal turnover depend on the environmental context in terms of the degree of novelty in the new information to be learned. In "adult" networks that had achieved mature synaptic connectivity upon prior learning of the Roman alphabet, imposition of apoptosis/neurogenesis before learning increasingly novel information (alternate Roman < Russian < Hebrew) reveals optimality of informatic cost benefits when rates of turnover are geared in proportion to the degree of novelty. These findings predict that flexible control of rates of apoptosis-neurogenesis within plastic, mature neural systems optimizes learning attributes under varying degrees of contextual change, and that failures in this regulation may define a role for adult hippocampal neurogenesis in novelty- and stress-responsive psychiatric disorders.

Optimization of Applications with Non-blocking Neighborhood Collectives via Multisends on the Blue Gene/P Supercomputer.

PubMed

Kumar, Sameer; Heidelberger, Philip; Chen, Dong; Hines, Michael

2010-04-19

We explore the multisend interface as a data mover interface to optimize applications with neighborhood collective communication operations. One of the limitations of the current MPI 2.1 standard is that the vector collective calls require counts and displacements (zero and nonzero bytes) to be specified for all the processors in the communicator. Further, all the collective calls in MPI 2.1 are blocking and do not permit overlap of communication with computation. We present the record replay persistent optimization to the multisend interface that minimizes the processor overhead of initiating the collective. We present four different case studies with the multisend API on Blue Gene/P (i) 3D-FFT, (ii) 4D nearest neighbor exchange as used in Quantum Chromodynamics, (iii) NAMD and (iv) neural network simulator NEURON. Performance results show 1.9× speedup with 32(3) 3D-FFTs, 1.9× speedup for 4D nearest neighbor exchange with the 2(4) problem, 1.6× speedup in NAMD and almost 3× speedup in NEURON with 256K cells and 1k connections/cell.

Atrophy and neuron loss: effects of a protein-deficient diet on sympathetic neurons.

PubMed

Gomes, Silvio Pires; Nyengaard, Jens Randel; Misawa, Rúbia; Girotti, Priscila Azevedo; Castelucci, Patrìcia; Blazquez, Francisco Hernandez Javier; de Melo, Mariana Pereira; Ribeiro, Antonio Augusto Coppi

2009-12-01

Protein deficiency is one of the biggest public health problems in the world, accounting for about 30-40% of hospital admissions in developing countries. Nutritional deficiencies lead to alterations in the peripheral nervous system and in the digestive system. Most studies have focused on the effects of protein-deficient diets on the enteric neurons, but not on sympathetic ganglia, which supply extrinsic sympathetic input to the digestive system. Hence, in this study, we investigated whether a protein-restricted diet would affect the quantitative structure of rat coeliac ganglion neurons. Five male Wistar rats (undernourished group) were given a pre- and postnatal hypoproteinic diet receiving 5% casein, whereas the nourished group (n = 5) was fed with 20% casein (normoproteinic diet). Blood tests were carried out on the animals, e.g., glucose, leptin, and triglyceride plasma concentrations. The main structural findings in this study were that a protein-deficient diet (5% casein) caused coeliac ganglion (78%) and coeliac ganglion neurons (24%) to atrophy and led to neuron loss (63%). Therefore, the fall in the total number of coeliac ganglion neurons in protein-restricted rats contrasts strongly with no neuron losses previously described for the enteric neurons of animals subjected to similar protein-restriction diets. Discrepancies between our figures and the data for enteric neurons (using very similar protein-restriction protocols) may be attributable to the counting method used. In light of this, further systematic investigations comparing 2-D and 3-D quantitative methods are warranted to provide even more advanced data on the effects that a protein-deficient diet may exert on sympathetic neurons. (c) 2009 Wiley-Liss, Inc. Copyright 2009 Wiley-Liss, Inc.

Advanced technique of infrared LED imaging of unstained cells and intracellular structures in isolated spinal cord, brainstem, ganglia and cerebellum.

PubMed

Szucs, Peter; Pinto, Vitor; Safronov, Boris V

2009-03-15

Light-emitting diodes (LEDs) have recently been used for the imaging of unstained living cells in the whole brain and spinal cord preparations, in which one cut was done to remove the overlying white matter. Here we show that in many cases the neurones can be visualized through the white matter in an intact nervous tissue (rats P0-P36 and mice P0-P2). We used an upright microscope with a water immersion objective and a powerful infrared LED (emission peak, 850 nm; maximum radiant intensity, 270 mW/sr) as a source of oblique illumination. In the isolated spinal cord, we were able to visualize lamina I and II neurones as well as motoneurones. In the brainstem, the neurones from the superficial nuclei were successfully viewed. In the sensory ganglion, we obtained images of unstained cells as well as intracellular structures, like endoplasmic reticulum, nucleus and nucleolus. In isolated cerebellum, parallel fibers, Purkinje and granule cells were viewed. Whole-cell recordings were done to fill spinal lamina I neurones, motoneurones and brainstem neurones with biocytin for detailed 2D-3D reconstruction of their dendritic and axonal arbores. Our imaging technique also allowed labelling individual intact neurones by injecting biocytin through the extracellular cell-attached pipette. This imaging technique opens broad possibilities for functional studies of neurones with completely preserved anatomical structures and synaptic inputs. We also show that the application of oblique infrared LED illumination allows a construction of a simple digital videomicroscope for the high-quality living cell imaging in intact nervous tissue.

Early functional impairment of sensory-motor connectivity in a mouse model of spinal muscular atrophy

PubMed Central

Mentis, George Z.; Blivis, Dvir; Liu, Wenfang; Drobac, Estelle; Crowder, Melissa E.; Kong, Lingling; Alvarez, Francisco J.; Sumner, Charlotte J.; O'Donovan, Michael J.

2011-01-01

SUMMARY To define alterations of neuronal connectivity that occur during motor neuron degeneration, we characterized the function and structure of spinal circuitry in spinal muscular atrophy (SMA) model mice. SMA motor neurons show reduced proprioceptive reflexes that correlate with decreased number and function of synapses on motor neuron somata and proximal dendrites. These abnormalities occur at an early stage of disease in motor neurons innervating proximal hindlimb muscles and medial motor neurons innervating axial muscles, but only at end-stage disease in motor neurons innervating distal hindlimb muscles. Motor neuron loss follows afferent synapse loss with the same temporal and topographical pattern. Trichostatin A, which improves motor behavior and survival of SMA mice, partially restores spinal reflexes illustrating the reversibility of these synaptic defects. De-afferentation of motor neurons is an early event in SMA and may be a primary cause of motor dysfunction that is amenable to therapeutic intervention. PMID:21315257

Fetal Therapy for Down Syndrome: Report of Three Cases and a Review of the Literature.

PubMed

Baggot, Patrick James; Baggot, Rocel Medina

2017-01-01

Down syndrome (trisomy 21) is a well-known cause of mental retardation. It can be diagnosed in early pregnancy. Scientists have made great strides in outlining the pathophysiologic mechanisms of mental retardation in Down syndrome. Much less has been published on human therapy. To our knowledge, these are the first published cases of fetal therapy for Down syndrome. Reports of three cases. In all cases, treatment was both biochemical (e.g. nutritional) and educational. In all cases, treatment was both before and after birth. All children lacked the characteristic faces usually seen in the children with Down syndrome. This suggests a treatment effect before birth. All children had better than expected development. Enhancement of development is proposed as a new therapeutic principle. Developing neurons exchange neurotrophic factors during development when they give or receive stimulation from other neurons. Neurons which receive neurotrophic stimulation survive, and those, which do not, are lost to apoptosis. The developmental therapeutic principle seeks to optimize brain development. Biochemical inputs (neurotransmitters, drugs, hormones, nutrients) and functional stimulation are integrated to optimize the growth and survival of neurons individually; other cells; subcellular organelles; and the brain as a whole. Treatment may be before and after birth, both biochemical and functional. These principles may be applied to Down syndrome, other conditions, and normal fetuses or children. Baggot PJ and Baggot RM (2014). Fetal Therapy for Down Syndrome: Report of three cases and review of the literature. J Am Phys Surg 19(1):20-24.

Light-neuron interactions: key to understanding the brain

NASA Astrophysics Data System (ADS)

Go, Mary Ann; Daria, Vincent R.

2017-02-01

In recent years, advances in light-based technology have driven an ongoing optical revolution in neuroscience. Synergistic technologies in laser microscopy, molecular biology, organic and synthetic chemistry, genetic engineering and materials science have allowed light to overcome the limitations of and to replace many conventional tools used by physiologists to record from and to manipulate single cells or whole cellular networks. Here we review the different optical techniques for stimulating neurons, influencing neuronal growth, manipulating neuronal structures and neurosurgery.

Dendrite regeneration of adult Drosophila sensory neurons diminishes with aging and is inhibited by epidermal-derived matrix metalloproteinase 2.

PubMed

DeVault, Laura; Li, Tun; Izabel, Sarah; Thompson-Peer, Katherine L; Jan, Lily Yeh; Jan, Yuh Nung

2018-03-01

Dendrites possess distinct structural and functional properties that enable neurons to receive information from the environment as well as other neurons. Despite their key role in neuronal function, current understanding of the ability of neurons to regenerate dendrites is lacking. This study characterizes the structural and functional capacity for dendrite regeneration in vivo in adult animals and examines the effect of neuronal maturation on dendrite regeneration. We focused on the class IV dendritic arborization (c4da) neuron of the Drosophila sensory system, which has a dendritic arbor that undergoes dramatic remodeling during the first 3 d of adult life and then maintains a relatively stable morphology thereafter. Using a laser severing paradigm, we monitored regeneration after acute and spatially restricted injury. We found that the capacity for regeneration was present in adult neurons but diminished as the animal aged. Regenerated dendrites recovered receptive function. Furthermore, we found that the regenerated dendrites show preferential alignment with the extracellular matrix (ECM). Finally, inhibition of ECM degradation by inhibition of matrix metalloproteinase 2 (Mmp2) to preserve the extracellular environment characteristics of young adults led to increased dendrite regeneration. These results demonstrate that dendrites retain regenerative potential throughout adulthood and that regenerative capacity decreases with aging. © 2018 DeVault et al.; Published by Cold Spring Harbor Laboratory Press.

Thy1.2 YFP-16 Transgenic Mouse Labels a Subset of Large-Diameter Sensory Neurons that Lack TRPV1 Expression

PubMed Central

Taylor-Clark, Thomas E.; Wu, Kevin Y.; Thompson, Julie-Ann; Yang, Kiseok; Bahia, Parmvir K.; Ajmo, Joanne M.

2015-01-01

The Thy1.2 YFP-16 mouse expresses yellow fluorescent protein (YFP) in specific subsets of peripheral and central neurons. The original characterization of this model suggested that YFP was expressed in all sensory neurons, and this model has been subsequently used to study sensory nerve structure and function. Here, we have characterized the expression of YFP in the sensory ganglia (DRG, trigeminal and vagal) of the Thy1.2 YFP-16 mouse, using biochemical, functional and anatomical analyses. Despite previous reports, we found that YFP was only expressed in approximately half of DRG and trigeminal neurons and less than 10% of vagal neurons. YFP-expression was only found in medium and large-diameter neurons that expressed neurofilament but not TRPV1. YFP-expressing neurons failed to respond to selective agonists for TRPV1, P2X2/3 and TRPM8 channels in Ca2+ imaging assays. Confocal analysis of glabrous skin, hairy skin of the back and ear and skeletal muscle indicated that YFP was expressed in some peripheral terminals with structures consistent with their presumed non-nociceptive nature. In summary, the Thy1.2 YFP-16 mouse expresses robust YFP expression in only a subset of sensory neurons. But this mouse model is not suitable for the study of nociceptive nerves or the function of such nerves in pain and neuropathies. PMID:25746468

Medullary neurons in the core white matter of the olfactory bulb: a new cell type.

PubMed

Paredes, Raúl G; Larriva-Sahd, Jorge

2010-02-01

The structure of a new cell type, termed the medullary neuron (MN) because of its intimate association with the rostral migratory stream (RMS) in the bulbar core, is described in the adult rat olfactory bulb. The MN is a triangular or polygonal interneuron whose soma lies between the cellular clusters of the RMS or, less frequently, among the neuron progenitors therein. MNs are easily distinguished from adjacent cells by their large size and differentiated structure. Two MN subtypes have been categorized by the Golgi technique: spiny pyramidal neurons and aspiny neurons. Both MN subtypes bear a large dendritic field impinged upon by axons in the core bulbar white matter. A set of collaterals from the adjacent axons appears to terminate on the MN dendrites. The MN axon passes in close apposition to adjacent neuron progenitors in the RMS. MNs are immunoreactive with antisera raised against gamma-aminobutyric acid and glutamate decarboxylase 65/67. Electron-microscopic observations confirm that MNs correspond to fully differentiated, mature neurons. MNs seem to be highly conserved among macrosmatic species as they occur in Nissl-stained brain sections from mouse, guinea pig, and hedgehog. Although the functional role of MNs remains to be determined, we suggest that MNs represent a cellular interface between endogenous olfactory activity and the differentiation of new neurons generated during adulthood.

Autapse-Induced Spiral Wave in Network of Neurons under Noise

PubMed Central

Qin, Huixin; Ma, Jun; Wang, Chunni; Wu, Ying

2014-01-01

Autapse plays an important role in regulating the electric activity of neuron by feedbacking time-delayed current on the membrane of neuron. Autapses are considered in a local area of regular network of neurons to investigate the development of spatiotemporal pattern, and emergence of spiral wave is observed while it fails to grow up and occupy the network completely. It is found that spiral wave can be induced to occupy more area in the network under optimized noise on the network with periodical or no-flux boundary condition being used. The developed spiral wave with self-sustained property can regulate the collective behaviors of neurons as a pacemaker. To detect the collective behaviors, a statistical factor of synchronization is calculated to investigate the emergence of ordered state in the network. The network keeps ordered state when self-sustained spiral wave is formed under noise and autapse in local area of network, and it independent of the selection of periodical or no-flux boundary condition. The developed stable spiral wave could be helpful for memory due to the distinct self-sustained property. PMID:24967577

Precise Spatiotemporal Control of Optogenetic Activation Using an Acousto-Optic Device

PubMed Central

Guo, Yanmeng; Song, Peipei; Zhang, Xiaohui; Zeng, Shaoqun; Wang, Zuoren

2011-01-01

Light activation and inactivation of neurons by optogenetic techniques has emerged as an important tool for studying neural circuit function. To achieve a high resolution, new methods are being developed to selectively manipulate the activity of individual neurons. Here, we report that the combination of an acousto-optic device (AOD) and single-photon laser was used to achieve rapid and precise spatiotemporal control of light stimulation at multiple points in a neural circuit with millisecond time resolution. The performance of this system in activating ChIEF expressed on HEK 293 cells as well as cultured neurons was first evaluated, and the laser stimulation patterns were optimized. Next, the spatiotemporally selective manipulation of multiple neurons was achieved in a precise manner. Finally, we demonstrated the versatility of this high-resolution method in dissecting neural circuits both in the mouse cortical slice and the Drosophila brain in vivo. Taken together, our results show that the combination of AOD-assisted laser stimulation and optogenetic tools provides a flexible solution for manipulating neuronal activity at high efficiency and with high temporal precision. PMID:22174813

Differential neuritogenic activities of two edible brown macroalgae, Undaria pinnatifida and Saccharina japonica.

PubMed

Hannan, Md Abdul; Mohibbullah, Md; Hwang, Seon-Yeong; Lee, Kyungyong; Kim, Yang-Chun; Hong, Yong-Ki; Moon, Il Soo

2014-01-01

Undaria pinnatifida (Harvey) Suringar and Saccharina japonica Areschoug are two common seaweeds, and both are known to have numerous pharmacological properties that include neuroprotective effects. In a previous study, we found that the ethanol extracts of U. pinnatifida (UPE) and S. japonica (SJE) had neurite promoting activities on developing hippocampal neurons. In the present study, we studied and compared the effects of UPE and SJE on neuronal maturation. Both UPE and SJE promoted neurite outgrowth in a dose-dependent manner with optimal concentrations of 5 and 15 μg/mL, respectively. Initial neuronal differentiation was significantly promoted by UPE and SJE. Subsequently, treatment with both increased indices of axonal and dendritic cytoarchitecture, such as, the numbers and lengths of primary processes, although only UPE had a significant effect on branching frequencies. In addition, UPE and SJE showed no evidence of cytotoxicity, rather they protected neurons from naturally occurring death in vitro. These results indicate that UPE and SJE promote axodendritic maturation and neuronal survival and suggest that these algal extracts, especially UPE, have beneficial effects on the nervous system.

Autapse-induced spiral wave in network of neurons under noise.

PubMed

Qin, Huixin; Ma, Jun; Wang, Chunni; Wu, Ying

2014-01-01

Autapse plays an important role in regulating the electric activity of neuron by feedbacking time-delayed current on the membrane of neuron. Autapses are considered in a local area of regular network of neurons to investigate the development of spatiotemporal pattern, and emergence of spiral wave is observed while it fails to grow up and occupy the network completely. It is found that spiral wave can be induced to occupy more area in the network under optimized noise on the network with periodical or no-flux boundary condition being used. The developed spiral wave with self-sustained property can regulate the collective behaviors of neurons as a pacemaker. To detect the collective behaviors, a statistical factor of synchronization is calculated to investigate the emergence of ordered state in the network. The network keeps ordered state when self-sustained spiral wave is formed under noise and autapse in local area of network, and it independent of the selection of periodical or no-flux boundary condition. The developed stable spiral wave could be helpful for memory due to the distinct self-sustained property.

Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity

PubMed Central

Abbott, L. F.; Sompolinsky, Haim

2017-01-01

Neurons and networks in the cerebral cortex must operate reliably despite multiple sources of noise. To evaluate the impact of both input and output noise, we determine the robustness of single-neuron stimulus selective responses, as well as the robustness of attractor states of networks of neurons performing memory tasks. We find that robustness to output noise requires synaptic connections to be in a balanced regime in which excitation and inhibition are strong and largely cancel each other. We evaluate the conditions required for this regime to exist and determine the properties of networks operating within it. A plausible synaptic plasticity rule for learning that balances weight configurations is presented. Our theory predicts an optimal ratio of the number of excitatory and inhibitory synapses for maximizing the encoding capacity of balanced networks for given statistics of afferent activations. Previous work has shown that balanced networks amplify spatiotemporal variability and account for observed asynchronous irregular states. Here we present a distinct type of balanced network that amplifies small changes in the impinging signals and emerges automatically from learning to perform neuronal and network functions robustly. PMID:29042519

Effects of spike-time-dependent plasticity on the stochastic resonance of small-world neuronal networks

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

Yu, Haitao; Guo, Xinmeng; Wang, Jiang, E-mail: jiangwang@tju.edu.cn

2014-09-01

The phenomenon of stochastic resonance in Newman-Watts small-world neuronal networks is investigated when the strength of synaptic connections between neurons is adaptively adjusted by spike-time-dependent plasticity (STDP). It is shown that irrespective of the synaptic connectivity is fixed or adaptive, the phenomenon of stochastic resonance occurs. The efficiency of network stochastic resonance can be largely enhanced by STDP in the coupling process. Particularly, the resonance for adaptive coupling can reach a much larger value than that for fixed one when the noise intensity is small or intermediate. STDP with dominant depression and small temporal window ratio is more efficient formore » the transmission of weak external signal in small-world neuronal networks. In addition, we demonstrate that the effect of stochastic resonance can be further improved via fine-tuning of the average coupling strength of the adaptive network. Furthermore, the small-world topology can significantly affect stochastic resonance of excitable neuronal networks. It is found that there exists an optimal probability of adding links by which the noise-induced transmission of weak periodic signal peaks.« less

Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins

PubMed Central

Li, Bian; Mendenhall, Jeffrey; Nguyen, Elizabeth Dong; Weiner, Brian E.; Fischer, Axel W.; Meiler, Jens

2017-01-01

Prediction of the three-dimensional (3D) structures of proteins by computational methods is acknowledged as an unsolved problem. Accurate prediction of important structural characteristics such as contact number is expected to accelerate the otherwise slow progress being made in the prediction of 3D structure of proteins. Here, we present a dropout neural network-based method, TMH-Expo, for predicting the contact number of transmembrane helix (TMH) residues from sequence. Neuronal dropout is a strategy where certain neurons of the network are excluded from back-propagation to prevent co-adaptation of hidden-layer neurons. By using neuronal dropout, overfitting was significantly reduced and performance was noticeably improved. For multi-spanning helical membrane proteins, TMH-Expo achieved a remarkable Pearson correlation coefficient of 0.69 between predicted and experimental values and a mean absolute error of only 1.68. In addition, among those membrane protein–membrane protein interface residues, 76.8% were correctly predicted. Mapping of predicted contact numbers onto structures indicates that contact numbers predicted by TMH-Expo reflect the exposure patterns of TMHs and reveal membrane protein–membrane protein interfaces, reinforcing the potential of predicted contact numbers to be used as restraints for 3D structure prediction and protein–protein docking. TMH-Expo can be accessed via a Web server at www.meilerlab.org. PMID:26804342

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