A Small World of Neuronal Synchrony

PubMed Central

Yu, Shan; Huang, Debin; Singer, Wolf

2008-01-01

A small-world network has been suggested to be an efficient solution for achieving both modular and global processing—a property highly desirable for brain computations. Here, we investigated functional networks of cortical neurons using correlation analysis to identify functional connectivity. To reconstruct the interaction network, we applied the Ising model based on the principle of maximum entropy. This allowed us to assess the interactions by measuring pairwise correlations and to assess the strength of coupling from the degree of synchrony. Visual responses were recorded in visual cortex of anesthetized cats, simultaneously from up to 24 neurons. First, pairwise correlations captured most of the patterns in the population's activity and, therefore, provided a reliable basis for the reconstruction of the interaction networks. Second, and most importantly, the resulting networks had small-world properties; the average path lengths were as short as in simulated random networks, but the clustering coefficients were larger. Neurons differed considerably with respect to the number and strength of interactions, suggesting the existence of “hubs” in the network. Notably, there was no evidence for scale-free properties. These results suggest that cortical networks are optimized for the coexistence of local and global computations: feature detection and feature integration or binding. PMID:18400792

Recruitment of local inhibitory networks by horizontal connections in layer 2/3 of ferret visual cortex.

PubMed

Tucker, Thomas R; Katz, Lawrence C

2003-01-01

To investigate how neurons in cortical layer 2/3 integrate horizontal inputs arising from widely distributed sites, we combined intracellular recording and voltage-sensitive dye imaging to visualize the spatiotemporal dynamics of neuronal activity evoked by electrical stimulation of multiple sites in visual cortex. Individual stimuli evoked characteristic patterns of optical activity, while delivering stimuli at multiple sites generated interacting patterns in the regions of overlap. We observed that neurons in overlapping regions received convergent horizontal activation that generated nonlinear responses due to the emergence of large inhibitory potentials. The results indicate that co-activation of multiple sets of horizontal connections recruit strong inhibition from local inhibitory networks, causing marked deviations from simple linear integration.

Functional neuronal processing of body odors differs from that of similar common odors.

PubMed

Lundström, Johan N; Boyle, Julie A; Zatorre, Robert J; Jones-Gotman, Marilyn

2008-06-01

Visual and auditory stimuli of high social and ecological importance are processed in the brain by specialized neuronal networks. To date, this has not been demonstrated for olfactory stimuli. By means of positron emission tomography, we sought to elucidate the neuronal substrates behind body odor perception to answer the question of whether the central processing of body odors differs from perceptually similar nonbody odors. Body odors were processed by a network that was distinctly separate from common odors, indicating a separation in the processing of odors based on their source. Smelling a friend's body odor activated regions previously seen for familiar stimuli, whereas smelling a stranger activated amygdala and insular regions akin to what has previously been demonstrated for fearful stimuli. The results provide evidence that social olfactory stimuli of high ecological relevance are processed by specialized neuronal networks similar to what has previously been demonstrated for auditory and visual stimuli.

An egalitarian network model for the emergence of simple and complex cells in visual cortex

PubMed Central

Tao, Louis; Shelley, Michael; McLaughlin, David; Shapley, Robert

2004-01-01

We explain how simple and complex cells arise in a large-scale neuronal network model of the primary visual cortex of the macaque. Our model consists of ≈4,000 integrate-and-fire, conductance-based point neurons, representing the cells in a small, 1-mm2 patch of an input layer of the primary visual cortex. In the model the local connections are isotropic and nonspecific, and convergent input from the lateral geniculate nucleus confers cortical cells with orientation and spatial phase preference. The balance between lateral connections and lateral geniculate nucleus drive determines whether individual neurons in this recurrent circuit are simple or complex. The model reproduces qualitatively the experimentally observed distributions of both extracellular and intracellular measures of simple and complex response. PMID:14695891

Endogenous Sequential Cortical Activity Evoked by Visual Stimuli

PubMed Central

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

2015-01-01

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

Cross-frequency synchronization connects networks of fast and slow oscillations during visual working memory maintenance.

PubMed

Siebenhühner, Felix; Wang, Sheng H; Palva, J Matias; Palva, Satu

2016-09-26

Neuronal activity in sensory and fronto-parietal (FP) areas underlies the representation and attentional control, respectively, of sensory information maintained in visual working memory (VWM). Within these regions, beta/gamma phase-synchronization supports the integration of sensory functions, while synchronization in theta/alpha bands supports the regulation of attentional functions. A key challenge is to understand which mechanisms integrate neuronal processing across these distinct frequencies and thereby the sensory and attentional functions. We investigated whether such integration could be achieved by cross-frequency phase synchrony (CFS). Using concurrent magneto- and electroencephalography, we found that CFS was load-dependently enhanced between theta and alpha-gamma and between alpha and beta-gamma oscillations during VWM maintenance among visual, FP, and dorsal attention (DA) systems. CFS also connected the hubs of within-frequency-synchronized networks and its strength predicted individual VWM capacity. We propose that CFS integrates processing among synchronized neuronal networks from theta to gamma frequencies to link sensory and attentional functions.

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.

PubMed

Hagen, Espen; Dahmen, David; Stavrinou, Maria L; Lindén, Henrik; Tetzlaff, Tom; van Albada, Sacha J; Grün, Sonja; Diesmann, Markus; Einevoll, Gaute T

2016-12-01

With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm 2 patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail. © The Author 2016. Published by Oxford University Press.

Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons.

PubMed

Zerlaut, Yann; Chemla, Sandrine; Chavane, Frederic; Destexhe, Alain

2018-02-01

Voltage-sensitive dye imaging (VSDi) has revealed fundamental properties of neocortical processing at macroscopic scales. Since for each pixel VSDi signals report the average membrane potential over hundreds of neurons, it seems natural to use a mean-field formalism to model such signals. Here, we present a mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions. We study a network of regular-spiking (RS) excitatory neurons and fast-spiking (FS) inhibitory neurons. We use a Master Equation formalism, together with a semi-analytic approach to the transfer function of AdEx neurons to describe the average dynamics of the coupled populations. We compare the predictions of this mean-field model to simulated networks of RS-FS cells, first at the level of the spontaneous activity of the network, which is well predicted by the analytical description. Second, we investigate the response of the network to time-varying external input, and show that the mean-field model predicts the response time course of the population. Finally, to model VSDi signals, we consider a one-dimensional ring model made of interconnected RS-FS mean-field units. We found that this model can reproduce the spatio-temporal patterns seen in VSDi of awake monkey visual cortex as a response to local and transient visual stimuli. Conversely, we show that the model allows one to infer physiological parameters from the experimentally-recorded spatio-temporal patterns.

A Model of Self-Organizing Head-Centered Visual Responses in Primate Parietal Areas

PubMed Central

Mender, Bedeho M. W.; Stringer, Simon M.

2013-01-01

We present a hypothesis for how head-centered visual representations in primate parietal areas could self-organize through visually-guided learning, and test this hypothesis using a neural network model. The model consists of a competitive output layer of neurons that receives afferent synaptic connections from a population of input neurons with eye position gain modulated retinal receptive fields. The synaptic connections in the model are trained with an associative trace learning rule which has the effect of encouraging output neurons to learn to respond to subsets of input patterns that tend to occur close together in time. This network architecture and synaptic learning rule is hypothesized to promote the development of head-centered output neurons during periods of time when the head remains fixed while the eyes move. This hypothesis is demonstrated to be feasible, and each of the core model components described is tested and found to be individually necessary for successful self-organization. PMID:24349064

Neural networks with local receptive fields and superlinear VC dimension.

PubMed

Schmitt, Michael

2002-04-01

Local receptive field neurons comprise such well-known and widely used unit types as radial basis function (RBF) neurons and neurons with center-surround receptive field. We study the Vapnik-Chervonenkis (VC) dimension of feedforward neural networks with one hidden layer of these units. For several variants of local receptive field neurons, we show that the VC dimension of these networks is superlinear. In particular, we establish the bound Omega(W log k) for any reasonably sized network with W parameters and k hidden nodes. This bound is shown to hold for discrete center-surround receptive field neurons, which are physiologically relevant models of cells in the mammalian visual system, for neurons computing a difference of gaussians, which are popular in computational vision, and for standard RBF neurons, a major alternative to sigmoidal neurons in artificial neural networks. The result for RBF neural networks is of particular interest since it answers a question that has been open for several years. The results also give rise to lower bounds for networks with fixed input dimension. Regarding constants, all bounds are larger than those known thus far for similar architectures with sigmoidal neurons. The superlinear lower bounds contrast with linear upper bounds for single local receptive field neurons also derived here.

The visual white matter: The application of diffusion MRI and fiber tractography to vision science

PubMed Central

Rokem, Ariel; Takemura, Hiromasa; Bock, Andrew S.; Scherf, K. Suzanne; Behrmann, Marlene; Wandell, Brian A.; Fine, Ione; Bridge, Holly; Pestilli, Franco

2017-01-01

Visual neuroscience has traditionally focused much of its attention on understanding the response properties of single neurons or neuronal ensembles. The visual white matter and the long-range neuronal connections it supports are fundamental in establishing such neuronal response properties and visual function. This review article provides an introduction to measurements and methods to study the human visual white matter using diffusion MRI. These methods allow us to measure the microstructural and macrostructural properties of the white matter in living human individuals; they allow us to trace long-range connections between neurons in different parts of the visual system and to measure the biophysical properties of these connections. We also review a range of findings from recent studies on connections between different visual field maps, the effects of visual impairment on the white matter, and the properties underlying networks that process visual information supporting visual face recognition. Finally, we discuss a few promising directions for future studies. These include new methods for analysis of MRI data, open datasets that are becoming available to study brain connectivity and white matter properties, and open source software for the analysis of these data. PMID:28196374

Activity of cardiorespiratory networks revealed by transsynaptic virus expressing GFP.

PubMed

Irnaten, M; Neff, R A; Wang, J; Loewy, A D; Mettenleiter, T C; Mendelowitz, D

2001-01-01

A fluorescent transneuronal marker capable of labeling individual neurons in a central network while maintaining their normal physiology would permit functional studies of neurons within entire networks responsible for complex behaviors such as cardiorespiratory reflexes. The Bartha strain of pseudorabies virus (PRV), an attenuated swine alpha herpesvirus, can be used as a transsynaptic marker of neural circuits. Bartha PRV invades neuronal networks in the CNS through peripherally projecting axons, replicates in these parent neurons, and then travels transsynaptically to continue labeling the second- and higher-order neurons in a time-dependent manner. A Bartha PRV mutant that expresses green fluorescent protein (GFP) was used to visualize and record from neurons that determine the vagal motor outflow to the heart. Here we show that Bartha PRV-GFP-labeled neurons retain their normal electrophysiological properties and that the labeled baroreflex pathways that control heart rate are unaltered by the virus. This novel transynaptic virus permits in vitro studies of identified neurons within functionally defined neuronal systems including networks that mediate cardiovascular and respiratory function and interactions. We also demonstrate superior laryngeal motorneurons fire spontaneously and synapse on cardiac vagal neurons in the nucleus ambiguus. This cardiorespiratory pathway provides a neural basis of respiratory sinus arrhythmias.

Multiple mechanisms switch an electrically coupled, synaptically inhibited neuron between competing rhythmic oscillators.

PubMed

Gutierrez, Gabrielle J; O'Leary, Timothy; Marder, Eve

2013-03-06

Rhythmic oscillations are common features of nervous systems. One of the fundamental questions posed by these rhythms is how individual neurons or groups of neurons are recruited into different network oscillations. We modeled competing fast and slow oscillators connected to a hub neuron with electrical and inhibitory synapses. We explore the patterns of coordination shown in the network as a function of the electrical coupling and inhibitory synapse strengths with the help of a novel visualization method that we call the "parameterscape." The hub neuron can be switched between the fast and slow oscillators by multiple network mechanisms, indicating that a given change in network state can be achieved by degenerate cellular mechanisms. These results have importance for interpreting experiments employing optogenetic, genetic, and pharmacological manipulations to understand circuit dynamics. Copyright © 2013 Elsevier Inc. All rights reserved.

Cross-frequency synchronization connects networks of fast and slow oscillations during visual working memory maintenance

PubMed Central

Siebenhühner, Felix; Wang, Sheng H; Palva, J Matias; Palva, Satu

2016-01-01

Neuronal activity in sensory and fronto-parietal (FP) areas underlies the representation and attentional control, respectively, of sensory information maintained in visual working memory (VWM). Within these regions, beta/gamma phase-synchronization supports the integration of sensory functions, while synchronization in theta/alpha bands supports the regulation of attentional functions. A key challenge is to understand which mechanisms integrate neuronal processing across these distinct frequencies and thereby the sensory and attentional functions. We investigated whether such integration could be achieved by cross-frequency phase synchrony (CFS). Using concurrent magneto- and electroencephalography, we found that CFS was load-dependently enhanced between theta and alpha–gamma and between alpha and beta-gamma oscillations during VWM maintenance among visual, FP, and dorsal attention (DA) systems. CFS also connected the hubs of within-frequency-synchronized networks and its strength predicted individual VWM capacity. We propose that CFS integrates processing among synchronized neuronal networks from theta to gamma frequencies to link sensory and attentional functions. DOI: http://dx.doi.org/10.7554/eLife.13451.001 PMID:27669146

GABAergic neurons in ferret visual cortex participate in functionally specific networks

PubMed Central

Wilson, Daniel E.; Smith, Gordon B.; Jacob, Amanda; Walker, Theo; Dimidschstein, Jordane; Fishell, Gord J.; Fitzpatrick, David

2017-01-01

Summary Functional circuits in the visual cortex require the coordinated activity of excitatory and inhibitory neurons. Molecular genetic approaches in the mouse have led to the ‘local nonspecific pooling principle’ of inhibitory connectivity, in which inhibitory neurons are untuned for stimulus features due to the random pooling of local inputs. However, it remains unclear whether this principle generalizes to species with a columnar organization of feature selectivity such as carnivores, primates, and humans. Here we use virally-mediated GABAergic-specific GCaMP6f expression to demonstrate that inhibitory neurons in ferret visual cortex respond robustly and selectively to oriented stimuli. We find that the tuning of inhibitory neurons is inconsistent with the local non-specific pooling of excitatory inputs, and that inhibitory neurons exhibit orientation-specific noise correlations with local and distant excitatory neurons. These findings challenge the generality of the non-specific pooling principle for inhibitory neurons, suggesting different rules for functional excitatory-inhibitory interactions in non-murine species. PMID:28279352

Network model of top-down influences on local gain and contextual interactions in visual cortex.

PubMed

Piëch, Valentin; Li, Wu; Reeke, George N; Gilbert, Charles D

2013-10-22

The visual system uses continuity as a cue for grouping oriented line segments that define object boundaries in complex visual scenes. Many studies support the idea that long-range intrinsic horizontal connections in early visual cortex contribute to this grouping. Top-down influences in primary visual cortex (V1) play an important role in the processes of contour integration and perceptual saliency, with contour-related responses being task dependent. This suggests an interaction between recurrent inputs to V1 and intrinsic connections within V1 that enables V1 neurons to respond differently under different conditions. We created a network model that simulates parametrically the control of local gain by hypothetical top-down modification of local recurrence. These local gain changes, as a consequence of network dynamics in our model, enable modulation of contextual interactions in a task-dependent manner. Our model displays contour-related facilitation of neuronal responses and differential foreground vs. background responses over the neuronal ensemble, accounting for the perceptual pop-out of salient contours. It quantitatively reproduces the results of single-unit recording experiments in V1, highlighting salient contours and replicating the time course of contextual influences. We show by means of phase-plane analysis that the model operates stably even in the presence of large inputs. Our model shows how a simple form of top-down modulation of the effective connectivity of intrinsic cortical connections among biophysically realistic neurons can account for some of the response changes seen in perceptual learning and task switching.

The visual development of hand-centered receptive fields in a neural network model of the primate visual system trained with experimentally recorded human gaze changes

PubMed Central

Galeazzi, Juan M.; Navajas, Joaquín; Mender, Bedeho M. W.; Quian Quiroga, Rodrigo; Minini, Loredana; Stringer, Simon M.

2016-01-01

ABSTRACT Neurons have been found in the primate brain that respond to objects in specific locations in hand-centered coordinates. A key theoretical challenge is to explain how such hand-centered neuronal responses may develop through visual experience. In this paper we show how hand-centered visual receptive fields can develop using an artificial neural network model, VisNet, of the primate visual system when driven by gaze changes recorded from human test subjects as they completed a jigsaw. A camera mounted on the head captured images of the hand and jigsaw, while eye movements were recorded using an eye-tracking device. This combination of data allowed us to reconstruct the retinal images seen as humans undertook the jigsaw task. These retinal images were then fed into the neural network model during self-organization of its synaptic connectivity using a biologically plausible trace learning rule. A trace learning mechanism encourages neurons in the model to learn to respond to input images that tend to occur in close temporal proximity. In the data recorded from human subjects, we found that the participant’s gaze often shifted through a sequence of locations around a fixed spatial configuration of the hand and one of the jigsaw pieces. In this case, trace learning should bind these retinal images together onto the same subset of output neurons. The simulation results consequently confirmed that some cells learned to respond selectively to the hand and a jigsaw piece in a fixed spatial configuration across different retinal views. PMID:27253452

The visual development of hand-centered receptive fields in a neural network model of the primate visual system trained with experimentally recorded human gaze changes.

PubMed

Galeazzi, Juan M; Navajas, Joaquín; Mender, Bedeho M W; Quian Quiroga, Rodrigo; Minini, Loredana; Stringer, Simon M

2016-01-01

Neurons have been found in the primate brain that respond to objects in specific locations in hand-centered coordinates. A key theoretical challenge is to explain how such hand-centered neuronal responses may develop through visual experience. In this paper we show how hand-centered visual receptive fields can develop using an artificial neural network model, VisNet, of the primate visual system when driven by gaze changes recorded from human test subjects as they completed a jigsaw. A camera mounted on the head captured images of the hand and jigsaw, while eye movements were recorded using an eye-tracking device. This combination of data allowed us to reconstruct the retinal images seen as humans undertook the jigsaw task. These retinal images were then fed into the neural network model during self-organization of its synaptic connectivity using a biologically plausible trace learning rule. A trace learning mechanism encourages neurons in the model to learn to respond to input images that tend to occur in close temporal proximity. In the data recorded from human subjects, we found that the participant's gaze often shifted through a sequence of locations around a fixed spatial configuration of the hand and one of the jigsaw pieces. In this case, trace learning should bind these retinal images together onto the same subset of output neurons. The simulation results consequently confirmed that some cells learned to respond selectively to the hand and a jigsaw piece in a fixed spatial configuration across different retinal views.

Effects of isoflurane anesthesia on ensemble patterns of Ca2+ activity in mouse v1: reduced direction selectivity independent of increased correlations in cellular activity.

PubMed

Goltstein, Pieter M; Montijn, Jorrit S; Pennartz, Cyriel M A

2015-01-01

Anesthesia affects brain activity at the molecular, neuronal and network level, but it is not well-understood how tuning properties of sensory neurons and network connectivity change under its influence. Using in vivo two-photon calcium imaging we matched neuron identity across episodes of wakefulness and anesthesia in the same mouse and recorded spontaneous and visually evoked activity patterns of neuronal ensembles in these two states. Correlations in spontaneous patterns of calcium activity between pairs of neurons were increased under anesthesia. While orientation selectivity remained unaffected by anesthesia, this treatment reduced direction selectivity, which was attributable to an increased response to the null-direction. As compared to anesthesia, populations of V1 neurons coded more mutual information on opposite stimulus directions during wakefulness, whereas information on stimulus orientation differences was lower. Increases in correlations of calcium activity during visual stimulation were correlated with poorer population coding, which raised the hypothesis that the anesthesia-induced increase in correlations may be causal to degrading directional coding. Visual stimulation under anesthesia, however, decorrelated ongoing activity patterns to a level comparable to wakefulness. Because visual stimulation thus appears to 'break' the strength of pairwise correlations normally found in spontaneous activity under anesthesia, the changes in correlational structure cannot explain the awake-anesthesia difference in direction coding. The population-wide decrease in coding for stimulus direction thus occurs independently of anesthesia-induced increments in correlations of spontaneous activity.

Effects of Isoflurane Anesthesia on Ensemble Patterns of Ca2+ Activity in Mouse V1: Reduced Direction Selectivity Independent of Increased Correlations in Cellular Activity

PubMed Central

Goltstein, Pieter M.; Montijn, Jorrit S.; Pennartz, Cyriel M. A.

2015-01-01

Anesthesia affects brain activity at the molecular, neuronal and network level, but it is not well-understood how tuning properties of sensory neurons and network connectivity change under its influence. Using in vivo two-photon calcium imaging we matched neuron identity across episodes of wakefulness and anesthesia in the same mouse and recorded spontaneous and visually evoked activity patterns of neuronal ensembles in these two states. Correlations in spontaneous patterns of calcium activity between pairs of neurons were increased under anesthesia. While orientation selectivity remained unaffected by anesthesia, this treatment reduced direction selectivity, which was attributable to an increased response to the null-direction. As compared to anesthesia, populations of V1 neurons coded more mutual information on opposite stimulus directions during wakefulness, whereas information on stimulus orientation differences was lower. Increases in correlations of calcium activity during visual stimulation were correlated with poorer population coding, which raised the hypothesis that the anesthesia-induced increase in correlations may be causal to degrading directional coding. Visual stimulation under anesthesia, however, decorrelated ongoing activity patterns to a level comparable to wakefulness. Because visual stimulation thus appears to ‘break’ the strength of pairwise correlations normally found in spontaneous activity under anesthesia, the changes in correlational structure cannot explain the awake-anesthesia difference in direction coding. The population-wide decrease in coding for stimulus direction thus occurs independently of anesthesia-induced increments in correlations of spontaneous activity. PMID:25706867

Mouse V1 population correlates of visual detection rely on heterogeneity within neuronal response patterns

PubMed Central

Montijn, Jorrit S; Goltstein, Pieter M; Pennartz, Cyriel MA

2015-01-01

Previous studies have demonstrated the importance of the primary sensory cortex for the detection, discrimination, and awareness of visual stimuli, but it is unknown how neuronal populations in this area process detected and undetected stimuli differently. Critical differences may reside in the mean strength of responses to visual stimuli, as reflected in bulk signals detectable in functional magnetic resonance imaging, electro-encephalogram, or magnetoencephalography studies, or may be more subtly composed of differentiated activity of individual sensory neurons. Quantifying single-cell Ca2+ responses to visual stimuli recorded with in vivo two-photon imaging, we found that visual detection correlates more strongly with population response heterogeneity rather than overall response strength. Moreover, neuronal populations showed consistencies in activation patterns across temporally spaced trials in association with hit responses, but not during nondetections. Contrary to models relying on temporally stable networks or bulk signaling, these results suggest that detection depends on transient differentiation in neuronal activity within cortical populations. DOI: http://dx.doi.org/10.7554/eLife.10163.001 PMID:26646184

Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene.

PubMed

Li, Jun; Mei, Xue; Prokhorov, Danil; Tao, Dacheng

2017-03-01

Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for successful analysis. This paper extends the framework of deep neural networks by accounting for the structural cues in the visual signals. In particular, two kinds of neural networks have been proposed. First, we develop a multitask deep convolutional network, which simultaneously detects the presence of the target and the geometric attributes (location and orientation) of the target with respect to the region of interest. Second, a recurrent neuron layer is adopted for structured visual detection. The recurrent neurons can deal with the spatial distribution of visible cues belonging to an object whose shape or structure is difficult to explicitly define. Both the networks are demonstrated by the practical task of detecting lane boundaries in traffic scenes. The multitask convolutional neural network provides auxiliary geometric information to help the subsequent modeling of the given lane structures. The recurrent neural network automatically detects lane boundaries, including those areas containing no marks, without any explicit prior knowledge or secondary modeling.

Integration of auditory and visual communication information in the primate ventrolateral prefrontal cortex.

PubMed

Sugihara, Tadashi; Diltz, Mark D; Averbeck, Bruno B; Romanski, Lizabeth M

2006-10-25

The integration of auditory and visual stimuli is crucial for recognizing objects, communicating effectively, and navigating through our complex world. Although the frontal lobes are involved in memory, communication, and language, there has been no evidence that the integration of communication information occurs at the single-cell level in the frontal lobes. Here, we show that neurons in the macaque ventrolateral prefrontal cortex (VLPFC) integrate audiovisual communication stimuli. The multisensory interactions included both enhancement and suppression of a predominantly auditory or a predominantly visual response, although multisensory suppression was the more common mode of response. The multisensory neurons were distributed across the VLPFC and within previously identified unimodal auditory and visual regions (O'Scalaidhe et al., 1997; Romanski and Goldman-Rakic, 2002). Thus, our study demonstrates, for the first time, that single prefrontal neurons integrate communication information from the auditory and visual domains, suggesting that these neurons are an important node in the cortical network responsible for communication.

Integration of Auditory and Visual Communication Information in the Primate Ventrolateral Prefrontal Cortex

PubMed Central

Sugihara, Tadashi; Diltz, Mark D.; Averbeck, Bruno B.; Romanski, Lizabeth M.

2009-01-01

The integration of auditory and visual stimuli is crucial for recognizing objects, communicating effectively, and navigating through our complex world. Although the frontal lobes are involved in memory, communication, and language, there has been no evidence that the integration of communication information occurs at the single-cell level in the frontal lobes. Here, we show that neurons in the macaque ventrolateral prefrontal cortex (VLPFC) integrate audiovisual communication stimuli. The multisensory interactions included both enhancement and suppression of a predominantly auditory or a predominantly visual response, although multisensory suppression was the more common mode of response. The multisensory neurons were distributed across the VLPFC and within previously identified unimodal auditory and visual regions (O’Scalaidhe et al., 1997; Romanski and Goldman-Rakic, 2002). Thus, our study demonstrates, for the first time, that single prefrontal neurons integrate communication information from the auditory and visual domains, suggesting that these neurons are an important node in the cortical network responsible for communication. PMID:17065454

Model-based analysis of pattern motion processing in mouse primary visual cortex

PubMed Central

Muir, Dylan R.; Roth, Morgane M.; Helmchen, Fritjof; Kampa, Björn M.

2015-01-01

Neurons in sensory areas of neocortex exhibit responses tuned to specific features of the environment. In visual cortex, information about features such as edges or textures with particular orientations must be integrated to recognize a visual scene or object. Connectivity studies in rodent cortex have revealed that neurons make specific connections within sub-networks sharing common input tuning. In principle, this sub-network architecture enables local cortical circuits to integrate sensory information. However, whether feature integration indeed occurs locally in rodent primary sensory areas has not been examined directly. We studied local integration of sensory features in primary visual cortex (V1) of the mouse by presenting drifting grating and plaid stimuli, while recording the activity of neuronal populations with two-photon calcium imaging. Using a Bayesian model-based analysis framework, we classified single-cell responses as being selective for either individual grating components or for moving plaid patterns. Rather than relying on trial-averaged responses, our model-based framework takes into account single-trial responses and can easily be extended to consider any number of arbitrary predictive models. Our analysis method was able to successfully classify significantly more responses than traditional partial correlation (PC) analysis, and provides a rigorous statistical framework to rank any number of models and reject poorly performing models. We also found a large proportion of cells that respond strongly to only one stimulus class. In addition, a quarter of selectively responding neurons had more complex responses that could not be explained by any simple integration model. Our results show that a broad range of pattern integration processes already take place at the level of V1. This diversity of integration is consistent with processing of visual inputs by local sub-networks within V1 that are tuned to combinations of sensory features. PMID:26300738

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

Computational Model of Primary Visual Cortex Combining Visual Attention for Action Recognition

PubMed Central

Shu, Na; Gao, Zhiyong; Chen, Xiangan; Liu, Haihua

2015-01-01

Humans can easily understand other people’s actions through visual systems, while computers cannot. Therefore, a new bio-inspired computational model is proposed in this paper aiming for automatic action recognition. The model focuses on dynamic properties of neurons and neural networks in the primary visual cortex (V1), and simulates the procedure of information processing in V1, which consists of visual perception, visual attention and representation of human action. In our model, a family of the three-dimensional spatial-temporal correlative Gabor filters is used to model the dynamic properties of the classical receptive field of V1 simple cell tuned to different speeds and orientations in time for detection of spatiotemporal information from video sequences. Based on the inhibitory effect of stimuli outside the classical receptive field caused by lateral connections of spiking neuron networks in V1, we propose surround suppressive operator to further process spatiotemporal information. Visual attention model based on perceptual grouping is integrated into our model to filter and group different regions. Moreover, in order to represent the human action, we consider the characteristic of the neural code: mean motion map based on analysis of spike trains generated by spiking neurons. The experimental evaluation on some publicly available action datasets and comparison with the state-of-the-art approaches demonstrate the superior performance of the proposed model. PMID:26132270

Coding the presence of visual objects in a recurrent neural network of visual cortex.

PubMed

Zwickel, Timm; Wachtler, Thomas; Eckhorn, Reinhard

2007-01-01

Before we can recognize a visual object, our visual system has to segregate it from its background. This requires a fast mechanism for establishing the presence and location of objects independently of their identity. Recently, border-ownership neurons were recorded in monkey visual cortex which might be involved in this task [Zhou, H., Friedmann, H., von der Heydt, R., 2000. Coding of border ownership in monkey visual cortex. J. Neurosci. 20 (17), 6594-6611]. In order to explain the basic mechanisms required for fast coding of object presence, we have developed a neural network model of visual cortex consisting of three stages. Feed-forward and lateral connections support coding of Gestalt properties, including similarity, good continuation, and convexity. Neurons of the highest area respond to the presence of an object and encode its position, invariant of its form. Feedback connections to the lowest area facilitate orientation detectors activated by contours belonging to potential objects, and thus generate the experimentally observed border-ownership property. This feedback control acts fast and significantly improves the figure-ground segregation required for the consecutive task of object recognition.

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.

A case for spiking neural network simulation based on configurable multiple-FPGA systems.

PubMed

Yang, Shufan; Wu, Qiang; Li, Renfa

2011-09-01

Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.

A reevaluation of the vestibulo-ocular reflex: new ideas of its purpose, properties, neural substrate, and disorders

NASA Technical Reports Server (NTRS)

Leigh, R. J.; Brandt, T.

1993-01-01

Conventional views of the vestibulo-ocular reflex (VOR) have emphasized testing with caloric stimuli and by passively rotating patients at low frequencies in a chair. The properties of the VOR tested under these conditions differ from the performance of this reflex during the natural function for which it evolved--locomotion. Only the VOR (and not visually mediated eye movements) can cope with the high-frequency angular and linear perturbations of the head that occur during locomotion; this is achieved by generating eye movements at short latency (< 16 msec). Interpretation of vestibular testing is enhanced by the realization that, although the di- and trisynaptic components of the VOR are essential for this short-latency response, the overall accuracy and plasticity of the VOR depend upon a distributed, parallel network of neurons involving the vestibular nuclei. Neurons in this network variously upon a distributed, parallel network of neurons involving the vestibular nuclei. Neurons in this network variously encode inputs from the labyrinthine semicircular canals and otoliths, as well as from the visual and somatosensory systems. The central vestibular pathways branch to contact vestibular cortex (for perception) and the spinal cord (for control of posture). Thus, the vestibular nuclei basically coordinate the stabilization of gaze and posture, and contribute to the perception of verticality and self-motion. Consequently, brainstem disorders that disrupt the VOR cause not just only nystagmus, but also instability of posture (eg, increased fore-aft sway in patients with downbeat nystagmus) and disturbance of spatial orientation (eg, tilt of the subjective visual vertical in Wallenberg's syndrome).

Chimera-like states in a neuronal network model of the cat brain

NASA Astrophysics Data System (ADS)

Santos, M. S.; Szezech, J. D.; Borges, F. S.; Iarosz, K. C.; Caldas, I. L.; Batista, A. M.; Viana, R. L.; Kurths, J.

2017-08-01

Neuronal systems have been modeled by complex networks in different description levels. Recently, it has been verified that networks can simultaneously exhibit one coherent and other incoherent domain, known as chimera states. In this work, we study the existence of chimera states in a network considering the connectivity matrix based on the cat cerebral cortex. The cerebral cortex of the cat can be separated in 65 cortical areas organised into the four cognitive regions: visual, auditory, somatosensory-motor and frontolimbic. We consider a network where the local dynamics is given by the Hindmarsh-Rose model. The Hindmarsh-Rose equations are a well known model of neuronal activity that has been considered to simulate membrane potential in neuron. Here, we analyse under which conditions chimera states are present, as well as the affects induced by intensity of coupling on them. We observe the existence of chimera states in that incoherent structure can be composed of desynchronised spikes or desynchronised bursts. Moreover, we find that chimera states with desynchronised bursts are more robust to neuronal noise than with desynchronised spikes.

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

Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule.

PubMed

Beyeler, Michael; Dutt, Nikil D; Krichmar, Jeffrey L

2013-12-01

Understanding how the human brain is able to efficiently perceive and understand a visual scene is still a field of ongoing research. Although many studies have focused on the design and optimization of neural networks to solve visual recognition tasks, most of them either lack neurobiologically plausible learning rules or decision-making processes. Here we present a large-scale model of a hierarchical spiking neural network (SNN) that integrates a low-level memory encoding mechanism with a higher-level decision process to perform a visual classification task in real-time. The model consists of Izhikevich neurons and conductance-based synapses for realistic approximation of neuronal dynamics, a spike-timing-dependent plasticity (STDP) synaptic learning rule with additional synaptic dynamics for memory encoding, and an accumulator model for memory retrieval and categorization. The full network, which comprised 71,026 neurons and approximately 133 million synapses, ran in real-time on a single off-the-shelf graphics processing unit (GPU). The network was constructed on a publicly available SNN simulator that supports general-purpose neuromorphic computer chips. The network achieved 92% correct classifications on MNIST in 100 rounds of random sub-sampling, which is comparable to other SNN approaches and provides a conservative and reliable performance metric. Additionally, the model correctly predicted reaction times from psychophysical experiments. Because of the scalability of the approach and its neurobiological fidelity, the current model can be extended to an efficient neuromorphic implementation that supports more generalized object recognition and decision-making architectures found in the brain. Copyright © 2013 Elsevier Ltd. All rights reserved.

Irregular synchronous activity in stochastically-coupled networks of integrate-and-fire neurons.

PubMed

Lin, J K; Pawelzik, K; Ernst, U; Sejnowski, T J

1998-08-01

We investigate the spatial and temporal aspects of firing patterns in a network of integrate-and-fire neurons arranged in a one-dimensional ring topology. The coupling is stochastic and shaped like a Mexican hat with local excitation and lateral inhibition. With perfect precision in the couplings, the attractors of activity in the network occur at every position in the ring. Inhomogeneities in the coupling break the translational invariance of localized attractors and lead to synchronization within highly active as well as weakly active clusters. The interspike interval variability is high, consistent with recent observations of spike time distributions in visual cortex. The robustness of our results is demonstrated with more realistic simulations on a network of McGregor neurons which model conductance changes and after-hyperpolarization potassium currents.

Simultaneous submicrometric 3D imaging of the micro-vascular network and the neuronal system in a mouse spinal cord

PubMed Central

Fratini, Michela; Bukreeva, Inna; Campi, Gaetano; Brun, Francesco; Tromba, Giuliana; Modregger, Peter; Bucci, Domenico; Battaglia, Giuseppe; Spanò, Raffaele; Mastrogiacomo, Maddalena; Requardt, Herwig; Giove, Federico; Bravin, Alberto; Cedola, Alessia

2015-01-01

Faults in vascular (VN) and neuronal networks of spinal cord are responsible for serious neurodegenerative pathologies. Because of inadequate investigation tools, the lacking knowledge of the complete fine structure of VN and neuronal system represents a crucial problem. Conventional 2D imaging yields incomplete spatial coverage leading to possible data misinterpretation, whereas standard 3D computed tomography imaging achieves insufficient resolution and contrast. We show that X-ray high-resolution phase-contrast tomography allows the simultaneous visualization of three-dimensional VN and neuronal systems of ex-vivo mouse spinal cord at scales spanning from millimeters to hundreds of nanometers, with nor contrast agent nor sectioning and neither destructive sample-preparation. We image both the 3D distribution of micro-capillary network and the micrometric nerve fibers, axon-bundles and neuron soma. Our approach is very suitable for pre-clinical investigation of neurodegenerative pathologies and spinal-cord-injuries, in particular to resolve the entangled relationship between VN and neuronal system. PMID:25686728

Functional Interactions between Newborn and Mature Neurons Leading to Integration into Established Neuronal Circuits.

PubMed

Boulanger-Weill, Jonathan; Candat, Virginie; Jouary, Adrien; Romano, Sebastián A; Pérez-Schuster, Verónica; Sumbre, Germán

2017-06-19

From development up to adulthood, the vertebrate brain is continuously supplied with newborn neurons that integrate into established mature circuits. However, how this process is coordinated during development remains unclear. Using two-photon imaging, GCaMP5 transgenic zebrafish larvae, and sparse electroporation in the larva's optic tectum, we monitored spontaneous and induced activity of large neuronal populations containing newborn and functionally mature neurons. We observed that the maturation of newborn neurons is a 4-day process. Initially, newborn neurons showed undeveloped dendritic arbors, no neurotransmitter identity, and were unresponsive to visual stimulation, although they displayed spontaneous calcium transients. Later on, newborn-labeled neurons began to respond to visual stimuli but in a very variable manner. At the end of the maturation period, newborn-labeled neurons exhibited visual tuning curves (spatial receptive fields and direction selectivity) and spontaneous correlated activity with neighboring functionally mature neurons. At this developmental stage, newborn-labeled neurons presented complex dendritic arbors and neurotransmitter identity (excitatory or inhibitory). Removal of retinal inputs significantly perturbed the integration of newborn neurons into the functionally mature tectal network. Our results provide a comprehensive description of the maturation of newborn neurons during development and shed light on potential mechanisms underlying their integration into a functionally mature neuronal circuit. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Single Canonical Model of Reflexive Memory and Spatial Attention

PubMed Central

Patel, Saumil S.; Red, Stuart; Lin, Eric; Sereno, Anne B.

2015-01-01

Many neurons in the dorsal and ventral visual stream have the property that after a brief visual stimulus presentation in their receptive field, the spiking activity in these neurons persists above their baseline levels for several seconds. This maintained activity is not always correlated with the monkey’s task and its origin is unknown. We have previously proposed a simple neural network model, based on shape selective neurons in monkey lateral intraparietal cortex, which predicts the valence and time course of reflexive (bottom-up) spatial attention. In the same simple model, we demonstrate here that passive maintained activity or short-term memory of specific visual events can result without need for an external or top-down modulatory signal. Mutual inhibition and neuronal adaptation play distinct roles in reflexive attention and memory. This modest 4-cell model provides the first simple and unified physiologically plausible mechanism of reflexive spatial attention and passive short-term memory processes. PMID:26493949

Negative Correlations in Visual Cortical Networks

PubMed Central

Chelaru, Mircea I.; Dragoi, Valentin

2016-01-01

The amount of information encoded by cortical circuits depends critically on the capacity of nearby neurons to exhibit trial-to-trial (noise) correlations in their responses. Depending on their sign and relationship to signal correlations, noise correlations can either increase or decrease the population code accuracy relative to uncorrelated neuronal firing. Whereas positive noise correlations have been extensively studied using experimental and theoretical tools, the functional role of negative correlations in cortical circuits has remained elusive. We addressed this issue by performing multiple-electrode recording in the superficial layers of the primary visual cortex (V1) of alert monkey. Despite the fact that positive noise correlations decayed exponentially with the difference in the orientation preference between cells, negative correlations were uniformly distributed across the population. Using a statistical model for Fisher Information estimation, we found that a mild increase in negative correlations causes a sharp increase in network accuracy even when mean correlations were held constant. To examine the variables controlling the strength of negative correlations, we implemented a recurrent spiking network model of V1. We found that increasing local inhibition and reducing excitation causes a decrease in the firing rates of neurons while increasing the negative noise correlations, which in turn increase the population signal-to-noise ratio and network accuracy. Altogether, these results contribute to our understanding of the neuronal mechanism involved in the generation of negative correlations and their beneficial impact on cortical circuit function. PMID:25217468

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.

Frequency-band signatures of visual responses to naturalistic input in ferret primary visual cortex during free viewing.

PubMed

Sellers, Kristin K; Bennett, Davis V; Fröhlich, Flavio

2015-02-19

Neuronal firing responses in visual cortex reflect the statistics of visual input and emerge from the interaction with endogenous network dynamics. Artificial visual stimuli presented to animals in which the network dynamics were constrained by anesthetic agents or trained behavioral tasks have provided fundamental understanding of how individual neurons in primary visual cortex respond to input. In contrast, very little is known about the mesoscale network dynamics and their relationship to microscopic spiking activity in the awake animal during free viewing of naturalistic visual input. To address this gap in knowledge, we recorded local field potential (LFP) and multiunit activity (MUA) simultaneously in all layers of primary visual cortex (V1) of awake, freely viewing ferrets presented with naturalistic visual input (nature movie clips). We found that naturalistic visual stimuli modulated the entire oscillation spectrum; low frequency oscillations were mostly suppressed whereas higher frequency oscillations were enhanced. In average across all cortical layers, stimulus-induced change in delta and alpha power negatively correlated with the MUA responses, whereas sensory-evoked increases in gamma power positively correlated with MUA responses. The time-course of the band-limited power in these frequency bands provided evidence for a model in which naturalistic visual input switched V1 between two distinct, endogenously present activity states defined by the power of low (delta, alpha) and high (gamma) frequency oscillatory activity. Therefore, the two mesoscale activity states delineated in this study may define the degree of engagement of the circuit with the processing of sensory input. Copyright © 2014 Elsevier B.V. All rights reserved.

Tau pathology does not affect experience-driven single-neuron and network-wide Arc/Arg3.1 responses.

PubMed

Rudinskiy, Nikita; Hawkes, Jonathan M; Wegmann, Susanne; Kuchibhotla, Kishore V; Muzikansky, Alona; Betensky, Rebecca A; Spires-Jones, Tara L; Hyman, Bradley T

2014-06-10

Intraneuronal neurofibrillary tangles (NFTs) - a characteristic pathological feature of Alzheimer's and several other neurodegenerative diseases - are considered a major target for drug development. Tangle load correlates well with the severity of cognitive symptoms and mouse models of tauopathy are behaviorally impaired. However, there is little evidence that NFTs directly impact physiological properties of host neurons. Here we used a transgenic mouse model of tauopathy to study how advanced tau pathology in different brain regions affects activity-driven expression of immediate-early gene Arc required for experience-dependent consolidation of long-term memories. We demonstrate in vivo that visual cortex neurons with tangles are as likely to express comparable amounts of Arc in response to structured visual stimulation as their neighbors without tangles. Probability of experience-dependent Arc response was not affected by tau tangles in both visual cortex and hippocampal pyramidal neurons as determined postmortem. Moreover, whole brain analysis showed that network-wide activity-driven Arc expression was not affected by tau pathology in any of the brain regions, including brain areas with the highest tangle load. Our findings suggest that intraneuronal NFTs do not affect signaling cascades leading to experience-dependent gene expression required for long-term synaptic plasticity.

Speed of feedforward and recurrent processing in multilayer networks of integrate-and-fire neurons.

PubMed

Panzeri, S; Rolls, E T; Battaglia, F; Lavis, R

2001-11-01

The speed of processing in the visual cortical areas can be fast, with for example the latency of neuronal responses increasing by only approximately 10 ms per area in the ventral visual system sequence V1 to V2 to V4 to inferior temporal visual cortex. This has led to the suggestion that rapid visual processing can only be based on the feedforward connections between cortical areas. To test this idea, we investigated the dynamics of information retrieval in multiple layer networks using a four-stage feedforward network modelled with continuous dynamics with integrate-and-fire neurons, and associative synaptic connections between stages with a synaptic time constant of 10 ms. Through the implementation of continuous dynamics, we found latency differences in information retrieval of only 5 ms per layer when local excitation was absent and processing was purely feedforward. However, information latency differences increased significantly when non-associative local excitation was included. We also found that local recurrent excitation through associatively modified synapses can contribute significantly to processing in as little as 15 ms per layer, including the feedforward and local feedback processing. Moreover, and in contrast to purely feed-forward processing, the contribution of local recurrent feedback was useful and approximately this rapid even when retrieval was made difficult by noise. These findings suggest that cortical information processing can benefit from recurrent circuits when the allowed processing time per cortical area is at least 15 ms long.

Interpretation of the function of the striate cortex

NASA Astrophysics Data System (ADS)

Garner, Bernardette M.; Paplinski, Andrew P.

2000-04-01

Biological neural networks do not require retraining every time objects move in the visual field. Conventional computer neural networks do not share this shift-invariance. The brain compensates for movements in the head, body, eyes and objects by allowing the sensory data to be tracked across the visual field. The neurons in the striate cortex respond to objects moving across the field of vision as is seen in many experiments. It is proposed, that the neurons in the striate cortex allow continuous angle changes needed to compensate for changes in orientation of the head, eyes and the motion of objects in the field of vision. It is hypothesized that the neurons in the striate cortex form a system that allows for the translation, some rotation and scaling of objects and provides a continuity of objects as they move relative to other objects. The neurons in the striate cortex respond to features which are fundamental to sight, such as orientation of lines, direction of motion, color and contrast. The neurons that respond to these features are arranged on the cortex in a way that depends on the features they are responding to and on the area of the retina from which they receive their inputs.

Visualizing deep neural network by alternately image blurring and deblurring.

PubMed

Wang, Feng; Liu, Haijun; Cheng, Jian

2018-01-01

Visualization from trained deep neural networks has drawn massive public attention in recent. One of the visualization approaches is to train images maximizing the activation of specific neurons. However, directly maximizing the activation would lead to unrecognizable images, which cannot provide any meaningful information. In this paper, we introduce a simple but effective technique to constrain the optimization route of the visualization. By adding two totally inverse transformations, image blurring and deblurring, to the optimization procedure, recognizable images can be created. Our algorithm is good at extracting the details in the images, which are usually filtered by previous methods in the visualizations. Extensive experiments on AlexNet, VGGNet and GoogLeNet illustrate that we can better understand the neural networks utilizing the knowledge obtained by the visualization. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparison of visual receptive fields in the dorsolateral prefrontal cortex and ventral intraparietal area in macaques.

PubMed

Viswanathan, Pooja; Nieder, Andreas

2017-12-01

The concept of receptive field (RF) describes the responsiveness of neurons to sensory space. Neurons in the primate association cortices have long been known to be spatially selective but a detailed characterisation and direct comparison of RFs between frontal and parietal association cortices are missing. We sampled the RFs of a large number of neurons from two interconnected areas of the frontal and parietal lobes, the dorsolateral prefrontal cortex (dlPFC) and ventral intraparietal area (VIP), of rhesus monkeys by systematically presenting a moving bar during passive fixation. We found that more than half of neurons in both areas showed spatial selectivity. Single neurons in both areas could be assigned to five classes according to the spatial response patterns: few non-uniform RFs with multiple discrete response maxima could be dissociated from the vast majority of uniform RFs showing a single maximum; the latter were further classified into full-field and confined foveal, contralateral and ipsilateral RFs. Neurons in dlPFC showed a preference for the contralateral visual space and collectively encoded the contralateral visual hemi-field. In contrast, VIP neurons preferred central locations, predominantly covering the foveal visual space. Putative pyramidal cells with broad-spiking waveforms in PFC had smaller RFs than putative interneurons showing narrow-spiking waveforms, but distributed similarly across the visual field. In VIP, however, both putative pyramidal cells and interneurons had similar RFs at similar eccentricities. We provide a first, thorough characterisation of visual RFs in two reciprocally connected areas of a fronto-parietal cortical network. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Feedforward Inhibition Allows Input Summation to Vary in Recurrent Cortical Networks

PubMed Central

2018-01-01

Abstract Brain computations depend on how neurons transform inputs to spike outputs. Here, to understand input-output transformations in cortical networks, we recorded spiking responses from visual cortex (V1) of awake mice of either sex while pairing sensory stimuli with optogenetic perturbation of excitatory and parvalbumin-positive inhibitory neurons. We found that V1 neurons’ average responses were primarily additive (linear). We used a recurrent cortical network model to determine whether these data, as well as past observations of nonlinearity, could be described by a common circuit architecture. Simulations showed that cortical input-output transformations can be changed from linear to sublinear with moderate (∼20%) strengthening of connections between inhibitory neurons, but this change away from linear scaling depends on the presence of feedforward inhibition. Simulating a variety of recurrent connection strengths showed that, compared with when input arrives only to excitatory neurons, networks produce a wider range of output spiking responses in the presence of feedforward inhibition. PMID:29682603

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

PubMed Central

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

2016-01-01

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

Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models

PubMed Central

Cowley, Benjamin R.; Doiron, Brent; Kohn, Adam

2016-01-01

Recent studies have applied dimensionality reduction methods to understand how the multi-dimensional structure of neural population activity gives rise to brain function. It is unclear, however, how the results obtained from dimensionality reduction generalize to recordings with larger numbers of neurons and trials or how these results relate to the underlying network structure. We address these questions by applying factor analysis to recordings in the visual cortex of non-human primates and to spiking network models that self-generate irregular activity through a balance of excitation and inhibition. We compared the scaling trends of two key outputs of dimensionality reduction—shared dimensionality and percent shared variance—with neuron and trial count. We found that the scaling properties of networks with non-clustered and clustered connectivity differed, and that the in vivo recordings were more consistent with the clustered network. Furthermore, recordings from tens of neurons were sufficient to identify the dominant modes of shared variability that generalize to larger portions of the network. These findings can help guide the interpretation of dimensionality reduction outputs in regimes of limited neuron and trial sampling and help relate these outputs to the underlying network structure. PMID:27926936

Spontaneously emerging direction selectivity maps in visual cortex through STDP.

PubMed

Wenisch, Oliver G; Noll, Joachim; Hemmen, J Leo van

2005-10-01

It is still an open question as to whether, and how, direction-selective neuronal responses in primary visual cortex are generated by feedforward thalamocortical or recurrent intracortical connections, or a combination of both. Here we present an investigation that concentrates on and, only for the sake of simplicity, restricts itself to intracortical circuits, in particular, with respect to the developmental aspects of direction selectivity through spike-timing-dependent synaptic plasticity. We show that directional responses can emerge in a recurrent network model of visual cortex with spiking neurons that integrate inputs mainly from a particular direction, thus giving rise to an asymmetrically shaped receptive field. A moving stimulus that enters the receptive field from this (preferred) direction will activate a neuron most strongly because of the increased number and/or strength of inputs from this direction and since delayed isotropic inhibition will neither overlap with, nor cancel excitation, as would be the case for other stimulus directions. It is demonstrated how direction-selective responses result from spatial asymmetries in the distribution of synaptic contacts or weights of inputs delivered to a neuron by slowly conducting intracortical axonal delay lines. By means of spike-timing-dependent synaptic plasticity with an asymmetric learning window this kind of coupling asymmetry develops naturally in a recurrent network of stochastically spiking neurons in a scenario where the neurons are activated by unidirectionally moving bar stimuli and even when only intrinsic spontaneous activity drives the learning process. We also present simulation results to show the ability of this model to produce direction preference maps similar to experimental findings.

Emulating the visual receptive-field properties of MST neurons with a template model of heading estimation

NASA Technical Reports Server (NTRS)

Perrone, J. A.; Stone, L. S.

1998-01-01

We have proposed previously a computational neural-network model by which the complex patterns of retinal image motion generated during locomotion (optic flow) can be processed by specialized detectors acting as templates for specific instances of self-motion. The detectors in this template model respond to global optic flow by sampling image motion over a large portion of the visual field through networks of local motion sensors with properties similar to those of neurons found in the middle temporal (MT) area of primate extrastriate visual cortex. These detectors, arranged within cortical-like maps, were designed to extract self-translation (heading) and self-rotation, as well as the scene layout (relative distances) ahead of a moving observer. We then postulated that heading from optic flow is directly encoded by individual neurons acting as heading detectors within the medial superior temporal (MST) area. Others have questioned whether individual MST neurons can perform this function because some of their receptive-field properties seem inconsistent with this role. To resolve this issue, we systematically compared MST responses with those of detectors from two different configurations of the model under matched stimulus conditions. We found that the characteristic physiological properties of MST neurons can be explained by the template model. We conclude that MST neurons are well suited to support self-motion estimation via a direct encoding of heading and that the template model provides an explicit set of testable hypotheses that can guide future exploration of MST and adjacent areas within the superior temporal sulcus.

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.

Lateral Information Processing by Spiking Neurons: A Theoretical Model of the Neural Correlate of Consciousness

PubMed Central

Ebner, Marc; Hameroff, Stuart

2011-01-01

Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on “autopilot”). Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the “conscious pilot”) suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious “auto-pilot” cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways “gap junctions” in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of synchrony, within which the contents of perception are represented and contained. This mobile zone can be viewed as a model of the neural correlate of consciousness in the brain. PMID:22046178

Lateral information processing by spiking neurons: a theoretical model of the neural correlate of consciousness.

PubMed

Ebner, Marc; Hameroff, Stuart

2011-01-01

Cognitive brain functions, for example, sensory perception, motor control and learning, are understood as computation by axonal-dendritic chemical synapses in networks of integrate-and-fire neurons. Cognitive brain functions may occur either consciously or nonconsciously (on "autopilot"). Conscious cognition is marked by gamma synchrony EEG, mediated largely by dendritic-dendritic gap junctions, sideways connections in input/integration layers. Gap-junction-connected neurons define a sub-network within a larger neural network. A theoretical model (the "conscious pilot") suggests that as gap junctions open and close, a gamma-synchronized subnetwork, or zone moves through the brain as an executive agent, converting nonconscious "auto-pilot" cognition to consciousness, and enhancing computation by coherent processing and collective integration. In this study we implemented sideways "gap junctions" in a single-layer artificial neural network to perform figure/ground separation. The set of neurons connected through gap junctions form a reconfigurable resistive grid or sub-network zone. In the model, outgoing spikes are temporally integrated and spatially averaged using the fixed resistive grid set up by neurons of similar function which are connected through gap-junctions. This spatial average, essentially a feedback signal from the neuron's output, determines whether particular gap junctions between neurons will open or close. Neurons connected through open gap junctions synchronize their output spikes. We have tested our gap-junction-defined sub-network in a one-layer neural network on artificial retinal inputs using real-world images. Our system is able to perform figure/ground separation where the laterally connected sub-network of neurons represents a perceived object. Even though we only show results for visual stimuli, our approach should generalize to other modalities. The system demonstrates a moving sub-network zone of synchrony, within which the contents of perception are represented and contained. This mobile zone can be viewed as a model of the neural correlate of consciousness in the brain.

Emulating the Visual Receptive Field Properties of MST Neurons with a Template Model of Heading Estimation

NASA Technical Reports Server (NTRS)

Perrone, John A.; Stone, Leland S.

1997-01-01

We have previously proposed a computational neural-network model by which the complex patterns of retinal image motion generated during locomotion (optic flow) can be processed by specialized detectors acting as templates for specific instances of self-motion. The detectors in this template model respond to global optic flow by sampling image motion over a large portion of the visual field through networks of local motion sensors with properties similar to neurons found in the middle temporal (MT) area of primate extrastriate visual cortex. The model detectors were designed to extract self-translation (heading), self-rotation, as well as the scene layout (relative distances) ahead of a moving observer, and are arranged in cortical-like heading maps to perform this function. Heading estimation from optic flow has been postulated by some to be implemented within the medial superior temporal (MST) area. Others have questioned whether MST neurons can fulfill this role because some of their receptive-field properties appear inconsistent with a role in heading estimation. To resolve this issue, we systematically compared MST single-unit responses with the outputs of model detectors under matched stimulus conditions. We found that the basic physiological properties of MST neurons can be explained by the template model. We conclude that MST neurons are well suited to support heading estimation and that the template model provides an explicit set of testable hypotheses which can guide future exploration of MST and adjacent areas within the primate superior temporal sulcus.

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.

Prefrontal spatial working memory network predicts animal's decision making in a free choice saccade task

PubMed Central

Mochizuki, Kei

2015-01-01

While neurons in the lateral prefrontal cortex (PFC) encode spatial information during the performance of working memory tasks, they are also known to participate in subjective behavior such as spatial attention and action selection. In the present study, we analyzed the activity of primate PFC neurons during the performance of a free choice memory-guided saccade task in which the monkeys needed to choose a saccade direction by themselves. In trials when the receptive field location was subsequently chosen by the animal, PFC neurons with spatially selective visual response started to show greater activation before cue onset. This result suggests that the fluctuation of firing before cue presentation prematurely biased the representation of a certain spatial location and eventually encouraged the subsequent choice of that location. In addition, modulation of the activity by the animal's choice was observed only in neurons with high sustainability of activation and was also dependent on the spatial configuration of the visual cues. These findings were consistent with known characteristics of PFC neurons in information maintenance in spatial working memory function. These results suggest that precue fluctuation of spatial representation was shared and enhanced through the working memory network in the PFC and could finally influence the animal's free choice of saccade direction. The present study revealed that the PFC plays an important role in decision making in a free choice condition and that the dynamics of decision making are constrained by the network architecture embedded in this cortical area. PMID:26490287

Barreloid Borders and Neuronal Activity Shape Panglial Gap Junction-Coupled Networks in the Mouse Thalamus.

PubMed

Claus, Lena; Philippot, Camille; Griemsmann, Stephanie; Timmermann, Aline; Jabs, Ronald; Henneberger, Christian; Kettenmann, Helmut; Steinhäuser, Christian

2018-01-01

The ventral posterior nucleus of the thalamus plays an important role in somatosensory information processing. It contains elongated cellular domains called barreloids, which are the structural basis for the somatotopic organization of vibrissae representation. So far, the organization of glial networks in these barreloid structures and its modulation by neuronal activity has not been studied. We have developed a method to visualize thalamic barreloid fields in acute slices. Combining electrophysiology, immunohistochemistry, and electroporation in transgenic mice with cell type-specific fluorescence labeling, we provide the first structure-function analyses of barreloidal glial gap junction networks. We observed coupled networks, which comprised both astrocytes and oligodendrocytes. The spread of tracers or a fluorescent glucose derivative through these networks was dependent on neuronal activity and limited by the barreloid borders, which were formed by uncoupled or weakly coupled oligodendrocytes. Neuronal somata were distributed homogeneously across barreloid fields with their processes running in parallel to the barreloid borders. Many astrocytes and oligodendrocytes were not part of the panglial networks. Thus, oligodendrocytes are the cellular elements limiting the communicating panglial network to a single barreloid, which might be important to ensure proper metabolic support to active neurons located within a particular vibrissae signaling pathway. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Feedback to distal dendrites links fMRI signals to neural receptive fields in a spiking network model of the visual cortex.

PubMed

Heikkinen, Hanna; Sharifian, Fariba; Vigario, Ricardo; Vanni, Simo

2015-07-01

The blood oxygenation level-dependent (BOLD) response has been strongly associated with neuronal activity in the brain. However, some neuronal tuning properties are consistently different from the BOLD response. We studied the spatial extent of neural and hemodynamic responses in the primary visual cortex, where the BOLD responses spread and interact over much longer distances than the small receptive fields of individual neurons would predict. Our model shows that a feedforward-feedback loop between V1 and a higher visual area can account for the observed spread of the BOLD response. In particular, anisotropic landing of inputs to compartmental neurons were necessary to account for the BOLD signal spread, while retaining realistic spiking responses. Our work shows that simple dendrites can separate tuning at the synapses and at the action potential output, thus bridging the BOLD signal to the neural receptive fields with high fidelity. Copyright © 2015 the American Physiological Society.

Learning alters theta amplitude, theta-gamma coupling and neuronal synchronization in inferotemporal cortex.

PubMed

Kendrick, Keith M; Zhan, Yang; Fischer, Hanno; Nicol, Alister U; Zhang, Xuejuan; Feng, Jianfeng

2011-06-09

How oscillatory brain rhythms alone, or in combination, influence cortical information processing to support learning has yet to be fully established. Local field potential and multi-unit neuronal activity recordings were made from 64-electrode arrays in the inferotemporal cortex of conscious sheep during and after visual discrimination learning of face or object pairs. A neural network model has been developed to simulate and aid functional interpretation of learning-evoked changes. Following learning the amplitude of theta (4-8 Hz), but not gamma (30-70 Hz) oscillations was increased, as was the ratio of theta to gamma. Over 75% of electrodes showed significant coupling between theta phase and gamma amplitude (theta-nested gamma). The strength of this coupling was also increased following learning and this was not simply a consequence of increased theta amplitude. Actual discrimination performance was significantly correlated with theta and theta-gamma coupling changes. Neuronal activity was phase-locked with theta but learning had no effect on firing rates or the magnitude or latencies of visual evoked potentials during stimuli. The neural network model developed showed that a combination of fast and slow inhibitory interneurons could generate theta-nested gamma. By increasing N-methyl-D-aspartate receptor sensitivity in the model similar changes were produced as in inferotemporal cortex after learning. The model showed that these changes could potentiate the firing of downstream neurons by a temporal desynchronization of excitatory neuron output without increasing the firing frequencies of the latter. This desynchronization effect was confirmed in IT neuronal activity following learning and its magnitude was correlated with discrimination performance. Face discrimination learning produces significant increases in both theta amplitude and the strength of theta-gamma coupling in the inferotemporal cortex which are correlated with behavioral performance. A network model which can reproduce these changes suggests that a key function of such learning-evoked alterations in theta and theta-nested gamma activity may be increased temporal desynchronization in neuronal firing leading to optimal timing of inputs to downstream neural networks potentiating their responses. In this way learning can produce potentiation in neural networks simply through altering the temporal pattern of their inputs.

Learning alters theta amplitude, theta-gamma coupling and neuronal synchronization in inferotemporal cortex

PubMed Central

2011-01-01

Background How oscillatory brain rhythms alone, or in combination, influence cortical information processing to support learning has yet to be fully established. Local field potential and multi-unit neuronal activity recordings were made from 64-electrode arrays in the inferotemporal cortex of conscious sheep during and after visual discrimination learning of face or object pairs. A neural network model has been developed to simulate and aid functional interpretation of learning-evoked changes. Results Following learning the amplitude of theta (4-8 Hz), but not gamma (30-70 Hz) oscillations was increased, as was the ratio of theta to gamma. Over 75% of electrodes showed significant coupling between theta phase and gamma amplitude (theta-nested gamma). The strength of this coupling was also increased following learning and this was not simply a consequence of increased theta amplitude. Actual discrimination performance was significantly correlated with theta and theta-gamma coupling changes. Neuronal activity was phase-locked with theta but learning had no effect on firing rates or the magnitude or latencies of visual evoked potentials during stimuli. The neural network model developed showed that a combination of fast and slow inhibitory interneurons could generate theta-nested gamma. By increasing N-methyl-D-aspartate receptor sensitivity in the model similar changes were produced as in inferotemporal cortex after learning. The model showed that these changes could potentiate the firing of downstream neurons by a temporal desynchronization of excitatory neuron output without increasing the firing frequencies of the latter. This desynchronization effect was confirmed in IT neuronal activity following learning and its magnitude was correlated with discrimination performance. Conclusions Face discrimination learning produces significant increases in both theta amplitude and the strength of theta-gamma coupling in the inferotemporal cortex which are correlated with behavioral performance. A network model which can reproduce these changes suggests that a key function of such learning-evoked alterations in theta and theta-nested gamma activity may be increased temporal desynchronization in neuronal firing leading to optimal timing of inputs to downstream neural networks potentiating their responses. In this way learning can produce potentiation in neural networks simply through altering the temporal pattern of their inputs. PMID:21658251

Stimulus Dependence of Correlated Variability across Cortical Areas

PubMed Central

Cohen, Marlene R.

2016-01-01

The way that correlated trial-to-trial variability between pairs of neurons in the same brain area (termed spike count or noise correlation, rSC) depends on stimulus or task conditions can constrain models of cortical circuits and of the computations performed by networks of neurons (Cohen and Kohn, 2011). In visual cortex, rSC tends not to depend on stimulus properties (Kohn and Smith, 2005; Huang and Lisberger, 2009) but does depend on cognitive factors like visual attention (Cohen and Maunsell, 2009; Mitchell et al., 2009). However, neurons across visual areas respond to any visual stimulus or contribute to any perceptual decision, and the way that information from multiple areas is combined to guide perception is unknown. To gain insight into these issues, we recorded simultaneously from neurons in two areas of visual cortex (primary visual cortex, V1, and the middle temporal area, MT) while rhesus monkeys viewed different visual stimuli in different attention conditions. We found that correlations between neurons in different areas depend on stimulus and attention conditions in very different ways than do correlations within an area. Correlations across, but not within, areas depend on stimulus direction and the presence of a second stimulus, and attention has opposite effects on correlations within and across areas. This observed pattern of cross-area correlations is predicted by a normalization model where MT units sum V1 inputs that are passed through a divisive nonlinearity. Together, our results provide insight into how neurons in different areas interact and constrain models of the neural computations performed across cortical areas. SIGNIFICANCE STATEMENT Correlations in the responses of pairs of neurons within the same cortical area have been a subject of growing interest in systems neuroscience. However, correlated variability between different cortical areas is likely just as important. We recorded simultaneously from neurons in primary visual cortex and the middle temporal area while rhesus monkeys viewed different visual stimuli in different attention conditions. We found that correlations between neurons in different areas depend on stimulus and attention conditions in very different ways than do correlations within an area. The observed pattern of cross-area correlations was predicted by a simple normalization model. Our results provide insight into how neurons in different areas interact and constrain models of the neural computations performed across cortical areas. PMID:27413163

Learning Peri-saccadic Remapping of Receptive Field from Experience in Lateral Intraparietal Area.

PubMed

Wang, Xiao; Wu, Yan; Zhang, Mingsha; Wu, Si

2017-01-01

Our eyes move constantly at a frequency of 3-5 times per second. These movements, called saccades, induce the sweeping of visual images on the retina, yet we perceive the world as stable. It has been suggested that the brain achieves this visual stability via predictive remapping of neuronal receptive field (RF). A recent experimental study disclosed details of this remapping process in the lateral intraparietal area (LIP), that is, about the time of the saccade, the neuronal RF expands along the saccadic trajectory temporally, covering the current RF (CRF), the future RF (FRF), and the region the eye will sweep through during the saccade. A cortical wave (CW) model was also proposed, which attributes the RF remapping as a consequence of neural activity propagating in the cortex, triggered jointly by a visual stimulus and the corollary discharge (CD) signal responsible for the saccade. In this study, we investigate how this CW model is learned naturally from visual experiences at the development of the brain. We build a two-layer network, with one layer consisting of LIP neurons and the other superior colliculus (SC) neurons. Initially, neuronal connections are random and non-selective. A saccade will cause a static visual image to sweep through the retina passively, creating the effect of the visual stimulus moving in the opposite direction of the saccade. According to the spiking-time-dependent-plasticity rule, the connection path in the opposite direction of the saccade between LIP neurons and the connection path from SC to LIP are enhanced. Over many such visual experiences, the CW model is developed, which generates the peri-saccadic RF remapping in LIP as observed in the experiment.

Learning Peri-saccadic Remapping of Receptive Field from Experience in Lateral Intraparietal Area

PubMed Central

Wang, Xiao; Wu, Yan; Zhang, Mingsha; Wu, Si

2017-01-01

Our eyes move constantly at a frequency of 3–5 times per second. These movements, called saccades, induce the sweeping of visual images on the retina, yet we perceive the world as stable. It has been suggested that the brain achieves this visual stability via predictive remapping of neuronal receptive field (RF). A recent experimental study disclosed details of this remapping process in the lateral intraparietal area (LIP), that is, about the time of the saccade, the neuronal RF expands along the saccadic trajectory temporally, covering the current RF (CRF), the future RF (FRF), and the region the eye will sweep through during the saccade. A cortical wave (CW) model was also proposed, which attributes the RF remapping as a consequence of neural activity propagating in the cortex, triggered jointly by a visual stimulus and the corollary discharge (CD) signal responsible for the saccade. In this study, we investigate how this CW model is learned naturally from visual experiences at the development of the brain. We build a two-layer network, with one layer consisting of LIP neurons and the other superior colliculus (SC) neurons. Initially, neuronal connections are random and non-selective. A saccade will cause a static visual image to sweep through the retina passively, creating the effect of the visual stimulus moving in the opposite direction of the saccade. According to the spiking-time-dependent-plasticity rule, the connection path in the opposite direction of the saccade between LIP neurons and the connection path from SC to LIP are enhanced. Over many such visual experiences, the CW model is developed, which generates the peri-saccadic RF remapping in LIP as observed in the experiment. PMID:29249953

Multiscale neural connectivity during human sensory processing in the brain

NASA Astrophysics Data System (ADS)

Maksimenko, Vladimir A.; Runnova, Anastasia E.; Frolov, Nikita S.; Makarov, Vladimir V.; Nedaivozov, Vladimir; Koronovskii, Alexey A.; Pisarchik, Alexander; Hramov, Alexander E.

2018-05-01

Stimulus-related brain activity is considered using wavelet-based analysis of neural interactions between occipital and parietal brain areas in alpha (8-12 Hz) and beta (15-30 Hz) frequency bands. We show that human sensory processing related to the visual stimuli perception induces brain response resulted in different ways of parieto-occipital interactions in these bands. In the alpha frequency band the parieto-occipital neuronal network is characterized by homogeneous increase of the interaction between all interconnected areas both within occipital and parietal lobes and between them. In the beta frequency band the occipital lobe starts to play a leading role in the dynamics of the occipital-parietal network: The perception of visual stimuli excites the visual center in the occipital area and then, due to the increase of parieto-occipital interactions, such excitation is transferred to the parietal area, where the attentional center takes place. In the case when stimuli are characterized by a high degree of ambiguity, we find greater increase of the interaction between interconnected areas in the parietal lobe due to the increase of human attention. Based on revealed mechanisms, we describe the complex response of the parieto-occipital brain neuronal network during the perception and primary processing of the visual stimuli. The results can serve as an essential complement to the existing theory of neural aspects of visual stimuli processing.

Nanotomography of brain networks

NASA Astrophysics Data System (ADS)

Saiga, Rino; Mizutani, Ryuta; Takekoshi, Susumu; Osawa, Motoki; Arai, Makoto; Takeuchi, Akihisa; Uesugi, Kentaro; Terada, Yasuko; Suzuki, Yoshio; de Andrade, Vincent; de Carlo, Francesco

The first step to understanding how the brain functions is to analyze its 3D network. The brain network consists of neurons having micrometer to nanometer sized structures. Therefore, 3D analysis of brain tissue at the relevant resolution is essential for elucidating brain's functional mechanisms. Here, we report 3D structures of human and fly brain networks revealed with synchrotron radiation nanotomography, or nano-CT. Neurons were stained with high-Z elements to visualize their structures with X-rays. Nano-CT experiments were then performed at the 32-ID beamline of the Advanced Photon Source, Argonne National Laboratory and at the BL37XU and BL47XU beamlines of SPring-8. Reconstructed 3D images illustrated precise structures of human neurons, including dendritic spines responsible for synaptic connections. The network of the fly brain hemisphere was traced to build a skeletonized wire model. An article reviewing our study appeared in MIT Technology Review. Movies of the obtained structures can be found in our YouTube channel.

Attentional modulation of neuronal variability in circuit models of cortex

PubMed Central

Kanashiro, Tatjana; Ocker, Gabriel Koch; Cohen, Marlene R; Doiron, Brent

2017-01-01

The circuit mechanisms behind shared neural variability (noise correlation) and its dependence on neural state are poorly understood. Visual attention is well-suited to constrain cortical models of response variability because attention both increases firing rates and their stimulus sensitivity, as well as decreases noise correlations. We provide a novel analysis of population recordings in rhesus primate visual area V4 showing that a single biophysical mechanism may underlie these diverse neural correlates of attention. We explore model cortical networks where top-down mediated increases in excitability, distributed across excitatory and inhibitory targets, capture the key neuronal correlates of attention. Our models predict that top-down signals primarily affect inhibitory neurons, whereas excitatory neurons are more sensitive to stimulus specific bottom-up inputs. Accounting for trial variability in models of state dependent modulation of neuronal activity is a critical step in building a mechanistic theory of neuronal cognition. DOI: http://dx.doi.org/10.7554/eLife.23978.001 PMID:28590902

NeuroCa: integrated framework for systematic analysis of spatiotemporal neuronal activity patterns from large-scale optical recording data

PubMed Central

Jang, Min Jee; Nam, Yoonkey

2015-01-01

Abstract. Optical recording facilitates monitoring the activity of a large neural network at the cellular scale, but the analysis and interpretation of the collected data remain challenging. Here, we present a MATLAB-based toolbox, named NeuroCa, for the automated processing and quantitative analysis of large-scale calcium imaging data. Our tool includes several computational algorithms to extract the calcium spike trains of individual neurons from the calcium imaging data in an automatic fashion. Two algorithms were developed to decompose the imaging data into the activity of individual cells and subsequently detect calcium spikes from each neuronal signal. Applying our method to dense networks in dissociated cultures, we were able to obtain the calcium spike trains of ∼1000 neurons in a few minutes. Further analyses using these data permitted the quantification of neuronal responses to chemical stimuli as well as functional mapping of spatiotemporal patterns in neuronal firing within the spontaneous, synchronous activity of a large network. These results demonstrate that our method not only automates time-consuming, labor-intensive tasks in the analysis of neural data obtained using optical recording techniques but also provides a systematic way to visualize and quantify the collective dynamics of a network in terms of its cellular elements. PMID:26229973

Cholinergic neurons and fibres in the rat visual cortex.

PubMed

Parnavelas, J G; Kelly, W; Franke, E; Eckenstein, F

1986-06-01

Choline acetyltransferase (ChAT), the acetylcholine synthesizing enzyme, was localized immunocytochemically in neurons and fibres in the rat visual cortex using a monoclonal antibody. ChAT-labelled cells were non-pyramidal neurons, primarily of the bipolar form, distributed in layers II through VI but concentrated in layers II & III. Their perikarya contained a large nucleus and a small amount of perinuclear cytoplasm. The somata and dendrites of all labelled cells received Gray's type I and type II synapses. ChAT-stained axons formed a dense and diffuse network throughout the visual cortex and particularly in layer V. Electron microscopy revealed that the great majority formed type II synaptic contacts with dendrites of various sizes, unlabelled non-pyramidal somata and, on a few occasions, with ChAT-labelled cells. However, a very small number of terminals appeared to form type I synaptic contacts. This study describes the morphological organization of the cholinergic system in the visual cortex, the function of which has been under extensive investigation.

A neural circuit for gamma-band coherence across the retinotopic map in mouse visual cortex

PubMed Central

Hakim, Richard; Shamardani, Kiarash

2018-01-01

Cortical gamma oscillations have been implicated in a variety of cognitive, behavioral, and circuit-level phenomena. However, the circuit mechanisms of gamma-band generation and synchronization across cortical space remain uncertain. Using optogenetic patterned illumination in acute brain slices of mouse visual cortex, we define a circuit composed of layer 2/3 (L2/3) pyramidal cells and somatostatin (SOM) interneurons that phase-locks ensembles across the retinotopic map. The network oscillations generated here emerge from non-periodic stimuli, and are stimulus size-dependent, coherent across cortical space, narrow band (30 Hz), and depend on SOM neuron but not parvalbumin (PV) neuron activity; similar to visually induced gamma oscillations observed in vivo. Gamma oscillations generated in separate cortical locations exhibited high coherence as far apart as 850 μm, and lateral gamma entrainment depended on SOM neuron activity. These data identify a circuit that is sufficient to mediate long-range gamma-band coherence in the primary visual cortex. PMID:29480803

Reduction in spontaneous firing of mouse excitatory layer 4 cortical neurons following visual classical conditioning

NASA Astrophysics Data System (ADS)

Bekisz, Marek; Shendye, Ninad; Raciborska, Ida; Wróbel, Andrzej; Waleszczyk, Wioletta J.

2017-08-01

The process of learning induces plastic changes in neuronal network of the brain. Our earlier studies on mice showed that classical conditioning in which monocular visual stimulation was paired with an electric shock to the tail enhanced GABA immunoreactivity within layer 4 of the monocular part of the primary visual cortex (V1), contralaterally to the stimulated eye. In the present experiment we investigated whether the same classical conditioning paradigm induces changes of neuronal excitability in this cortical area. Two experimental groups were used: mice that underwent 7-day visual classical conditioning and controls. Patch-clamp whole-cell recordings were performed from ex vivo slices of mouse V1. The slices were perfused with the modified artificial cerebrospinal fluid, the composition of which better mimics the brain interstitial fluid in situ and induces spontaneous activity. The neuronal excitability was characterized by measuring the frequency of spontaneous action potentials. We found that layer 4 star pyramidal cells located in the monocular representation of the "trained" eye in V1 had lower frequency of spontaneous activity in comparison with neurons from the same cortical region of control animals. Weaker spontaneous firing indicates decreased general excitability of star pyramidal neurons within layer 4 of the monocular representation of the "trained" eye in V1. Such effect could result from enhanced inhibitory processes accompanying learning in this cortical area.

Integrate-and-fire vs Poisson models of LGN input to V1 cortex: noisier inputs reduce orientation selectivity

PubMed Central

Lin, I-Chun; Xing, Dajun; Shapley, Robert

2014-01-01

One of the reasons the visual cortex has attracted the interest of computational neuroscience is that it has well-defined inputs. The lateral geniculate nucleus (LGN) of the thalamus is the source of visual signals to the primary visual cortex (V1). Most large-scale cortical network models approximate the spike trains of LGN neurons as simple Poisson point processes. However, many studies have shown that neurons in the early visual pathway are capable of spiking with high temporal precision and their discharges are not Poisson-like. To gain an understanding of how response variability in the LGN influences the behavior of V1, we study response properties of model V1 neurons that receive purely feedforward inputs from LGN cells modeled either as noisy leaky integrate-and-fire (NLIF) neurons or as inhomogeneous Poisson processes. We first demonstrate that the NLIF model is capable of reproducing many experimentally observed statistical properties of LGN neurons. Then we show that a V1 model in which the LGN input to a V1 neuron is modeled as a group of NLIF neurons produces higher orientation selectivity than the one with Poisson LGN input. The second result implies that statistical characteristics of LGN spike trains are important for V1's function. We conclude that physiologically motivated models of V1 need to include more realistic LGN spike trains that are less noisy than inhomogeneous Poisson processes. PMID:22684587

Integrate-and-fire vs Poisson models of LGN input to V1 cortex: noisier inputs reduce orientation selectivity.

PubMed

Lin, I-Chun; Xing, Dajun; Shapley, Robert

2012-12-01

One of the reasons the visual cortex has attracted the interest of computational neuroscience is that it has well-defined inputs. The lateral geniculate nucleus (LGN) of the thalamus is the source of visual signals to the primary visual cortex (V1). Most large-scale cortical network models approximate the spike trains of LGN neurons as simple Poisson point processes. However, many studies have shown that neurons in the early visual pathway are capable of spiking with high temporal precision and their discharges are not Poisson-like. To gain an understanding of how response variability in the LGN influences the behavior of V1, we study response properties of model V1 neurons that receive purely feedforward inputs from LGN cells modeled either as noisy leaky integrate-and-fire (NLIF) neurons or as inhomogeneous Poisson processes. We first demonstrate that the NLIF model is capable of reproducing many experimentally observed statistical properties of LGN neurons. Then we show that a V1 model in which the LGN input to a V1 neuron is modeled as a group of NLIF neurons produces higher orientation selectivity than the one with Poisson LGN input. The second result implies that statistical characteristics of LGN spike trains are important for V1's function. We conclude that physiologically motivated models of V1 need to include more realistic LGN spike trains that are less noisy than inhomogeneous Poisson processes.

Learning spatially coherent properties of the visual world in connectionist networks

NASA Astrophysics Data System (ADS)

Becker, Suzanna; Hinton, Geoffrey E.

1991-10-01

In the unsupervised learning paradigm, a network of neuron-like units is presented with an ensemble of input patterns from a structured environment, such as the visual world, and learns to represent the regularities in that input. The major goal in developing unsupervised learning algorithms is to find objective functions that characterize the quality of the network's representation without explicitly specifying the desired outputs of any of the units. The sort of objective functions considered cause a unit to become tuned to spatially coherent features of visual images (such as texture, depth, shading, and surface orientation), by learning to predict the outputs of other units which have spatially adjacent receptive fields. Simulations show that using an information-theoretic algorithm called IMAX, a network can be trained to represent depth by observing random dot stereograms of surfaces with continuously varying disparities. Once a layer of depth-tuned units has developed, subsequent layers are trained to perform surface interpolation of curved surfaces, by learning to predict the depth of one image region based on depth measurements in surrounding regions. An extension of the basic model allows a population of competing neurons to learn a distributed code for disparity, which naturally gives rise to a representation of discontinuities.

Theory of correlation in a network with synaptic depression

NASA Astrophysics Data System (ADS)

Igarashi, Yasuhiko; Oizumi, Masafumi; Okada, Masato

2012-01-01

Synaptic depression affects not only the mean responses of neurons but also the correlation of response variability in neural populations. Although previous studies have constructed a theory of correlation in a spiking neuron model by using the mean-field theory framework, synaptic depression has not been taken into consideration. We expanded the previous theoretical framework in this study to spiking neuron models with short-term synaptic depression. On the basis of this theory we analytically calculated neural correlations in a ring attractor network with Mexican-hat-type connectivity, which was used as a model of the primary visual cortex. The results revealed that synaptic depression reduces neural correlation, which could be beneficial for sensory coding. Furthermore, our study opens the way for theoretical studies on the effect of interaction change on the linear response function in large stochastic networks.

Specific excitatory connectivity for feature integration in mouse primary visual cortex

PubMed Central

Molina-Luna, Patricia; Roth, Morgane M.

2017-01-01

Local excitatory connections in mouse primary visual cortex (V1) are stronger and more prevalent between neurons that share similar functional response features. However, the details of how functional rules for local connectivity shape neuronal responses in V1 remain unknown. We hypothesised that complex responses to visual stimuli may arise as a consequence of rules for selective excitatory connectivity within the local network in the superficial layers of mouse V1. In mouse V1 many neurons respond to overlapping grating stimuli (plaid stimuli) with highly selective and facilitatory responses, which are not simply predicted by responses to single gratings presented alone. This complexity is surprising, since excitatory neurons in V1 are considered to be mainly tuned to single preferred orientations. Here we examined the consequences for visual processing of two alternative connectivity schemes: in the first case, local connections are aligned with visual properties inherited from feedforward input (a ‘like-to-like’ scheme specifically connecting neurons that share similar preferred orientations); in the second case, local connections group neurons into excitatory subnetworks that combine and amplify multiple feedforward visual properties (a ‘feature binding’ scheme). By comparing predictions from large scale computational models with in vivo recordings of visual representations in mouse V1, we found that responses to plaid stimuli were best explained by assuming feature binding connectivity. Unlike under the like-to-like scheme, selective amplification within feature-binding excitatory subnetworks replicated experimentally observed facilitatory responses to plaid stimuli; explained selective plaid responses not predicted by grating selectivity; and was consistent with broad anatomical selectivity observed in mouse V1. Our results show that visual feature binding can occur through local recurrent mechanisms without requiring feedforward convergence, and that such a mechanism is consistent with visual responses and cortical anatomy in mouse V1. PMID:29240769

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

Dynamics of excitatory and inhibitory networks are differentially altered by selective attention.

PubMed

Snyder, Adam C; Morais, Michael J; Smith, Matthew A

2016-10-01

Inhibition and excitation form two fundamental modes of neuronal interaction, yet we understand relatively little about their distinct roles in service of perceptual and cognitive processes. We developed a multidimensional waveform analysis to identify fast-spiking (putative inhibitory) and regular-spiking (putative excitatory) neurons in vivo and used this method to analyze how attention affects these two cell classes in visual area V4 of the extrastriate cortex of rhesus macaques. We found that putative inhibitory neurons had both greater increases in firing rate and decreases in correlated variability with attention compared with putative excitatory neurons. Moreover, the time course of attention effects for putative inhibitory neurons more closely tracked the temporal statistics of target probability in our task. Finally, the session-to-session variability in a behavioral measure of attention covaried with the magnitude of this effect. Together, these results suggest that selective targeting of inhibitory neurons and networks is a critical mechanism for attentional modulation. Copyright © 2016 the American Physiological Society.

Dynamics of excitatory and inhibitory networks are differentially altered by selective attention

PubMed Central

Snyder, Adam C.; Morais, Michael J.

2016-01-01

Inhibition and excitation form two fundamental modes of neuronal interaction, yet we understand relatively little about their distinct roles in service of perceptual and cognitive processes. We developed a multidimensional waveform analysis to identify fast-spiking (putative inhibitory) and regular-spiking (putative excitatory) neurons in vivo and used this method to analyze how attention affects these two cell classes in visual area V4 of the extrastriate cortex of rhesus macaques. We found that putative inhibitory neurons had both greater increases in firing rate and decreases in correlated variability with attention compared with putative excitatory neurons. Moreover, the time course of attention effects for putative inhibitory neurons more closely tracked the temporal statistics of target probability in our task. Finally, the session-to-session variability in a behavioral measure of attention covaried with the magnitude of this effect. Together, these results suggest that selective targeting of inhibitory neurons and networks is a critical mechanism for attentional modulation. PMID:27466133

Linking Neurons to Network Function and Behavior by Two-Photon Holographic Optogenetics and Volumetric Imaging.

PubMed

Dal Maschio, Marco; Donovan, Joseph C; Helmbrecht, Thomas O; Baier, Herwig

2017-05-17

We introduce a flexible method for high-resolution interrogation of circuit function, which combines simultaneous 3D two-photon stimulation of multiple targeted neurons, volumetric functional imaging, and quantitative behavioral tracking. This integrated approach was applied to dissect how an ensemble of premotor neurons in the larval zebrafish brain drives a basic motor program, the bending of the tail. We developed an iterative photostimulation strategy to identify minimal subsets of channelrhodopsin (ChR2)-expressing neurons that are sufficient to initiate tail movements. At the same time, the induced network activity was recorded by multiplane GCaMP6 imaging across the brain. From this dataset, we computationally identified activity patterns associated with distinct components of the elicited behavior and characterized the contributions of individual neurons. Using photoactivatable GFP (paGFP), we extended our protocol to visualize single functionally identified neurons and reconstruct their morphologies. Together, this toolkit enables linking behavior to circuit activity with unprecedented resolution. Copyright © 2017 Elsevier Inc. All rights reserved.

Distribution of Orientation Selectivity in Recurrent Networks of Spiking Neurons with Different Random Topologies

PubMed Central

Sadeh, Sadra; Rotter, Stefan

2014-01-01

Neurons in the primary visual cortex are more or less selective for the orientation of a light bar used for stimulation. A broad distribution of individual grades of orientation selectivity has in fact been reported in all species. A possible reason for emergence of broad distributions is the recurrent network within which the stimulus is being processed. Here we compute the distribution of orientation selectivity in randomly connected model networks that are equipped with different spatial patterns of connectivity. We show that, for a wide variety of connectivity patterns, a linear theory based on firing rates accurately approximates the outcome of direct numerical simulations of networks of spiking neurons. Distance dependent connectivity in networks with a more biologically realistic structure does not compromise our linear analysis, as long as the linearized dynamics, and hence the uniform asynchronous irregular activity state, remain stable. We conclude that linear mechanisms of stimulus processing are indeed responsible for the emergence of orientation selectivity and its distribution in recurrent networks with functionally heterogeneous synaptic connectivity. PMID:25469704

Prefrontal spatial working memory network predicts animal's decision making in a free choice saccade task.

PubMed

Mochizuki, Kei; Funahashi, Shintaro

2016-01-01

While neurons in the lateral prefrontal cortex (PFC) encode spatial information during the performance of working memory tasks, they are also known to participate in subjective behavior such as spatial attention and action selection. In the present study, we analyzed the activity of primate PFC neurons during the performance of a free choice memory-guided saccade task in which the monkeys needed to choose a saccade direction by themselves. In trials when the receptive field location was subsequently chosen by the animal, PFC neurons with spatially selective visual response started to show greater activation before cue onset. This result suggests that the fluctuation of firing before cue presentation prematurely biased the representation of a certain spatial location and eventually encouraged the subsequent choice of that location. In addition, modulation of the activity by the animal's choice was observed only in neurons with high sustainability of activation and was also dependent on the spatial configuration of the visual cues. These findings were consistent with known characteristics of PFC neurons in information maintenance in spatial working memory function. These results suggest that precue fluctuation of spatial representation was shared and enhanced through the working memory network in the PFC and could finally influence the animal's free choice of saccade direction. The present study revealed that the PFC plays an important role in decision making in a free choice condition and that the dynamics of decision making are constrained by the network architecture embedded in this cortical area. Copyright © 2016 the American Physiological Society.

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

PubMed

Rangan, Aaditya V; Cai, David

2007-02-01

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

Audiovisual integration in hemianopia: A neurocomputational account based on cortico-collicular interaction.

PubMed

Magosso, Elisa; Bertini, Caterina; Cuppini, Cristiano; Ursino, Mauro

2016-10-01

Hemianopic patients retain some abilities to integrate audiovisual stimuli in the blind hemifield, showing both modulation of visual perception by auditory stimuli and modulation of auditory perception by visual stimuli. Indeed, conscious detection of a visual target in the blind hemifield can be improved by a spatially coincident auditory stimulus (auditory enhancement of visual detection), while a visual stimulus in the blind hemifield can improve localization of a spatially coincident auditory stimulus (visual enhancement of auditory localization). To gain more insight into the neural mechanisms underlying these two perceptual phenomena, we propose a neural network model including areas of neurons representing the retina, primary visual cortex (V1), extrastriate visual cortex, auditory cortex and the Superior Colliculus (SC). The visual and auditory modalities in the network interact via both direct cortical-cortical connections and subcortical-cortical connections involving the SC; the latter, in particular, integrates visual and auditory information and projects back to the cortices. Hemianopic patients were simulated by unilaterally lesioning V1, and preserving spared islands of V1 tissue within the lesion, to analyze the role of residual V1 neurons in mediating audiovisual integration. The network is able to reproduce the audiovisual phenomena in hemianopic patients, linking perceptions to neural activations, and disentangles the individual contribution of specific neural circuits and areas via sensitivity analyses. The study suggests i) a common key role of SC-cortical connections in mediating the two audiovisual phenomena; ii) a different role of visual cortices in the two phenomena: auditory enhancement of conscious visual detection being conditional on surviving V1 islands, while visual enhancement of auditory localization persisting even after complete V1 damage. The present study may contribute to advance understanding of the audiovisual dialogue between cortical and subcortical structures in healthy and unisensory deficit conditions. Copyright © 2016 Elsevier Ltd. All rights reserved.

An insect-inspired model for visual binding II: functional analysis and visual attention.

PubMed

Northcutt, Brandon D; Higgins, Charles M

2017-04-01

We have developed a neural network model capable of performing visual binding inspired by neuronal circuitry in the optic glomeruli of flies: a brain area that lies just downstream of the optic lobes where early visual processing is performed. This visual binding model is able to detect objects in dynamic image sequences and bind together their respective characteristic visual features-such as color, motion, and orientation-by taking advantage of their common temporal fluctuations. Visual binding is represented in the form of an inhibitory weight matrix which learns over time which features originate from a given visual object. In the present work, we show that information represented implicitly in this weight matrix can be used to explicitly count the number of objects present in the visual image, to enumerate their specific visual characteristics, and even to create an enhanced image in which one particular object is emphasized over others, thus implementing a simple form of visual attention. Further, we present a detailed analysis which reveals the function and theoretical limitations of the visual binding network and in this context describe a novel network learning rule which is optimized for visual binding.

An insect-inspired model for visual binding I: learning objects and their characteristics.

PubMed

Northcutt, Brandon D; Dyhr, Jonathan P; Higgins, Charles M

2017-04-01

Visual binding is the process of associating the responses of visual interneurons in different visual submodalities all of which are responding to the same object in the visual field. Recently identified neuropils in the insect brain termed optic glomeruli reside just downstream of the optic lobes and have an internal organization that could support visual binding. Working from anatomical similarities between optic and olfactory glomeruli, we have developed a model of visual binding based on common temporal fluctuations among signals of independent visual submodalities. Here we describe and demonstrate a neural network model capable both of refining selectivity of visual information in a given visual submodality, and of associating visual signals produced by different objects in the visual field by developing inhibitory neural synaptic weights representing the visual scene. We also show that this model is consistent with initial physiological data from optic glomeruli. Further, we discuss how this neural network model may be implemented in optic glomeruli at a neuronal level.

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

PubMed

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

2017-05-29

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

Automated quantification of neuronal networks and single-cell calcium dynamics using calcium imaging

PubMed Central

Patel, Tapan P.; Man, Karen; Firestein, Bonnie L.; Meaney, David F.

2017-01-01

Background Recent advances in genetically engineered calcium and membrane potential indicators provide the potential to estimate the activation dynamics of individual neurons within larger, mesoscale networks (100s–1000 +neurons). However, a fully integrated automated workflow for the analysis and visualization of neural microcircuits from high speed fluorescence imaging data is lacking. New method Here we introduce FluoroSNNAP, Fluorescence Single Neuron and Network Analysis Package. FluoroSNNAP is an open-source, interactive software developed in MATLAB for automated quantification of numerous biologically relevant features of both the calcium dynamics of single-cells and network activity patterns. FluoroSNNAP integrates and improves upon existing tools for spike detection, synchronization analysis, and inference of functional connectivity, making it most useful to experimentalists with little or no programming knowledge. Results We apply FluoroSNNAP to characterize the activity patterns of neuronal microcircuits undergoing developmental maturation in vitro. Separately, we highlight the utility of single-cell analysis for phenotyping a mixed population of neurons expressing a human mutant variant of the microtubule associated protein tau and wild-type tau. Comparison with existing method(s) We show the performance of semi-automated cell segmentation using spatiotemporal independent component analysis and significant improvement in detecting calcium transients using a template-based algorithm in comparison to peak-based or wavelet-based detection methods. Our software further enables automated analysis of microcircuits, which is an improvement over existing methods. Conclusions We expect the dissemination of this software will facilitate a comprehensive analysis of neuronal networks, promoting the rapid interrogation of circuits in health and disease. PMID:25629800

Temporal sequence learning in winner-take-all networks of spiking neurons demonstrated in a brain-based device.

PubMed

McKinstry, Jeffrey L; Edelman, Gerald M

2013-01-01

Animal behavior often involves a temporally ordered sequence of actions learned from experience. Here we describe simulations of interconnected networks of spiking neurons that learn to generate patterns of activity in correct temporal order. The simulation consists of large-scale networks of thousands of excitatory and inhibitory neurons that exhibit short-term synaptic plasticity and spike-timing dependent synaptic plasticity. The neural architecture within each area is arranged to evoke winner-take-all (WTA) patterns of neural activity that persist for tens of milliseconds. In order to generate and switch between consecutive firing patterns in correct temporal order, a reentrant exchange of signals between these areas was necessary. To demonstrate the capacity of this arrangement, we used the simulation to train a brain-based device responding to visual input by autonomously generating temporal sequences of motor actions.

View-Invariant Visuomotor Processing in Computational Mirror Neuron System for Humanoid

PubMed Central

Dawood, Farhan; Loo, Chu Kiong

2016-01-01

Mirror neurons are visuo-motor neurons found in primates and thought to be significant for imitation learning. The proposition that mirror neurons result from associative learning while the neonate observes his own actions has received noteworthy empirical support. Self-exploration is regarded as a procedure by which infants become perceptually observant to their own body and engage in a perceptual communication with themselves. We assume that crude sense of self is the prerequisite for social interaction. However, the contribution of mirror neurons in encoding the perspective from which the motor acts of others are seen have not been addressed in relation to humanoid robots. In this paper we present a computational model for development of mirror neuron system for humanoid based on the hypothesis that infants acquire MNS by sensorimotor associative learning through self-exploration capable of sustaining early imitation skills. The purpose of our proposed model is to take into account the view-dependency of neurons as a probable outcome of the associative connectivity between motor and visual information. In our experiment, a humanoid robot stands in front of a mirror (represented through self-image using camera) in order to obtain the associative relationship between his own motor generated actions and his own visual body-image. In the learning process the network first forms mapping from each motor representation onto visual representation from the self-exploratory perspective. Afterwards, the representation of the motor commands is learned to be associated with all possible visual perspectives. The complete architecture was evaluated by simulation experiments performed on DARwIn-OP humanoid robot. PMID:26998923

View-Invariant Visuomotor Processing in Computational Mirror Neuron System for Humanoid.

PubMed

Dawood, Farhan; Loo, Chu Kiong

2016-01-01

Mirror neurons are visuo-motor neurons found in primates and thought to be significant for imitation learning. The proposition that mirror neurons result from associative learning while the neonate observes his own actions has received noteworthy empirical support. Self-exploration is regarded as a procedure by which infants become perceptually observant to their own body and engage in a perceptual communication with themselves. We assume that crude sense of self is the prerequisite for social interaction. However, the contribution of mirror neurons in encoding the perspective from which the motor acts of others are seen have not been addressed in relation to humanoid robots. In this paper we present a computational model for development of mirror neuron system for humanoid based on the hypothesis that infants acquire MNS by sensorimotor associative learning through self-exploration capable of sustaining early imitation skills. The purpose of our proposed model is to take into account the view-dependency of neurons as a probable outcome of the associative connectivity between motor and visual information. In our experiment, a humanoid robot stands in front of a mirror (represented through self-image using camera) in order to obtain the associative relationship between his own motor generated actions and his own visual body-image. In the learning process the network first forms mapping from each motor representation onto visual representation from the self-exploratory perspective. Afterwards, the representation of the motor commands is learned to be associated with all possible visual perspectives. The complete architecture was evaluated by simulation experiments performed on DARwIn-OP humanoid robot.

Shedding Light on Words and Sentences: Near-Infrared Spectroscopy in Language Research

ERIC Educational Resources Information Center

Rossi, Sonja; Telkemeyer, Silke; Wartenburger, Isabell; Obrig, Hellmuth

2012-01-01

Investigating the neuronal network underlying language processing may contribute to a better understanding of how the brain masters this complex cognitive function with surprising ease and how language is acquired at a fast pace in infancy. Modern neuroimaging methods permit to visualize the evolvement and the function of the language network. The…

Efficient spiking neural network model of pattern motion selectivity in visual cortex.

PubMed

Beyeler, Michael; Richert, Micah; Dutt, Nikil D; Krichmar, Jeffrey L

2014-07-01

Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction-selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation outperforms a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40 × 40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available.

Network and external perturbation induce burst synchronisation in cat cerebral cortex

NASA Astrophysics Data System (ADS)

Lameu, Ewandson L.; Borges, Fernando S.; Borges, Rafael R.; Batista, Antonio M.; Baptista, Murilo S.; Viana, Ricardo L.

2016-05-01

The brain of mammals are divided into different cortical areas that are anatomically connected forming larger networks which perform cognitive tasks. The cat cerebral cortex is composed of 65 areas organised into the visual, auditory, somatosensory-motor and frontolimbic cognitive regions. We have built a network of networks, in which networks are connected among themselves according to the connections observed in the cat cortical areas aiming to study how inputs drive the synchronous behaviour in this cat brain-like network. We show that without external perturbations it is possible to observe high level of bursting synchronisation between neurons within almost all areas, except for the auditory area. Bursting synchronisation appears between neurons in the auditory region when an external perturbation is applied in another cognitive area. This is a clear evidence that burst synchronisation and collective behaviour in the brain might be a process mediated by other brain areas under stimulation.

Universality in the Evolution of Orientation Columns in the Visual Cortex

PubMed Central

Kaschube, Matthias; Schnabel, Michael; Löwel, Siegrid; Coppola, David M.; White, Leonard E.; Wolf, Fred

2011-01-01

The brain’s visual cortex processes information concerning form, pattern, and motion within functional maps that reflect the layout of neuronal circuits. We analyzed functional maps of orientation preference in the ferret, tree shrew, and galago—three species separated since the basal radiation of placental mammals more than 65 million years ago—and found a common organizing principle. A symmetry-based class of models for the self-organization of cortical networks predicts all essential features of the layout of these neuronal circuits, but only if suppressive long-range interactions dominate development. We show mathematically that orientation-selective long-range connectivity can mediate the required interactions. Our results suggest that self-organization has canalized the evolution of the neuronal circuitry underlying orientation preference maps into a single common design. PMID:21051599

Effective visual working memory capacity: an emergent effect from the neural dynamics in an attractor network.

PubMed

Dempere-Marco, Laura; Melcher, David P; Deco, Gustavo

2012-01-01

The study of working memory capacity is of outmost importance in cognitive psychology as working memory is at the basis of general cognitive function. Although the working memory capacity limit has been thoroughly studied, its origin still remains a matter of strong debate. Only recently has the role of visual saliency in modulating working memory storage capacity been assessed experimentally and proved to provide valuable insights into working memory function. In the computational arena, attractor networks have successfully accounted for psychophysical and neurophysiological data in numerous working memory tasks given their ability to produce a sustained elevated firing rate during a delay period. Here we investigate the mechanisms underlying working memory capacity by means of a biophysically-realistic attractor network with spiking neurons while accounting for two recent experimental observations: 1) the presence of a visually salient item reduces the number of items that can be held in working memory, and 2) visually salient items are commonly kept in memory at the cost of not keeping as many non-salient items. Our model suggests that working memory capacity is determined by two fundamental processes: encoding of visual items into working memory and maintenance of the encoded items upon their removal from the visual display. While maintenance critically depends on the constraints that lateral inhibition imposes to the mnemonic activity, encoding is limited by the ability of the stimulated neural assemblies to reach a sufficiently high level of excitation, a process governed by the dynamics of competition and cooperation among neuronal pools. Encoding is therefore contingent upon the visual working memory task and has led us to introduce the concept of effective working memory capacity (eWMC) in contrast to the maximal upper capacity limit only reached under ideal conditions.

Effective Visual Working Memory Capacity: An Emergent Effect from the Neural Dynamics in an Attractor Network

PubMed Central

Dempere-Marco, Laura; Melcher, David P.; Deco, Gustavo

2012-01-01

The study of working memory capacity is of outmost importance in cognitive psychology as working memory is at the basis of general cognitive function. Although the working memory capacity limit has been thoroughly studied, its origin still remains a matter of strong debate. Only recently has the role of visual saliency in modulating working memory storage capacity been assessed experimentally and proved to provide valuable insights into working memory function. In the computational arena, attractor networks have successfully accounted for psychophysical and neurophysiological data in numerous working memory tasks given their ability to produce a sustained elevated firing rate during a delay period. Here we investigate the mechanisms underlying working memory capacity by means of a biophysically-realistic attractor network with spiking neurons while accounting for two recent experimental observations: 1) the presence of a visually salient item reduces the number of items that can be held in working memory, and 2) visually salient items are commonly kept in memory at the cost of not keeping as many non-salient items. Our model suggests that working memory capacity is determined by two fundamental processes: encoding of visual items into working memory and maintenance of the encoded items upon their removal from the visual display. While maintenance critically depends on the constraints that lateral inhibition imposes to the mnemonic activity, encoding is limited by the ability of the stimulated neural assemblies to reach a sufficiently high level of excitation, a process governed by the dynamics of competition and cooperation among neuronal pools. Encoding is therefore contingent upon the visual working memory task and has led us to introduce the concept of effective working memory capacity (eWMC) in contrast to the maximal upper capacity limit only reached under ideal conditions. PMID:22952608

Resting-state functional connectivity in multiple sclerosis: an examination of group differences and individual differences.

PubMed

Janssen, Alisha L; Boster, Aaron; Patterson, Beth A; Abduljalil, Amir; Prakash, Ruchika Shaurya

2013-11-01

Multiple sclerosis (MS) is a neurodegenerative, inflammatory disease of the central nervous system, resulting in physical and cognitive disturbances. The goal of the current study was to examine the association between network integrity and composite measures of cognition and disease severity in individuals with relapsing-remitting MS (RRMS), relative to healthy controls. All participants underwent a neuropsychological and neuroimaging session, where resting-state data was collected. Independent component analysis and dual regression were employed to examine network integrity in individuals with MS, relative to healthy controls. The MS sample exhibited less connectivity in the motor and visual networks, relative to healthy controls, after controlling for group differences in gray matter volume. However, no alterations were observed in the frontoparietal, executive control, or default-mode networks, despite previous evidence of altered neuronal patterns during tasks of exogenous processing. Whole-brain, voxel-wise regression analyses with disease severity and processing speed composites were also performed to elucidate the brain-behavior relationship with neuronal network integrity. Individuals with higher levels of disease severity demonstrated reduced intra-network connectivity of the motor network, and the executive control network, while higher disease burden was associated with greater inter-network connectivity between the medial visual network and areas involved in visuomotor learning. Our findings underscore the importance of examining resting-state oscillations in this population, both as a biomarker of disease progression and a potential target for therapeutic intervention. Copyright © 2013 Elsevier Ltd. All rights reserved.

Simbrain 3.0: A flexible, visually-oriented neural network simulator.

PubMed

Tosi, Zachary; Yoshimi, Jeffrey

2016-11-01

Simbrain 3.0 is a software package for neural network design and analysis, which emphasizes flexibility (arbitrarily complex networks can be built using a suite of basic components) and a visually rich, intuitive interface. These features support both students and professionals. Students can study all of the major classes of neural networks in a familiar graphical setting, and can easily modify simulations, experimenting with networks and immediately seeing the results of their interventions. With the 3.0 release, Simbrain supports models on the order of thousands of neurons and a million synapses. This allows the same features that support education to support research professionals, who can now use the tool to quickly design, run, and analyze the behavior of large, highly customizable simulations. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neuronal pathway finding: from neurons to initial neural networks.

PubMed

Roscigno, Cecelia I

2004-10-01

Neuronal pathway finding is crucial for structured cellular organization and development of neural circuits within the nervous system. Neuronal pathway finding within the visual system has been extensively studied and therefore is used as a model to review existing knowledge regarding concepts of this developmental process. General principles of neuron pathway finding throughout the nervous system exist. Comprehension of these concepts guides neuroscience nurses in gaining an understanding of the developmental course of action, the implications of different anomalies, as well as the theoretical basis and nursing implications of some provocative new therapies being proposed to treat neurodegenerative diseases and neurologic injuries. These therapies have limitations in light of current ethical, developmental, and delivery modes and what is known about the development of neuronal pathways.

Shining light on neurons--elucidation of neuronal functions by photostimulation.

PubMed

Eder, Matthias; Zieglgänsberger, Walter; Dodt, Hans-Ulrich

2004-01-01

Many neuronal functions can be elucidated by techniques that allow for a precise stimulation of defined regions of a neuron and its afferents. Photolytic release of neurotransmitters from 'caged' derivates in the vicinity of visualized neurons in living brain slices meets this request. This technique allows the study of the subcellular distribution and properties of functional native neurotransmitter receptors. These are prerequisites for a detailed analysis of the expression and spatial specificity of synaptic plasticity. Photostimulation can further be used to fast map the synaptic connectivity between nearby and, more importantly, distant cells in a neuronal network. Here we give a personal review of some of the technical aspects of photostimulation and recent findings, which illustrate the advantages of this technique.

Synchronous circadian voltage rhythms with asynchronous calcium rhythms in the suprachiasmatic nucleus

PubMed Central

Enoki, Ryosuke; Oda, Yoshiaki; Mieda, Michihiro; Ono, Daisuke; Honma, Sato; Honma, Ken-ichi

2017-01-01

The suprachiasmatic nucleus (SCN), the master circadian clock, contains a network composed of multiple types of neurons which are thought to form a hierarchical and multioscillator system. The molecular clock machinery in SCN neurons drives membrane excitability and sends time cue signals to various brain regions and peripheral organs. However, how and at what time of the day these neurons transmit output signals remain largely unknown. Here, we successfully visualized circadian voltage rhythms optically for many days using a genetically encoded voltage sensor, ArcLightD. Unexpectedly, the voltage rhythms are synchronized across the entire SCN network of cultured slices, whereas simultaneously recorded Ca2+ rhythms are topologically specific to the dorsal and ventral regions. We further found that the temporal order of these two rhythms is cell-type specific: The Ca2+ rhythms phase-lead the voltage rhythms in AVP neurons but Ca2+ and voltage rhythms are nearly in phase in VIP neurons. We confirmed that circadian firing rhythms are also synchronous and are coupled with the voltage rhythms. These results indicate that SCN networks with asynchronous Ca2+ rhythms produce coherent voltage rhythms. PMID:28270612

Eye evolution at high resolution: the neuron as a unit of homology.

PubMed

Erclik, Ted; Hartenstein, Volker; McInnes, Roderick R; Lipshitz, Howard D

2009-08-01

Based on differences in morphology, photoreceptor-type usage and lens composition it has been proposed that complex eyes have evolved independently many times. The remarkable observation that different eye types rely on a conserved network of genes (including Pax6/eyeless) for their formation has led to the revised proposal that disparate complex eye types have evolved from a shared and simpler prototype. Did this ancestral eye already contain the neural circuitry required for image processing? And what were the evolutionary events that led to the formation of complex visual systems, such as those found in vertebrates and insects? The recent identification of unexpected cell-type homologies between neurons in the vertebrate and Drosophila visual systems has led to two proposed models for the evolution of complex visual systems from a simple prototype. The first, as an extension of the finding that the neurons of the vertebrate retina share homologies with both insect (rhabdomeric) and vertebrate (ciliary) photoreceptor cell types, suggests that the vertebrate retina is a composite structure, made up of neurons that have evolved from two spatially separate ancestral photoreceptor populations. The second model, based largely on the conserved role for the Vsx homeobox genes in photoreceptor-target neuron development, suggests that the last common ancestor of vertebrates and flies already possessed a relatively sophisticated visual system that contained a mixture of rhabdomeric and ciliary photoreceptors as well as their first- and second-order target neurons. The vertebrate retina and fly visual system would have subsequently evolved by elaborating on this ancestral neural circuit. Here we present evidence for these two cell-type homology-based models and discuss their implications.

Network-State Modulation of Power-Law Frequency-Scaling in Visual Cortical Neurons

PubMed Central

Béhuret, Sébastien; Baudot, Pierre; Yger, Pierre; Bal, Thierry; Destexhe, Alain; Frégnac, Yves

2009-01-01

Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of Vm activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the Vm reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the “effective” connectivity responsible for the dynamical signature of the population signals measured at different integration levels, from Vm to LFP, EEG and fMRI. PMID:19779556

Network-state modulation of power-law frequency-scaling in visual cortical neurons.

PubMed

El Boustani, Sami; Marre, Olivier; Béhuret, Sébastien; Baudot, Pierre; Yger, Pierre; Bal, Thierry; Destexhe, Alain; Frégnac, Yves

2009-09-01

Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of V(m) activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the V(m) reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the "effective" connectivity responsible for the dynamical signature of the population signals measured at different integration levels, from Vm to LFP, EEG and fMRI.

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

Chronic multiunit recordings in behaving animals: advantages and limitations.

PubMed

Supèr, Hans; Roelfsema, Pieter R

2005-01-01

By simultaneous recording from neural responses at many different loci at the same time, we can understand the interaction between neurons, and thereby gain insight into the network properties of neural processing, instead of the functioning of individual neurons. Here we will discuss a method for recording in behaving animals that uses chronically implanted micro-electrodes that allow one to track neural responses over a long period of time. In a majority of cases, multiunit activity, which is the aggregate spiking activity of a number of neurons in the vicinity of an electrode tip, is recorded through these electrodes, and occasionally single neurons can be isolated. Here we compare the properties of multiunit responses to the responses of single neurons in the primary visual cortex. We also discuss the advantages and disadvantages of the multiunit signal as opposed to a signal of single neurons. We demonstrate that multiunit recording provides a reliable and useful technique in cases where the neurons at the electrodes have similar response properties. Multiunit recording is therefore especially valuable when task variables have an effect that is consistent across the population of neurons. In the primary visual cortex, this is the case for figure-ground segregation and visual attention. Multiunit recording also has clear advantages for cross-correlation analysis. We show that the cross-correlation function between multiunit signals gives a reliable estimate of the average single-unit cross-correlation function. By the use of multiunit recording, it becomes much easier to detect relatively weak interactions between neurons at different cortical locations.

A Biophysical Neural Model To Describe Spatial Visual Attention

NASA Astrophysics Data System (ADS)

Hugues, Etienne; José, Jorge V.

2008-02-01

Visual scenes have enormous spatial and temporal information that are transduced into neural spike trains. Psychophysical experiments indicate that only a small portion of a spatial image is consciously accessible. Electrophysiological experiments in behaving monkeys have revealed a number of modulations of the neural activity in special visual area known as V4, when the animal is paying attention directly towards a particular stimulus location. The nature of the attentional input to V4, however, remains unknown as well as to the mechanisms responsible for these modulations. We use a biophysical neural network model of V4 to address these issues. We first constrain our model to reproduce the experimental results obtained for different external stimulus configurations and without paying attention. To reproduce the known neuronal response variability, we found that the neurons should receive about equal, or balanced, levels of excitatory and inhibitory inputs and whose levels are high as they are in in vivo conditions. Next we consider attentional inputs that can induce and reproduce the observed spiking modulations. We also elucidate the role played by the neural network to generate these modulations.

Neural dynamics for landmark orientation and angular path integration

PubMed Central

Seelig, Johannes D.; Jayaraman, Vivek

2015-01-01

Summary Many animals navigate using a combination of visual landmarks and path integration. In mammalian brains, head direction cells integrate these two streams of information by representing an animal's heading relative to landmarks, yet maintaining their directional tuning in darkness based on self-motion cues. Here we use two-photon calcium imaging in head-fixed flies walking on a ball in a virtual reality arena to demonstrate that landmark-based orientation and angular path integration are combined in the population responses of neurons whose dendrites tile the ellipsoid body — a toroidal structure in the center of the fly brain. The population encodes the fly's azimuth relative to its environment, tracking visual landmarks when available and relying on self-motion cues in darkness. When both visual and self-motion cues are absent, a representation of the animal's orientation is maintained in this network through persistent activity — a potential substrate for short-term memory. Several features of the population dynamics of these neurons and their circular anatomical arrangement are suggestive of ring attractors — network structures proposed to support the function of navigational brain circuits. PMID:25971509

A Biophysical Neural Model To Describe Spatial Visual Attention

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

Hugues, Etienne; Jose, Jorge V.

2008-02-14

Visual scenes have enormous spatial and temporal information that are transduced into neural spike trains. Psychophysical experiments indicate that only a small portion of a spatial image is consciously accessible. Electrophysiological experiments in behaving monkeys have revealed a number of modulations of the neural activity in special visual area known as V4, when the animal is paying attention directly towards a particular stimulus location. The nature of the attentional input to V4, however, remains unknown as well as to the mechanisms responsible for these modulations. We use a biophysical neural network model of V4 to address these issues. We firstmore » constrain our model to reproduce the experimental results obtained for different external stimulus configurations and without paying attention. To reproduce the known neuronal response variability, we found that the neurons should receive about equal, or balanced, levels of excitatory and inhibitory inputs and whose levels are high as they are in in vivo conditions. Next we consider attentional inputs that can induce and reproduce the observed spiking modulations. We also elucidate the role played by the neural network to generate these modulations.« less

STDP-based spiking deep convolutional neural networks for object recognition.

PubMed

Kheradpisheh, Saeed Reza; Ganjtabesh, Mohammad; Thorpe, Simon J; Masquelier, Timothée

2018-03-01

Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively shallow architectures, and only one layer was trainable. Another line of research has demonstrated - using rate-based neural networks trained with back-propagation - that having many layers increases the recognition robustness, an approach known as deep learning. We thus designed a deep SNN, comprising several convolutional (trainable with STDP) and pooling layers. We used a temporal coding scheme where the most strongly activated neurons fire first, and less activated neurons fire later or not at all. The network was exposed to natural images. Thanks to STDP, neurons progressively learned features corresponding to prototypical patterns that were both salient and frequent. Only a few tens of examples per category were required and no label was needed. After learning, the complexity of the extracted features increased along the hierarchy, from edge detectors in the first layer to object prototypes in the last layer. Coding was very sparse, with only a few thousands spikes per image, and in some cases the object category could be reasonably well inferred from the activity of a single higher-order neuron. More generally, the activity of a few hundreds of such neurons contained robust category information, as demonstrated using a classifier on Caltech 101, ETH-80, and MNIST databases. We also demonstrate the superiority of STDP over other unsupervised techniques such as random crops (HMAX) or auto-encoders. Taken together, our results suggest that the combination of STDP with latency coding may be a key to understanding the way that the primate visual system learns, its remarkable processing speed and its low energy consumption. These mechanisms are also interesting for artificial vision systems, particularly for hardware solutions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Maintaining network activity in submerged hippocampal slices: importance of oxygen supply.

PubMed

Hájos, Norbert; Ellender, Tommas J; Zemankovics, Rita; Mann, Edward O; Exley, Richard; Cragg, Stephanie J; Freund, Tamás F; Paulsen, Ole

2009-01-01

Studies in brain slices have provided a wealth of data on the basic features of neurons and synapses. In the intact brain, these properties may be strongly influenced by ongoing network activity. Although physiologically realistic patterns of network activity have been successfully induced in brain slices maintained in interface-type recording chambers, they have been harder to obtain in submerged-type chambers, which offer significant experimental advantages, including fast exchange of pharmacological agents, visually guided patch-clamp recordings, and imaging techniques. Here, we investigated conditions for the emergence of network oscillations in submerged slices prepared from the hippocampus of rats and mice. We found that the local oxygen level is critical for generation and propagation of both spontaneously occurring sharp wave-ripple oscillations and cholinergically induced fast oscillations. We suggest three ways to improve the oxygen supply to slices under submerged conditions: (i) optimizing chamber design for laminar flow of superfusion fluid; (ii) increasing the flow rate of superfusion fluid; and (iii) superfusing both surfaces of the slice. These improvements to the recording conditions enable detailed studies of neurons under more realistic conditions of network activity, which are essential for a better understanding of neuronal network operation.

How does the brain solve visual object recognition?

PubMed Central

Zoccolan, Davide; Rust, Nicole C.

2012-01-01

Mounting evidence suggests that “core object recognition,” the ability to rapidly recognize objects despite substantial appearance variation, is solved in the brain via a cascade of reflexive, largely feedforward computations that culminate in a powerful neuronal representation in the inferior temporal cortex. However, the algorithm that produces this solution remains little-understood. Here we review evidence ranging from individual neurons, to neuronal populations, to behavior, to computational models. We propose that understanding this algorithm will require using neuronal and psychophysical data to sift through many computational models, each based on building blocks of small, canonical sub-networks with a common functional goal. PMID:22325196

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.

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding.

PubMed

Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

2017-01-01

The recent "deep learning revolution" in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems.

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding

PubMed Central

Testolin, Alberto; De Filippo De Grazia, Michele; Zorzi, Marco

2017-01-01

The recent “deep learning revolution” in artificial neural networks had strong impact and widespread deployment for engineering applications, but the use of deep learning for neurocomputational modeling has been so far limited. In this article we argue that unsupervised deep learning represents an important step forward for improving neurocomputational models of perception and cognition, because it emphasizes the role of generative learning as opposed to discriminative (supervised) learning. As a case study, we present a series of simulations investigating the emergence of neural coding of visual space for sensorimotor transformations. We compare different network architectures commonly used as building blocks for unsupervised deep learning by systematically testing the type of receptive fields and gain modulation developed by the hidden neurons. In particular, we compare Restricted Boltzmann Machines (RBMs), which are stochastic, generative networks with bidirectional connections trained using contrastive divergence, with autoencoders, which are deterministic networks trained using error backpropagation. For both learning architectures we also explore the role of sparse coding, which has been identified as a fundamental principle of neural computation. The unsupervised models are then compared with supervised, feed-forward networks that learn an explicit mapping between different spatial reference frames. Our simulations show that both architectural and learning constraints strongly influenced the emergent coding of visual space in terms of distribution of tuning functions at the level of single neurons. Unsupervised models, and particularly RBMs, were found to more closely adhere to neurophysiological data from single-cell recordings in the primate parietal cortex. These results provide new insights into how basic properties of artificial neural networks might be relevant for modeling neural information processing in biological systems. PMID:28377709

Spatial interactions reveal inhibitory cortical networks in human amblyopia.

PubMed

Wong, Erwin H; Levi, Dennis M; McGraw, Paul V

2005-10-01

Humans with amblyopia have a well-documented loss of sensitivity for first-order, or luminance defined, visual information. Recent studies show that they also display a specific loss of sensitivity for second-order, or contrast defined, visual information; a type of image structure encoded by neurons found predominantly in visual area A18/V2. In the present study, we investigate whether amblyopia disrupts the normal architecture of spatial interactions in V2 by determining the contrast detection threshold of a second-order target in the presence of second-order flanking stimuli. Adjacent flanks facilitated second-order detectability in normal observers. However, in marked contrast, they suppressed detection in each eye of the majority of amblyopic observers. Furthermore, strabismic observers with no loss of visual acuity show a similar pattern of detection suppression. We speculate that amblyopia results in predominantly inhibitory cortical interactions between second-order neurons.

Characteristic and intermingled neocortical circuits encode different visual object discriminations.

PubMed

Zhang, Guo-Rong; Zhao, Hua; Cook, Nathan; Svestka, Michael; Choi, Eui M; Jan, Mary; Cook, Robert G; Geller, Alfred I

2017-07-28

Synaptic plasticity and neural network theories hypothesize that the essential information for advanced cognitive tasks is encoded in specific circuits and neurons within distributed neocortical networks. However, these circuits are incompletely characterized, and we do not know if a specific discrimination is encoded in characteristic circuits among multiple animals. Here, we determined the spatial distribution of active neurons for a circuit that encodes some of the essential information for a cognitive task. We genetically activated protein kinase C pathways in several hundred spatially-grouped glutamatergic and GABAergic neurons in rat postrhinal cortex, a multimodal associative area that is part of a distributed circuit that encodes visual object discriminations. We previously established that this intervention enhances accuracy for specific discriminations. Moreover, the genetically-modified, local circuit in POR cortex encodes some of the essential information, and this local circuit is preferentially activated during performance, as shown by activity-dependent gene imaging. Here, we mapped the positions of the active neurons, which revealed that two image sets are encoded in characteristic and different circuits. While characteristic circuits are known to process sensory information, in sensory areas, this is the first demonstration that characteristic circuits encode specific discriminations, in a multimodal associative area. Further, the circuits encoding the two image sets are intermingled, and likely overlapping, enabling efficient encoding. Consistent with reconsolidation theories, intermingled and overlapping encoding could facilitate formation of associations between related discriminations, including visually similar discriminations or discriminations learned at the same time or place. Copyright © 2017 Elsevier B.V. All rights reserved.

Vision restoration after brain and retina damage: the "residual vision activation theory".

PubMed

Sabel, Bernhard A; Henrich-Noack, Petra; Fedorov, Anton; Gall, Carolin

2011-01-01

Vision loss after retinal or cerebral visual injury (CVI) was long considered to be irreversible. However, there is considerable potential for vision restoration and recovery even in adulthood. Here, we propose the "residual vision activation theory" of how visual functions can be reactivated and restored. CVI is usually not complete, but some structures are typically spared by the damage. They include (i) areas of partial damage at the visual field border, (ii) "islands" of surviving tissue inside the blind field, (iii) extrastriate pathways unaffected by the damage, and (iv) downstream, higher-level neuronal networks. However, residual structures have a triple handicap to be fully functional: (i) fewer neurons, (ii) lack of sufficient attentional resources because of the dominant intact hemisphere caused by excitation/inhibition dysbalance, and (iii) disturbance in their temporal processing. Because of this resulting activation loss, residual structures are unable to contribute much to everyday vision, and their "non-use" further impairs synaptic strength. However, residual structures can be reactivated by engaging them in repetitive stimulation by different means: (i) visual experience, (ii) visual training, or (iii) noninvasive electrical brain current stimulation. These methods lead to strengthening of synaptic transmission and synchronization of partially damaged structures (within-systems plasticity) and downstream neuronal networks (network plasticity). Just as in normal perceptual learning, synaptic plasticity can improve vision and lead to vision restoration. This can be induced at any time after the lesion, at all ages and in all types of visual field impairments after retinal or brain damage (stroke, neurotrauma, glaucoma, amblyopia, age-related macular degeneration). If and to what extent vision restoration can be achieved is a function of the amount of residual tissue and its activation state. However, sustained improvements require repetitive stimulation which, depending on the method, may take days (noninvasive brain stimulation) or months (behavioral training). By becoming again engaged in everyday vision, (re)activation of areas of residual vision outlasts the stimulation period, thus contributing to lasting vision restoration and improvements in quality of life. Copyright © 2011 Elsevier B.V. All rights reserved.

Audiovisual Modulation in Mouse Primary Visual Cortex Depends on Cross-Modal Stimulus Configuration and Congruency.

PubMed

Meijer, Guido T; Montijn, Jorrit S; Pennartz, Cyriel M A; Lansink, Carien S

2017-09-06

The sensory neocortex is a highly connected associative network that integrates information from multiple senses, even at the level of the primary sensory areas. Although a growing body of empirical evidence supports this view, the neural mechanisms of cross-modal integration in primary sensory areas, such as the primary visual cortex (V1), are still largely unknown. Using two-photon calcium imaging in awake mice, we show that the encoding of audiovisual stimuli in V1 neuronal populations is highly dependent on the features of the stimulus constituents. When the visual and auditory stimulus features were modulated at the same rate (i.e., temporally congruent), neurons responded with either an enhancement or suppression compared with unisensory visual stimuli, and their prevalence was balanced. Temporally incongruent tones or white-noise bursts included in audiovisual stimulus pairs resulted in predominant response suppression across the neuronal population. Visual contrast did not influence multisensory processing when the audiovisual stimulus pairs were congruent; however, when white-noise bursts were used, neurons generally showed response suppression when the visual stimulus contrast was high whereas this effect was absent when the visual contrast was low. Furthermore, a small fraction of V1 neurons, predominantly those located near the lateral border of V1, responded to sound alone. These results show that V1 is involved in the encoding of cross-modal interactions in a more versatile way than previously thought. SIGNIFICANCE STATEMENT The neural substrate of cross-modal integration is not limited to specialized cortical association areas but extends to primary sensory areas. Using two-photon imaging of large groups of neurons, we show that multisensory modulation of V1 populations is strongly determined by the individual and shared features of cross-modal stimulus constituents, such as contrast, frequency, congruency, and temporal structure. Congruent audiovisual stimulation resulted in a balanced pattern of response enhancement and suppression compared with unisensory visual stimuli, whereas incongruent or dissimilar stimuli at full contrast gave rise to a population dominated by response-suppressing neurons. Our results indicate that V1 dynamically integrates nonvisual sources of information while still attributing most of its resources to coding visual information. Copyright © 2017 the authors 0270-6474/17/378783-14$15.00/0.

Graph properties of synchronized cortical networks during visual working memory maintenance.

PubMed

Palva, Satu; Monto, Simo; Palva, J Matias

2010-02-15

Oscillatory synchronization facilitates communication in neuronal networks and is intimately associated with human cognition. Neuronal activity in the human brain can be non-invasively imaged with magneto- (MEG) and electroencephalography (EEG), but the large-scale structure of synchronized cortical networks supporting cognitive processing has remained uncharacterized. We combined simultaneous MEG and EEG (MEEG) recordings with minimum-norm-estimate-based inverse modeling to investigate the structure of oscillatory phase synchronized networks that were active during visual working memory (VWM) maintenance. Inter-areal phase-synchrony was quantified as a function of time and frequency by single-trial phase-difference estimates of cortical patches covering the entire cortical surfaces. The resulting networks were characterized with a number of network metrics that were then compared between delta/theta- (3-6 Hz), alpha- (7-13 Hz), beta- (16-25 Hz), and gamma- (30-80 Hz) frequency bands. We found several salient differences between frequency bands. Alpha- and beta-band networks were more clustered and small-world like but had smaller global efficiency than the networks in the delta/theta and gamma bands. Alpha- and beta-band networks also had truncated-power-law degree distributions and high k-core numbers. The data converge on showing that during the VWM-retention period, human cortical alpha- and beta-band networks have a memory-load dependent, scale-free small-world structure with densely connected core-like structures. These data further show that synchronized dynamic networks underlying a specific cognitive state can exhibit distinct frequency-dependent network structures that could support distinct functional roles. Copyright 2009 Elsevier Inc. All rights reserved.

Horizontal integration and cortical dynamics.

PubMed

Gilbert, C D

1992-07-01

We have discussed several results that lead to a view that cells in the visual system are endowed with dynamic properties, influenced by context, expectation, and long-term modifications of the cortical network. These observations will be important for understanding how neuronal ensembles produce a system that perceives, remembers, and adapts to injury. The advantage to being able to observe changes at early stages in a sensory pathway is that one may be able to understand the way in which neuronal ensembles encode and represent images at the level of their receptive field properties, of cortical topographies, and of the patterns of connections between cells participating in a network.

Visual patch clamp recording of neurons in thick portions of the adult spinal cord.

PubMed

Munch, Anders Sonne; Smith, Morten; Moldovan, Mihai; Perrier, Jean-François

2010-07-15

The study of visually identified neurons in slice preparations from the central nervous system offers considerable advantages over in vivo preparations including high mechanical stability in the absence of anaesthesia and full control of the extracellular medium. However, because of their relative thinness, slices are not appropriate for investigating how individual neurons integrate synaptic inputs generated by large numbers of neurons. Here we took advantage of the exceptional resistance of the turtle to anoxia to make slices of increasing thicknesses (from 300 to 3000 microm) from the lumbar enlargement of the spinal cord. With a conventional upright microscope in which the light condenser was carefully adjusted, we could visualize neurons present at the surface of the slice and record them with the whole-cell patch clamp technique. We show that neurons present in the middle of the preparation remain alive and capable of generating action potentials. By stimulating the lateral funiculus we can evoke intense synaptic activity associated with large increases in conductance of the recorded neurons. The conductance increases substantially more in neurons recorded in thick slices suggesting that the size of the network recruited with the stimulation increases with the thickness of the slices. We also find that that the number of spontaneous excitatory postsynaptic currents (EPSCs) is higher in thick slices compared with thin slices while the number of spontaneous inhibitory postsynaptic currents (IPSCs) remains constant. These preliminary data suggest that inhibitory and excitatory synaptic connections are balanced locally while excitation dominates long-range connections in the spinal cord. Copyright 2010 Elsevier B.V. All rights reserved.

Sensory-driven and spontaneous gamma oscillations engage distinct cortical circuitry

PubMed Central

2015-01-01

Gamma oscillations are a robust component of sensory responses but are also part of the background spontaneous activity of the brain. To determine whether the properties of gamma oscillations in cortex are specific to their mechanism of generation, we compared in mouse visual cortex in vivo the laminar geometry and single-neuron rhythmicity of oscillations produced during sensory representation with those occurring spontaneously in the absence of stimulation. In mouse visual cortex under anesthesia (isoflurane and xylazine), visual stimulation triggered oscillations mainly between 20 and 50 Hz, which, because of their similar functional significance to gamma oscillations in higher mammals, we define here as gamma range. Sensory representation in visual cortex specifically increased gamma oscillation amplitude in the supragranular (L2/3) and granular (L4) layers and strongly entrained putative excitatory and inhibitory neurons in infragranular layers, while spontaneous gamma oscillations were distributed evenly through the cortical depth and primarily entrained putative inhibitory neurons in the infragranular (L5/6) cortical layers. The difference in laminar distribution of gamma oscillations during the two different conditions may result from differences in the source of excitatory input to the cortex. In addition, modulation of superficial gamma oscillation amplitude did not result in a corresponding change in deep-layer oscillations, suggesting that superficial and deep layers of cortex may utilize independent but related networks for gamma generation. These results demonstrate that stimulus-driven gamma oscillations engage cortical circuitry in a manner distinct from spontaneous oscillations and suggest multiple networks for the generation of gamma oscillations in cortex. PMID:26719085

Biocytin-Derived MRI Contrast Agent for Longitudinal Brain Connectivity Studies

PubMed Central

2011-01-01

To investigate the connectivity of brain networks noninvasively and dynamically, we have developed a new strategy to functionalize neuronal tracers and designed a biocompatible probe that can be visualized in vivo using magnetic resonance imaging (MRI). Furthermore, the multimodal design used allows combined ex vivo studies with microscopic spatial resolution by conventional histochemical techniques. We present data on the functionalization of biocytin, a well-known neuronal tract tracer, and demonstrate the validity of the approach by showing brain networks of cortical connectivity in live rats under MRI, together with the corresponding microscopic details, such as fibers and neuronal morphology under light microscopy. We further demonstrate that the developed molecule is the first MRI-visible probe to preferentially trace retrograde connections. Our study offers a new platform for the development of multimodal molecular imaging tools of broad interest in neuroscience, that capture in vivo the dynamics of large scale neural networks together with their microscopic characteristics, thereby spanning several organizational levels. PMID:22860157

Implications on visual apperception: energy, duration, structure and synchronization.

PubMed

Bókkon, I; Vimal, Ram Lakhan Pandey

2010-07-01

Although primary visual cortex (V1 or striate) activity per se is not sufficient for visual apperception (normal conscious visual experiences and conscious functions such as detection, discrimination, and recognition), the same is also true for extrastriate visual areas (such as V2, V3, V4/V8/VO, V5/M5/MST, IT, and GF). In the lack of V1 area, visual signals can still reach several extrastriate parts but appear incapable of generating normal conscious visual experiences. It is scarcely emphasized in the scientific literature that conscious perceptions and representations must have also essential energetic conditions. These energetic conditions are achieved by spatiotemporal networks of dynamic mitochondrial distributions inside neurons. However, the highest density of neurons in neocortex (number of neurons per degree of visual angle) devoted to representing the visual field is found in retinotopic V1. It means that the highest mitochondrial (energetic) activity can be achieved in mitochondrial cytochrome oxidase-rich V1 areas. Thus, V1 bear the highest energy allocation for visual representation. In addition, the conscious perceptions also demand structural conditions, presence of adequate duration of information representation, and synchronized neural processes and/or 'interactive hierarchical structuralism.' For visual apperception, various visual areas are involved depending on context such as stimulus characteristics such as color, form/shape, motion, and other features. Here, we focus primarily on V1 where specific mitochondrial-rich retinotopic structures are found; we will concisely discuss V2 where smaller riches of these structures are found. We also point out that residual brain states are not fully reflected in active neural patterns after visual perception. Namely, after visual perception, subliminal residual states are not being reflected in passive neural recording techniques, but require active stimulation to be revealed.

Target dependence of orientation and direction selectivity of corticocortical projection neurons in the mouse V1

PubMed Central

Matsui, Teppei; Ohki, Kenichi

2013-01-01

Higher order visual areas that receive input from the primary visual cortex (V1) are specialized for the processing of distinct features of visual information. However, it is still incompletely understood how this functional specialization is acquired. Here we used in vivo two photon calcium imaging in the mouse visual cortex to investigate whether this functional distinction exists at as early as the level of projections from V1 to two higher order visual areas, AL and LM. Specifically, we examined whether sharpness of orientation and direction selectivity and optimal spatial and temporal frequency of projection neurons from V1 to higher order visual areas match with that of target areas. We found that the V1 input to higher order visual areas were indeed functionally distinct: AL preferentially received inputs from V1 that were more orientation and direction selective and tuned for lower spatial frequency compared to projection of V1 to LM, consistent with functional differences between AL and LM. The present findings suggest that selective projections from V1 to higher order visual areas initiates parallel processing of sensory information in the visual cortical network. PMID:24068987

Distributed Fading Memory for Stimulus Properties in the Primary Visual Cortex

PubMed Central

Singer, Wolf; Maass, Wolfgang

2009-01-01

It is currently not known how distributed neuronal responses in early visual areas carry stimulus-related information. We made multielectrode recordings from cat primary visual cortex and applied methods from machine learning in order to analyze the temporal evolution of stimulus-related information in the spiking activity of large ensembles of around 100 neurons. We used sequences of up to three different visual stimuli (letters of the alphabet) presented for 100 ms and with intervals of 100 ms or larger. Most of the information about visual stimuli extractable by sophisticated methods of machine learning, i.e., support vector machines with nonlinear kernel functions, was also extractable by simple linear classification such as can be achieved by individual neurons. New stimuli did not erase information about previous stimuli. The responses to the most recent stimulus contained about equal amounts of information about both this and the preceding stimulus. This information was encoded both in the discharge rates (response amplitudes) of the ensemble of neurons and, when using short time constants for integration (e.g., 20 ms), in the precise timing of individual spikes (≤∼20 ms), and persisted for several 100 ms beyond the offset of stimuli. The results indicate that the network from which we recorded is endowed with fading memory and is capable of performing online computations utilizing information about temporally sequential stimuli. This result challenges models assuming frame-by-frame analyses of sequential inputs. PMID:20027205

Multiplicative and additive modulation of neuronal tuning with population activity affects encoded information

PubMed Central

Arandia-Romero, Iñigo; Tanabe, Seiji; Drugowitsch, Jan; Kohn, Adam; Moreno-Bote, Rubén

2016-01-01

Numerous studies have shown that neuronal responses are modulated by stimulus properties, and also by the state of the local network. However, little is known about how activity fluctuations of neuronal populations modulate the sensory tuning of cells and affect their encoded information. We found that fluctuations in ongoing and stimulus-evoked population activity in primate visual cortex modulate the tuning of neurons in a multiplicative and additive manner. While distributed on a continuum, neurons with stronger multiplicative effects tended to have less additive modulation, and vice versa. The information encoded by multiplicatively-modulated neurons increased with greater population activity, while that of additively-modulated neurons decreased. These effects offset each other, so that population activity had little effect on total information. Our results thus suggest that intrinsic activity fluctuations may act as a `traffic light' that determines which subset of neurons are most informative. PMID:26924437

Visual Stimuli Induce Waves of Electrical Activity in Turtle Cortex

NASA Astrophysics Data System (ADS)

Prechtl, J. C.; Cohen, L. B.; Pesaran, B.; Mitra, P. P.; Kleinfeld, D.

1997-07-01

The computations involved in the processing of a visual scene invariably involve the interactions among neurons throughout all of visual cortex. One hypothesis is that the timing of neuronal activity, as well as the amplitude of activity, provides a means to encode features of objects. The experimental data from studies on cat [Gray, C. M., Konig, P., Engel, A. K. & Singer, W. (1989) Nature (London) 338, 334-337] support a view in which only synchronous (no phase lags) activity carries information about the visual scene. In contrast, theoretical studies suggest, on the one hand, the utility of multiple phases within a population of neurons as a means to encode independent visual features and, on the other hand, the likely existence of timing differences solely on the basis of network dynamics. Here we use widefield imaging in conjunction with voltage-sensitive dyes to record electrical activity from the virtually intact, unanesthetized turtle brain. Our data consist of single-trial measurements. We analyze our data in the frequency domain to isolate coherent events that lie in different frequency bands. Low frequency oscillations (<5 Hz) are seen in both ongoing activity and activity induced by visual stimuli. These oscillations propagate parallel to the afferent input. Higher frequency activity, with spectral peaks near 10 and 20 Hz, is seen solely in response to stimulation. This activity consists of plane waves and spiral-like waves, as well as more complex patterns. The plane waves have an average phase gradient of ≈ π /2 radians/mm and propagate orthogonally to the low frequency waves. Our results show that large-scale differences in neuronal timing are present and persistent during visual processing.

Visual stimuli induce waves of electrical activity in turtle cortex

PubMed Central

Prechtl, J. C.; Cohen, L. B.; Pesaran, B.; Mitra, P. P.; Kleinfeld, D.

1997-01-01

The computations involved in the processing of a visual scene invariably involve the interactions among neurons throughout all of visual cortex. One hypothesis is that the timing of neuronal activity, as well as the amplitude of activity, provides a means to encode features of objects. The experimental data from studies on cat [Gray, C. M., Konig, P., Engel, A. K. & Singer, W. (1989) Nature (London) 338, 334–337] support a view in which only synchronous (no phase lags) activity carries information about the visual scene. In contrast, theoretical studies suggest, on the one hand, the utility of multiple phases within a population of neurons as a means to encode independent visual features and, on the other hand, the likely existence of timing differences solely on the basis of network dynamics. Here we use widefield imaging in conjunction with voltage-sensitive dyes to record electrical activity from the virtually intact, unanesthetized turtle brain. Our data consist of single-trial measurements. We analyze our data in the frequency domain to isolate coherent events that lie in different frequency bands. Low frequency oscillations (<5 Hz) are seen in both ongoing activity and activity induced by visual stimuli. These oscillations propagate parallel to the afferent input. Higher frequency activity, with spectral peaks near 10 and 20 Hz, is seen solely in response to stimulation. This activity consists of plane waves and spiral-like waves, as well as more complex patterns. The plane waves have an average phase gradient of ≈π/2 radians/mm and propagate orthogonally to the low frequency waves. Our results show that large-scale differences in neuronal timing are present and persistent during visual processing. PMID:9207142

Mechanisms of perceptual organization provide auto-zoom and auto-localization for attention to objects

PubMed Central

Mihalas, Stefan; Dong, Yi; von der Heydt, Rüdiger; Niebur, Ernst

2011-01-01

Visual attention is often understood as a modulatory field acting at early stages of processing, but the mechanisms that direct and fit the field to the attended object are not known. We show that a purely spatial attention field propagating downward in the neuronal network responsible for perceptual organization will be reshaped, repositioned, and sharpened to match the object's shape and scale. Key features of the model are grouping neurons integrating local features into coherent tentative objects, excitatory feedback to the same local feature neurons that caused grouping neuron activation, and inhibition between incompatible interpretations both at the local feature level and at the object representation level. PMID:21502489

Opposite patterns of age-associated changes in neurons and glial cells of the thalamus of human brain.

PubMed

Guidolin, D; Zunarelli, E; Genedani, S; Trentini, G P; De Gaetani, C; Fuxe, K; Benegiamo, C; Agnati, L F

2008-06-01

In an autopsy series of 19 individuals, age-ranged 24-94, a relatively age-spared region, the anterior-ventral thalamus, was analyzed by immunohistochemical techniques to visualize neurons (neurofilament protein), astrocytes (glial fibrillary acidic protein), microglial cells (CD68) and amyloid precursor protein. The pattern of immunoreactivity was determined by surface fractal dimension and lacunarity, the size by the field area (FA) and the spatial uniformity by the uniformity index. From the normalized FA values of immunoreactivity for the four markers studied, a global parameter was defined to give an overall characterization of the age-dependent changes in the glio-neuronal networks. A significant exponential decline of the GP was observed with increasing age. This finding suggests that early in life (age<50 years) an adaptive response might be triggered, involving the glio-neuronal networks in plastic adaptive adjustments to cope with the environmental challenges and the continuous wearing off of the neuronal structures. The slow decay of the GP observed in a later phase (age>70 years) could be due to the non-trophic reserve still available.

A Component-Based Extension Framework for Large-Scale Parallel Simulations in NEURON

PubMed Central

King, James G.; Hines, Michael; Hill, Sean; Goodman, Philip H.; Markram, Henry; Schürmann, Felix

2008-01-01

As neuronal simulations approach larger scales with increasing levels of detail, the neurosimulator software represents only a part of a chain of tools ranging from setup, simulation, interaction with virtual environments to analysis and visualizations. Previously published approaches to abstracting simulator engines have not received wide-spread acceptance, which in part may be to the fact that they tried to address the challenge of solving the model specification problem. Here, we present an approach that uses a neurosimulator, in this case NEURON, to describe and instantiate the network model in the simulator's native model language but then replaces the main integration loop with its own. Existing parallel network models are easily adopted to run in the presented framework. The presented approach is thus an extension to NEURON but uses a component-based architecture to allow for replaceable spike exchange components and pluggable components for monitoring, analysis, or control that can run in this framework alongside with the simulation. PMID:19430597

Mutual information and redundancy in spontaneous communication between cortical neurons.

PubMed

Szczepanski, J; Arnold, M; Wajnryb, E; Amigó, J M; Sanchez-Vives, M V

2011-03-01

An important question in neural information processing is how neurons cooperate to transmit information. To study this question, we resort to the concept of redundancy in the information transmitted by a group of neurons and, at the same time, we introduce a novel concept for measuring cooperation between pairs of neurons called relative mutual information (RMI). Specifically, we studied these two parameters for spike trains generated by neighboring neurons from the primary visual cortex in the awake, freely moving rat. The spike trains studied here were spontaneously generated in the cortical network, in the absence of visual stimulation. Under these conditions, our analysis revealed that while the value of RMI oscillated slightly around an average value, the redundancy exhibited a behavior characterized by a higher variability. We conjecture that this combination of approximately constant RMI and greater variable redundancy makes information transmission more resistant to noise disturbances. Furthermore, the redundancy values suggest that neurons can cooperate in a flexible way during information transmission. This mostly occurs via a leading neuron with higher transmission rate or, less frequently, through the information rate of the whole group being higher than the sum of the individual information rates-in other words in a synergetic manner. The proposed method applies not only to the stationary, but also to locally stationary neural signals.

Statistics of Visual Responses to Image Object Stimuli from Primate AIT Neurons to DNN Neurons.

PubMed

Dong, Qiulei; Wang, Hong; Hu, Zhanyi

2018-02-01

Under the goal-driven paradigm, Yamins et al. ( 2014 ; Yamins & DiCarlo, 2016 ) have shown that by optimizing only the final eight-way categorization performance of a four-layer hierarchical network, not only can its top output layer quantitatively predict IT neuron responses but its penultimate layer can also automatically predict V4 neuron responses. Currently, deep neural networks (DNNs) in the field of computer vision have reached image object categorization performance comparable to that of human beings on ImageNet, a data set that contains 1.3 million training images of 1000 categories. We explore whether the DNN neurons (units in DNNs) possess image object representational statistics similar to monkey IT neurons, particularly when the network becomes deeper and the number of image categories becomes larger, using VGG19, a typical and widely used deep network of 19 layers in the computer vision field. Following Lehky, Kiani, Esteky, and Tanaka ( 2011 , 2014 ), where the response statistics of 674 IT neurons to 806 image stimuli are analyzed using three measures (kurtosis, Pareto tail index, and intrinsic dimensionality), we investigate the three issues in this letter using the same three measures: (1) the similarities and differences of the neural response statistics between VGG19 and primate IT cortex, (2) the variation trends of the response statistics of VGG19 neurons at different layers from low to high, and (3) the variation trends of the response statistics of VGG19 neurons when the numbers of stimuli and neurons increase. We find that the response statistics on both single-neuron selectivity and population sparseness of VGG19 neurons are fundamentally different from those of IT neurons in most cases; by increasing the number of neurons in different layers and the number of stimuli, the response statistics of neurons at different layers from low to high do not substantially change; and the estimated intrinsic dimensionality values at the low convolutional layers of VGG19 are considerably larger than the value of approximately 100 reported for IT neurons in Lehky et al. ( 2014 ), whereas those at the high fully connected layers are close to or lower than 100. To the best of our knowledge, this work is the first attempt to analyze the response statistics of DNN neurons with respect to primate IT neurons in image object representation.

Microfluidic device for unidirectional axon growth

NASA Astrophysics Data System (ADS)

Malishev, E.; Pimashkin, A.; Gladkov, A.; Pigareva, Y.; Bukatin, A.; Kazantsev, V.; Mukhina, I.; Dubina, M.

2015-11-01

In order to better understand the communication and connectivity development of neuron networks, we designed microfluidic devices with several chambers for growing dissociated neuronal cultures from mice fetal hippocampus (E18). The chambers were connected with microchannels providing unidirectional axonal growth between “Source” and “Target” neural sub-networks. Experiments were performed in a hippocampal cultures plated in a poly-dimethylsiloxane (PDMS) microfluidic chip, aligned with a 60 microelectrode array (MEA). Axonal growth through microchannels was observed with brightfield, phase-contrast and fluorescence microscopy, and after 7 days in vitro electrical activity was recorded. Visual inspection and spike propagation analysis showed the predominant axonal growth in microchannels in a direction from “Source” to “Target”.

Modulation of neuronal oscillatory activity in the beta- and gamma-band is associated with current individual anxiety levels.

PubMed

Schneider, Till R; Hipp, Joerg F; Domnick, Claudia; Carl, Christine; Büchel, Christian; Engel, Andreas K

2018-05-26

Human faces are among the most salient visual stimuli and act both as socially and emotionally relevant signals. Faces and especially faces with emotional expression receive prioritized processing in the human brain and activate a distributed network of brain areas reflected, e.g., in enhanced oscillatory neuronal activity. However, an inconsistent picture emerged so far regarding neuronal oscillatory activity across different frequency-bands modulated by emotionally and socially relevant stimuli. The individual level of anxiety among healthy populations might be one explanation for these inconsistent findings. Therefore, we tested the hypothesis whether oscillatory neuronal activity is associated with individual anxiety levels during perception of faces with neutral and fearful facial expressions. We recorded neuronal activity using magnetoencephalography (MEG) in 27 healthy participants and determined their individual state anxiety levels. Images of human faces with neutral and fearful expressions, and physically matched visual control stimuli were presented while participants performed a simple color detection task. Spectral analyses revealed that face processing and in particular processing of fearful faces was characterized by enhanced neuronal activity in the theta- and gamma-band and decreased activity in the beta-band in early visual cortex and the fusiform gyrus (FFG). Moreover, the individuals' state anxiety levels correlated positively with the gamma-band response and negatively with the beta response in the FFG and the amygdala. Our results suggest that oscillatory neuronal activity plays an important role in affective face processing and is dependent on the individual level of state anxiety. Our work provides new insights on the role of oscillatory neuronal activity underlying processing of faces. Copyright © 2018. Published by Elsevier Inc.

Integrated workflows for spiking neuronal network simulations

PubMed Central

Antolík, Ján; Davison, Andrew P.

2013-01-01

The increasing availability of computational resources is enabling more detailed, realistic modeling in computational neuroscience, resulting in a shift toward more heterogeneous models of neuronal circuits, and employment of complex experimental protocols. This poses a challenge for existing tool chains, as the set of tools involved in a typical modeler's workflow is expanding concomitantly, with growing complexity in the metadata flowing between them. For many parts of the workflow, a range of tools is available; however, numerous areas lack dedicated tools, while integration of existing tools is limited. This forces modelers to either handle the workflow manually, leading to errors, or to write substantial amounts of code to automate parts of the workflow, in both cases reducing their productivity. To address these issues, we have developed Mozaik: a workflow system for spiking neuronal network simulations written in Python. Mozaik integrates model, experiment and stimulation specification, simulation execution, data storage, data analysis and visualization into a single automated workflow, ensuring that all relevant metadata are available to all workflow components. It is based on several existing tools, including PyNN, Neo, and Matplotlib. It offers a declarative way to specify models and recording configurations using hierarchically organized configuration files. Mozaik automatically records all data together with all relevant metadata about the experimental context, allowing automation of the analysis and visualization stages. Mozaik has a modular architecture, and the existing modules are designed to be extensible with minimal programming effort. Mozaik increases the productivity of running virtual experiments on highly structured neuronal networks by automating the entire experimental cycle, while increasing the reliability of modeling studies by relieving the user from manual handling of the flow of metadata between the individual workflow stages. PMID:24368902

Integrated workflows for spiking neuronal network simulations.

PubMed

Antolík, Ján; Davison, Andrew P

2013-01-01

The increasing availability of computational resources is enabling more detailed, realistic modeling in computational neuroscience, resulting in a shift toward more heterogeneous models of neuronal circuits, and employment of complex experimental protocols. This poses a challenge for existing tool chains, as the set of tools involved in a typical modeler's workflow is expanding concomitantly, with growing complexity in the metadata flowing between them. For many parts of the workflow, a range of tools is available; however, numerous areas lack dedicated tools, while integration of existing tools is limited. This forces modelers to either handle the workflow manually, leading to errors, or to write substantial amounts of code to automate parts of the workflow, in both cases reducing their productivity. To address these issues, we have developed Mozaik: a workflow system for spiking neuronal network simulations written in Python. Mozaik integrates model, experiment and stimulation specification, simulation execution, data storage, data analysis and visualization into a single automated workflow, ensuring that all relevant metadata are available to all workflow components. It is based on several existing tools, including PyNN, Neo, and Matplotlib. It offers a declarative way to specify models and recording configurations using hierarchically organized configuration files. Mozaik automatically records all data together with all relevant metadata about the experimental context, allowing automation of the analysis and visualization stages. Mozaik has a modular architecture, and the existing modules are designed to be extensible with minimal programming effort. Mozaik increases the productivity of running virtual experiments on highly structured neuronal networks by automating the entire experimental cycle, while increasing the reliability of modeling studies by relieving the user from manual handling of the flow of metadata between the individual workflow stages.

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

Molecular Mechanisms at the Basis of Plasticity in the Developing Visual Cortex: Epigenetic Processes and Gene Programs

PubMed Central

Maya-Vetencourt, José Fernando; Pizzorusso, Tommaso

2013-01-01

Neuronal circuitries in the mammalian visual system change as a function of experience. Sensory experience modifies neuronal networks connectivity via the activation of different physiological processes such as excitatory/inhibitory synaptic transmission, neurotrophins, and signaling of extracellular matrix molecules. Long-lasting phenomena of plasticity occur when intracellular signal transduction pathways promote epigenetic alterations of chromatin structure that regulate the induction of transcription factors that in turn drive the expression of downstream targets, the products of which then work via the activation of structural and functional mechanisms that modify synaptic connectivity. Here, we review recent findings in the field of visual cortical plasticity while focusing on how physiological mechanisms associated with experience promote structural changes that determine functional modifications of neural circuitries in V1. We revise the role of microRNAs as molecular transducers of environmental stimuli and the role of immediate early genes that control gene expression programs underlying plasticity in the developing visual cortex. PMID:25157210

Polarized skylight navigation in insects: model and electrophysiology of e-vector coding by neurons in the central complex.

PubMed

Sakura, Midori; Lambrinos, Dimitrios; Labhart, Thomas

2008-02-01

Many insects exploit skylight polarization for visual compass orientation or course control. As found in crickets, the peripheral visual system (optic lobe) contains three types of polarization-sensitive neurons (POL neurons), which are tuned to different ( approximately 60 degrees diverging) e-vector orientations. Thus each e-vector orientation elicits a specific combination of activities among the POL neurons coding any e-vector orientation by just three neural signals. In this study, we hypothesize that in the presumed orientation center of the brain (central complex) e-vector orientation is population-coded by a set of "compass neurons." Using computer modeling, we present a neural network model transforming the signal triplet provided by the POL neurons to compass neuron activities coding e-vector orientation by a population code. Using intracellular electrophysiology and cell marking, we present evidence that neurons with the response profile of the presumed compass neurons do indeed exist in the insect brain: each of these compass neuron-like (CNL) cells is activated by a specific e-vector orientation only and otherwise remains silent. Morphologically, CNL cells are tangential neurons extending from the lateral accessory lobe to the lower division of the central body. Surpassing the modeled compass neurons in performance, CNL cells are insensitive to the degree of polarization of the stimulus between 99% and at least down to 18% polarization and thus largely disregard variations of skylight polarization due to changing solar elevations or atmospheric conditions. This suggests that the polarization vision system includes a gain control circuit keeping the output activity at a constant level.

Monosynaptic rabies virus reveals premotor network organization and synaptic specificity of cholinergic partition cells.

PubMed

Stepien, Anna E; Tripodi, Marco; Arber, Silvia

2010-11-04

Movement is the behavioral output of neuronal activity in the spinal cord. Motor neurons are grouped into motor neuron pools, the functional units innervating individual muscles. Here we establish an anatomical rabies virus-based connectivity assay in early postnatal mice. We employ it to study the connectivity scheme of premotor neurons, the neuronal cohorts monosynaptically connected to motor neurons, unveiling three aspects of organization. First, motor neuron pools are connected to segmentally widely distributed yet stereotypic interneuron populations, differing for pools innervating functionally distinct muscles. Second, depending on subpopulation identity, interneurons take on local or segmentally distributed positions. Third, cholinergic partition cells involved in the regulation of motor neuron excitability segregate into ipsilaterally and bilaterally projecting populations, the latter exhibiting preferential connections to functionally equivalent motor neuron pools bilaterally. Our study visualizes the widespread yet precise nature of the connectivity matrix for premotor interneurons and reveals exquisite synaptic specificity for bilaterally projecting cholinergic partition cells. Copyright © 2010 Elsevier Inc. All rights reserved.

The Population Tracking Model: A Simple, Scalable Statistical Model for Neural Population Data

PubMed Central

O'Donnell, Cian; alves, J. Tiago Gonç; Whiteley, Nick; Portera-Cailliau, Carlos; Sejnowski, Terrence J.

2017-01-01

Our understanding of neural population coding has been limited by a lack of analysis methods to characterize spiking data from large populations. The biggest challenge comes from the fact that the number of possible network activity patterns scales exponentially with the number of neurons recorded (∼2Neurons). Here we introduce a new statistical method for characterizing neural population activity that requires semi-independent fitting of only as many parameters as the square of the number of neurons, requiring drastically smaller data sets and minimal computation time. The model works by matching the population rate (the number of neurons synchronously active) and the probability that each individual neuron fires given the population rate. We found that this model can accurately fit synthetic data from up to 1000 neurons. We also found that the model could rapidly decode visual stimuli from neural population data from macaque primary visual cortex about 65 ms after stimulus onset. Finally, we used the model to estimate the entropy of neural population activity in developing mouse somatosensory cortex and, surprisingly, found that it first increases, and then decreases during development. This statistical model opens new options for interrogating neural population data and can bolster the use of modern large-scale in vivo Ca2+ and voltage imaging tools. PMID:27870612

Synchrony and the binding problem in macaque visual cortex

PubMed Central

Dong, Yi; Mihalas, Stefan; Qiu, Fangtu; von der Heydt, Rüdiger; Niebur, Ernst

2009-01-01

We tested the binding-by-synchrony hypothesis which proposes that object representations are formed by synchronizing spike activity between neurons that code features of the same object. We studied responses of 32 pairs of neurons recorded with microelectrodes 3 mm apart in the visual cortex of macaques performing a fixation task. Upon mapping the receptive fields of the neurons, a quadrilateral was generated so that two of its sides were centered in the receptive fields at the optimal orientations. This one-figure condition was compared with a two-figure condition in which the neurons were stimulated by two separate figures, keeping the local edges in the receptive fields identical. For each neuron, we also determined its border ownership selectivity (H. Zhou, H. S. Friedman, & R. von der Heydt, 2000). We examined both synchronization and correlation at nonzero time lag. After correcting for effects of the firing rate, we found that synchrony did not depend on the binding condition. However, finding synchrony in a pair of neurons was correlated with finding border-ownership selectivity in both members of the pair. This suggests that the synchrony reflected the connectivity in the network that generates border ownership assignment. Thus, we have not found evidence to support the binding-by-synchrony hypothesis. PMID:19146262

Development of a Bayesian Estimator for Audio-Visual Integration: A Neurocomputational Study

PubMed Central

Ursino, Mauro; Crisafulli, Andrea; di Pellegrino, Giuseppe; Magosso, Elisa; Cuppini, Cristiano

2017-01-01

The brain integrates information from different sensory modalities to generate a coherent and accurate percept of external events. Several experimental studies suggest that this integration follows the principle of Bayesian estimate. However, the neural mechanisms responsible for this behavior, and its development in a multisensory environment, are still insufficiently understood. We recently presented a neural network model of audio-visual integration (Neural Computation, 2017) to investigate how a Bayesian estimator can spontaneously develop from the statistics of external stimuli. Model assumes the presence of two unimodal areas (auditory and visual) topologically organized. Neurons in each area receive an input from the external environment, computed as the inner product of the sensory-specific stimulus and the receptive field synapses, and a cross-modal input from neurons of the other modality. Based on sensory experience, synapses were trained via Hebbian potentiation and a decay term. Aim of this work is to improve the previous model, including a more realistic distribution of visual stimuli: visual stimuli have a higher spatial accuracy at the central azimuthal coordinate and a lower accuracy at the periphery. Moreover, their prior probability is higher at the center, and decreases toward the periphery. Simulations show that, after training, the receptive fields of visual and auditory neurons shrink to reproduce the accuracy of the input (both at the center and at the periphery in the visual case), thus realizing the likelihood estimate of unimodal spatial position. Moreover, the preferred positions of visual neurons contract toward the center, thus encoding the prior probability of the visual input. Finally, a prior probability of the co-occurrence of audio-visual stimuli is encoded in the cross-modal synapses. The model is able to simulate the main properties of a Bayesian estimator and to reproduce behavioral data in all conditions examined. In particular, in unisensory conditions the visual estimates exhibit a bias toward the fovea, which increases with the level of noise. In cross modal conditions, the SD of the estimates decreases when using congruent audio-visual stimuli, and a ventriloquism effect becomes evident in case of spatially disparate stimuli. Moreover, the ventriloquism decreases with the eccentricity. PMID:29046631

What Brain Research Suggests for Teaching Reading Strategies

ERIC Educational Resources Information Center

Willis, Judy

2009-01-01

How the brain learns to read has been the subject of much neuroscience educational research. Evidence is mounting for identifiable networks of connected neurons that are particularly active during reading processes such as response to visual and auditory stimuli, relating new information to prior knowledge, long-term memory storage, comprehension,…

Connectomics-based analysis of information flow in the Drosophila brain.

PubMed

Shih, Chi-Tin; Sporns, Olaf; Yuan, Shou-Li; Su, Ta-Shun; Lin, Yen-Jen; Chuang, Chao-Chun; Wang, Ting-Yuan; Lo, Chung-Chuang; Greenspan, Ralph J; Chiang, Ann-Shyn

2015-05-18

Understanding the overall patterns of information flow within the brain has become a major goal of neuroscience. In the current study, we produced a first draft of the Drosophila connectome at the mesoscopic scale, reconstructed from 12,995 images of neuron projections collected in FlyCircuit (version 1.1). Neuron polarities were predicted according to morphological criteria, with nodes of the network corresponding to brain regions designated as local processing units (LPUs). The weight of each directed edge linking a pair of LPUs was determined by the number of neuron terminals that connected one LPU to the other. The resulting network showed hierarchical structure and small-world characteristics and consisted of five functional modules that corresponded to sensory modalities (olfactory, mechanoauditory, and two visual) and the pre-motor center. Rich-club organization was present in this network and involved LPUs in all sensory centers, and rich-club members formed a putative motor center of the brain. Major intra- and inter-modular loops were also identified that could play important roles for recurrent and reverberant information flow. The present analysis revealed whole-brain patterns of network structure and information flow. Additionally, we propose that the overall organizational scheme showed fundamental similarities to the network structure of the mammalian brain. Copyright © 2015 Elsevier Ltd. All rights reserved.

Top-down control of visual perception: attention in natural vision.

PubMed

Rolls, Edmund T

2008-01-01

Top-down perceptual influences can bias (or pre-empt) perception. In natural scenes, the receptive fields of neurons in the inferior temporal visual cortex (IT) shrink to become close to the size of objects. This facilitates the read-out of information from the ventral visual system, because the information is primarily about the object at the fovea. Top-down attentional influences are much less evident in natural scenes than when objects are shown against blank backgrounds, though are still present. It is suggested that the reduced receptive-field size in natural scenes, and the effects of top-down attention contribute to change blindness. The receptive fields of IT neurons in complex scenes, though including the fovea, are frequently asymmetric around the fovea, and it is proposed that this is the solution the IT uses to represent multiple objects and their relative spatial positions in a scene. Networks that implement probabilistic decision-making are described, and it is suggested that, when in perceptual systems they take decisions (or 'test hypotheses'), they influence lower-level networks to bias visual perception. Finally, it is shown that similar processes extend to systems involved in the processing of emotion-provoking sensory stimuli, in that word-level cognitive states provide top-down biasing that reaches as far down as the orbitofrontal cortex, where, at the first stage of affective representations, olfactory, taste, flavour, and touch processing is biased (or pre-empted) in humans.

Natural asynchronies in audiovisual communication signals regulate neuronal multisensory interactions in voice-sensitive cortex.

PubMed

Perrodin, Catherine; Kayser, Christoph; Logothetis, Nikos K; Petkov, Christopher I

2015-01-06

When social animals communicate, the onset of informative content in one modality varies considerably relative to the other, such as when visual orofacial movements precede a vocalization. These naturally occurring asynchronies do not disrupt intelligibility or perceptual coherence. However, they occur on time scales where they likely affect integrative neuronal activity in ways that have remained unclear, especially for hierarchically downstream regions in which neurons exhibit temporally imprecise but highly selective responses to communication signals. To address this, we exploited naturally occurring face- and voice-onset asynchronies in primate vocalizations. Using these as stimuli we recorded cortical oscillations and neuronal spiking responses from functional MRI (fMRI)-localized voice-sensitive cortex in the anterior temporal lobe of macaques. We show that the onset of the visual face stimulus resets the phase of low-frequency oscillations, and that the face-voice asynchrony affects the prominence of two key types of neuronal multisensory responses: enhancement or suppression. Our findings show a three-way association between temporal delays in audiovisual communication signals, phase-resetting of ongoing oscillations, and the sign of multisensory responses. The results reveal how natural onset asynchronies in cross-sensory inputs regulate network oscillations and neuronal excitability in the voice-sensitive cortex of macaques, a suggested animal model for human voice areas. These findings also advance predictions on the impact of multisensory input on neuronal processes in face areas and other brain regions.

Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.

PubMed

Kriegeskorte, Nikolaus

2015-11-24

Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

Searching for collective behavior in a large network of sensory neurons.

PubMed

Tkačik, Gašper; Marre, Olivier; Amodei, Dario; Schneidman, Elad; Bialek, William; Berry, Michael J

2014-01-01

Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such "K-pairwise" models--being systematic extensions of the previously used pairwise Ising models--provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population's capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.

Searching for Collective Behavior in a Large Network of Sensory Neurons

PubMed Central

Tkačik, Gašper; Marre, Olivier; Amodei, Dario; Schneidman, Elad; Bialek, William; Berry, Michael J.

2014-01-01

Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such “K-pairwise” models—being systematic extensions of the previously used pairwise Ising models—provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population's capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction. PMID:24391485

Simultaneous selection by object-based attention in visual and frontal cortex

PubMed Central

Pooresmaeili, Arezoo; Poort, Jasper; Roelfsema, Pieter R.

2014-01-01

Models of visual attention hold that top-down signals from frontal cortex influence information processing in visual cortex. It is unknown whether situations exist in which visual cortex actively participates in attentional selection. To investigate this question, we simultaneously recorded neuronal activity in the frontal eye fields (FEF) and primary visual cortex (V1) during a curve-tracing task in which attention shifts are object-based. We found that accurate performance was associated with similar latencies of attentional selection in both areas and that the latency in both areas increased if the task was made more difficult. The amplitude of the attentional signals in V1 saturated early during a trial, whereas these selection signals kept increasing for a longer time in FEF, until the moment of an eye movement, as if FEF integrated attentional signals present in early visual cortex. In erroneous trials, we observed an interareal latency difference because FEF selected the wrong curve before V1 and imposed its erroneous decision onto visual cortex. The neuronal activity in visual and frontal cortices was correlated across trials, and this trial-to-trial coupling was strongest for the attended curve. These results imply that selective attention relies on reciprocal interactions within a large network of areas that includes V1 and FEF. PMID:24711379

Skimming Digits: Neuromorphic Classification of Spike-Encoded Images

PubMed Central

Cohen, Gregory K.; Orchard, Garrick; Leng, Sio-Hoi; Tapson, Jonathan; Benosman, Ryad B.; van Schaik, André

2016-01-01

The growing demands placed upon the field of computer vision have renewed the focus on alternative visual scene representations and processing paradigms. Silicon retinea provide an alternative means of imaging the visual environment, and produce frame-free spatio-temporal data. This paper presents an investigation into event-based digit classification using N-MNIST, a neuromorphic dataset created with a silicon retina, and the Synaptic Kernel Inverse Method (SKIM), a learning method based on principles of dendritic computation. As this work represents the first large-scale and multi-class classification task performed using the SKIM network, it explores different training patterns and output determination methods necessary to extend the original SKIM method to support multi-class problems. Making use of SKIM networks applied to real-world datasets, implementing the largest hidden layer sizes and simultaneously training the largest number of output neurons, the classification system achieved a best-case accuracy of 92.87% for a network containing 10,000 hidden layer neurons. These results represent the highest accuracies achieved against the dataset to date and serve to validate the application of the SKIM method to event-based visual classification tasks. Additionally, the study found that using a square pulse as the supervisory training signal produced the highest accuracy for most output determination methods, but the results also demonstrate that an exponential pattern is better suited to hardware implementations as it makes use of the simplest output determination method based on the maximum value. PMID:27199646

Locally induced neuronal synchrony precisely propagates to specific cortical areas without rhythm distortion.

PubMed

Toda, Haruo; Kawasaki, Keisuke; Sato, Sho; Horie, Masao; Nakahara, Kiyoshi; Bepari, Asim K; Sawahata, Hirohito; Suzuki, Takafumi; Okado, Haruo; Takebayashi, Hirohide; Hasegawa, Isao

2018-05-16

Propagation of oscillatory spike firing activity at specific frequencies plays an important role in distributed cortical networks. However, there is limited evidence for how such frequency-specific signals are induced or how the signal spectra of the propagating signals are modulated during across-layer (radial) and inter-areal (tangential) neuronal interactions. To directly evaluate the direction specificity of spectral changes in a spiking cortical network, we selectively photostimulated infragranular excitatory neurons in the rat primary visual cortex (V1) at a supra-threshold level with various frequencies, and recorded local field potentials (LFPs) at the infragranular stimulation site, the cortical surface site immediately above the stimulation site in V1, and cortical surface sites outside V1. We found a significant reduction of LFP powers during radial propagation, especially at high-frequency stimulation conditions. Moreover, low-gamma-band dominant rhythms were transiently induced during radial propagation. Contrastingly, inter-areal LFP propagation, directed to specific cortical sites, accompanied no significant signal reduction nor gamma-band power induction. We propose an anisotropic mechanism for signal processing in the spiking cortical network, in which the neuronal rhythms are locally induced/modulated along the radial direction, and then propagate without distortion via intrinsic horizontal connections for spatiotemporally precise, inter-areal communication.

The Temporal Resolution of Flight Attitude Control in Dragonflies and Locusts: Lessons for the Design of Flapping-Wing MAVs

DTIC Science & Technology

2007-12-04

central nevous system , consisting of a self- excited neuronal network. Even in the absence of any sensory inputs this network will 4 produce, in two...is not necessary in smaller systems . Introduction Conventional aircraft can be designed such that steady-state aerodynamics apply. Thus, it is...active damping by visual inputs, whereas the same is not necessary in smaller systems . 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17

Synaptic Plasticity in Visual Cortex: Comparison of Theory with Experiment

DTIC Science & Technology

1990-01-01

Hubel DH, Wiesel TN (1961) Integrative action in the cat’s lateral geniculate body . J. Physiol. 155:385-398. Hubel DH, Wiesel TN (1962) Receptive...fibers from the lateral geniculate nucleus (LGN) onto a single cortical neuron. Scofield and Cooper (1985) extended this to a network of interconnected...connected network was later 1 simplified by Cooper and Scofield (1988) with the introduction of a mean-field theory, which in effect replaces all of the

Functional connectivity analysis in resting state fMRI with echo-state networks and non-metric clustering for network structure recovery

NASA Astrophysics Data System (ADS)

Wismüller, Axel; DSouza, Adora M.; Abidin, Anas Z.; Wang, Xixi; Hobbs, Susan K.; Nagarajan, Mahesh B.

2015-03-01

Echo state networks (ESN) are recurrent neural networks where the hidden layer is replaced with a fixed reservoir of neurons. Unlike feed-forward networks, neuron training in ESN is restricted to the output neurons alone thereby providing a computational advantage. We demonstrate the use of such ESNs in our mutual connectivity analysis (MCA) framework for recovering the primary motor cortex network associated with hand movement from resting state functional MRI (fMRI) data. Such a framework consists of two steps - (1) defining a pair-wise affinity matrix between different pixel time series within the brain to characterize network activity and (2) recovering network components from the affinity matrix with non-metric clustering. Here, ESNs are used to evaluate pair-wise cross-estimation performance between pixel time series to create the affinity matrix, which is subsequently subject to non-metric clustering with the Louvain method. For comparison, the ground truth of the motor cortex network structure is established with a task-based fMRI sequence. Overlap between the primary motor cortex network recovered with our model free MCA approach and the ground truth was measured with the Dice coefficient. Our results show that network recovery with our proposed MCA approach is in close agreement with the ground truth. Such network recovery is achieved without requiring low-pass filtering of the time series ensembles prior to analysis, an fMRI preprocessing step that has courted controversy in recent years. Thus, we conclude our MCA framework can allow recovery and visualization of the underlying functionally connected networks in the brain on resting state fMRI.

Complex Network Analysis of CA3 Transcriptome Reveals Pathogenic and Compensatory Pathways in Refractory Temporal Lobe Epilepsy

PubMed Central

Bando, Silvia Yumi; Silva, Filipi Nascimento; Costa, Luciano da Fontoura; Silva, Alexandre V.; Pimentel-Silva, Luciana R.; Castro, Luiz HM.; Wen, Hung-Tzu; Amaro, Edson; Moreira-Filho, Carlos Alberto

2013-01-01

We previously described – studying transcriptional signatures of hippocampal CA3 explants – that febrile (FS) and afebrile (NFS) forms of refractory mesial temporal lobe epilepsy constitute two distinct genomic phenotypes. That network analysis was based on a limited number (hundreds) of differentially expressed genes (DE networks) among a large set of valid transcripts (close to two tens of thousands). Here we developed a methodology for complex network visualization (3D) and analysis that allows the categorization of network nodes according to distinct hierarchical levels of gene-gene connections (node degree) and of interconnection between node neighbors (concentric node degree). Hubs are highly connected nodes, VIPs have low node degree but connect only with hubs, and high-hubs have VIP status and high overall number of connections. Studying the whole set of CA3 valid transcripts we: i) obtained complete transcriptional networks (CO) for FS and NFS phenotypic groups; ii) examined how CO and DE networks are related; iii) characterized genomic and molecular mechanisms underlying FS and NFS phenotypes, identifying potential novel targets for therapeutic interventions. We found that: i) DE hubs and VIPs are evenly distributed inside the CO networks; ii) most DE hubs and VIPs are related to synaptic transmission and neuronal excitability whereas most CO hubs, VIPs and high hubs are related to neuronal differentiation, homeostasis and neuroprotection, indicating compensatory mechanisms. Complex network visualization and analysis is a useful tool for systems biology approaches to multifactorial diseases. Network centrality observed for hubs, VIPs and high hubs of CO networks, is consistent with the network disease model, where a group of nodes whose perturbation leads to a disease phenotype occupies a central position in the network. Conceivably, the chance for exerting therapeutic effects through the modulation of particular genes will be higher if these genes are highly interconnected in transcriptional networks. PMID:24278214

Network adaptation improves temporal representation of naturalistic stimuli in Drosophila eye: I dynamics.

PubMed

Zheng, Lei; Nikolaev, Anton; Wardill, Trevor J; O'Kane, Cahir J; de Polavieja, Gonzalo G; Juusola, Mikko

2009-01-01

Because of the limited processing capacity of eyes, retinal networks must adapt constantly to best present the ever changing visual world to the brain. However, we still know little about how adaptation in retinal networks shapes neural encoding of changing information. To study this question, we recorded voltage responses from photoreceptors (R1-R6) and their output neurons (LMCs) in the Drosophila eye to repeated patterns of contrast values, collected from natural scenes. By analyzing the continuous photoreceptor-to-LMC transformations of these graded-potential neurons, we show that the efficiency of coding is dynamically improved by adaptation. In particular, adaptation enhances both the frequency and amplitude distribution of LMC output by improving sensitivity to under-represented signals within seconds. Moreover, the signal-to-noise ratio of LMC output increases in the same time scale. We suggest that these coding properties can be used to study network adaptation using the genetic tools in Drosophila, as shown in a companion paper (Part II).

Network Adaptation Improves Temporal Representation of Naturalistic Stimuli in Drosophila Eye: I Dynamics

PubMed Central

Wardill, Trevor J.; O'Kane, Cahir J.; de Polavieja, Gonzalo G.; Juusola, Mikko

2009-01-01

Because of the limited processing capacity of eyes, retinal networks must adapt constantly to best present the ever changing visual world to the brain. However, we still know little about how adaptation in retinal networks shapes neural encoding of changing information. To study this question, we recorded voltage responses from photoreceptors (R1–R6) and their output neurons (LMCs) in the Drosophila eye to repeated patterns of contrast values, collected from natural scenes. By analyzing the continuous photoreceptor-to-LMC transformations of these graded-potential neurons, we show that the efficiency of coding is dynamically improved by adaptation. In particular, adaptation enhances both the frequency and amplitude distribution of LMC output by improving sensitivity to under-represented signals within seconds. Moreover, the signal-to-noise ratio of LMC output increases in the same time scale. We suggest that these coding properties can be used to study network adaptation using the genetic tools in Drosophila, as shown in a companion paper (Part II). PMID:19180196

Neural Action Fields for Optic Flow Based Navigation: A Simulation Study of the Fly Lobula Plate Network

PubMed Central

Borst, Alexander; Weber, Franz

2011-01-01

Optic flow based navigation is a fundamental way of visual course control described in many different species including man. In the fly, an essential part of optic flow analysis is performed in the lobula plate, a retinotopic map of motion in the environment. There, the so-called lobula plate tangential cells possess large receptive fields with different preferred directions in different parts of the visual field. Previous studies demonstrated an extensive connectivity between different tangential cells, providing, in principle, the structural basis for their large and complex receptive fields. We present a network simulation of the tangential cells, comprising most of the neurons studied so far (22 on each hemisphere) with all the known connectivity between them. On their dendrite, model neurons receive input from a retinotopic array of Reichardt-type motion detectors. Model neurons exhibit receptive fields much like their natural counterparts, demonstrating that the connectivity between the lobula plate tangential cells indeed can account for their complex receptive field structure. We describe the tuning of a model neuron to particular types of ego-motion (rotation as well as translation around/along a given body axis) by its ‘action field’. As we show for model neurons of the vertical system (VS-cells), each of them displays a different type of action field, i.e., responds maximally when the fly is rotating around a particular body axis. However, the tuning width of the rotational action fields is relatively broad, comparable to the one with dendritic input only. The additional intra-lobula-plate connectivity mainly reduces their translational action field amplitude, i.e., their sensitivity to translational movements along any body axis of the fly. PMID:21305019

Experimental and Computational Studies of Cortical Neural Network Properties Through Signal Processing

NASA Astrophysics Data System (ADS)

Clawson, Wesley Patrick

Previous studies, both theoretical and experimental, of network level dynamics in the cerebral cortex show evidence for a statistical phenomenon called criticality; a phenomenon originally studied in the context of phase transitions in physical systems and that is associated with favorable information processing in the context of the brain. The focus of this thesis is to expand upon past results with new experimentation and modeling to show a relationship between criticality and the ability to detect and discriminate sensory input. A line of theoretical work predicts maximal sensory discrimination as a functional benefit of criticality, which can then be characterized using mutual information between sensory input, visual stimulus, and neural response,. The primary finding of our experiments in the visual cortex in turtles and neuronal network modeling confirms this theoretical prediction. We show that sensory discrimination is maximized when visual cortex operates near criticality. In addition to presenting this primary finding in detail, this thesis will also address our preliminary results on change-point-detection in experimentally measured cortical dynamics.

Eigenvalue distributions for a class of covariance matrices with application to Bienenstock-Cooper-Munro neurons under noisy conditions.

PubMed

Bazzani, Armando; Castellani, Gastone C; Cooper, Leon N

2010-05-01

We analyze the effects of noise correlations in the input to, or among, Bienenstock-Cooper-Munro neurons using the Wigner semicircular law to construct random, positive-definite symmetric correlation matrices and compute their eigenvalue distributions. In the finite dimensional case, we compare our analytic results with numerical simulations and show the effects of correlations on the lifetimes of synaptic strengths in various visual environments. These correlations can be due either to correlations in the noise from the input lateral geniculate nucleus neurons, or correlations in the variability of lateral connections in a network of neurons. In particular, we find that for fixed dimensionality, a large noise variance can give rise to long lifetimes of synaptic strengths. This may be of physiological significance.

The stimulus-evoked population response in visual cortex of awake monkey is a propagating wave

PubMed Central

Muller, Lyle; Reynaud, Alexandre; Chavane, Frédéric; Destexhe, Alain

2014-01-01

Propagating waves occur in many excitable media and were recently found in neural systems from retina to neocortex. While propagating waves are clearly present under anaesthesia, whether they also appear during awake and conscious states remains unclear. One possibility is that these waves are systematically missed in trial-averaged data, due to variability. Here we present a method for detecting propagating waves in noisy multichannel recordings. Applying this method to single-trial voltage-sensitive dye imaging data, we show that the stimulus-evoked population response in primary visual cortex of the awake monkey propagates as a travelling wave, with consistent dynamics across trials. A network model suggests that this reliability is the hallmark of the horizontal fibre network of superficial cortical layers. Propagating waves with similar properties occur independently in secondary visual cortex, but maintain precise phase relations with the waves in primary visual cortex. These results show that, in response to a visual stimulus, propagating waves are systematically evoked in several visual areas, generating a consistent spatiotemporal frame for further neuronal interactions. PMID:24770473

Dynamics of feature categorization.

PubMed

Martí, Daniel; Rinzel, John

2013-01-01

In visual and auditory scenes, we are able to identify shared features among sensory objects and group them according to their similarity. This grouping is preattentive and fast and is thought of as an elementary form of categorization by which objects sharing similar features are clustered in some abstract perceptual space. It is unclear what neuronal mechanisms underlie this fast categorization. Here we propose a neuromechanistic model of fast feature categorization based on the framework of continuous attractor networks. The mechanism for category formation does not rely on learning and is based on biologically plausible assumptions, for example, the existence of populations of neurons tuned to feature values, feature-specific interactions, and subthreshold-evoked responses upon the presentation of single objects. When the network is presented with a sequence of stimuli characterized by some feature, the network sums the evoked responses and provides a running estimate of the distribution of features in the input stream. If the distribution of features is structured into different components or peaks (i.e., is multimodal), recurrent excitation amplifies the response of activated neurons, and categories are singled out as emerging localized patterns of elevated neuronal activity (bumps), centered at the centroid of each cluster. The emergence of bump states through sequential, subthreshold activation and the dependence on input statistics is a novel application of attractor networks. We show that the extraction and representation of multiple categories are facilitated by the rich attractor structure of the network, which can sustain multiple stable activity patterns for a robust range of connectivity parameters compatible with cortical physiology.

Deep neural networks for modeling visual perceptual learning.

PubMed

Wenliang, Li; Seitz, Aaron R

2018-05-23

Understanding visual perceptual learning (VPL) has become increasingly more challenging as new phenomena are discovered with novel stimuli and training paradigms. While existing models aid our knowledge of critical aspects of VPL, the connections shown by these models between behavioral learning and plasticity across different brain areas are typically superficial. Most models explain VPL as readout from simple perceptual representations to decision areas and are not easily adaptable to explain new findings. Here, we show that a well-known instance of deep neural network (DNN), while not designed specifically for VPL, provides a computational model of VPL with enough complexity to be studied at many levels of analyses. After learning a Gabor orientation discrimination task, the DNN model reproduced key behavioral results, including increasing specificity with higher task precision, and also suggested that learning precise discriminations could asymmetrically transfer to coarse discriminations when the stimulus conditions varied. In line with the behavioral findings, the distribution of plasticity moved towards lower layers when task precision increased, and this distribution was also modulated by tasks with different stimulus types. Furthermore, learning in the network units demonstrated close resemblance to extant electrophysiological recordings in monkey visual areas. Altogether, the DNN fulfilled predictions of existing theories regarding specificity and plasticity, and reproduced findings of tuning changes in neurons of the primate visual areas. Although the comparisons were mostly qualitative, the DNN provides a new method of studying VPL and can serve as a testbed for theories and assist in generating predictions for physiological investigations. SIGNIFICANCE STATEMENT Visual perceptual learning (VPL) has been found to cause changes at multiple stages of the visual hierarchy. We found that training a deep neural network (DNN) on an orientation discrimination task produced similar behavioral and physiological patterns found in human and monkey experiments. Unlike existing VPL models, the DNN was pre-trained on natural images to reach high performance in object recognition but was not designed specifically for VPL, and yet it fulfilled predictions of existing theories regarding specificity and plasticity, and reproduced findings of tuning changes in neurons of the primate visual areas. When used with care, this unbiased and deep-hierarchical model can provide new ways of studying VPL from behavior to physiology. Copyright © 2018 the authors.

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

The effects of tDCS upon sustained visual attention are dependent on cognitive load.

PubMed

Roe, James M; Nesheim, Mathias; Mathiesen, Nina C; Moberget, Torgeir; Alnæs, Dag; Sneve, Markus H

2016-01-08

Transcranial Direct Current Stimulation (tDCS) modulates the excitability of neuronal responses and consequently can affect performance on a variety of cognitive tasks. However, the interaction between cognitive load and the effects of tDCS is currently not well-understood. We recorded the performance accuracy of participants on a bilateral multiple object tracking task while undergoing bilateral stimulation assumed to enhance (anodal) and decrease (cathodal) neuronal excitability. Stimulation was applied to the posterior parietal cortex (PPC), a region inferred to be at the centre of an attentional tracking network that shows load-dependent activation. 34 participants underwent three separate stimulation conditions across three days. Each subject received (1) left cathodal / right anodal PPC tDCS, (2) left anodal / right cathodal PPC tDCS, and (3) sham tDCS. The number of targets-to-be-tracked was also manipulated, giving a low (one target per visual field), medium (two targets per visual field) or high (three targets per visual field) tracking load condition. It was found that tracking performance at high attentional loads was significantly reduced in both stimulation conditions relative to sham, and this was apparent in both visual fields, regardless of the direction of polarity upon the brain's hemispheres. We interpret this as an interaction between cognitive load and tDCS, and suggest that tDCS may degrade attentional performance when cognitive networks become overtaxed and unable to compensate as a result. Systematically varying cognitive load may therefore be a fruitful direction to elucidate the effects of tDCS upon cognitive functions. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

A Simple Rule for Dendritic Spine and Axonal Bouton Formation Can Account for Cortical Reorganization after Focal Retinal Lesions

PubMed Central

Butz, Markus; van Ooyen, Arjen

2013-01-01

Lasting alterations in sensory input trigger massive structural and functional adaptations in cortical networks. The principles governing these experience-dependent changes are, however, poorly understood. Here, we examine whether a simple rule based on the neurons' need for homeostasis in electrical activity may serve as driving force for cortical reorganization. According to this rule, a neuron creates new spines and boutons when its level of electrical activity is below a homeostatic set-point and decreases the number of spines and boutons when its activity exceeds this set-point. In addition, neurons need a minimum level of activity to form spines and boutons. Spine and bouton formation depends solely on the neuron's own activity level, and synapses are formed by merging spines and boutons independently of activity. Using a novel computational model, we show that this simple growth rule produces neuron and network changes as observed in the visual cortex after focal retinal lesions. In the model, as in the cortex, the turnover of dendritic spines was increased strongest in the center of the lesion projection zone, while axonal boutons displayed a marked overshoot followed by pruning. Moreover, the decrease in external input was compensated for by the formation of new horizontal connections, which caused a retinotopic remapping. Homeostatic regulation may provide a unifying framework for understanding cortical reorganization, including network repair in degenerative diseases or following focal stroke. PMID:24130472

Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit

PubMed Central

Bharioke, Arjun; Chklovskii, Dmitri B.

2015-01-01

Neurons must faithfully encode signals that can vary over many orders of magnitude despite having only limited dynamic ranges. For a correlated signal, this dynamic range constraint can be relieved by subtracting away components of the signal that can be predicted from the past, a strategy known as predictive coding, that relies on learning the input statistics. However, the statistics of input natural signals can also vary over very short time scales e.g., following saccades across a visual scene. To maintain a reduced transmission cost to signals with rapidly varying statistics, neuronal circuits implementing predictive coding must also rapidly adapt their properties. Experimentally, in different sensory modalities, sensory neurons have shown such adaptations within 100 ms of an input change. Here, we show first that linear neurons connected in a feedback inhibitory circuit can implement predictive coding. We then show that adding a rectification nonlinearity to such a feedback inhibitory circuit allows it to automatically adapt and approximate the performance of an optimal linear predictive coding network, over a wide range of inputs, while keeping its underlying temporal and synaptic properties unchanged. We demonstrate that the resulting changes to the linearized temporal filters of this nonlinear network match the fast adaptations observed experimentally in different sensory modalities, in different vertebrate species. Therefore, the nonlinear feedback inhibitory network can provide automatic adaptation to fast varying signals, maintaining the dynamic range necessary for accurate neuronal transmission of natural inputs. PMID:26247884

Characterizing structural association alterations within brain networks in normal aging using Gaussian Bayesian networks.

PubMed

Guo, Xiaojuan; Wang, Yan; Chen, Kewei; Wu, Xia; Zhang, Jiacai; Li, Ke; Jin, Zhen; Yao, Li

2014-01-01

Recent multivariate neuroimaging studies have revealed aging-related alterations in brain structural networks. However, the sensory/motor networks such as the auditory, visual and motor networks, have obtained much less attention in normal aging research. In this study, we used Gaussian Bayesian networks (BN), an approach investigating possible inter-regional directed relationship, to characterize aging effects on structural associations between core brain regions within each of these structural sensory/motor networks using volumetric MRI data. We then further examined the discriminability of BN models for the young (N = 109; mean age =22.73 years, range 20-28) and old (N = 82; mean age =74.37 years, range 60-90) groups. The results of the BN modeling demonstrated that structural associations exist between two homotopic brain regions from the left and right hemispheres in each of the three networks. In particular, compared with the young group, the old group had significant connection reductions in each of the three networks and lesser connection numbers in the visual network. Moreover, it was found that the aging-related BN models could distinguish the young and old individuals with 90.05, 73.82, and 88.48% accuracy for the auditory, visual, and motor networks, respectively. Our findings suggest that BN models can be used to investigate the normal aging process with reliable statistical power. Moreover, these differences in structural inter-regional interactions may help elucidate the neuronal mechanism of anatomical changes in normal aging.

Visual sensation during pecking in pigeons.

PubMed

Ostheim, J

1997-10-01

During the final down-thrust of a pigeon's head, the eyes are closed gradually, a response that was thought to block visual input. This phase of pecking was therefore assumed to be under feed-forward control exclusively. Analysis of high resolution video-recordings showed that visual information collected during the down-thrust of the head could be used for 'on-line' modulations of pecks in progress. We thus concluded that the final down-thrust of the head is not exclusively controlled by feed-forward mechanisms but also by visual feedback components. We could further establish that as a rule the eyes are never closed completely but instead the eyelids form a slit which leaves a part of the pupil uncovered. The width of the slit between the pigeon' eyelids is highly sensitive to both, ambient luminance and the visual background against which seeds are offered. It was concluded that eyelid slits increase the focal depth of retinal images at extreme near-field viewing-conditions. Applying pharmacological methods we could confirm that pupil size and eyelid slit width are controlled through conjoint neuronal mechanisms. This shared neuronal network is particularly sensitive to drugs that affect dopamine receptors.

Natural asynchronies in audiovisual communication signals regulate neuronal multisensory interactions in voice-sensitive cortex

PubMed Central

Perrodin, Catherine; Kayser, Christoph; Logothetis, Nikos K.; Petkov, Christopher I.

2015-01-01

When social animals communicate, the onset of informative content in one modality varies considerably relative to the other, such as when visual orofacial movements precede a vocalization. These naturally occurring asynchronies do not disrupt intelligibility or perceptual coherence. However, they occur on time scales where they likely affect integrative neuronal activity in ways that have remained unclear, especially for hierarchically downstream regions in which neurons exhibit temporally imprecise but highly selective responses to communication signals. To address this, we exploited naturally occurring face- and voice-onset asynchronies in primate vocalizations. Using these as stimuli we recorded cortical oscillations and neuronal spiking responses from functional MRI (fMRI)-localized voice-sensitive cortex in the anterior temporal lobe of macaques. We show that the onset of the visual face stimulus resets the phase of low-frequency oscillations, and that the face–voice asynchrony affects the prominence of two key types of neuronal multisensory responses: enhancement or suppression. Our findings show a three-way association between temporal delays in audiovisual communication signals, phase-resetting of ongoing oscillations, and the sign of multisensory responses. The results reveal how natural onset asynchronies in cross-sensory inputs regulate network oscillations and neuronal excitability in the voice-sensitive cortex of macaques, a suggested animal model for human voice areas. These findings also advance predictions on the impact of multisensory input on neuronal processes in face areas and other brain regions. PMID:25535356

Temporal Processing in the Visual Cortex of the Awake and Anesthetized Rat.

PubMed

Aasebø, Ida E J; Lepperød, Mikkel E; Stavrinou, Maria; Nøkkevangen, Sandra; Einevoll, Gaute; Hafting, Torkel; Fyhn, Marianne

2017-01-01

The activity pattern and temporal dynamics within and between neuron ensembles are essential features of information processing and believed to be profoundly affected by anesthesia. Much of our general understanding of sensory information processing, including computational models aimed at mathematically simulating sensory information processing, rely on parameters derived from recordings conducted on animals under anesthesia. Due to the high variety of neuronal subtypes in the brain, population-based estimates of the impact of anesthesia may conceal unit- or ensemble-specific effects of the transition between states. Using chronically implanted tetrodes into primary visual cortex (V1) of rats, we conducted extracellular recordings of single units and followed the same cell ensembles in the awake and anesthetized states. We found that the transition from wakefulness to anesthesia involves unpredictable changes in temporal response characteristics. The latency of single-unit responses to visual stimulation was delayed in anesthesia, with large individual variations between units. Pair-wise correlations between units increased under anesthesia, indicating more synchronized activity. Further, the units within an ensemble show reproducible temporal activity patterns in response to visual stimuli that is changed between states, suggesting state-dependent sequences of activity. The current dataset, with recordings from the same neural ensembles across states, is well suited for validating and testing computational network models. This can lead to testable predictions, bring a deeper understanding of the experimental findings and improve models of neural information processing. Here, we exemplify such a workflow using a Brunel network model.

Temporal Processing in the Visual Cortex of the Awake and Anesthetized Rat

PubMed Central

Aasebø, Ida E. J.; Stavrinou, Maria; Nøkkevangen, Sandra; Einevoll, Gaute

2017-01-01

Abstract The activity pattern and temporal dynamics within and between neuron ensembles are essential features of information processing and believed to be profoundly affected by anesthesia. Much of our general understanding of sensory information processing, including computational models aimed at mathematically simulating sensory information processing, rely on parameters derived from recordings conducted on animals under anesthesia. Due to the high variety of neuronal subtypes in the brain, population-based estimates of the impact of anesthesia may conceal unit- or ensemble-specific effects of the transition between states. Using chronically implanted tetrodes into primary visual cortex (V1) of rats, we conducted extracellular recordings of single units and followed the same cell ensembles in the awake and anesthetized states. We found that the transition from wakefulness to anesthesia involves unpredictable changes in temporal response characteristics. The latency of single-unit responses to visual stimulation was delayed in anesthesia, with large individual variations between units. Pair-wise correlations between units increased under anesthesia, indicating more synchronized activity. Further, the units within an ensemble show reproducible temporal activity patterns in response to visual stimuli that is changed between states, suggesting state-dependent sequences of activity. The current dataset, with recordings from the same neural ensembles across states, is well suited for validating and testing computational network models. This can lead to testable predictions, bring a deeper understanding of the experimental findings and improve models of neural information processing. Here, we exemplify such a workflow using a Brunel network model. PMID:28791331

Role of the visual experience-dependent nascent proteome in neuronal plasticity

PubMed Central

Liu, Han-Hsuan; McClatchy, Daniel B; Schiapparelli, Lucio; Shen, Wanhua; Yates, John R

2018-01-01

Experience-dependent synaptic plasticity refines brain circuits during development. To identify novel protein synthesis-dependent mechanisms contributing to experience-dependent plasticity, we conducted a quantitative proteomic screen of the nascent proteome in response to visual experience in Xenopus optic tectum using bio-orthogonal metabolic labeling (BONCAT). We identified 83 differentially synthesized candidate plasticity proteins (CPPs). The CPPs form strongly interconnected networks and are annotated to a variety of biological functions, including RNA splicing, protein translation, and chromatin remodeling. Functional analysis of select CPPs revealed the requirement for eukaryotic initiation factor three subunit A (eIF3A), fused in sarcoma (FUS), and ribosomal protein s17 (RPS17) in experience-dependent structural plasticity in tectal neurons and behavioral plasticity in tadpoles. These results demonstrate that the nascent proteome is dynamic in response to visual experience and that de novo synthesis of machinery that regulates RNA splicing and protein translation is required for experience-dependent plasticity. PMID:29412139

A quantitative analysis of the local connectivity between pyramidal neurons in layers 2/3 of the rat visual cortex.

PubMed

Hellwig, B

2000-02-01

This study provides a detailed quantitative estimate for local synaptic connectivity between neocortical pyramidal neurons. A new way of obtaining such an estimate is presented. In acute slices of the rat visual cortex, four layer 2 and four layer 3 pyramidal neurons were intracellularly injected with biocytin. Axonal and dendritic arborizations were three-dimensionally reconstructed with the aid of a computer-based camera lucida system. In a computer experiment, pairs of pre- and postsynaptic neurons were formed and potential synaptic contacts were calculated. For each pair, the calculations were carried out for a whole range of distances (0 to 500 microm) between the presynaptic and the postsynaptic neuron, in order to estimate cortical connectivity as a function of the spatial separation of neurons. It was also differentiated whether neurons were situated in the same or in different cortical layers. The data thus obtained was used to compute connection probabilities, the average number of contacts between neurons, the frequency of specific numbers of contacts and the total number of contacts a dendritic tree receives from the surrounding cortical volume. Connection probabilities ranged from 50% to 80% for directly adjacent neurons and from 0% to 15% for neurons 500 microm apart. In many cases, connections were mediated by one contact only. However, close neighbors made on average up to 3 contacts with each other. The question as to whether the method employed in this study yields a realistic estimate of synaptic connectivity is discussed. It is argued that the results can be used as a detailed blueprint for building artificial neural networks with a cortex-like architecture.

Reinforcement Learning of Linking and Tracing Contours in Recurrent Neural Networks

PubMed Central

Brosch, Tobias; Neumann, Heiko; Roelfsema, Pieter R.

2015-01-01

The processing of a visual stimulus can be subdivided into a number of stages. Upon stimulus presentation there is an early phase of feedforward processing where the visual information is propagated from lower to higher visual areas for the extraction of basic and complex stimulus features. This is followed by a later phase where horizontal connections within areas and feedback connections from higher areas back to lower areas come into play. In this later phase, image elements that are behaviorally relevant are grouped by Gestalt grouping rules and are labeled in the cortex with enhanced neuronal activity (object-based attention in psychology). Recent neurophysiological studies revealed that reward-based learning influences these recurrent grouping processes, but it is not well understood how rewards train recurrent circuits for perceptual organization. This paper examines the mechanisms for reward-based learning of new grouping rules. We derive a learning rule that can explain how rewards influence the information flow through feedforward, horizontal and feedback connections. We illustrate the efficiency with two tasks that have been used to study the neuronal correlates of perceptual organization in early visual cortex. The first task is called contour-integration and demands the integration of collinear contour elements into an elongated curve. We show how reward-based learning causes an enhancement of the representation of the to-be-grouped elements at early levels of a recurrent neural network, just as is observed in the visual cortex of monkeys. The second task is curve-tracing where the aim is to determine the endpoint of an elongated curve composed of connected image elements. If trained with the new learning rule, neural networks learn to propagate enhanced activity over the curve, in accordance with neurophysiological data. We close the paper with a number of model predictions that can be tested in future neurophysiological and computational studies. PMID:26496502

Effect of synapse dilution on the memory retrieval in structured attractor neural networks

NASA Astrophysics Data System (ADS)

Brunel, N.

1993-08-01

We investigate a simple model of structured attractor neural network (ANN). In this network a module codes for the category of the stored information, while another group of neurons codes for the remaining information. The probability distribution of stabilities of the patterns and the prototypes of the categories are calculated, for two different synaptic structures. The stability of the prototypes is shown to increase when the fraction of neurons coding for the category goes down. Then the effect of synapse destruction on the retrieval is studied in two opposite situations : first analytically in sparsely connected networks, then numerically in completely connected ones. In both cases the behaviour of the structured network and that of the usual homogeneous networks are compared. When lesions increase, two transitions are shown to appear in the behaviour of the structured network when one of the patterns is presented to the network. After the first transition the network recognizes the category of the pattern but not the individual pattern. After the second transition the network recognizes nothing. These effects are similar to syndromes caused by lesions in the central visual system, namely prosopagnosia and agnosia. In both types of networks (structured or homogeneous) the stability of the prototype is greater than the stability of individual patterns, however the first transition, for completely connected networks, occurs only when the network is structured.

Representing Where along with What Information in a Model of a Cortical Patch

PubMed Central

Roudi, Yasser; Treves, Alessandro

2008-01-01

Behaving in the real world requires flexibly combining and maintaining information about both continuous and discrete variables. In the visual domain, several lines of evidence show that neurons in some cortical networks can simultaneously represent information about the position and identity of objects, and maintain this combined representation when the object is no longer present. The underlying network mechanism for this combined representation is, however, unknown. In this paper, we approach this issue through a theoretical analysis of recurrent networks. We present a model of a cortical network that can retrieve information about the identity of objects from incomplete transient cues, while simultaneously representing their spatial position. Our results show that two factors are important in making this possible: A) a metric organisation of the recurrent connections, and B) a spatially localised change in the linear gain of neurons. Metric connectivity enables a localised retrieval of information about object identity, while gain modulation ensures localisation in the correct position. Importantly, we find that the amount of information that the network can retrieve and retain about identity is strongly affected by the amount of information it maintains about position. This balance can be controlled by global signals that change the neuronal gain. These results show that anatomical and physiological properties, which have long been known to characterise cortical networks, naturally endow them with the ability to maintain a conjunctive representation of the identity and location of objects. PMID:18369416

A Balanced Comparison of Object Invariances in Monkey IT Neurons.

PubMed

Ratan Murty, N Apurva; Arun, Sripati P

2017-01-01

Our ability to recognize objects across variations in size, position, or rotation is based on invariant object representations in higher visual cortex. However, we know little about how these invariances are related. Are some invariances harder than others? Do some invariances arise faster than others? These comparisons can be made only upon equating image changes across transformations. Here, we targeted invariant neural representations in the monkey inferotemporal (IT) cortex using object images with balanced changes in size, position, and rotation. Across the recorded population, IT neurons generalized across size and position both stronger and faster than to rotations in the image plane as well as in depth. We obtained a similar ordering of invariances in deep neural networks but not in low-level visual representations. Thus, invariant neural representations dynamically evolve in a temporal order reflective of their underlying computational complexity.

Fluorescent tagging of rhythmically active respiratory neurons within the pre-Bötzinger complex of rat medullary slice preparations.

PubMed

Pagliardini, Silvia; Adachi, Tadafumi; Ren, Jun; Funk, Gregory D; Greer, John J

2005-03-09

Elucidation of the neuronal mechanisms underlying respiratory rhythmogenesis is a major focal point in respiratory physiology. An area of the ventrolateral medulla, the pre-Bötzinger complex (preBotC), is a critical site. Attention is now focused on understanding the cellular and network properties within the preBotC that underlie this critical function. The inability to clearly identify key "rhythm-generating" neurons within the heterogeneous population of preBotC neurons has been a significant limitation. Here we report an advancement allowing precise targeting of neurons expressing neurokinin-1 receptors (NK1Rs), which are hypothesized to be essential for respiratory rhythmogenesis. The internalization of tetramethylrhodamine conjugated substance P in rhythmically active medullary slice preparations provided clear visualization of NK1R-expressing neurons for subsequent whole-cell patch-clamp recordings. Among labeled neurons, 82% were inspiratory modulated, and 25% had pacemaker properties. We propose that this approach can be used to greatly expedite progress toward understanding the neuronal processes underlying the control of breathing.

Neuronal nonlinearity explains greater visual spatial resolution for darks than lights.

PubMed

Kremkow, Jens; Jin, Jianzhong; Komban, Stanley J; Wang, Yushi; Lashgari, Reza; Li, Xiaobing; Jansen, Michael; Zaidi, Qasim; Alonso, Jose-Manuel

2014-02-25

Astronomers and physicists noticed centuries ago that visual spatial resolution is higher for dark than light stimuli, but the neuronal mechanisms for this perceptual asymmetry remain unknown. Here we demonstrate that the asymmetry is caused by a neuronal nonlinearity in the early visual pathway. We show that neurons driven by darks (OFF neurons) increase their responses roughly linearly with luminance decrements, independent of the background luminance. However, neurons driven by lights (ON neurons) saturate their responses with small increases in luminance and need bright backgrounds to approach the linearity of OFF neurons. We show that, as a consequence of this difference in linearity, receptive fields are larger in ON than OFF thalamic neurons, and cortical neurons are more strongly driven by darks than lights at low spatial frequencies. This ON/OFF asymmetry in linearity could be demonstrated in the visual cortex of cats, monkeys, and humans and in the cat visual thalamus. Furthermore, in the cat visual thalamus, we show that the neuronal nonlinearity is present at the ON receptive field center of ON-center neurons and ON receptive field surround of OFF-center neurons, suggesting an origin at the level of the photoreceptor. These results demonstrate a fundamental difference in visual processing between ON and OFF channels and reveal a competitive advantage for OFF neurons over ON neurons at low spatial frequencies, which could be important during cortical development when retinal images are blurred by immature optics in infant eyes.

PyNN: A Common Interface for Neuronal Network Simulators.

PubMed

Davison, Andrew P; Brüderle, Daniel; Eppler, Jochen; Kremkow, Jens; Muller, Eilif; Pecevski, Dejan; Perrinet, Laurent; Yger, Pierre

2008-01-01

Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN.

PyNN: A Common Interface for Neuronal Network Simulators

PubMed Central

Davison, Andrew P.; Brüderle, Daniel; Eppler, Jochen; Kremkow, Jens; Muller, Eilif; Pecevski, Dejan; Perrinet, Laurent; Yger, Pierre

2008-01-01

Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN. PMID:19194529

Towards systems neuroscience of ADHD: A meta-analysis of 55 fMRI studies

PubMed Central

Cortese, Samuele; Kelly, Clare; Chabernaud, Camille; Proal, Erika; Di Martino, Adriana; Milham, Michael P.; Castellanos, F. Xavier

2013-01-01

Objective To perform a comprehensive meta-analysis of task-based functional MRI studies of Attention-Deficit/Hyperactivity Disorder (ADHD). Method PubMed, Ovid, EMBASE, Web of Science, ERIC, CINHAL, and NeuroSynth were searched for studies published through 06/30/2011. Significant differences in activation of brain regions between individuals with ADHD and comparisons were detected using activation likelihood estimation meta-analysis (p<0.05, corrected). Dysfunctional regions in ADHD were related to seven reference neuronal systems. We performed a set of meta-analyses focused on age groups (children; adults), clinical characteristics (history of stimulant treatment; presence of psychiatric comorbidities), and specific neuropsychological tasks (inhibition; working memory; vigilance/attention). Results Fifty-five studies were included (39 in children, 16 in adults). In children, hypoactivation in ADHD vs. comparisons was found mostly in systems involved in executive functions (frontoparietal network) and attention (ventral attentional network). Significant hyperactivation in ADHD vs. comparisons was observed predominantly within the default, ventral attention, and somatomotor networks. In adults, ADHD-related hypoactivation was predominant in the frontoparietal system, while ADHD-related hyperactivation was present in the visual, dorsal attention, and default networks. Significant ADHD-related dysfunction largely reflected task features and was detected even in the absence of comorbid mental disorders or history of stimulant treatment. Conclusions A growing literature provides evidence of ADHD-related dysfunction within multiple neuronal systems involved in higher-level cognitive functions but also in sensorimotor processes, including the visual system, and in the default network. This meta-analytic evidence extends early models of ADHD pathophysiology focused on prefrontal-striatal circuits. PMID:22983386

Attentional impulsivity in binge eating disorder modulates response inhibition performance and frontal brain networks.

PubMed

Hege, M A; Stingl, K T; Kullmann, S; Schag, K; Giel, K E; Zipfel, S; Preissl, H

2015-02-01

A subgroup of overweight and obese people is characterized by binge eating disorder (BED). Increased impulsivity has been suggested to cause binge eating and subsequent weight gain. In the current study, neuronal correlates of increased impulsivity in binge eating disorder during behavioral response inhibition were investigated. Magnetic brain activity and behavioral responses of 37 overweight and obese individuals with and without diagnosed BED were recorded while performing a food-related visual go-nogo task. Trait impulsivity was assessed with the Barratt Impulsiveness Scale (BIS-11). Specifically, increased attentional impulsiveness (a subscale of the BIS-11) in BED was related to decreased response inhibition performance and hypoactivity in the prefrontal control network, which was activated when response inhibition was required. Furthermore, participants with BED showed a trend for a food-specific inhibition performance decline. This was possibly related to the absence of a food-specific activity increase in the prefrontal control network in BED, as observed in the control group. In addition, an increase in activity related to the actual button press during prepotent responses and alterations in visual processing were observed. Our results suggest an attentional impulsiveness-related attenuation in response inhibition performance in individuals with BED. This might have been related to increased reward responsiveness and limited resources to activate the prefrontal control network involved in response inhibition. Our results substantiate the importance of neuronal markers for investigating prevention and treatment of obesity, especially in specific subgroups at risk such as BED.

Curtailing effect of awakening on visual responses of cortical neurons by cholinergic activation of inhibitory circuits.

PubMed

Kimura, Rui; Safari, Mir-Shahram; Mirnajafi-Zadeh, Javad; Kimura, Rie; Ebina, Teppei; Yanagawa, Yuchio; Sohya, Kazuhiro; Tsumoto, Tadaharu

2014-07-23

Visual responsiveness of cortical neurons changes depending on the brain state. Neural circuit mechanism underlying this change is unclear. By applying the method of in vivo two-photon functional calcium imaging to transgenic rats in which GABAergic neurons express fluorescent protein, we analyzed changes in visual response properties of cortical neurons when animals became awakened from anesthesia. In the awake state, the magnitude and reliability of visual responses of GABAergic neurons increased whereas the decay of responses of excitatory neurons became faster. To test whether the basal forebrain (BF) cholinergic projection is involved in these changes, we analyzed effects of electrical and optogenetic activation of BF on visual responses of mouse cortical neurons with in vivo imaging and whole-cell recordings. Electrical BF stimulation in anesthetized animals induced the same direction of changes in visual responses of both groups of neurons as awakening. Optogenetic activation increased the frequency of visually evoked action potentials in GABAergic neurons but induced the delayed hyperpolarization that ceased the late generation of action potentials in excitatory neurons. Pharmacological analysis in slice preparations revealed that photoactivation-induced depolarization of layer 1 GABAergic neurons was blocked by a nicotinic receptor antagonist, whereas non-fast-spiking layer 2/3 GABAergic neurons was blocked only by the application of both nicotinic and muscarinic receptor antagonists. These results suggest that the effect of awakening is mediated mainly through nicotinic activation of layer 1 GABAergic neurons and mixed nicotinic/muscarinic activation of layer 2/3 non-fast-spiking GABAergic neurons, which together curtails the visual responses of excitatory neurons. Copyright © 2014 the authors 0270-6474/14/3410122-12$15.00/0.

iPlasticity: induced juvenile-like plasticity in the adult brain as a mechanism of antidepressants.

PubMed

Umemori, Juzoh; Winkel, Frederike; Didio, Giuliano; Llach Pou, Maria; Castrén, Eero

2018-05-26

The network hypothesis of depression proposes that mood disorders reflect problems in information processing within particular neural networks. Antidepressants, including selective serotonin reuptake inhibitors (SSRIs), function by gradually improving information processing within these networks. Antidepressants have been shown to induce a state of juvenile-like plasticity comparable to that observed during developmental critical periods: such critical-period-like plasticity allows brain networks to better adapt to extrinsic and intrinsic signals. We have coined this drug-induced state of juvenile-like plasticity iPlasticity. A combination of iPlasticity induced by chronic SSRI treatment together with training, rehabilitation, or psychotherapy improves symptoms of neuropsychiatric disorders and issues underlying the developmentally- or genetically-malfunctioning networks. We have proposed that iPlasticity might be a critical component of antidepressant action. We have demonstrated that iPlasticity occurs in the visual cortex, fear erasure network, extinction of aggression caused by social isolation, and spatial reversal memory in rodent models. Chronic SSRI treatment is known to promote neurogenesis and to cause dematuration of granule cells in the dentate gyrus and of interneurons, especially parvalbumin interneurons enwrapped by perineuronal nets in the prefrontal cortex, visual cortex, and amygdala. Brain-derived neurotrophic factor (BDNF), via its receptor Tropomyosin kinase receptor B (TrkB), is involved in processes of the synaptic plasticity, including neurogenesis, neuronal differentiation, weight of synapses, and gene regulation of synaptic formation. BDNF can be activated by both chronic SSRI treatment and neuronal activity. Accordingly, the BDNF/TrkB pathway is critical for iPlasticity, but further analyses will be needed to provide mechanical insight into the processes of iPlasticity. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

A Possible Role for End-Stopped V1 Neurons in the Perception of Motion: A Computational Model

PubMed Central

Zarei Eskikand, Parvin; Kameneva, Tatiana; Ibbotson, Michael R.; Burkitt, Anthony N.; Grayden, David B.

2016-01-01

We present a model of the early stages of processing in the visual cortex, in particular V1 and MT, to investigate the potential role of end-stopped V1 neurons in solving the aperture problem. A hierarchical network is used in which the incoming motion signals provided by complex V1 neurons and end-stopped V1 neurons proceed to MT neurons at the next stage. MT neurons are categorized into two types based on their function: integration and segmentation. The role of integration neurons is to propagate unambiguous motion signals arriving from those V1 neurons that emphasize object terminators (e.g. corners). Segmentation neurons detect the discontinuities in the input stimulus to control the activity of integration neurons. Although the activity of the complex V1 neurons at the terminators of the object accurately represents the direction of the motion, their level of activity is less than the activity of the neurons along the edges. Therefore, a model incorporating end-stopped neurons is essential to suppress ambiguous motion signals along the edges of the stimulus. It is shown that the unambiguous motion signals at terminators propagate over the rest of the object to achieve an accurate representation of motion. PMID:27741307

ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains

PubMed Central

Canova, Carlos; Denker, Michael; Gerstein, George; Helias, Moritz

2016-01-01

With the ability to observe the activity from large numbers of neurons simultaneously using modern recording technologies, the chance to identify sub-networks involved in coordinated processing increases. Sequences of synchronous spike events (SSEs) constitute one type of such coordinated spiking that propagates activity in a temporally precise manner. The synfire chain was proposed as one potential model for such network processing. Previous work introduced a method for visualization of SSEs in massively parallel spike trains, based on an intersection matrix that contains in each entry the degree of overlap of active neurons in two corresponding time bins. Repeated SSEs are reflected in the matrix as diagonal structures of high overlap values. The method as such, however, leaves the task of identifying these diagonal structures to visual inspection rather than to a quantitative analysis. Here we present ASSET (Analysis of Sequences of Synchronous EvenTs), an improved, fully automated method which determines diagonal structures in the intersection matrix by a robust mathematical procedure. The method consists of a sequence of steps that i) assess which entries in the matrix potentially belong to a diagonal structure, ii) cluster these entries into individual diagonal structures and iii) determine the neurons composing the associated SSEs. We employ parallel point processes generated by stochastic simulations as test data to demonstrate the performance of the method under a wide range of realistic scenarios, including different types of non-stationarity of the spiking activity and different correlation structures. Finally, the ability of the method to discover SSEs is demonstrated on complex data from large network simulations with embedded synfire chains. Thus, ASSET represents an effective and efficient tool to analyze massively parallel spike data for temporal sequences of synchronous activity. PMID:27420734

Altered Gradients of Glutamate and Gamma-Aminobutyric Acid Transcripts in the Cortical Visuospatial Working Memory Network in Schizophrenia.

PubMed

Hoftman, Gil D; Dienel, Samuel J; Bazmi, Holly H; Zhang, Yun; Chen, Kehui; Lewis, David A

2018-04-15

Visuospatial working memory (vsWM), which is impaired in schizophrenia, requires information transfer across multiple nodes in the cerebral cortex, including visual, posterior parietal, and dorsolateral prefrontal regions. Information is conveyed across these regions via the excitatory projections of glutamatergic pyramidal neurons located in layer 3, whose activity is modulated by local inhibitory gamma-aminobutyric acidergic (GABAergic) neurons. Key properties of these neurons differ across these cortical regions. Consequently, in schizophrenia, alterations in the expression of gene products regulating these properties could disrupt vsWM function in different ways, depending on the region(s) affected. Here, we quantified the expression of markers of glutamate and GABA neurotransmission selectively in layer 3 of four cortical regions in the vsWM network from 20 matched pairs of schizophrenia and unaffected comparison subjects. In comparison subjects, levels of glutamate transcripts tended to increase, whereas GABA transcript levels tended to decrease, from caudal to rostral, across cortical regions of the vsWM network. Composite measures across all transcripts revealed a significant effect of region, with the glutamate measure lowest in the primary visual cortex and highest in the dorsolateral prefrontal cortex, whereas the GABA measure showed the opposite pattern. In schizophrenia subjects, the expression levels of many of these transcripts were altered. However, this disease effect differed across regions, such that the caudal-to-rostral increase in the glutamate measure was blunted and the caudal-to-rostral decline in the GABA measure was enhanced in the illness. Differential alterations in layer 3 glutamate and GABA neurotransmission across cortical regions may contribute to vsWM deficits in schizophrenia. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Different cortical projections from three subdivisions of the rat lateral posterior thalamic nucleus: a single-neuron tracing study with viral vectors.

PubMed

Nakamura, Hisashi; Hioki, Hiroyuki; Furuta, Takahiro; Kaneko, Takeshi

2015-05-01

The lateral posterior thalamic nucleus (LP) is one of the components of the extrageniculate pathway in the rat visual system, and is cytoarchitecturally divided into three subdivisions--lateral (LPl), rostromedial (LPrm), and caudomedial (LPcm) portions. To clarify the differences in the dendritic fields and axonal arborisations among the three subdivisions, we applied a single-neuron labeling technique with viral vectors to LP neurons. The proximal dendrites of LPl neurons were more numerous than those of LPrm and LPcm neurons, and LPrm neurons tended to have wider dendritic fields than LPl neurons. We then analysed the axonal arborisations of LP neurons by reconstructing the axon fibers in the cortex. The LPl, LPrm and LPcm were different from one another in terms of the projection targets--the main target cortical regions of LPl and LPrm neurons were the secondary and primary visual areas, whereas those of LPcm neurons were the postrhinal and temporal association areas. Furthermore, the principal target cortical layers of LPl neurons in the visual areas were middle layers, but that of LPrm neurons was layer 1. This indicates that LPl and LPrm neurons can be categorised into the core and matrix types of thalamic neurons, respectively, in the visual areas. In addition, LPl neurons formed multiple axonal clusters within the visual areas, whereas the fibers of LPrm neurons were widely and diffusely distributed. It is therefore presumed that these two types of neurons play different roles in visual information processing by dual thalamocortical innervation of the visual areas. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Potential roles of cholinergic modulation in the neural coding of location and movement speed

PubMed Central

Dannenberg, Holger; Hinman, James R.; Hasselmo, Michael E.

2016-01-01

Behavioral data suggest that cholinergic modulation may play a role in certain aspects of spatial memory, and neurophysiological data demonstrate neurons that fire in response to spatial dimensions, including grid cells and place cells that respond on the basis of location and running speed. These neurons show firing responses that depend upon the visual configuration of the environment, due to coding in visually-responsive regions of the neocortex. This review focuses on the physiological effects of acetylcholine that may influence the sensory coding of spatial dimensions relevant to behavior. In particular, the local circuit effects of acetylcholine within the cortex regulate the influence of sensory input relative to internal memory representations, via presynaptic inhibition of excitatory and inhibitory synaptic transmission, and the modulation of intrinsic currents in cortical excitatory and inhibitory neurons. In addition, circuit effects of acetylcholine regulate the dynamics of cortical circuits including oscillations at theta and gamma frequencies. These effects of acetylcholine on local circuits and network dynamics could underlie the role of acetylcholine in coding of spatial information for the performance of spatial memory tasks. PMID:27677935

Space coding for sensorimotor transformations can emerge through unsupervised learning.

PubMed

De Filippo De Grazia, Michele; Cutini, Simone; Lisi, Matteo; Zorzi, Marco

2012-08-01

The posterior parietal cortex (PPC) is fundamental for sensorimotor transformations because it combines multiple sensory inputs and posture signals into different spatial reference frames that drive motor programming. Here, we present a computational model mimicking the sensorimotor transformations occurring in the PPC. A recurrent neural network with one layer of hidden neurons (restricted Boltzmann machine) learned a stochastic generative model of the sensory data without supervision. After the unsupervised learning phase, the activity of the hidden neurons was used to compute a motor program (a population code on a bidimensional map) through a simple linear projection and delta rule learning. The average motor error, calculated as the difference between the expected and the computed output, was less than 3°. Importantly, analyses of the hidden neurons revealed gain-modulated visual receptive fields, thereby showing that space coding for sensorimotor transformations similar to that observed in the PPC can emerge through unsupervised learning. These results suggest that gain modulation is an efficient coding strategy to integrate visual and postural information toward the generation of motor commands.

Behavior Selection of Mobile Robot Based on Integration of Multimodal Information

NASA Astrophysics Data System (ADS)

Chen, Bin; Kaneko, Masahide

Recently, biologically inspired robots have been developed to acquire the capacity for directing visual attention to salient stimulus generated from the audiovisual environment. On purpose to realize this behavior, a general method is to calculate saliency maps to represent how much the external information attracts the robot's visual attention, where the audiovisual information and robot's motion status should be involved. In this paper, we represent a visual attention model where three modalities, that is, audio information, visual information and robot's motor status are considered, while the previous researches have not considered all of them. Firstly, we introduce a 2-D density map, on which the value denotes how much the robot pays attention to each spatial location. Then we model the attention density using a Bayesian network where the robot's motion statuses are involved. Secondly, the information from both of audio and visual modalities is integrated with the attention density map in integrate-fire neurons. The robot can direct its attention to the locations where the integrate-fire neurons are fired. Finally, the visual attention model is applied to make the robot select the visual information from the environment, and react to the content selected. Experimental results show that it is possible for robots to acquire the visual information related to their behaviors by using the attention model considering motion statuses. The robot can select its behaviors to adapt to the dynamic environment as well as to switch to another task according to the recognition results of visual attention.

Neural attractor network for application in visual field data classification.

PubMed

Fink, Wolfgang

2004-07-07

The purpose was to introduce a novel method for computer-based classification of visual field data derived from perimetric examination, that may act as a 'counsellor', providing an independent 'second opinion' to the diagnosing physician. The classification system consists of a Hopfield-type neural attractor network that obtains its input data from perimetric examination results. An iterative relaxation process determines the states of the neurons dynamically. Therefore, even 'noisy' perimetric output, e.g., early stages of a disease, may eventually be classified correctly according to the predefined idealized visual field defect (scotoma) patterns, stored as attractors of the network, that are found with diseases of the eye, optic nerve and the central nervous system. Preliminary tests of the classification system on real visual field data derived from perimetric examinations have shown a classification success of over 80%. Some of the main advantages of the Hopfield-attractor-network-based approach over feed-forward type neural networks are: (1) network architecture is defined by the classification problem; (2) no training is required to determine the neural coupling strengths; (3) assignment of an auto-diagnosis confidence level is possible by means of an overlap parameter and the Hamming distance. In conclusion, the novel method for computer-based classification of visual field data, presented here, furnishes a valuable first overview and an independent 'second opinion' in judging perimetric examination results, pointing towards a final diagnosis by a physician. It should not be considered a substitute for the diagnosing physician. Thanks to the worldwide accessibility of the Internet, the classification system offers a promising perspective towards modern computer-assisted diagnosis in both medicine and tele-medicine, for example and in particular, with respect to non-ophthalmic clinics or in communities where perimetric expertise is not readily available.

Auditory and audio-vocal responses of single neurons in the monkey ventral premotor cortex.

PubMed

Hage, Steffen R

2018-03-20

Monkey vocalization is a complex behavioral pattern, which is flexibly used in audio-vocal communication. A recently proposed dual neural network model suggests that cognitive control might be involved in this behavior, originating from a frontal cortical network in the prefrontal cortex and mediated via projections from the rostral portion of the ventral premotor cortex (PMvr) and motor cortex to the primary vocal motor network in the brainstem. For the rapid adjustment of vocal output to external acoustic events, strong interconnections between vocal motor and auditory sites are needed, which are present at cortical and subcortical levels. However, the role of the PMvr in audio-vocal integration processes remains unclear. In the present study, single neurons in the PMvr were recorded in rhesus monkeys (Macaca mulatta) while volitionally producing vocalizations in a visual detection task or passively listening to monkey vocalizations. Ten percent of randomly selected neurons in the PMvr modulated their discharge rate in response to acoustic stimulation with species-specific calls. More than four-fifths of these auditory neurons showed an additional modulation of their discharge rates either before and/or during the monkeys' motor production of the vocalization. Based on these audio-vocal interactions, the PMvr might be well positioned to mediate higher order auditory processing with cognitive control of the vocal motor output to the primary vocal motor network. Such audio-vocal integration processes in the premotor cortex might constitute a precursor for the evolution of complex learned audio-vocal integration systems, ultimately giving rise to human speech. Copyright © 2018 Elsevier B.V. All rights reserved.

Use of a Neural Net to Model the Impact of Optical Coherence Tomography Abnormalities on Vision in Age-related Macular Degeneration.

PubMed

Aslam, Tariq M; Zaki, Haider R; Mahmood, Sajjad; Ali, Zaria C; Ahmad, Nur A; Thorell, Mariana R; Balaskas, Konstantinos

2018-01-01

To develop a neural network for the estimation of visual acuity from optical coherence tomography (OCT) images of patients with neovascular age-related macular degeneration (AMD) and to demonstrate its use to model the impact of specific controlled OCT changes on vision. Artificial intelligence (neural network) study. We assessed 1400 OCT scans of patients with neovascular AMD. Fifteen physical features for each eligible OCT, as well as patient age, were used as input data and corresponding recorded visual acuity as the target data to train, validate, and test a supervised neural network. We then applied this network to model the impact on acuity of defined OCT changes in subretinal fluid, subretinal hyperreflective material, and loss of external limiting membrane (ELM) integrity. A total of 1210 eligible OCT scans were analyzed, resulting in 1210 data points, which were each 16-dimensional. A 10-layer feed-forward neural network with 1 hidden layer of 10 neurons was trained to predict acuity and demonstrated a root mean square error of 8.2 letters for predicted compared to actual visual acuity and a mean regression coefficient of 0.85. A virtual model using this network demonstrated the relationship of visual acuity to specific, programmed changes in OCT characteristics. When ELM is intact, there is a shallow decline in acuity with increasing subretinal fluid but a much steeper decline with equivalent increasing subretinal hyperreflective material. When ELM is not intact, all visual acuities are reduced. Increasing subretinal hyperreflective material or subretinal fluid in this circumstance reduces vision further still, but with a smaller gradient than when ELM is intact. The supervised machine learning neural network developed is able to generate an estimated visual acuity value from OCT images in a population of patients with AMD. These findings should be of clinical and research interest in macular degeneration, for example in estimating visual prognosis or highlighting the importance of developing treatments targeting more visually destructive pathologies. Copyright © 2017 Elsevier Inc. All rights reserved.

Multiplicative mixing of object identity and image attributes in single inferior temporal neurons.

PubMed

Ratan Murty, N Apurva; Arun, S P

2018-04-03

Object recognition is challenging because the same object can produce vastly different images, mixing signals related to its identity with signals due to its image attributes, such as size, position, rotation, etc. Previous studies have shown that both signals are present in high-level visual areas, but precisely how they are combined has remained unclear. One possibility is that neurons might encode identity and attribute signals multiplicatively so that each can be efficiently decoded without interference from the other. Here, we show that, in high-level visual cortex, responses of single neurons can be explained better as a product rather than a sum of tuning for object identity and tuning for image attributes. This subtle effect in single neurons produced substantially better population decoding of object identity and image attributes in the neural population as a whole. This property was absent both in low-level vision models and in deep neural networks. It was also unique to invariances: when tested with two-part objects, neural responses were explained better as a sum than as a product of part tuning. Taken together, our results indicate that signals requiring separate decoding, such as object identity and image attributes, are combined multiplicatively in IT neurons, whereas signals that require integration (such as parts in an object) are combined additively. Copyright © 2018 the Author(s). Published by PNAS.

Neuronify: An Educational Simulator for Neural Circuits.

PubMed

Dragly, Svenn-Arne; Hobbi Mobarhan, Milad; Våvang Solbrå, Andreas; Tennøe, Simen; Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne; Hafting, Torkel; Einevoll, Gaute T

2017-01-01

Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux).

Neuronify: An Educational Simulator for Neural Circuits

PubMed Central

Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne

2017-01-01

Abstract Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux). PMID:28321440

A Balanced Comparison of Object Invariances in Monkey IT Neurons

PubMed Central

2017-01-01

Abstract Our ability to recognize objects across variations in size, position, or rotation is based on invariant object representations in higher visual cortex. However, we know little about how these invariances are related. Are some invariances harder than others? Do some invariances arise faster than others? These comparisons can be made only upon equating image changes across transformations. Here, we targeted invariant neural representations in the monkey inferotemporal (IT) cortex using object images with balanced changes in size, position, and rotation. Across the recorded population, IT neurons generalized across size and position both stronger and faster than to rotations in the image plane as well as in depth. We obtained a similar ordering of invariances in deep neural networks but not in low-level visual representations. Thus, invariant neural representations dynamically evolve in a temporal order reflective of their underlying computational complexity. PMID:28413827

Rapid learning in visual cortical networks.

PubMed

Wang, Ye; Dragoi, Valentin

2015-08-26

Although changes in brain activity during learning have been extensively examined at the single neuron level, the coding strategies employed by cell populations remain mysterious. We examined cell populations in macaque area V4 during a rapid form of perceptual learning that emerges within tens of minutes. Multiple single units and LFP responses were recorded as monkeys improved their performance in an image discrimination task. We show that the increase in behavioral performance during learning is predicted by a tight coordination of spike timing with local population activity. More spike-LFP theta synchronization is correlated with higher learning performance, while high-frequency synchronization is unrelated with changes in performance, but these changes were absent once learning had stabilized and stimuli became familiar, or in the absence of learning. These findings reveal a novel mechanism of plasticity in visual cortex by which elevated low-frequency synchronization between individual neurons and local population activity accompanies the improvement in performance during learning.

Network feedback regulates motor output across a range of modulatory neuron activity

PubMed Central

Spencer, Robert M.

2016-01-01

Modulatory projection neurons alter network neuron synaptic and intrinsic properties to elicit multiple different outputs. Sensory and other inputs elicit a range of modulatory neuron activity that is further shaped by network feedback, yet little is known regarding how the impact of network feedback on modulatory neurons regulates network output across a physiological range of modulatory neuron activity. Identified network neurons, a fully described connectome, and a well-characterized, identified modulatory projection neuron enabled us to address this issue in the crab (Cancer borealis) stomatogastric nervous system. The modulatory neuron modulatory commissural neuron 1 (MCN1) activates and modulates two networks that generate rhythms via different cellular mechanisms and at distinct frequencies. MCN1 is activated at rates of 5–35 Hz in vivo and in vitro. Additionally, network feedback elicits MCN1 activity time-locked to motor activity. We asked how network activation, rhythm speed, and neuron activity levels are regulated by the presence or absence of network feedback across a physiological range of MCN1 activity rates. There were both similarities and differences in responses of the two networks to MCN1 activity. Many parameters in both networks were sensitive to network feedback effects on MCN1 activity. However, for most parameters, MCN1 activity rate did not determine the extent to which network output was altered by the addition of network feedback. These data demonstrate that the influence of network feedback on modulatory neuron activity is an important determinant of network output and feedback can be effective in shaping network output regardless of the extent of network modulation. PMID:27030739

Resolving ability and image discretization in the visual system.

PubMed

Shelepin, Yu E; Bondarko, V M

2004-02-01

Psychophysiological studies were performed to measure the spatial threshold for resolution of two "points" and the thresholds for discriminating their orientations depending on the distance between the two points. Data were compared with the scattering of the "point" by the eye's optics, the packing density of cones in the fovea, and the characteristics of the receptive fields of ganglion cells in the foveal area of the retina and neurons in the corresponding projection zones of the primary visual cortex. The effective zone was shown to have to contain a scattering function for several receptors, as this allowed preliminary blurring of the image by the eye's optics to decrease the subsequent (at the level of receptors) discretization noise created by a matrix of receptors. The concordance of these parameters supports the optical operation of the spatial elements of the neural network determining the resolving ability of the visual system at different levels of visual information processing. It is suggested that the special geometry of the receptive fields of neurons in the striate cortex, which are concordant with the statistics of natural scenes, results in a further increase in the signal:noise ratio.

An Efficient and Versatile System for Visualization and Genetic Modification of Dopaminergic Neurons in Transgenic Mice

PubMed Central

Kramer, Edgar R.

2015-01-01

Background & Aims The brain dopaminergic (DA) system is involved in fine tuning many behaviors and several human diseases are associated with pathological alterations of the DA system such as Parkinson’s disease (PD) and drug addiction. Because of its complex network integration, detailed analyses of physiological and pathophysiological conditions are only possible in a whole organism with a sophisticated tool box for visualization and functional modification. Methods & Results Here, we have generated transgenic mice expressing the tetracycline-regulated transactivator (tTA) or the reverse tetracycline-regulated transactivator (rtTA) under control of the tyrosine hydroxylase (TH) promoter, TH-tTA (tet-OFF) and TH-rtTA (tet-ON) mice, to visualize and genetically modify DA neurons. We show their tight regulation and efficient use to overexpress proteins under the control of tet-responsive elements or to delete genes of interest with tet-responsive Cre. In combination with mice encoding tet-responsive luciferase, we visualized the DA system in living mice progressively over time. Conclusion These experiments establish TH-tTA and TH-rtTA mice as a powerful tool to generate and monitor mouse models for DA system diseases. PMID:26291828

Development of novel two-photon microscopy for living brain and neuron.

PubMed

Nemoto, Tomomi

2014-11-01

"In vivo" two-photon microscopy (TPLSM) has revealed vital information on neural activity for brain function, even in light of its limitation in imaging events at depths greater than a several hundred micrometers from the brain surface. To break the limit of this penetration depth, we introduced a novel light source based on a semiconductor laser [1]. The light source successfully visualized not only cortex layer V pyramidal neurons spreading to all cortex layers at a superior S/N ratio, but visualize hippocampal CA1 neurons in young adult mice [2]. These results indicate that the penetration depth of this laser was ∼1.4 mm. In vivo TPLSM with a laser emitting a longer wavelength might give us insights on activities of neurons in the cortex or the hippocampus. This deep imaging method could be applicable to other living organs including tumor tissues. In addition, we developed liquid crystal devices to convert linearly polarized beams (LP) to vector beams [3]. A liquid device generated a vector beam called higher-order radially polarized (HRP) beam, which enabled that each of the aggregated 0.17 m beads was distinguished individually, whereas in conventional confocal microscopy or TPLSM they could not. We also visualized the finer structures of networks of filamentous cytoskeleton microtubule fluorescently-labeled in the COS-7, and primary culture of mouse neurons. Moreover, by taking an advantage of the LCDs that can utilize various wavelengths including near-infrared, we could employ an HRP beam for improving TPLSM. An HRP beam visualized fine intracellular structures not only in fixed cells stained with various dyes, but also in living cells expressing a fluorescent protein [4]. HRP beam also visualized finer structures of microtubules in fixed cells. Here, we will discuss these improvements and future application on the basis of our recent data.jmicro;63/suppl_1/i7/DFU087F1F1DFU087F1Fig. 1."in vivo" imaging of living mouse brain (H-line). © The Author 2014. Published by Oxford University Press on behalf of The Japanese Society of Microscopy. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Feature-Specific Organization of Feedback Pathways in Mouse Visual Cortex.

PubMed

Huh, Carey Y L; Peach, John P; Bennett, Corbett; Vega, Roxana M; Hestrin, Shaul

2018-01-08

Higher and lower cortical areas in the visual hierarchy are reciprocally connected [1]. Although much is known about how feedforward pathways shape receptive field properties of visual neurons, relatively little is known about the role of feedback pathways in visual processing. Feedback pathways are thought to carry top-down signals, including information about context (e.g., figure-ground segmentation and surround suppression) [2-5], and feedback has been demonstrated to sharpen orientation tuning of neurons in the primary visual cortex (V1) [6, 7]. However, the response characteristics of feedback neurons themselves and how feedback shapes V1 neurons' tuning for other features, such as spatial frequency (SF), remain largely unknown. Here, using a retrograde virus, targeted electrophysiological recordings, and optogenetic manipulations, we show that putatively feedback neurons in layer 5 (hereafter "L5 feedback") in higher visual areas, AL (anterolateral area) and PM (posteromedial area), display distinct visual properties in awake head-fixed mice. AL L5 feedback neurons prefer significantly lower SF (mean: 0.04 cycles per degree [cpd]) compared to PM L5 feedback neurons (0.15 cpd). Importantly, silencing AL L5 feedback reduced visual responses of V1 neurons preferring low SF (mean change in firing rate: -8.0%), whereas silencing PM L5 feedback suppressed responses of high-SF-preferring V1 neurons (-20.4%). These findings suggest that feedback connections from higher visual areas convey distinctly tuned visual inputs to V1 that serve to boost V1 neurons' responses to SF. Such like-to-like functional organization may represent an important feature of feedback pathways in sensory systems and in the nervous system in general. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mirror neurons as a model for the science and treatment of stuttering.

PubMed

Snyder, Gregory J; Waddell, Dwight E; Blanchet, Paul

2016-01-06

Persistent developmental stuttering is generally considered a speech disorder and affects ∼1% of the global population. While mainstream treatments continue to rely on unreliable behavioral speech motor targets, an emerging research perspective utilizes the mirror neuron system hypothesis as a neural substrate in the science and treatment of stuttering. The purpose of this exploratory study is to test the viability of the mirror neuron system hypothesis in the fluency enhancement of those who stutter. Participants were asked to speak while they were producing self-generated manual gestures, producing and visually perceiving self-generated manual gestures, and visually perceiving manual gestures, relative to a nonmanual gesture control speaking condition. Data reveal that all experimental speaking conditions enhanced fluent speech in all research participants, and the simultaneous perception and production of manual gesturing trended toward greater efficacious fluency enhancement. Coupled with existing research, we interpret these data as suggestive of fluency enhancement through subcortical involvement within multiple levels of an action understanding mirror neuron network. In addition, incidental findings report that stuttering moments were observed to simultaneously occur both orally and manually. Consequently, these data suggest that stuttering behaviors are compensatory, distal manifestations over multiple expressive modalities to an underlying centralized genetic neural substrate of the disorder.

Network feedback regulates motor output across a range of modulatory neuron activity.

PubMed

Spencer, Robert M; Blitz, Dawn M

2016-06-01

Modulatory projection neurons alter network neuron synaptic and intrinsic properties to elicit multiple different outputs. Sensory and other inputs elicit a range of modulatory neuron activity that is further shaped by network feedback, yet little is known regarding how the impact of network feedback on modulatory neurons regulates network output across a physiological range of modulatory neuron activity. Identified network neurons, a fully described connectome, and a well-characterized, identified modulatory projection neuron enabled us to address this issue in the crab (Cancer borealis) stomatogastric nervous system. The modulatory neuron modulatory commissural neuron 1 (MCN1) activates and modulates two networks that generate rhythms via different cellular mechanisms and at distinct frequencies. MCN1 is activated at rates of 5-35 Hz in vivo and in vitro. Additionally, network feedback elicits MCN1 activity time-locked to motor activity. We asked how network activation, rhythm speed, and neuron activity levels are regulated by the presence or absence of network feedback across a physiological range of MCN1 activity rates. There were both similarities and differences in responses of the two networks to MCN1 activity. Many parameters in both networks were sensitive to network feedback effects on MCN1 activity. However, for most parameters, MCN1 activity rate did not determine the extent to which network output was altered by the addition of network feedback. These data demonstrate that the influence of network feedback on modulatory neuron activity is an important determinant of network output and feedback can be effective in shaping network output regardless of the extent of network modulation. Copyright © 2016 the American Physiological Society.

Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality.

PubMed

Li, Zhongyu; Butler, Erik; Li, Kang; Lu, Aidong; Ji, Shuiwang; Zhang, Shaoting

2018-02-12

Recently released large-scale neuron morphological data has greatly facilitated the research in neuroinformatics. However, the sheer volume and complexity of these data pose significant challenges for efficient and accurate neuron exploration. In this paper, we propose an effective retrieval framework to address these problems, based on frontier techniques of deep learning and binary coding. For the first time, we develop a deep learning based feature representation method for the neuron morphological data, where the 3D neurons are first projected into binary images and then learned features using an unsupervised deep neural network, i.e., stacked convolutional autoencoders (SCAEs). The deep features are subsequently fused with the hand-crafted features for more accurate representation. Considering the exhaustive search is usually very time-consuming in large-scale databases, we employ a novel binary coding method to compress feature vectors into short binary codes. Our framework is validated on a public data set including 58,000 neurons, showing promising retrieval precision and efficiency compared with state-of-the-art methods. In addition, we develop a novel neuron visualization program based on the techniques of augmented reality (AR), which can help users take a deep exploration of neuron morphologies in an interactive and immersive manner.

Eye Velocity Gain Fields in MSTd During Optokinetic Stimulation

PubMed Central

Brostek, Lukas; Büttner, Ulrich; Mustari, Michael J.; Glasauer, Stefan

2015-01-01

Lesion studies argue for an involvement of cortical area dorsal medial superior temporal area (MSTd) in the control of optokinetic response (OKR) eye movements to planar visual stimulation. Neural recordings during OKR suggested that MSTd neurons directly encode stimulus velocity. On the other hand, studies using radial visual flow together with voluntary smooth pursuit eye movements showed that visual motion responses were modulated by eye movement-related signals. Here, we investigated neural responses in MSTd during continuous optokinetic stimulation using an information-theoretic approach for characterizing neural tuning with high resolution. We show that the majority of MSTd neurons exhibit gain-field-like tuning functions rather than directly encoding one variable. Neural responses showed a large diversity of tuning to combinations of retinal and extraretinal input. Eye velocity-related activity was observed prior to the actual eye movements, reflecting an efference copy. The observed tuning functions resembled those emerging in a network model trained to perform summation of 2 population-coded signals. Together, our findings support the hypothesis that MSTd implements the visuomotor transformation from retinal to head-centered stimulus velocity signals for the control of OKR. PMID:24557636

Occipital Alpha Activity during Stimulus Processing Gates the Information Flow to Object-Selective Cortex

PubMed Central

Zumer, Johanna M.; Scheeringa, René; Schoffelen, Jan-Mathijs; Norris, David G.; Jensen, Ole

2014-01-01

Given the limited processing capabilities of the sensory system, it is essential that attended information is gated to downstream areas, whereas unattended information is blocked. While it has been proposed that alpha band (8–13 Hz) activity serves to route information to downstream regions by inhibiting neuronal processing in task-irrelevant regions, this hypothesis remains untested. Here we investigate how neuronal oscillations detected by electroencephalography in visual areas during working memory encoding serve to gate information reflected in the simultaneously recorded blood-oxygenation-level-dependent (BOLD) signals recorded by functional magnetic resonance imaging in downstream ventral regions. We used a paradigm in which 16 participants were presented with faces and landscapes in the right and left hemifields; one hemifield was attended and the other unattended. We observed that decreased alpha power contralateral to the attended object predicted the BOLD signal representing the attended object in ventral object-selective regions. Furthermore, increased alpha power ipsilateral to the attended object predicted a decrease in the BOLD signal representing the unattended object. We also found that the BOLD signal in the dorsal attention network inversely correlated with visual alpha power. This is the first demonstration, to our knowledge, that oscillations in the alpha band are implicated in the gating of information from the visual cortex to the ventral stream, as reflected in the representationally specific BOLD signal. This link of sensory alpha to downstream activity provides a neurophysiological substrate for the mechanism of selective attention during stimulus processing, which not only boosts the attended information but also suppresses distraction. Although previous studies have shown a relation between the BOLD signal from the dorsal attention network and the alpha band at rest, we demonstrate such a relation during a visuospatial task, indicating that the dorsal attention network exercises top-down control of visual alpha activity. PMID:25333286

A Simple Network Architecture Accounts for Diverse Reward Time Responses in Primary Visual Cortex

PubMed Central

Hussain Shuler, Marshall G.; Shouval, Harel Z.

2015-01-01

Many actions performed by animals and humans depend on an ability to learn, estimate, and produce temporal intervals of behavioral relevance. Exemplifying such learning of cued expectancies is the observation of reward-timing activity in the primary visual cortex (V1) of rodents, wherein neural responses to visual cues come to predict the time of future reward as behaviorally experienced in the past. These reward-timing responses exhibit significant heterogeneity in at least three qualitatively distinct classes: sustained increase or sustained decrease in firing rate until the time of expected reward, and a class of cells that reach a peak in firing at the expected delay. We elaborate upon our existing model by including inhibitory and excitatory units while imposing simple connectivity rules to demonstrate what role these inhibitory elements and the simple architectures play in sculpting the response dynamics of the network. We find that simply adding inhibition is not sufficient for obtaining the different distinct response classes, and that a broad distribution of inhibitory projections is necessary for obtaining peak-type responses. Furthermore, although changes in connection strength that modulate the effects of inhibition onto excitatory units have a strong impact on the firing rate profile of these peaked responses, the network exhibits robustness in its overall ability to predict the expected time of reward. Finally, we demonstrate how the magnitude of expected reward can be encoded at the expected delay in the network and how peaked responses express this reward expectancy. SIGNIFICANCE STATEMENT Heterogeneity in single-neuron responses is a common feature of neuronal systems, although sometimes, in theoretical approaches, it is treated as a nuisance and seldom considered as conveying a different aspect of a signal. In this study, we focus on the heterogeneous responses in the primary visual cortex of rodents trained with a predictable delayed reward time. We describe under what conditions this heterogeneity can arise by self-organization, and what information it can convey. This study, while focusing on a specific system, provides insight onto how heterogeneity can arise in general while also shedding light onto mechanisms of reinforcement learning using realistic biological assumptions. PMID:26377457

Direction selectivity of blowfly motion-sensitive neurons is computed in a two-stage process.

PubMed Central

Borst, A; Egelhaaf, M

1990-01-01

Direction selectivity of motion-sensitive neurons is generally thought to result from the nonlinear interaction between the signals derived from adjacent image points. Modeling of motion-sensitive networks, however, reveals that such elements may still respond to motion in a rather poor directionally selective way. Direction selectivity can be significantly enhanced if the nonlinear interaction is followed by another processing stage in which the signals of elements with opposite preferred directions are subtracted from each other. Our electrophysiological experiments in the fly visual system suggest that here direction selectivity is acquired in such a two-stage process. Images PMID:2251278

Neuronal basis of covert spatial attention in the frontal eye field.

PubMed

Thompson, Kirk G; Biscoe, Keri L; Sato, Takashi R

2005-10-12

The influential "premotor theory of attention" proposes that developing oculomotor commands mediate covert visual spatial attention. A likely source of this attentional bias is the frontal eye field (FEF), an area of the frontal cortex involved in converting visual information into saccade commands. We investigated the link between FEF activity and covert spatial attention by recording from FEF visual and saccade-related neurons in monkeys performing covert visual search tasks without eye movements. Here we show that the source of attention signals in the FEF is enhanced activity of visually responsive neurons. At the time attention is allocated to the visual search target, nonvisually responsive saccade-related movement neurons are inhibited. Therefore, in the FEF, spatial attention signals are independent of explicit saccade command signals. We propose that spatially selective activity in FEF visually responsive neurons corresponds to the mental spotlight of attention via modulation of ongoing visual processing.

Efficient encoding of motion is mediated by gap junctions in the fly visual system.

PubMed

Wang, Siwei; Borst, Alexander; Zaslavsky, Noga; Tishby, Naftali; Segev, Idan

2017-12-01

Understanding the computational implications of specific synaptic connectivity patterns is a fundamental goal in neuroscience. In particular, the computational role of ubiquitous electrical synapses operating via gap junctions remains elusive. In the fly visual system, the cells in the vertical-system network, which play a key role in visual processing, primarily connect to each other via axonal gap junctions. This network therefore provides a unique opportunity to explore the functional role of gap junctions in sensory information processing. Our information theoretical analysis of a realistic VS network model shows that within 10 ms following the onset of the visual input, the presence of axonal gap junctions enables the VS system to efficiently encode the axis of rotation, θ, of the fly's ego motion. This encoding efficiency, measured in bits, is near-optimal with respect to the physical limits of performance determined by the statistical structure of the visual input itself. The VS network is known to be connected to downstream pathways via a subset of triplets of the vertical system cells; we found that because of the axonal gap junctions, the efficiency of this subpopulation in encoding θ is superior to that of the whole vertical system network and is robust to a wide range of signal to noise ratios. We further demonstrate that this efficient encoding of motion by this subpopulation is necessary for the fly's visually guided behavior, such as banked turns in evasive maneuvers. Because gap junctions are formed among the axons of the vertical system cells, they only impact the system's readout, while maintaining the dendritic input intact, suggesting that the computational principles implemented by neural circuitries may be much richer than previously appreciated based on point neuron models. Our study provides new insights as to how specific network connectivity leads to efficient encoding of sensory stimuli.

Mechanisms for Rapid Adaptive Control of Motion Processing in Macaque Visual Cortex.

PubMed

McLelland, Douglas; Baker, Pamela M; Ahmed, Bashir; Kohn, Adam; Bair, Wyeth

2015-07-15

A key feature of neural networks is their ability to rapidly adjust their function, including signal gain and temporal dynamics, in response to changes in sensory inputs. These adjustments are thought to be important for optimizing the sensitivity of the system, yet their mechanisms remain poorly understood. We studied adaptive changes in temporal integration in direction-selective cells in macaque primary visual cortex, where specific hypotheses have been proposed to account for rapid adaptation. By independently stimulating direction-specific channels, we found that the control of temporal integration of motion at one direction was independent of motion signals driven at the orthogonal direction. We also found that individual neurons can simultaneously support two different profiles of temporal integration for motion in orthogonal directions. These findings rule out a broad range of adaptive mechanisms as being key to the control of temporal integration, including untuned normalization and nonlinearities of spike generation and somatic adaptation in the recorded direction-selective cells. Such mechanisms are too broadly tuned, or occur too far downstream, to explain the channel-specific and multiplexed temporal integration that we observe in single neurons. Instead, we are compelled to conclude that parallel processing pathways are involved, and we demonstrate one such circuit using a computer model. This solution allows processing in different direction/orientation channels to be separately optimized and is sensible given that, under typical motion conditions (e.g., translation or looming), speed on the retina is a function of the orientation of image components. Many neurons in visual cortex are understood in terms of their spatial and temporal receptive fields. It is now known that the spatiotemporal integration underlying visual responses is not fixed but depends on the visual input. For example, neurons that respond selectively to motion direction integrate signals over a shorter time window when visual motion is fast and a longer window when motion is slow. We investigated the mechanisms underlying this useful adaptation by recording from neurons as they responded to stimuli moving in two different directions at different speeds. Computer simulations of our results enabled us to rule out several candidate theories in favor of a model that integrates across multiple parallel channels that operate at different time scales. Copyright © 2015 the authors 0270-6474/15/3510268-13$15.00/0.

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.

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.

Visual processing in the central bee brain.

PubMed

Paulk, Angelique C; Dacks, Andrew M; Phillips-Portillo, James; Fellous, Jean-Marc; Gronenberg, Wulfila

2009-08-12

Visual scenes comprise enormous amounts of information from which nervous systems extract behaviorally relevant cues. In most model systems, little is known about the transformation of visual information as it occurs along visual pathways. We examined how visual information is transformed physiologically as it is communicated from the eye to higher-order brain centers using bumblebees, which are known for their visual capabilities. We recorded intracellularly in vivo from 30 neurons in the central bumblebee brain (the lateral protocerebrum) and compared these neurons to 132 neurons from more distal areas along the visual pathway, namely the medulla and the lobula. In these three brain regions (medulla, lobula, and central brain), we examined correlations between the neurons' branching patterns and their responses primarily to color, but also to motion stimuli. Visual neurons projecting to the anterior central brain were generally color sensitive, while neurons projecting to the posterior central brain were predominantly motion sensitive. The temporal response properties differed significantly between these areas, with an increase in spike time precision across trials and a decrease in average reliable spiking as visual information processing progressed from the periphery to the central brain. These data suggest that neurons along the visual pathway to the central brain not only are segregated with regard to the physical features of the stimuli (e.g., color and motion), but also differ in the way they encode stimuli, possibly to allow for efficient parallel processing to occur.

ACTIVIS: Visual Exploration of Industry-Scale Deep Neural Network Models.

PubMed

Kahng, Minsuk; Andrews, Pierre Y; Kalro, Aditya; Polo Chau, Duen Horng

2017-08-30

While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ACTIVIS, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance- and subset-level. ACTIVIS has been deployed on Facebook's machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ACTIVIS may work with different models.

Distinct learning-induced changes in stimulus selectivity and interactions of GABAergic interneuron classes in visual cortex.

PubMed

Khan, Adil G; Poort, Jasper; Chadwick, Angus; Blot, Antonin; Sahani, Maneesh; Mrsic-Flogel, Thomas D; Hofer, Sonja B

2018-06-01

How learning enhances neural representations for behaviorally relevant stimuli via activity changes of cortical cell types remains unclear. We simultaneously imaged responses of pyramidal cells (PYR) along with parvalbumin (PV), somatostatin (SOM), and vasoactive intestinal peptide (VIP) inhibitory interneurons in primary visual cortex while mice learned to discriminate visual patterns. Learning increased selectivity for task-relevant stimuli of PYR, PV and SOM subsets but not VIP cells. Strikingly, PV neurons became as selective as PYR cells, and their functional interactions reorganized, leading to the emergence of stimulus-selective PYR-PV ensembles. Conversely, SOM activity became strongly decorrelated from the network, and PYR-SOM coupling before learning predicted selectivity increases in individual PYR cells. Thus, learning differentially shapes the activity and interactions of multiple cell classes: while SOM inhibition may gate selectivity changes, PV interneurons become recruited into stimulus-specific ensembles and provide more selective inhibition as the network becomes better at discriminating behaviorally relevant stimuli.

Perceptual learning as improved probabilistic inference in early sensory areas.

PubMed

Bejjanki, Vikranth R; Beck, Jeffrey M; Lu, Zhong-Lin; Pouget, Alexandre

2011-05-01

Extensive training on simple tasks such as fine orientation discrimination results in large improvements in performance, a form of learning known as perceptual learning. Previous models have argued that perceptual learning is due to either sharpening and amplification of tuning curves in early visual areas or to improved probabilistic inference in later visual areas (at the decision stage). However, early theories are inconsistent with the conclusions of psychophysical experiments manipulating external noise, whereas late theories cannot explain the changes in neural responses that have been reported in cortical areas V1 and V4. Here we show that we can capture both the neurophysiological and behavioral aspects of perceptual learning by altering only the feedforward connectivity in a recurrent network of spiking neurons so as to improve probabilistic inference in early visual areas. The resulting network shows modest changes in tuning curves, in line with neurophysiological reports, along with a marked reduction in the amplitude of pairwise noise correlations.

Rhythmogenic neuronal networks, emergent leaders, and k-cores.

PubMed

Schwab, David J; Bruinsma, Robijn F; Feldman, Jack L; Levine, Alex J

2010-11-01

Neuronal network behavior results from a combination of the dynamics of individual neurons and the connectivity of the network that links them together. We study a simplified model, based on the proposal of Feldman and Del Negro (FDN) [Nat. Rev. Neurosci. 7, 232 (2006)], of the preBötzinger Complex, a small neuronal network that participates in the control of the mammalian breathing rhythm through periodic firing bursts. The dynamics of this randomly connected network of identical excitatory neurons differ from those of a uniformly connected one. Specifically, network connectivity determines the identity of emergent leader neurons that trigger the firing bursts. When neuronal desensitization is controlled by the number of input signals to the neurons (as proposed by FDN), the network's collective desensitization--required for successful burst termination--is mediated by k-core clusters of neurons.

Measuring multiple spike train synchrony.

PubMed

Kreuz, Thomas; Chicharro, Daniel; Andrzejak, Ralph G; Haas, Julie S; Abarbanel, Henry D I

2009-10-15

Measures of multiple spike train synchrony are essential in order to study issues such as spike timing reliability, network synchronization, and neuronal coding. These measures can broadly be divided in multivariate measures and averages over bivariate measures. One of the most recent bivariate approaches, the ISI-distance, employs the ratio of instantaneous interspike intervals (ISIs). In this study we propose two extensions of the ISI-distance, the straightforward averaged bivariate ISI-distance and the multivariate ISI-diversity based on the coefficient of variation. Like the original measure these extensions combine many properties desirable in applications to real data. In particular, they are parameter-free, time scale independent, and easy to visualize in a time-resolved manner, as we illustrate with in vitro recordings from a cortical neuron. Using a simulated network of Hindemarsh-Rose neurons as a controlled configuration we compare the performance of our methods in distinguishing different levels of multi-neuron spike train synchrony to the performance of six other previously published measures. We show and explain why the averaged bivariate measures perform better than the multivariate ones and why the multivariate ISI-diversity is the best performer among the multivariate methods. Finally, in a comparison against standard methods that rely on moving window estimates, we use single-unit monkey data to demonstrate the advantages of the instantaneous nature of our methods.

A Reevaluation of the Vestibulo-Ocular Reflex: New Ideas of its Purpose, Properties, Neural Substrate, and Disorders

NASA Technical Reports Server (NTRS)

Leigh, R. John; Brandt, Thomas

1992-01-01

Conventional views of the Vestibulo-Ocular Reflex (VOR) have emphasized testing with caloric stimuli and by passively rotating patients at low frequencies in a chair. The properties of the VOR tested under these conditions differ from the performance of this reflex during the natural function for which it evolved-locomotion. Only the VOR (and not visually mediated eye movements) can cope with the high-frequency angular and linear perturbations of the head that occur during locomotion; this is achieved by generating eye movements at short latency (less than 16 msec). Interpretation of vestibular testing is enhanced by the realization that, although the di- and trisynaptic components of the VOR are essential for this short-latency response, the overall accuracy and plasticity of the VOR depend upon a distributed, parallel network of neurons involving the vestibular nuclei. Neurons in this network variously encode inputs from the labyrinthine semicircular canals and otoliths, as well as from the visual and somatosensory systems. The central vestibular pathways branch to contact vestibular cortex (for perception) and the spinal cord (for control of posture). Thus, the vestibular nuclei basically coordinate the stabilization of gaze and posture, and contribute to the perception of verticality and self-motion. Consequently, brainstem disorders that disrupt the VOR cause not just only nystagmus, but also instability of posture (eg, increased fore-aft sway in patients with downbeat nystagmus) and disturbance of spatial orientation (eg, tilt of the subjective visual vertical in Wallenberg's syndrome).

Attention Increases Spike Count Correlations between Visual Cortical Areas.

PubMed

Ruff, Douglas A; Cohen, Marlene R

2016-07-13

Visual attention, which improves perception of attended locations or objects, has long been known to affect many aspects of the responses of neuronal populations in visual cortex. There are two nonmutually exclusive hypotheses concerning the neuronal mechanisms that underlie these perceptual improvements. The first hypothesis, that attention improves the information encoded by a population of neurons in a particular cortical area, has considerable physiological support. The second hypothesis is that attention improves perception by selectively communicating relevant visual information. This idea has been tested primarily by measuring interactions between neurons on very short timescales, which are mathematically nearly independent of neuronal interactions on longer timescales. We tested the hypothesis that attention changes the way visual information is communicated between cortical areas on longer timescales by recording simultaneously from neurons in primary visual cortex (V1) and the middle temporal area (MT) in rhesus monkeys. We used two independent and complementary approaches. Our correlative experiment showed that attention increases the trial-to-trial response variability that is shared between the two areas. In our causal experiment, we electrically microstimulated V1 and found that attention increased the effect of stimulation on MT responses. Together, our results suggest that attention affects both the way visual stimuli are encoded within a cortical area and the extent to which visual information is communicated between areas on behaviorally relevant timescales. Visual attention dramatically improves the perception of attended stimuli. Attention has long been thought to act by selecting relevant visual information for further processing. It has been hypothesized that this selection is accomplished by increasing communication between neurons that encode attended information in different cortical areas. We recorded simultaneously from neurons in primary visual cortex and the middle temporal area while rhesus monkeys performed an attention task. We found that attention increased shared variability between neurons in the two areas and that attention increased the effect of microstimulation in V1 on the firing rates of MT neurons. Our results provide support for the hypothesis that attention increases communication between neurons in different brain areas on behaviorally relevant timescales. Copyright © 2016 the authors 0270-6474/16/367523-12$15.00/0.

Attention Increases Spike Count Correlations between Visual Cortical Areas

PubMed Central

Cohen, Marlene R.

2016-01-01

Visual attention, which improves perception of attended locations or objects, has long been known to affect many aspects of the responses of neuronal populations in visual cortex. There are two nonmutually exclusive hypotheses concerning the neuronal mechanisms that underlie these perceptual improvements. The first hypothesis, that attention improves the information encoded by a population of neurons in a particular cortical area, has considerable physiological support. The second hypothesis is that attention improves perception by selectively communicating relevant visual information. This idea has been tested primarily by measuring interactions between neurons on very short timescales, which are mathematically nearly independent of neuronal interactions on longer timescales. We tested the hypothesis that attention changes the way visual information is communicated between cortical areas on longer timescales by recording simultaneously from neurons in primary visual cortex (V1) and the middle temporal area (MT) in rhesus monkeys. We used two independent and complementary approaches. Our correlative experiment showed that attention increases the trial-to-trial response variability that is shared between the two areas. In our causal experiment, we electrically microstimulated V1 and found that attention increased the effect of stimulation on MT responses. Together, our results suggest that attention affects both the way visual stimuli are encoded within a cortical area and the extent to which visual information is communicated between areas on behaviorally relevant timescales. SIGNIFICANCE STATEMENT Visual attention dramatically improves the perception of attended stimuli. Attention has long been thought to act by selecting relevant visual information for further processing. It has been hypothesized that this selection is accomplished by increasing communication between neurons that encode attended information in different cortical areas. We recorded simultaneously from neurons in primary visual cortex and the middle temporal area while rhesus monkeys performed an attention task. We found that attention increased shared variability between neurons in the two areas and that attention increased the effect of microstimulation in V1 on the firing rates of MT neurons. Our results provide support for the hypothesis that attention increases communication between neurons in different brain areas on behaviorally relevant timescales. PMID:27413161

NeuroTessMesh: A Tool for the Generation and Visualization of Neuron Meshes and Adaptive On-the-Fly Refinement.

PubMed

Garcia-Cantero, Juan J; Brito, Juan P; Mata, Susana; Bayona, Sofia; Pastor, Luis

2017-01-01

Gaining a better understanding of the human brain continues to be one of the greatest challenges for science, largely because of the overwhelming complexity of the brain and the difficulty of analyzing the features and behavior of dense neural networks. Regarding analysis, 3D visualization has proven to be a useful tool for the evaluation of complex systems. However, the large number of neurons in non-trivial circuits, together with their intricate geometry, makes the visualization of a neuronal scenario an extremely challenging computational problem. Previous work in this area dealt with the generation of 3D polygonal meshes that approximated the cells' overall anatomy but did not attempt to deal with the extremely high storage and computational cost required to manage a complex scene. This paper presents NeuroTessMesh, a tool specifically designed to cope with many of the problems associated with the visualization of neural circuits that are comprised of large numbers of cells. In addition, this method facilitates the recovery and visualization of the 3D geometry of cells included in databases, such as NeuroMorpho, and provides the tools needed to approximate missing information such as the soma's morphology. This method takes as its only input the available compact, yet incomplete, morphological tracings of the cells as acquired by neuroscientists. It uses a multiresolution approach that combines an initial, coarse mesh generation with subsequent on-the-fly adaptive mesh refinement stages using tessellation shaders. For the coarse mesh generation, a novel approach, based on the Finite Element Method, allows approximation of the 3D shape of the soma from its incomplete description. Subsequently, the adaptive refinement process performed in the graphic card generates meshes that provide good visual quality geometries at a reasonable computational cost, both in terms of memory and rendering time. All the described techniques have been integrated into NeuroTessMesh, available to the scientific community, to generate, visualize, and save the adaptive resolution meshes.

Visual Attention Model Based on Statistical Properties of Neuron Responses

PubMed Central

Duan, Haibin; Wang, Xiaohua

2015-01-01

Visual attention is a mechanism of the visual system that can select relevant objects from a specific scene. Interactions among neurons in multiple cortical areas are considered to be involved in attentional allocation. However, the characteristics of the encoded features and neuron responses in those attention related cortices are indefinite. Therefore, further investigations carried out in this study aim at demonstrating that unusual regions arousing more attention generally cause particular neuron responses. We suppose that visual saliency is obtained on the basis of neuron responses to contexts in natural scenes. A bottom-up visual attention model is proposed based on the self-information of neuron responses to test and verify the hypothesis. Four different color spaces are adopted and a novel entropy-based combination scheme is designed to make full use of color information. Valuable regions are highlighted while redundant backgrounds are suppressed in the saliency maps obtained by the proposed model. Comparative results reveal that the proposed model outperforms several state-of-the-art models. This study provides insights into the neuron responses based saliency detection and may underlie the neural mechanism of early visual cortices for bottom-up visual attention. PMID:25747859

Development of a computational model on the neural activity patterns of a visual working memory in a hierarchical feedforward Network

NASA Astrophysics Data System (ADS)

An, Soyoung; Choi, Woochul; Paik, Se-Bum

2015-11-01

Understanding the mechanism of information processing in the human brain remains a unique challenge because the nonlinear interactions between the neurons in the network are extremely complex and because controlling every relevant parameter during an experiment is difficult. Therefore, a simulation using simplified computational models may be an effective approach. In the present study, we developed a general model of neural networks that can simulate nonlinear activity patterns in the hierarchical structure of a neural network system. To test our model, we first examined whether our simulation could match the previously-observed nonlinear features of neural activity patterns. Next, we performed a psychophysics experiment for a simple visual working memory task to evaluate whether the model could predict the performance of human subjects. Our studies show that the model is capable of reproducing the relationship between memory load and performance and may contribute, in part, to our understanding of how the structure of neural circuits can determine the nonlinear neural activity patterns in the human brain.

Neuronvisio: A Graphical User Interface with 3D Capabilities for NEURON.

PubMed

Mattioni, Michele; Cohen, Uri; Le Novère, Nicolas

2012-01-01

The NEURON simulation environment is a commonly used tool to perform electrical simulation of neurons and neuronal networks. The NEURON User Interface, based on the now discontinued InterViews library, provides some limited facilities to explore models and to plot their simulation results. Other limitations include the inability to generate a three-dimensional visualization, no standard mean to save the results of simulations, or to store the model geometry within the results. Neuronvisio (http://neuronvisio.org) aims to address these deficiencies through a set of well designed python APIs and provides an improved UI, allowing users to explore and interact with the model. Neuronvisio also facilitates access to previously published models, allowing users to browse, download, and locally run NEURON models stored in ModelDB. Neuronvisio uses the matplotlib library to plot simulation results and uses the HDF standard format to store simulation results. Neuronvisio can be viewed as an extension of NEURON, facilitating typical user workflows such as model browsing, selection, download, compilation, and simulation. The 3D viewer simplifies the exploration of complex model structure, while matplotlib permits the plotting of high-quality graphs. The newly introduced ability of saving numerical results allows users to perform additional analysis on their previous simulations.

Putative pyramidal neurons and interneurons in the monkey parietal cortex make different contributions to the performance of a visual grouping task.

PubMed

Yokoi, Isao; Komatsu, Hidehiko

2010-09-01

Visual grouping of discrete elements is an important function for object recognition. We recently conducted an experiment to study neural correlates of visual grouping. We recorded neuronal activities while monkeys performed a grouping detection task in which they discriminated visual patterns composed of discrete dots arranged in a cross and detected targets in which dots with the same contrast were aligned horizontally or vertically. We found that some neurons in the lateral bank of the intraparietal sulcus exhibit activity related to visual grouping. In the present study, we analyzed how different types of neurons contribute to visual grouping. We classified the recorded neurons as putative pyramidal neurons or putative interneurons, depending on the duration of their action potentials. We found that putative pyramidal neurons exhibited selectivity for the orientation of the target, and this selectivity was enhanced by attention to a particular target orientation. By contrast, putative interneurons responded more strongly to the target stimuli than to the nontargets, regardless of the orientation of the target. These results suggest that different classes of parietal neurons contribute differently to the grouping of discrete elements.

Figure-ground segregation in a recurrent network architecture.

PubMed

Roelfsema, Pieter R; Lamme, Victor A F; Spekreijse, Henk; Bosch, Holger

2002-05-15

Here we propose a model of how the visual brain segregates textured scenes into figures and background. During texture segregation, locations where the properties of texture elements change abruptly are assigned to boundaries, whereas image regions that are relatively homogeneous are grouped together. Boundary detection and grouping of image regions require different connection schemes, which are accommodated in a single network architecture by implementing them in different layers. As a result, all units carry signals related to boundary detection as well as grouping of image regions, in accordance with cortical physiology. Boundaries yield an early enhancement of network responses, but at a later point, an entire figural region is grouped together, because units that respond to it are labeled with enhanced activity. The model predicts which image regions are preferentially perceived as figure or as background and reproduces the spatio-temporal profile of neuronal activity in the visual cortex during texture segregation in intact animals, as well as in animals with cortical lesions.

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

PubMed

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

2014-06-01

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

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.

Distribution of neurons in functional areas of the mouse cerebral cortex reveals quantitatively different cortical zones

PubMed Central

Herculano-Houzel, Suzana; Watson, Charles; Paxinos, George

2013-01-01

How are neurons distributed along the cortical surface and across functional areas? Here we use the isotropic fractionator (Herculano-Houzel and Lent, 2005) to analyze the distribution of neurons across the entire isocortex of the mouse, divided into 18 functional areas defined anatomically. We find that the number of neurons underneath a surface area (the N/A ratio) varies 4.5-fold across functional areas and neuronal density varies 3.2-fold. The face area of S1 contains the most neurons, followed by motor cortex and the primary visual cortex. Remarkably, while the distribution of neurons across functional areas does not accompany the distribution of surface area, it mirrors closely the distribution of cortical volumes—with the exception of the visual areas, which hold more neurons than expected for their volume. Across the non-visual cortex, the volume of individual functional areas is a shared linear function of their number of neurons, while in the visual areas, neuronal densities are much higher than in all other areas. In contrast, the 18 functional areas cluster into three different zones according to the relationship between the N/A ratio and cortical thickness and neuronal density: these three clusters can be called visual, sensory, and, possibly, associative. These findings are remarkably similar to those in the human cerebral cortex (Ribeiro et al., 2013) and suggest that, like the human cerebral cortex, the mouse cerebral cortex comprises two zones that differ in how neurons form the cortical volume, and three zones that differ in how neurons are distributed underneath the cortical surface, possibly in relation to local differences in connectivity through the white matter. Our results suggest that beyond the developmental divide into visual and non-visual cortex, functional areas initially share a common distribution of neurons along the parenchyma that become delimited into functional areas according to the pattern of connectivity established later. PMID:24155697

A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition

PubMed Central

Fu, Si-Yao; Yang, Guo-Sheng; Kuai, Xin-Kai

2012-01-01

In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people's facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism. PMID:23193391

A spiking neural network based cortex-like mechanism and application to facial expression recognition.

PubMed

Fu, Si-Yao; Yang, Guo-Sheng; Kuai, Xin-Kai

2012-01-01

In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people's facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism.

Efficient Training of Supervised Spiking Neural Network via Accurate Synaptic-Efficiency Adjustment Method.

PubMed

Xie, Xiurui; Qu, Hong; Yi, Zhang; Kurths, Jurgen

2017-06-01

The spiking neural network (SNN) is the third generation of neural networks and performs remarkably well in cognitive tasks, such as pattern recognition. The temporal neural encode mechanism found in biological hippocampus enables SNN to possess more powerful computation capability than networks with other encoding schemes. However, this temporal encoding approach requires neurons to process information serially on time, which reduces learning efficiency significantly. To keep the powerful computation capability of the temporal encoding mechanism and to overcome its low efficiency in the training of SNNs, a new training algorithm, the accurate synaptic-efficiency adjustment method is proposed in this paper. Inspired by the selective attention mechanism of the primate visual system, our algorithm selects only the target spike time as attention areas, and ignores voltage states of the untarget ones, resulting in a significant reduction of training time. Besides, our algorithm employs a cost function based on the voltage difference between the potential of the output neuron and the firing threshold of the SNN, instead of the traditional precise firing time distance. A normalized spike-timing-dependent-plasticity learning window is applied to assigning this error to different synapses for instructing their training. Comprehensive simulations are conducted to investigate the learning properties of our algorithm, with input neurons emitting both single spike and multiple spikes. Simulation results indicate that our algorithm possesses higher learning performance than the existing other methods and achieves the state-of-the-art efficiency in the training of SNN.

Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons.

PubMed

Burbank, Kendra S

2015-12-01

The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field's Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks.

Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons

PubMed Central

Burbank, Kendra S.

2015-01-01

The autoencoder algorithm is a simple but powerful unsupervised method for training neural networks. Autoencoder networks can learn sparse distributed codes similar to those seen in cortical sensory areas such as visual area V1, but they can also be stacked to learn increasingly abstract representations. Several computational neuroscience models of sensory areas, including Olshausen & Field’s Sparse Coding algorithm, can be seen as autoencoder variants, and autoencoders have seen extensive use in the machine learning community. Despite their power and versatility, autoencoders have been difficult to implement in a biologically realistic fashion. The challenges include their need to calculate differences between two neuronal activities and their requirement for learning rules which lead to identical changes at feedforward and feedback connections. Here, we study a biologically realistic network of integrate-and-fire neurons with anatomical connectivity and synaptic plasticity that closely matches that observed in cortical sensory areas. Our choice of synaptic plasticity rules is inspired by recent experimental and theoretical results suggesting that learning at feedback connections may have a different form from learning at feedforward connections, and our results depend critically on this novel choice of plasticity rules. Specifically, we propose that plasticity rules at feedforward versus feedback connections are temporally opposed versions of spike-timing dependent plasticity (STDP), leading to a symmetric combined rule we call Mirrored STDP (mSTDP). We show that with mSTDP, our network follows a learning rule that approximately minimizes an autoencoder loss function. When trained with whitened natural image patches, the learned synaptic weights resemble the receptive fields seen in V1. Our results use realistic synaptic plasticity rules to show that the powerful autoencoder learning algorithm could be within the reach of real biological networks. PMID:26633645

Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general purpose computing on graphics processing units.

PubMed

Igarashi, Jun; Shouno, Osamu; Fukai, Tomoki; Tsujino, Hiroshi

2011-11-01

Real-time simulation of a biologically realistic spiking neural network is necessary for evaluation of its capacity to interact with real environments. However, the real-time simulation of such a neural network is difficult due to its high computational costs that arise from two factors: (1) vast network size and (2) the complicated dynamics of biologically realistic neurons. In order to address these problems, mainly the latter, we chose to use general purpose computing on graphics processing units (GPGPUs) for simulation of such a neural network, taking advantage of the powerful computational capability of a graphics processing unit (GPU). As a target for real-time simulation, we used a model of the basal ganglia that has been developed according to electrophysiological and anatomical knowledge. The model consists of heterogeneous populations of 370 spiking model neurons, including computationally heavy conductance-based models, connected by 11,002 synapses. Simulation of the model has not yet been performed in real-time using a general computing server. By parallelization of the model on the NVIDIA Geforce GTX 280 GPU in data-parallel and task-parallel fashion, faster-than-real-time simulation was robustly realized with only one-third of the GPU's total computational resources. Furthermore, we used the GPU's full computational resources to perform faster-than-real-time simulation of three instances of the basal ganglia model; these instances consisted of 1100 neurons and 33,006 synapses and were synchronized at each calculation step. Finally, we developed software for simultaneous visualization of faster-than-real-time simulation output. These results suggest the potential power of GPGPU techniques in real-time simulation of realistic neural networks. Copyright © 2011 Elsevier Ltd. All rights reserved.

Regulating Cortical Oscillations in an Inhibition-Stabilized Network.

PubMed

Jadi, Monika P; Sejnowski, Terrence J

2014-04-21

Understanding the anatomical and functional architecture of the brain is essential for designing neurally inspired intelligent systems. Theoretical and empirical studies suggest a role for narrowband oscillations in shaping the functional architecture of the brain through their role in coding and communication of information. Such oscillations are ubiquitous signals in the electrical activity recorded from the brain. In the cortex, oscillations detected in the gamma range (30-80 Hz) are modulated by behavioral states and sensory features in complex ways. How is this regulation achieved? Although several underlying principles for the genesis of these oscillations have been proposed, a unifying account for their regulation has remained elusive. In a network of excitatory and inhibitory neurons operating in an inhibition-stabilized regime, we show that strongly superlinear responses of inhibitory neurons facilitate bidirectional regulation of oscillation frequency and power. In such a network, the balance of drives to the excitatory and inhibitory populations determines how the power and frequency of oscillations are modulated. The model accounts for the puzzling increase in their frequency with the salience of visual stimuli, and a decrease with their size. Oscillations in our model grow stronger as the mean firing level is reduced, accounting for the size dependence of visually evoked gamma rhythms, and suggesting a role for oscillations in improving the signal-to-noise ratio (SNR) of signals in the brain. Empirically testing such predictions is still challenging, and implementing the proposed coding and communication strategies in neuromorphic systems could assist in our understanding of the biological system.

Emergence of transformation-tolerant representations of visual objects in rat lateral extrastriate cortex

PubMed Central

Tafazoli, Sina; Safaai, Houman; De Franceschi, Gioia; Rosselli, Federica Bianca; Vanzella, Walter; Riggi, Margherita; Buffolo, Federica; Panzeri, Stefano; Zoccolan, Davide

2017-01-01

Rodents are emerging as increasingly popular models of visual functions. Yet, evidence that rodent visual cortex is capable of advanced visual processing, such as object recognition, is limited. Here we investigate how neurons located along the progression of extrastriate areas that, in the rat brain, run laterally to primary visual cortex, encode object information. We found a progressive functional specialization of neural responses along these areas, with: (1) a sharp reduction of the amount of low-level, energy-related visual information encoded by neuronal firing; and (2) a substantial increase in the ability of both single neurons and neuronal populations to support discrimination of visual objects under identity-preserving transformations (e.g., position and size changes). These findings strongly argue for the existence of a rat object-processing pathway, and point to the rodents as promising models to dissect the neuronal circuitry underlying transformation-tolerant recognition of visual objects. DOI: http://dx.doi.org/10.7554/eLife.22794.001 PMID:28395730

Potentiometric Dye Imaging for Pheochromocytoma and Cortical Neurons with a Novel Measurement System Using an Integrated Complementary Metal-Oxide-Semiconductor Imaging Device

NASA Astrophysics Data System (ADS)

Kobayashi, Takuma; Tagawa, Ayato; Noda, Toshihiko; Sasagawa, Kiyotaka; Tokuda, Takashi; Hatanaka, Yumiko; Tamura, Hideki; Ishikawa, Yasuyuki; Shiosaka, Sadao; Ohta, Jun

2010-11-01

The combination of optical imaging with voltage-sensitive dyes is a powerful tool for studying the spatiotemporal patterns of neural activity and understanding the neural networks of the brain. To visualize the potential status of multiple neurons simultaneously using a compact instrument with high density and a wide range, we present a novel measurement system using an implantable biomedical photonic LSI device with a red absorptive light filter for voltage-sensitive dye imaging (BpLSI-red). The BpLSI-red was developed for sensing fluorescence by the on-chip LSI, which was designed by using complementary metal-oxide-semiconductor (CMOS) technology. A micro-electro-mechanical system (MEMS) microfabrication technique was used to postprocess the CMOS sensor chip; light-emitting diodes (LEDs) were integrated for illumination and to enable long-term cell culture. Using the device, we succeeded in visualizing the membrane potential of 2000-3000 cells and the process of depolarization of pheochromocytoma cells (PC12 cells) and mouse cerebral cortical neurons in a primary culture with cellular resolution. Therefore, our measurement application enables the detection of multiple neural activities simultaneously.

Embodied learning of a generative neural model for biological motion perception and inference

PubMed Central

Schrodt, Fabian; Layher, Georg; Neumann, Heiko; Butz, Martin V.

2015-01-01

Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons. PMID:26217215

Clonal selection versus clonal cooperation: the integrated perception of immune objects

PubMed Central

Nataf, Serge

2016-01-01

Analogies between the immune and nervous systems were first envisioned by the immunologist Niels Jerne who introduced the concepts of antigen "recognition" and immune "memory". However, since then, it appears that only the cognitive immunology paradigm proposed by Irun Cohen, attempted to further theorize the immune system functions through the prism of neurosciences. The present paper is aimed at revisiting this analogy-based reasoning. In particular, a parallel is drawn between the brain pathways of visual perception and the processes allowing the global perception of an "immune object". Thus, in the visual system, distinct features of a visual object (shape, color, motion) are perceived separately by distinct neuronal populations during a primary perception task. The output signals generated during this first step instruct then an integrated perception task performed by other neuronal networks. Such a higher order perception step is by essence a cooperative task that is mandatory for the global perception of visual objects. Based on a re-interpretation of recent experimental data, it is suggested that similar general principles drive the integrated perception of immune objects in secondary lymphoid organs (SLOs). In this scheme, the four main categories of signals characterizing an immune object (antigenic, contextual, temporal and localization signals) are first perceived separately by distinct networks of immunocompetent cells.  Then, in a multitude of SLO niches, the output signals generated during this primary perception step are integrated by TH-cells at the single cell level. This process eventually generates a multitude of T-cell and B-cell clones that perform, at the scale of SLOs, an integrated perception of immune objects. Overall, this new framework proposes that integrated immune perception and, consequently, integrated immune responses, rely essentially on clonal cooperation rather than clonal selection. PMID:27830060

Embodied learning of a generative neural model for biological motion perception and inference.

PubMed

Schrodt, Fabian; Layher, Georg; Neumann, Heiko; Butz, Martin V

2015-01-01

Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be established, even if the biologically motion is visually perceived from a new vantage point. We introduce a learning artificial neural network model and evaluate it on full body motion tracking recordings. The model implements an embodied, predictive inference approach. It first learns to correlate and segment multimodal sensory streams of own bodily motion. In doing so, it becomes able to anticipate motion progression, to complete missing modal information, and to self-generate learned motion sequences. When biological motion of another person is observed, this self-knowledge is utilized to recognize similar motion patterns and predict their progress. Due to the relative encodings, the model shows strong robustness in recognition despite observing rather large varieties of body morphology and posture dynamics. By additionally equipping the model with the capability to rotate its visual frame of reference, it is able to deduce the visual perspective onto the observed person, establishing full consistency to the embodied self-motion encodings by means of active inference. In further support of its neuro-cognitive plausibility, we also model typical bistable perceptions when crucial depth information is missing. In sum, the introduced neural model proposes a solution to the problem of how the human brain may establish correspondence between observed bodily motion and its own motor system, thus offering a mechanism that supports the development of mirror neurons.

Simulation of Code Spectrum and Code Flow of Cultured Neuronal Networks.

PubMed

Tamura, Shinichi; Nishitani, Yoshi; Hosokawa, Chie; Miyoshi, Tomomitsu; Sawai, Hajime

2016-01-01

It has been shown that, in cultured neuronal networks on a multielectrode, pseudorandom-like sequences (codes) are detected, and they flow with some spatial decay constant. Each cultured neuronal network is characterized by a specific spectrum curve. That is, we may consider the spectrum curve as a "signature" of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution. In the present study, we used an integrate-and-fire model of neurons with intrinsic and instantaneous fluctuations of characteristics for performing a simulation of a code spectrum from multielectrodes on a 2D mesh neural network. We showed that it is possible to estimate the characteristics of neurons such as the distribution of number of neurons around each electrode and their refractory periods. Although this process is a reverse problem and theoretically the solutions are not sufficiently guaranteed, the parameters seem to be consistent with those of neurons. That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network. Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence.

Neural circuits underlying visually evoked escapes in larval zebrafish

PubMed Central

Dunn, Timothy W.; Gebhardt, Christoph; Naumann, Eva A.; Riegler, Clemens; Ahrens, Misha B.; Engert, Florian; Del Bene, Filippo

2015-01-01

SUMMARY Escape behaviors deliver organisms away from imminent catastrophe. Here, we characterize behavioral responses of freely swimming larval zebrafish to looming visual stimuli simulating predators. We report that the visual system alone can recruit lateralized, rapid escape motor programs, similar to those elicited by mechanosensory modalities. Two-photon calcium imaging of retino-recipient midbrain regions isolated the optic tectum as an important center processing looming stimuli, with ensemble activity encoding the critical image size determining escape latency. Furthermore, we describe activity in retinal ganglion cell terminals and superficial inhibitory interneurons in the tectum during looming and propose a model for how temporal dynamics in tectal periventricular neurons might arise from computations between these two fundamental constituents. Finally, laser ablations of hindbrain circuitry confirmed that visual and mechanosensory modalities share the same premotor output network. Together, we establish a circuit for the processing of aversive stimuli in the context of an innate visual behavior. PMID:26804997

Charles Bonnet Syndrome in a Patient With Right Medial Occipital Lobe Infarction: Epileptic or Deafferentation Phenomenon?

PubMed

Kumral, Emre; Uluakay, Arzu; Dönmez, İlknur

2015-07-01

Charles Bonnet syndrome (CBS) is an uncommon disorder characterized by complex and recurrent visual hallucinations in patients with visual pathway pathologic defects. To describe a patient who experienced complex visual hallucinations following infarction in the right occipital lobe and epileptic seizure who was diagnosed as having CBS. A 65-year-old man presented acute ischemic stroke caused by artery to artery embolism involving the right occipital lobe. Following ischemic stroke, complex visual hallucinations in the left visual field not associated with loss of consciousness or delusion developed in the patient. Hallucinations persisted for >1 month and during hallucination, no electrographic seizures were recorded through 24 hours of videoelectroencephalographic monitoring. CBS may develop in a patient with occipital lobe infarction following an embolic event. CBS associated with medial occipital lobe infarction and epilepsy may coexist and reflects the abnormal functioning of an integrated neuronal network.

Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures.

PubMed

Tibau, Elisenda; Valencia, Miguel; Soriano, Jordi

2013-01-01

Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks.

NeuroLines: A Subway Map Metaphor for Visualizing Nanoscale Neuronal Connectivity.

PubMed

Al-Awami, Ali K; Beyer, Johanna; Strobelt, Hendrik; Kasthuri, Narayanan; Lichtman, Jeff W; Pfister, Hanspeter; Hadwiger, Markus

2014-12-01

We present NeuroLines, a novel visualization technique designed for scalable detailed analysis of neuronal connectivity at the nanoscale level. The topology of 3D brain tissue data is abstracted into a multi-scale, relative distance-preserving subway map visualization that allows domain scientists to conduct an interactive analysis of neurons and their connectivity. Nanoscale connectomics aims at reverse-engineering the wiring of the brain. Reconstructing and analyzing the detailed connectivity of neurons and neurites (axons, dendrites) will be crucial for understanding the brain and its development and diseases. However, the enormous scale and complexity of nanoscale neuronal connectivity pose big challenges to existing visualization techniques in terms of scalability. NeuroLines offers a scalable visualization framework that can interactively render thousands of neurites, and that supports the detailed analysis of neuronal structures and their connectivity. We describe and analyze the design of NeuroLines based on two real-world use-cases of our collaborators in developmental neuroscience, and investigate its scalability to large-scale neuronal connectivity data.

Early-life exposure to caffeine affects the construction and activity of cortical networks in mice.

PubMed

Fazeli, Walid; Zappettini, Stefania; Marguet, Stephan Lawrence; Grendel, Jasper; Esclapez, Monique; Bernard, Christophe; Isbrandt, Dirk

2017-09-01

The consumption of psychoactive drugs during pregnancy can have deleterious effects on newborns. It remains unclear whether early-life exposure to caffeine, the most widely consumed psychoactive substance, alters brain development. We hypothesized that maternal caffeine ingestion during pregnancy and the early postnatal period in mice affects the construction and activity of cortical networks in offspring. To test this hypothesis, we focused on primary visual cortex (V1) as a model neocortical region. In a study design mimicking the daily consumption of approximately three cups of coffee during pregnancy in humans, caffeine was added to the drinking water of female mice and their offspring were compared to control offspring. Caffeine altered the construction of GABAergic neuronal networks in V1, as reflected by a reduced number of somatostatin-containing GABA neurons at postnatal days 6-7, with the remaining ones showing poorly developed dendritic arbors. These findings were accompanied by increased synaptic activity in vitro and elevated network activity in vivo in V1. Similarly, in vivo hippocampal network activity was altered from the neonatal period until adulthood. Finally, caffeine-exposed offspring showed increased seizure susceptibility in a hyperthermia-induced seizure model. In summary, our results indicate detrimental effects of developmental caffeine exposure on mouse brain development. Copyright © 2017 Elsevier Inc. All rights reserved.

Fractal Interfaces for Stimulating and Recording Neural Implants

NASA Astrophysics Data System (ADS)

Watterson, William James

From investigating movement in an insect to deciphering cognition in a human brain to treating Parkinson's disease, hearing loss, or even blindness, electronic implants are an essential tool for understanding the brain and treating neural diseases. Currently, the stimulating and recording resolution of these implants remains low. For instance, they can record all the neuron activity associated with movement in an insect, but are quite far from recording, at an individual neuron resolution, the large volumes of brain tissue associated with cognition. Likewise, there is remarkable success in the cochlear implant restoring hearing due to the relatively simple anatomy of the auditory nerves, but are failing to restore vision to the blind due to poor signal fidelity and transmission in stimulating the more complex anatomy of the visual nerves. The critically important research needed to improve the resolution of these implants is to optimize the neuron-electrode interface. This thesis explores geometrical and material modifications to both stimulating and recording electrodes which can improve the neuron-electrode interface. First, we introduce a fractal electrode geometry which radically improves the restored visual acuity achieved by retinal implants and leads to safe, long-term operation of the implant. Next, we demonstrate excellent neuron survival and neurite outgrowth on carbon nanotube electrodes, thus providing a safe biomaterial which forms a strong connection between the electrode and neurons. Additional preliminary evidence suggests carbon nanotubes patterned into a fractal geometry will provide further benefits in improving the electrode-neuron interface. Finally, we propose a novel implant based off field effect transistor technology which utilizes an interconnecting fractal network of semiconducting carbon nanotubes to record from thousands of neurons simutaneously at an individual neuron resolution. Taken together, these improvements have the potential to radically improve our understanding of the brain and our ability to restore function to patients of neural diseases.

Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems

NASA Astrophysics Data System (ADS)

Giulioni, Massimiliano; Corradi, Federico; Dante, Vittorio; Del Giudice, Paolo

2015-10-01

Neuromorphic chips embody computational principles operating in the nervous system, into microelectronic devices. In this domain it is important to identify computational primitives that theory and experiments suggest as generic and reusable cognitive elements. One such element is provided by attractor dynamics in recurrent networks. Point attractors are equilibrium states of the dynamics (up to fluctuations), determined by the synaptic structure of the network; a ‘basin’ of attraction comprises all initial states leading to a given attractor upon relaxation, hence making attractor dynamics suitable to implement robust associative memory. The initial network state is dictated by the stimulus, and relaxation to the attractor state implements the retrieval of the corresponding memorized prototypical pattern. In a previous work we demonstrated that a neuromorphic recurrent network of spiking neurons and suitably chosen, fixed synapses supports attractor dynamics. Here we focus on learning: activating on-chip synaptic plasticity and using a theory-driven strategy for choosing network parameters, we show that autonomous learning, following repeated presentation of simple visual stimuli, shapes a synaptic connectivity supporting stimulus-selective attractors. Associative memory develops on chip as the result of the coupled stimulus-driven neural activity and ensuing synaptic dynamics, with no artificial separation between learning and retrieval phases.

Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems.

PubMed

Giulioni, Massimiliano; Corradi, Federico; Dante, Vittorio; del Giudice, Paolo

2015-10-14

Neuromorphic chips embody computational principles operating in the nervous system, into microelectronic devices. In this domain it is important to identify computational primitives that theory and experiments suggest as generic and reusable cognitive elements. One such element is provided by attractor dynamics in recurrent networks. Point attractors are equilibrium states of the dynamics (up to fluctuations), determined by the synaptic structure of the network; a 'basin' of attraction comprises all initial states leading to a given attractor upon relaxation, hence making attractor dynamics suitable to implement robust associative memory. The initial network state is dictated by the stimulus, and relaxation to the attractor state implements the retrieval of the corresponding memorized prototypical pattern. In a previous work we demonstrated that a neuromorphic recurrent network of spiking neurons and suitably chosen, fixed synapses supports attractor dynamics. Here we focus on learning: activating on-chip synaptic plasticity and using a theory-driven strategy for choosing network parameters, we show that autonomous learning, following repeated presentation of simple visual stimuli, shapes a synaptic connectivity supporting stimulus-selective attractors. Associative memory develops on chip as the result of the coupled stimulus-driven neural activity and ensuing synaptic dynamics, with no artificial separation between learning and retrieval phases.

Spatial and Feature-Based Attention in a Layered Cortical Microcircuit Model

PubMed Central

Wagatsuma, Nobuhiko; Potjans, Tobias C.; Diesmann, Markus; Sakai, Ko; Fukai, Tomoki

2013-01-01

Directing attention to the spatial location or the distinguishing feature of a visual object modulates neuronal responses in the visual cortex and the stimulus discriminability of subjects. However, the spatial and feature-based modes of attention differently influence visual processing by changing the tuning properties of neurons. Intriguingly, neurons' tuning curves are modulated similarly across different visual areas under both these modes of attention. Here, we explored the mechanism underlying the effects of these two modes of visual attention on the orientation selectivity of visual cortical neurons. To do this, we developed a layered microcircuit model. This model describes multiple orientation-specific microcircuits sharing their receptive fields and consisting of layers 2/3, 4, 5, and 6. These microcircuits represent a functional grouping of cortical neurons and mutually interact via lateral inhibition and excitatory connections between groups with similar selectivity. The individual microcircuits receive bottom-up visual stimuli and top-down attention in different layers. A crucial assumption of the model is that feature-based attention activates orientation-specific microcircuits for the relevant feature selectively, whereas spatial attention activates all microcircuits homogeneously, irrespective of their orientation selectivity. Consequently, our model simultaneously accounts for the multiplicative scaling of neuronal responses in spatial attention and the additive modulations of orientation tuning curves in feature-based attention, which have been observed widely in various visual cortical areas. Simulations of the model predict contrasting differences between excitatory and inhibitory neurons in the two modes of attentional modulations. Furthermore, the model replicates the modulation of the psychophysical discriminability of visual stimuli in the presence of external noise. Our layered model with a biologically suggested laminar structure describes the basic circuit mechanism underlying the attention-mode specific modulations of neuronal responses and visual perception. PMID:24324628

Visual Working Memory Load-Related Changes in Neural Activity and Functional Connectivity

PubMed Central

Li, Ling; Zhang, Jin-Xiang; Jiang, Tao

2011-01-01

Background Visual working memory (VWM) helps us store visual information to prepare for subsequent behavior. The neuronal mechanisms for sustaining coherent visual information and the mechanisms for limited VWM capacity have remained uncharacterized. Although numerous studies have utilized behavioral accuracy, neural activity, and connectivity to explore the mechanism of VWM retention, little is known about the load-related changes in functional connectivity for hemi-field VWM retention. Methodology/Principal Findings In this study, we recorded electroencephalography (EEG) from 14 normal young adults while they performed a bilateral visual field memory task. Subjects had more rapid and accurate responses to the left visual field (LVF) memory condition. The difference in mean amplitude between the ipsilateral and contralateral event-related potential (ERP) at parietal-occipital electrodes in retention interval period was obtained with six different memory loads. Functional connectivity between 128 scalp regions was measured by EEG phase synchronization in the theta- (4–8 Hz), alpha- (8–12 Hz), beta- (12–32 Hz), and gamma- (32–40 Hz) frequency bands. The resulting matrices were converted to graphs, and mean degree, clustering coefficient and shortest path length was computed as a function of memory load. The results showed that brain networks of theta-, alpha-, beta-, and gamma- frequency bands were load-dependent and visual-field dependent. The networks of theta- and alpha- bands phase synchrony were most predominant in retention period for right visual field (RVF) WM than for LVF WM. Furthermore, only for RVF memory condition, brain network density of theta-band during the retention interval were linked to the delay of behavior reaction time, and the topological property of alpha-band network was negative correlation with behavior accuracy. Conclusions/Significance We suggest that the differences in theta- and alpha- bands between LVF and RVF conditions in functional connectivity and topological properties during retention period may result in the decline of behavioral performance in RVF task. PMID:21789253

Computational properties of networks of synchronous groups of spiking neurons.

PubMed

Dayhoff, Judith E

2007-09-01

We demonstrate a model in which synchronously firing ensembles of neurons are networked to produce computational results. Each ensemble is a group of biological integrate-and-fire spiking neurons, with probabilistic interconnections between groups. An analogy is drawn in which each individual processing unit of an artificial neural network corresponds to a neuronal group in a biological model. The activation value of a unit in the artificial neural network corresponds to the fraction of active neurons, synchronously firing, in a biological neuronal group. Weights of the artificial neural network correspond to the product of the interconnection density between groups, the group size of the presynaptic group, and the postsynaptic potential heights in the synchronous group model. All three of these parameters can modulate connection strengths between neuronal groups in the synchronous group models. We give an example of nonlinear classification (XOR) and a function approximation example in which the capability of the artificial neural network can be captured by a neural network model with biological integrate-and-fire neurons configured as a network of synchronously firing ensembles of such neurons. We point out that the general function approximation capability proven for feedforward artificial neural networks appears to be approximated by networks of neuronal groups that fire in synchrony, where the groups comprise integrate-and-fire neurons. We discuss the advantages of this type of model for biological systems, its possible learning mechanisms, and the associated timing relationships.

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.

Computational modeling of the neural representation of object shape in the primate ventral visual system

PubMed Central

Eguchi, Akihiro; Mender, Bedeho M. W.; Evans, Benjamin D.; Humphreys, Glyn W.; Stringer, Simon M.

2015-01-01

Neurons in successive stages of the primate ventral visual pathway encode the spatial structure of visual objects. In this paper, we investigate through computer simulation how these cell firing properties may develop through unsupervised visually-guided learning. Individual neurons in the model are shown to exploit statistical regularity and temporal continuity of the visual inputs during training to learn firing properties that are similar to neurons in V4 and TEO. Neurons in V4 encode the conformation of boundary contour elements at a particular position within an object regardless of the location of the object on the retina, while neurons in TEO integrate information from multiple boundary contour elements. This representation goes beyond mere object recognition, in which neurons simply respond to the presence of a whole object, but provides an essential foundation from which the brain is subsequently able to recognize the whole object. PMID:26300766

A visual pathway links brain structures active during magnetic compass orientation in migratory birds.

PubMed

Heyers, Dominik; Manns, Martina; Luksch, Harald; Güntürkün, Onur; Mouritsen, Henrik

2007-09-26

The magnetic compass of migratory birds has been suggested to be light-dependent. Retinal cryptochrome-expressing neurons and a forebrain region, "Cluster N", show high neuronal activity when night-migratory songbirds perform magnetic compass orientation. By combining neuronal tracing with behavioral experiments leading to sensory-driven gene expression of the neuronal activity marker ZENK during magnetic compass orientation, we demonstrate a functional neuronal connection between the retinal neurons and Cluster N via the visual thalamus. Thus, the two areas of the central nervous system being most active during magnetic compass orientation are part of an ascending visual processing stream, the thalamofugal pathway. Furthermore, Cluster N seems to be a specialized part of the visual wulst. These findings strongly support the hypothesis that migratory birds use their visual system to perceive the reference compass direction of the geomagnetic field and that migratory birds "see" the reference compass direction provided by the geomagnetic field.

Integrating Visualizations into Modeling NEST Simulations

PubMed Central

Nowke, Christian; Zielasko, Daniel; Weyers, Benjamin; Peyser, Alexander; Hentschel, Bernd; Kuhlen, Torsten W.

2015-01-01

Modeling large-scale spiking neural networks showing realistic biological behavior in their dynamics is a complex and tedious task. Since these networks consist of millions of interconnected neurons, their simulation produces an immense amount of data. In recent years it has become possible to simulate even larger networks. However, solutions to assist researchers in understanding the simulation's complex emergent behavior by means of visualization are still lacking. While developing tools to partially fill this gap, we encountered the challenge to integrate these tools easily into the neuroscientists' daily workflow. To understand what makes this so challenging, we looked into the workflows of our collaborators and analyzed how they use the visualizations to solve their daily problems. We identified two major issues: first, the analysis process can rapidly change focus which requires to switch the visualization tool that assists in the current problem domain. Second, because of the heterogeneous data that results from simulations, researchers want to relate data to investigate these effectively. Since a monolithic application model, processing and visualizing all data modalities and reflecting all combinations of possible workflows in a holistic way, is most likely impossible to develop and to maintain, a software architecture that offers specialized visualization tools that run simultaneously and can be linked together to reflect the current workflow, is a more feasible approach. To this end, we have developed a software architecture that allows neuroscientists to integrate visualization tools more closely into the modeling tasks. In addition, it forms the basis for semantic linking of different visualizations to reflect the current workflow. In this paper, we present this architecture and substantiate the usefulness of our approach by common use cases we encountered in our collaborative work. PMID:26733860

Network reconfiguration and neuronal plasticity in rhythm-generating networks.

PubMed

Koch, Henner; Garcia, Alfredo J; Ramirez, Jan-Marino

2011-12-01

Neuronal networks are highly plastic and reconfigure in a state-dependent manner. The plasticity at the network level emerges through multiple intrinsic and synaptic membrane properties that imbue neurons and their interactions with numerous nonlinear properties. These properties are continuously regulated by neuromodulators and homeostatic mechanisms that are critical to maintain not only network stability and also adapt networks in a short- and long-term manner to changes in behavioral, developmental, metabolic, and environmental conditions. This review provides concrete examples from neuronal networks in invertebrates and vertebrates, and illustrates that the concepts and rules that govern neuronal networks and behaviors are universal.

Neural adaptation accounts for the dynamic resizing of peripersonal space: evidence from a psychophysical-computational approach.

PubMed

Noel, Jean-Paul; Blanke, Olaf; Magosso, Elisa; Serino, Andrea

2018-06-01

Interactions between the body and the environment occur within the peripersonal space (PPS), the space immediately surrounding the body. The PPS is encoded by multisensory (audio-tactile, visual-tactile) neurons that possess receptive fields (RFs) anchored on the body and restricted in depth. The extension in depth of PPS neurons' RFs has been documented to change dynamically as a function of the velocity of incoming stimuli, but the underlying neural mechanisms are still unknown. Here, by integrating a psychophysical approach with neural network modeling, we propose a mechanistic explanation behind this inherent dynamic property of PPS. We psychophysically mapped the size of participant's peri-face and peri-trunk space as a function of the velocity of task-irrelevant approaching auditory stimuli. Findings indicated that the peri-trunk space was larger than the peri-face space, and, importantly, as for the neurophysiological delineation of RFs, both of these representations enlarged as the velocity of incoming sound increased. We propose a neural network model to mechanistically interpret these findings: the network includes reciprocal connections between unisensory areas and higher order multisensory neurons, and it implements neural adaptation to persistent stimulation as a mechanism sensitive to stimulus velocity. The network was capable of replicating the behavioral observations of PPS size remapping and relates behavioral proxies of PPS size to neurophysiological measures of multisensory neurons' RF size. We propose that a biologically plausible neural adaptation mechanism embedded within the network encoding for PPS can be responsible for the dynamic alterations in PPS size as a function of the velocity of incoming stimuli. NEW & NOTEWORTHY Interactions between body and environment occur within the peripersonal space (PPS). PPS neurons are highly dynamic, adapting online as a function of body-object interactions. The mechanistic underpinning PPS dynamic properties are unexplained. We demonstrate with a psychophysical approach that PPS enlarges as incoming stimulus velocity increases, efficiently preventing contacts with faster approaching objects. We present a neurocomputational model of multisensory PPS implementing neural adaptation to persistent stimulation to propose a neurophysiological mechanism underlying this effect.

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

PubMed Central

Rich, Scott; Booth, Victoria; Zochowski, Michal

2016-01-01

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

Modeling the functional genomics of autism using human neurons.

PubMed

Konopka, G; Wexler, E; Rosen, E; Mukamel, Z; Osborn, G E; Chen, L; Lu, D; Gao, F; Gao, K; Lowe, J K; Geschwind, D H

2012-02-01

Human neural progenitors from a variety of sources present new opportunities to model aspects of human neuropsychiatric disease in vitro. Such in vitro models provide the advantages of a human genetic background combined with rapid and easy manipulation, making them highly useful adjuncts to animal models. Here, we examined whether a human neuronal culture system could be utilized to assess the transcriptional program involved in human neural differentiation and to model some of the molecular features of a neurodevelopmental disorder, such as autism. Primary normal human neuronal progenitors (NHNPs) were differentiated into a post-mitotic neuronal state through addition of specific growth factors and whole-genome gene expression was examined throughout a time course of neuronal differentiation. After 4 weeks of differentiation, a significant number of genes associated with autism spectrum disorders (ASDs) are either induced or repressed. This includes the ASD susceptibility gene neurexin 1, which showed a distinct pattern from neurexin 3 in vitro, and which we validated in vivo in fetal human brain. Using weighted gene co-expression network analysis, we visualized the network structure of transcriptional regulation, demonstrating via this unbiased analysis that a significant number of ASD candidate genes are coordinately regulated during the differentiation process. As NHNPs are genetically tractable and manipulable, they can be used to study both the effects of mutations in multiple ASD candidate genes on neuronal differentiation and gene expression in combination with the effects of potential therapeutic molecules. These data also provide a step towards better understanding of the signaling pathways disrupted in ASD.

Aberrant within- and between-network connectivity of the mirror neuron system network and the mentalizing network in first episode psychosis.

PubMed

Choe, Eugenie; Lee, Tae Young; Kim, Minah; Hur, Ji-Won; Yoon, Youngwoo Bryan; Cho, Kang-Ik K; Kwon, Jun Soo

2018-03-26

It has been suggested that the mentalizing network and the mirror neuron system network support important social cognitive processes that are impaired in schizophrenia. However, the integrity and interaction of these two networks have not been sufficiently studied, and their effects on social cognition in schizophrenia remain unclear. Our study included 26 first-episode psychosis (FEP) patients and 26 healthy controls. We utilized resting-state functional connectivity to examine the a priori-defined mirror neuron system network and the mentalizing network and to assess the within- and between-network connectivities of the networks in FEP patients. We also assessed the correlation between resting-state functional connectivity measures and theory of mind performance. FEP patients showed altered within-network connectivity of the mirror neuron system network, and aberrant between-network connectivity between the mirror neuron system network and the mentalizing network. The within-network connectivity of the mirror neuron system network was noticeably correlated with theory of mind task performance in FEP patients. The integrity and interaction of the mirror neuron system network and the mentalizing network may be altered during the early stages of psychosis. Additionally, this study suggests that alterations in the integrity of the mirror neuron system network are highly related to deficient theory of mind in schizophrenia, and this problem would be present from the early stage of psychosis. Copyright © 2018 Elsevier B.V. All rights reserved.

Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation

PubMed Central

Nowke, Christian; Diaz-Pier, Sandra; Weyers, Benjamin; Hentschel, Bernd; Morrison, Abigail; Kuhlen, Torsten W.; Peyser, Alexander

2018-01-01

Simulation models in many scientific fields can have non-unique solutions or unique solutions which can be difficult to find. Moreover, in evolving systems, unique final state solutions can be reached by multiple different trajectories. Neuroscience is no exception. Often, neural network models are subject to parameter fitting to obtain desirable output comparable to experimental data. Parameter fitting without sufficient constraints and a systematic exploration of the possible solution space can lead to conclusions valid only around local minima or around non-minima. To address this issue, we have developed an interactive tool for visualizing and steering parameters in neural network simulation models. In this work, we focus particularly on connectivity generation, since finding suitable connectivity configurations for neural network models constitutes a complex parameter search scenario. The development of the tool has been guided by several use cases—the tool allows researchers to steer the parameters of the connectivity generation during the simulation, thus quickly growing networks composed of multiple populations with a targeted mean activity. The flexibility of the software allows scientists to explore other connectivity and neuron variables apart from the ones presented as use cases. With this tool, we enable an interactive exploration of parameter spaces and a better understanding of neural network models and grapple with the crucial problem of non-unique network solutions and trajectories. In addition, we observe a reduction in turn around times for the assessment of these models, due to interactive visualization while the simulation is computed. PMID:29937723

Computational exploration of neuron and neural network models in neurobiology.

PubMed

Prinz, Astrid A

2007-01-01

The electrical activity of individual neurons and neuronal networks is shaped by the complex interplay of a large number of non-linear processes, including the voltage-dependent gating of ion channels and the activation of synaptic receptors. These complex dynamics make it difficult to understand how individual neuron or network parameters-such as the number of ion channels of a given type in a neuron's membrane or the strength of a particular synapse-influence neural system function. Systematic exploration of cellular or network model parameter spaces by computational brute force can overcome this difficulty and generate comprehensive data sets that contain information about neuron or network behavior for many different combinations of parameters. Searching such data sets for parameter combinations that produce functional neuron or network output provides insights into how narrowly different neural system parameters have to be tuned to produce a desired behavior. This chapter describes the construction and analysis of databases of neuron or neuronal network models and describes some of the advantages and downsides of such exploration methods.

Uniting functional network topology and oscillations in the fronto-parietal single unit network of behaving primates.

PubMed

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

2016-08-15

The functional communication of neurons in cortical networks underlies higher cognitive processes. Yet, little is known about the organization of the single neuron network or its relationship to the synchronization processes that are essential for its formation. Here, we show that the functional single neuron network of three fronto-parietal areas during active behavior of macaque monkeys is highly complex. The network was closely connected (small-world) and consisted of functional modules spanning these areas. Surprisingly, the importance of different neurons to the network was highly heterogeneous with a small number of neurons contributing strongly to the network function (hubs), which were in turn strongly inter-connected (rich-club). Examination of the network synchronization revealed that the identified rich-club consisted of neurons that were synchronized in the beta or low frequency range, whereas other neurons were mostly non-oscillatory synchronized. Therefore, oscillatory synchrony may be a central communication mechanism for highly organized functional spiking networks.

Selective Attention to Visual Stimuli Using Auditory Distractors Is Altered in Alpha-9 Nicotinic Receptor Subunit Knock-Out Mice.

PubMed

Terreros, Gonzalo; Jorratt, Pascal; Aedo, Cristian; Elgoyhen, Ana Belén; Delano, Paul H

2016-07-06

During selective attention, subjects voluntarily focus their cognitive resources on a specific stimulus while ignoring others. Top-down filtering of peripheral sensory responses by higher structures of the brain has been proposed as one of the mechanisms responsible for selective attention. A prerequisite to accomplish top-down modulation of the activity of peripheral structures is the presence of corticofugal pathways. The mammalian auditory efferent system is a unique neural network that originates in the auditory cortex and projects to the cochlear receptor through the olivocochlear bundle, and it has been proposed to function as a top-down filter of peripheral auditory responses during attention to cross-modal stimuli. However, to date, there is no conclusive evidence of the involvement of olivocochlear neurons in selective attention paradigms. Here, we trained wild-type and α-9 nicotinic receptor subunit knock-out (KO) mice, which lack cholinergic transmission between medial olivocochlear neurons and outer hair cells, in a two-choice visual discrimination task and studied the behavioral consequences of adding different types of auditory distractors. In addition, we evaluated the effects of contralateral noise on auditory nerve responses as a measure of the individual strength of the olivocochlear reflex. We demonstrate that KO mice have a reduced olivocochlear reflex strength and perform poorly in a visual selective attention paradigm. These results confirm that an intact medial olivocochlear transmission aids in ignoring auditory distraction during selective attention to visual stimuli. The auditory efferent system is a neural network that originates in the auditory cortex and projects to the cochlear receptor through the olivocochlear system. It has been proposed to function as a top-down filter of peripheral auditory responses during attention to cross-modal stimuli. However, to date, there is no conclusive evidence of the involvement of olivocochlear neurons in selective attention paradigms. Here, we studied the behavioral consequences of adding different types of auditory distractors in a visual selective attention task in wild-type and α-9 nicotinic receptor knock-out (KO) mice. We demonstrate that KO mice perform poorly in the selective attention paradigm and that an intact medial olivocochlear transmission aids in ignoring auditory distractors during attention. Copyright © 2016 the authors 0270-6474/16/367198-12$15.00/0.

Evaluating the potential of using quantum dots for monitoring electrical signals in neurons

NASA Astrophysics Data System (ADS)

Efros, Alexander L.; Delehanty, James B.; Huston, Alan L.; Medintz, Igor L.; Barbic, Mladen; Harris, Timothy D.

2018-04-01

Success in the projects aimed at providing an advanced understanding of the brain is directly predicated on making critical advances in nanotechnology. This Perspective addresses the unique interface of neuroscience and nanomaterials by considering the foundational problem of sensing neuron membrane voltage and offers a potential solution that may be facilitated by a prototypical nanomaterial. Despite substantial improvements, the visualization of instantaneous voltage changes within individual neurons, whether in cell culture or in vivo, at both the single-cell and network level at high speed remains complex and problematic. The unique properties of semiconductor quantum dots (QDs) have made them powerful fluorophores for bioimaging. What is not widely appreciated, however, is that QD photoluminescence is exquisitely sensitive to proximal electric fields. This property should be suitable for sensing voltage changes that occur in the active neuronal membrane. Here, we examine the potential role of QDs in addressing the important challenge of real-time optical voltage imaging.

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.

The Drosophila Clock Neuron Network Features Diverse Coupling Modes and Requires Network-wide Coherence for Robust Circadian Rhythms.

PubMed

Yao, Zepeng; Bennett, Amelia J; Clem, Jenna L; Shafer, Orie T

2016-12-13

In animals, networks of clock neurons containing molecular clocks orchestrate daily rhythms in physiology and behavior. However, how various types of clock neurons communicate and coordinate with one another to produce coherent circadian rhythms is not well understood. Here, we investigate clock neuron coupling in the brain of Drosophila and demonstrate that the fly's various groups of clock neurons display unique and complex coupling relationships to core pacemaker neurons. Furthermore, we find that coordinated free-running rhythms require molecular clock synchrony not only within the well-characterized lateral clock neuron classes but also between lateral clock neurons and dorsal clock neurons. These results uncover unexpected patterns of coupling in the clock neuron network and reveal that robust free-running behavioral rhythms require a coherence of molecular oscillations across most of the fly's clock neuron network. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Relationship between inter-stimulus-intervals and intervals of autonomous activities in a neuronal network.

PubMed

Ito, Hidekatsu; Minoshima, Wataru; Kudoh, Suguru N

2015-08-01

To investigate relationships between neuronal network activity and electrical stimulus, we analyzed autonomous activity before and after electrical stimulus. Recordings of autonomous activity were performed using dissociated culture of rat hippocampal neurons on a multi-electrodes array (MEA) dish. Single stimulus and pared stimuli were applied to a cultured neuronal network. Single stimulus was applied every 1 min, and paired stimuli was performed by two sequential stimuli every 1 min. As a result, the patterns of synchronized activities of a neuronal network were changed after stimulus. Especially, long range synchronous activities were induced by paired stimuli. When 1 s inter-stimulus-intervals (ISI) and 1.5 s ISI paired stimuli are applied to a neuronal network, relatively long range synchronous activities expressed in case of 1.5 s ISI. Temporal synchronous activity of neuronal network is changed according to inter-stimulus-intervals (ISI) of electrical stimulus. In other words, dissociated neuronal network can maintain given information in temporal pattern and a certain type of an information maintenance mechanism was considered to be implemented in a semi-artificial dissociated neuronal network. The result is useful toward manipulation technology of neuronal activity in a brain system.

A computational model of the development of separate representations of facial identity and expression in the primate visual system.

PubMed

Tromans, James Matthew; Harris, Mitchell; Stringer, Simon Maitland

2011-01-01

Experimental studies have provided evidence that the visual processing areas of the primate brain represent facial identity and facial expression within different subpopulations of neurons. For example, in non-human primates there is evidence that cells within the inferior temporal gyrus (TE) respond primarily to facial identity, while cells within the superior temporal sulcus (STS) respond to facial expression. More recently, it has been found that the orbitofrontal cortex (OFC) of non-human primates contains some cells that respond exclusively to changes in facial identity, while other cells respond exclusively to facial expression. How might the primate visual system develop physically separate representations of facial identity and expression given that the visual system is always exposed to simultaneous combinations of facial identity and expression during learning? In this paper, a biologically plausible neural network model, VisNet, of the ventral visual pathway is trained on a set of carefully-designed cartoon faces with different identities and expressions. The VisNet model architecture is composed of a hierarchical series of four Self-Organising Maps (SOMs), with associative learning in the feedforward synaptic connections between successive layers. During learning, the network develops separate clusters of cells that respond exclusively to either facial identity or facial expression. We interpret the performance of the network in terms of the learning properties of SOMs, which are able to exploit the statistical indendependence between facial identity and expression.

Modularity in the Organization of Mouse Primary Visual Cortex

PubMed Central

Ji, Weiqing; Gămănuţ, Răzvan; Bista, Pawan; D’Souza, Rinaldo D.; Wang, Quanxin; Burkhalter, Andreas

2015-01-01

SUMMARY Layer 1 (L1) of primary visual cortex (V1) is the target of projections from many brain regions outside of V1. We found that inputs to the non-columnar mouse V1 from the dorsal lateral geniculate nucleus and feedback projections from multiple higher cortical areas to L1 are patchy. The patches are matched to a pattern of M2 muscarinic acetylcholine receptor expression at fixed locations of mouse, rat and monkey V1. Neurons in L2/3 aligned with M2-rich patches have high spatial acuity whereas cells in M2-poor zones exhibited high temporal acuity. Together M2+ and M2− zones form constant-size domains that are repeated across V1. Domains map subregions of the receptive field, such that multiple copies are contained within the point image. The results suggest that the modular network in mouse V1 selects spatiotemporally distinct clusters of neurons within the point image for top-down control and differential routing of inputs to cortical streams. PMID:26247867

Neuronal population coding of perceived and memorized visual features in the lateral prefrontal cortex

PubMed Central

Mendoza-Halliday, Diego; Martinez-Trujillo, Julio C.

2017-01-01

The primate lateral prefrontal cortex (LPFC) encodes visual stimulus features while they are perceived and while they are maintained in working memory. However, it remains unclear whether perceived and memorized features are encoded by the same or different neurons and population activity patterns. Here we record LPFC neuronal activity while monkeys perceive the motion direction of a stimulus that remains visually available, or memorize the direction if the stimulus disappears. We find neurons with a wide variety of combinations of coding strength for perceived and memorized directions: some neurons encode both to similar degrees while others preferentially or exclusively encode either one. Reading out the combined activity of all neurons, a machine-learning algorithm reliably decode the motion direction and determine whether it is perceived or memorized. Our results indicate that a functionally diverse population of LPFC neurons provides a substrate for discriminating between perceptual and mnemonic representations of visual features. PMID:28569756

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.

An integrative approach for analyzing hundreds of neurons in task performing mice using wide-field calcium imaging.

PubMed

Mohammed, Ali I; Gritton, Howard J; Tseng, Hua-an; Bucklin, Mark E; Yao, Zhaojie; Han, Xue

2016-02-08

Advances in neurotechnology have been integral to the investigation of neural circuit function in systems neuroscience. Recent improvements in high performance fluorescent sensors and scientific CMOS cameras enables optical imaging of neural networks at a much larger scale. While exciting technical advances demonstrate the potential of this technique, further improvement in data acquisition and analysis, especially those that allow effective processing of increasingly larger datasets, would greatly promote the application of optical imaging in systems neuroscience. Here we demonstrate the ability of wide-field imaging to capture the concurrent dynamic activity from hundreds to thousands of neurons over millimeters of brain tissue in behaving mice. This system allows the visualization of morphological details at a higher spatial resolution than has been previously achieved using similar functional imaging modalities. To analyze the expansive data sets, we developed software to facilitate rapid downstream data processing. Using this system, we show that a large fraction of anatomically distinct hippocampal neurons respond to discrete environmental stimuli associated with classical conditioning, and that the observed temporal dynamics of transient calcium signals are sufficient for exploring certain spatiotemporal features of large neural networks.

Quantitative distribution of GABA-immunoreactive neurons in cetacean visual cortex is similar to that in land mammals.

PubMed

Garey, L J; Takács, J; Revishchin, A V; Hámori, J

1989-04-24

Sections of the anterior portion of the visual cortex in the lateral gyrus of the Black Sea porpoise were studied to determine the neuronal architecture and numerical density, and the distribution of neurons immunoreactive to gamma-aminobutyric acid (GABA). Cytoarchitecture and neuronal density are similar to those described in another cetacean, the bottlenose dolphin. GABA-positive neurons are distributed through all layers of the visual cortex but are especially dense in layers II and III, and comprise some 20% of the total neuronal population in this part of the cortex. The distribution of GABA-positive neurons is similar to that found in land mammals.

Human embryonic stem cell-derived neurons adopt and regulate the activity of an established neural network

PubMed Central

Weick, Jason P.; Liu, Yan; Zhang, Su-Chun

2011-01-01

Whether hESC-derived neurons can fully integrate with and functionally regulate an existing neural network remains unknown. Here, we demonstrate that hESC-derived neurons receive unitary postsynaptic currents both in vitro and in vivo and adopt the rhythmic firing behavior of mouse cortical networks via synaptic integration. Optical stimulation of hESC-derived neurons expressing Channelrhodopsin-2 elicited both inhibitory and excitatory postsynaptic currents and triggered network bursting in mouse neurons. Furthermore, light stimulation of hESC-derived neurons transplanted to the hippocampus of adult mice triggered postsynaptic currents in host pyramidal neurons in acute slice preparations. Thus, hESC-derived neurons can participate in and modulate neural network activity through functional synaptic integration, suggesting they are capable of contributing to neural network information processing both in vitro and in vivo. PMID:22106298

Visual perception and imagery: a new molecular hypothesis.

PubMed

Bókkon, I

2009-05-01

Here, we put forward a redox molecular hypothesis about the natural biophysical substrate of visual perception and visual imagery. This hypothesis is based on the redox and bioluminescent processes of neuronal cells in retinotopically organized cytochrome oxidase-rich visual areas. Our hypothesis is in line with the functional roles of reactive oxygen and nitrogen species in living cells that are not part of haphazard process, but rather a very strict mechanism used in signaling pathways. We point out that there is a direct relationship between neuronal activity and the biophoton emission process in the brain. Electrical and biochemical processes in the brain represent sensory information from the external world. During encoding or retrieval of information, electrical signals of neurons can be converted into synchronized biophoton signals by bioluminescent radical and non-radical processes. Therefore, information in the brain appears not only as an electrical (chemical) signal but also as a regulated biophoton (weak optical) signal inside neurons. During visual perception, the topological distribution of photon stimuli on the retina is represented by electrical neuronal activity in retinotopically organized visual areas. These retinotopic electrical signals in visual neurons can be converted into synchronized biophoton signals by radical and non-radical processes in retinotopically organized mitochondria-rich areas. As a result, regulated bioluminescent biophotons can create intrinsic pictures (depictive representation) in retinotopically organized cytochrome oxidase-rich visual areas during visual imagery and visual perception. The long-term visual memory is interpreted as epigenetic information regulated by free radicals and redox processes. This hypothesis does not claim to solve the secret of consciousness, but proposes that the evolution of higher levels of complexity made the intrinsic picture representation of the external visual world possible by regulated redox and bioluminescent reactions in the visual system during visual perception and visual imagery.

Visual development in primates: Neural mechanisms and critical periods

PubMed Central

Kiorpes, Lynne

2015-01-01

Despite many decades of research into the development of visual cortex, it remains unclear what neural processes set limitations on the development of visual function and define its vulnerability to abnormal visual experience. This selected review examines the development of visual function and its neural correlates, and highlights the fact that in most cases receptive field properties of infant neurons are substantially more mature than infant visual function. One exception is temporal resolution, which can be accounted for by resolution of neurons at the level of the LGN. In terms of spatial vision, properties of single neurons alone are not sufficient to account for visual development. Different visual functions develop over different time courses. Their onset may be limited by the existence of neural response properties that support a given perceptual ability, but the subsequent time course of maturation to adult levels remains unexplained. Several examples are offered suggesting that taking account of weak signaling by infant neurons, correlated firing, and pooled responses of populations of neurons brings us closer to an understanding of the relationship between neural and behavioral development. PMID:25649764

Contribution of amygdalar and lateral hypothalamic neurons to visual information processing of food and nonfood in monkey.

PubMed

Ono, T; Tamura, R; Nishijo, H; Nakamura, K; Tabuchi, E

1989-02-01

Visual information processing was investigated in the inferotemporal cortical (ITCx)-amygdalar (AM)-lateral hypothalamic (LHA) axis which contributes to food-nonfood discrimination. Neuronal activity was recorded from monkey AM and LHA during discrimination of sensory stimuli including sight of food or nonfood. The task had four phases: control, visual, bar press, and ingestion. Of 710 AM neurons tested, 220 (31.0%) responded during visual phase: 48 to only visual stimulation, 13 (1.9%) to visual plus oral sensory stimulation, 142 (20.0%) to multimodal stimulation and 17 (2.4%) to one affectively significant item. Of 669 LHA neurons tested, 106 (15.8%) responded in the visual phase. Of 80 visual-related neurons tested systematically, 33 (41.2%) responded selectively to the sight of any object predicting the availability of reward, and 47 (58.8%) responded nondifferentially to both food and nonfood. Many of AM neuron responses were graded according to the degree of affective significance of sensory stimuli (sensory-affective association), but responses of LHA food responsive neurons did not depend on the kind of reward indicated by the sensory stimuli (stimulus-reinforcement association). Some AM and LHA food responses were modulated by extinction or reversal. Dynamic information processing in ITCx-AM-LHA axis was investigated by reversible deficits of bilateral ITCx or AM by cooling. ITCx cooling suppressed discrimination by vision responding AM neurons (8/17). AM cooling suppressed LHA responses to food (9/22). We suggest deep AM-LHA involvement in food-nonfood discrimination based on AM sensory-affective association and LHA stimulus-reinforcement association.

Cultured Neuronal Networks Express Complex Patterns of Activity and Morphological Memory

NASA Astrophysics Data System (ADS)

Raichman, Nadav; Rubinsky, Liel; Shein, Mark; Baruchi, Itay; Volman, Vladislav; Ben-Jacob, Eshel

The following sections are included: * Cultured Neuronal Networks * Recording the Network Activity * Network Engineering * The Formation of Synchronized Bursting Events * The Characterization of the SBEs * Highly-Active Neurons * Function-Form Relations in Cultured Networks * Analyzing the SBEs Motifs * Network Repertoire * Network under Hypothermia * Summary * Acknowledgments * References

Local activity determines functional connectivity in the resting human brain: a simultaneous FDG-PET/fMRI study.

PubMed

Riedl, Valentin; Bienkowska, Katarzyna; Strobel, Carola; Tahmasian, Masoud; Grimmer, Timo; Förster, Stefan; Friston, Karl J; Sorg, Christian; Drzezga, Alexander

2014-04-30

Over the last decade, synchronized resting-state fluctuations of blood oxygenation level-dependent (BOLD) signals between remote brain areas [so-called BOLD resting-state functional connectivity (rs-FC)] have gained enormous relevance in systems and clinical neuroscience. However, the neural underpinnings of rs-FC are still incompletely understood. Using simultaneous positron emission tomography/magnetic resonance imaging we here directly investigated the relationship between rs-FC and local neuronal activity in humans. Computational models suggest a mechanistic link between the dynamics of local neuronal activity and the functional coupling among distributed brain regions. Therefore, we hypothesized that the local activity (LA) of a region at rest determines its rs-FC. To test this hypothesis, we simultaneously measured both LA (glucose metabolism) and rs-FC (via synchronized BOLD fluctuations) during conditions of eyes closed or eyes open. During eyes open, LA increased in the visual system, and the salience network (i.e., cingulate and insular cortices) and the pattern of elevated LA coincided almost exactly with the spatial pattern of increased rs-FC. Specifically, the voxelwise regional profile of LA in these areas strongly correlated with the regional pattern of rs-FC among the same regions (e.g., LA in primary visual cortex accounts for ∼ 50%, and LA in anterior cingulate accounts for ∼ 20% of rs-FC with the visual system). These data provide the first direct evidence in humans that local neuronal activity determines BOLD FC at rest. Beyond its relevance for the neuronal basis of coherent BOLD signal fluctuations, our procedure may translate into clinical research particularly to investigate potentially aberrant links between local dynamics and remote functional coupling in patients with neuropsychiatric disorders.

Adaptation disrupts motion integration in the primate dorsal stream

PubMed Central

Patterson, Carlyn A.; Wissig, Stephanie C.; Kohn, Adam

2014-01-01

Summary Sensory systems adjust continuously to the environment. The effects of recent sensory experience—or adaptation—are typically assayed by recording in a relevant subcortical or cortical network. However, adaptation effects cannot be localized to a single, local network. Adjustments in one circuit or area will alter the input provided to others, with unclear consequences for computations implemented in the downstream circuit. Here we show that prolonged adaptation with drifting gratings, which alters responses in the early visual system, impedes the ability of area MT neurons to integrate motion signals in plaid stimuli. Perceptual experiments reveal a corresponding loss of plaid coherence. A simple computational model shows how the altered representation of motion signals in early cortex can derail integration in MT. Our results suggest that the effects of adaptation cascade through the visual system, derailing the downstream representation of distinct stimulus attributes. PMID:24507198

NeuroPhysics: Studying how neurons create the perception of space-time using Physics' tools and techniques

NASA Astrophysics Data System (ADS)

Dhingra, Shonali; Sandler, Roman; Rios, Rodrigo; Vuong, Cliff; Mehta, Mayank

All animals naturally perceive the abstract concept of space-time. A brain region called the Hippocampus is known to be important in creating these perceptions, but the underlying mechanisms are unknown. In our lab we employ several experimental and computational techniques from Physics to tackle this fundamental puzzle. Experimentally, we use ideas from Nanoscience and Materials Science to develop techniques to measure the activity of hippocampal neurons, in freely-behaving animals. Computationally, we develop models to study neuronal activity patterns, which are point processes that are highly stochastic and multidimensional. We then apply these techniques to collect and analyze neuronal signals from rodents while they're exploring space in Real World or Virtual Reality with various stimuli. Our findings show that under these conditions neuronal activity depends on various parameters, such as sensory cues including visual and auditory, and behavioral cues including, linear and angular, position and velocity. Further, neuronal networks create internally-generated rhythms, which influence perception of space and time. In totality, these results further our understanding of how the brain develops a cognitive map of our surrounding space, and keep track of time.

Three-dimensional neural cultures produce networks that mimic native brain activity.

PubMed

Bourke, Justin L; Quigley, Anita F; Duchi, Serena; O'Connell, Cathal D; Crook, Jeremy M; Wallace, Gordon G; Cook, Mark J; Kapsa, Robert M I

2018-02-01

Development of brain function is critically dependent on neuronal networks organized through three dimensions. Culture of central nervous system neurons has traditionally been limited to two dimensions, restricting growth patterns and network formation to a single plane. Here, with the use of multichannel extracellular microelectrode arrays, we demonstrate that neurons cultured in a true three-dimensional environment recapitulate native neuronal network formation and produce functional outcomes more akin to in vivo neuronal network activity. Copyright © 2017 John Wiley & Sons, Ltd.

Numerical simulation of coherent resonance in a model network of Rulkov neurons

NASA Astrophysics Data System (ADS)

Andreev, Andrey V.; Runnova, Anastasia E.; Pisarchik, Alexander N.

2018-04-01

In this paper we study the spiking behaviour of a neuronal network consisting of Rulkov elements. We find that the regularity of this behaviour maximizes at a certain level of environment noise. This effect referred to as coherence resonance is demonstrated in a random complex network of Rulkov neurons. An external stimulus added to some of neurons excites them, and then activates other neurons in the network. The network coherence is also maximized at the certain stimulus amplitude.

Ultra-Rapid serial visual presentation reveals dynamics of feedforward and feedback processes in the ventral visual pathway.

PubMed

Mohsenzadeh, Yalda; Qin, Sheng; Cichy, Radoslaw M; Pantazis, Dimitrios

2018-06-21

Human visual recognition activates a dense network of overlapping feedforward and recurrent neuronal processes, making it hard to disentangle processing in the feedforward from the feedback direction. Here, we used ultra-rapid serial visual presentation to suppress sustained activity that blurs the boundaries of processing steps, enabling us to resolve two distinct stages of processing with MEG multivariate pattern classification. The first processing stage was the rapid activation cascade of the bottom-up sweep, which terminated early as visual stimuli were presented at progressively faster rates. The second stage was the emergence of categorical information with peak latency that shifted later in time with progressively faster stimulus presentations, indexing time-consuming recurrent processing. Using MEG-fMRI fusion with representational similarity, we localized recurrent signals in early visual cortex. Together, our findings segregated an initial bottom-up sweep from subsequent feedback processing, and revealed the neural signature of increased recurrent processing demands for challenging viewing conditions. © 2018, Mohsenzadeh et al.

Tracking Plasticity: Effects of Long-Term Rehearsal in Expert Dancers Encoding Music to Movement

PubMed Central

Bar, Rachel J.; DeSouza, Joseph F. X.

2016-01-01

Our knowledge of neural plasticity suggests that neural networks show adaptation to environmental and intrinsic change. In particular, studies investigating the neuroplastic changes associated with learning and practicing motor tasks have shown that practicing such tasks results in an increase in neural activation in several specific brain regions. However, studies comparing experts and non-experts suggest that experts employ less neuronal activation than non-experts when performing a familiar motor task. Here, we aimed to determine the long-term changes in neural networks associated with learning a new dance in professional ballet dancers over 34 weeks. Subjects visualized dance movements to music while undergoing fMRI scanning at four time points over 34-weeks. Results demonstrated that initial learning and performance at seven weeks led to increases in activation in cortical regions during visualization compared to the first week. However, at 34 weeks, the cortical networks showed reduced activation compared to week seven. Specifically, motor learning and performance over the 34 weeks showed the typical inverted-U-shaped function of learning. Further, our result demonstrate that learning of a motor sequence of dance movements to music in the real world can be visualized by expert dancers using fMRI and capture highly significant modeled fits of the brain network variance of BOLD signals from early learning to expert level performance. PMID:26824475

Figure-ground mechanisms provide structure for selective attention.

PubMed

Qiu, Fangtu T; Sugihara, Tadashi; von der Heydt, Rüdiger

2007-11-01

Attention depends on figure-ground organization: figures draw attention, whereas shapes of the ground tend to be ignored. Recent research has revealed mechanisms for figure-ground organization in the visual cortex, but how these mechanisms relate to the attention process remains unclear. Here we show that the influences of figure-ground organization and volitional (top-down) attention converge in single neurons of area V2 in Macaca mulatta. Although we found assignment of border ownership for attended and for ignored figures, attentional modulation was stronger when the attended figure was located on the neuron's preferred side of border ownership. When the border between two overlapping figures was placed in the receptive field, responses depended on the side of attention, and enhancement was generally found on the neuron's preferred side of border ownership. This correlation suggests that the neural network that creates figure-ground organization also provides the interface for the top-down selection process.

Reciprocal Inhibitory Connections Within a Neural Network for Rotational Optic-Flow Processing

PubMed Central

Haag, Juergen; Borst, Alexander

2007-01-01

Neurons in the visual system of the blowfly have large receptive fields that are selective for specific optic flow fields. Here, we studied the neural mechanisms underlying flow–field selectivity in proximal Vertical System (VS)-cells, a particular subset of tangential cells in the fly. These cells have local preferred directions that are distributed such as to match the flow field occurring during a rotation of the fly. However, the neural circuitry leading to this selectivity is not fully understood. Through dual intracellular recordings from proximal VS cells and other tangential cells, we characterized the specific wiring between VS cells themselves and between proximal VS cells and horizontal sensitive tangential cells. We discovered a spiking neuron (Vi) involved in this circuitry that has not been described before. This neuron turned out to be connected to proximal VS cells via gap junctions and, in addition, it was found to be inhibitory onto VS1. PMID:18982122

Brain-wide neuronal dynamics during motor adaptation in zebrafish.

PubMed

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

2012-05-09

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

Uniting functional network topology and oscillations in the fronto-parietal single unit network of behaving primates

PubMed Central

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

2016-01-01

The functional communication of neurons in cortical networks underlies higher cognitive processes. Yet, little is known about the organization of the single neuron network or its relationship to the synchronization processes that are essential for its formation. Here, we show that the functional single neuron network of three fronto-parietal areas during active behavior of macaque monkeys is highly complex. The network was closely connected (small-world) and consisted of functional modules spanning these areas. Surprisingly, the importance of different neurons to the network was highly heterogeneous with a small number of neurons contributing strongly to the network function (hubs), which were in turn strongly inter-connected (rich-club). Examination of the network synchronization revealed that the identified rich-club consisted of neurons that were synchronized in the beta or low frequency range, whereas other neurons were mostly non-oscillatory synchronized. Therefore, oscillatory synchrony may be a central communication mechanism for highly organized functional spiking networks. DOI: http://dx.doi.org/10.7554/eLife.15719.001 PMID:27525488

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

Muscarinic Acetylcholine Receptors in Macaque V1 Are Most Frequently Expressed by Parvalbumin-Immunoreactive Neurons

PubMed Central

Disney, Anita A.; Aoki, Chiye

2010-01-01

Acetylcholine (ACh) is believed to underlie mechanisms of arousal and attention in mammals. ACh also has a demonstrated functional effect in visual cortex that is both diverse and profound. We have reported previously that cholinergic modulation in V1 of the macaque monkey is strongly targeted toward GABAergic interneurons. Here we examine the localization of m1 and m2 muscarinic receptor subtypes across subpopulations of GABAergic interneurons—identified by their expression of the calcium-binding proteins parvalbumin, calbindin, and calretinin—using dual-immunofluorescence confocal microscopy in V1 of the macaque monkey. In doing so, we find that the vast majority (87%) of parvalbumin-immunoreactive neurons express m1-type muscarinic ACh receptors. m1 receptors are also expressed by 60% of calbindin-immunoreactive neurons and 40% of calretinin-immunoreactive neurons. m2 AChRs, on the other hand, are expressed by only 31% of parvalbumin neurons, 23% of calbindin neurons, and 25% of calretinin neurons. Parvalbumin-immunoreactive cells comprise ≈75% of the inhibitory neuronal population in V1 and included in this large subpopulation are neurons known to veto and regulate the synchrony of principal cell spiking. Through the expression of m1 ACh receptors on nearly all of these PV cells, the cholinergic system avails itself of powerful control of information flow through and processing within the network of principal cells in the cortical circuit. PMID:18265004

Visual Receptive Field Heterogeneity and Functional Connectivity of Adjacent Neurons in Primate Frontoparietal Association Cortices.

PubMed

Viswanathan, Pooja; Nieder, Andreas

2017-09-13

The basic organization principles of the primary visual cortex (V1) are commonly assumed to also hold in the association cortex such that neurons within a cortical column share functional connectivity patterns and represent the same region of the visual field. We mapped the visual receptive fields (RFs) of neurons recorded at the same electrode in the ventral intraparietal area (VIP) and the lateral prefrontal cortex (PFC) of rhesus monkeys. We report that the spatial characteristics of visual RFs between adjacent neurons differed considerably, with increasing heterogeneity from VIP to PFC. In addition to RF incongruences, we found differential functional connectivity between putative inhibitory interneurons and pyramidal cells in PFC and VIP. These findings suggest that local RF topography vanishes with hierarchical distance from visual cortical input and argue for increasingly modified functional microcircuits in noncanonical association cortices that contrast V1. SIGNIFICANCE STATEMENT Our visual field is thought to be represented faithfully by the early visual brain areas; all the information from a certain region of the visual field is conveyed to neurons situated close together within a functionally defined cortical column. We examined this principle in the association areas, PFC, and ventral intraparietal area of rhesus monkeys and found that adjacent neurons represent markedly different areas of the visual field. This is the first demonstration of such noncanonical organization of these brain areas. Copyright © 2017 the authors 0270-6474/17/378919-10$15.00/0.

Functional neural substrates of posterior cortical atrophy patients.

PubMed

Shames, H; Raz, N; Levin, Netta

2015-07-01

Posterior cortical atrophy (PCA) is a neurodegenerative syndrome in which the most pronounced pathologic involvement is in the occipito-parietal visual regions. Herein, we aimed to better define the cortical reflection of this unique syndrome using a thorough battery of behavioral and functional MRI (fMRI) tests. Eight PCA patients underwent extensive testing to map their visual deficits. Assessments included visual functions associated with lower and higher components of the cortical hierarchy, as well as dorsal- and ventral-related cortical functions. fMRI was performed on five patients to examine the neuronal substrate of their visual functions. The PCA patient cohort exhibited stereopsis, saccadic eye movements and higher dorsal stream-related functional impairments, including simultant perception, image orientation, figure-from-ground segregation, closure and spatial orientation. In accordance with the behavioral findings, fMRI revealed intact activation in the ventral visual regions of face and object perception while more dorsal aspects of perception, including motion and gestalt perception, revealed impaired patterns of activity. In most of the patients, there was a lack of activity in the word form area, which is known to be linked to reading disorders. Finally, there was evidence of reduced cortical representation of the peripheral visual field, corresponding to the behaviorally assessed peripheral visual deficit. The findings are discussed in the context of networks extending from parietal regions, which mediate navigationally related processing, visually guided actions, eye movement control and working memory, suggesting that damage to these networks might explain the wide range of deficits in PCA patients.

Computational model of electrically coupled, intrinsically distinct pacemaker neurons.

PubMed

Soto-Treviño, Cristina; Rabbah, Pascale; Marder, Eve; Nadim, Farzan

2005-07-01

Electrical coupling between neurons with similar properties is often studied. Nonetheless, the role of electrical coupling between neurons with widely different intrinsic properties also occurs, but is less well understood. Inspired by the pacemaker group of the crustacean pyloric network, we developed a multicompartment, conductance-based model of a small network of intrinsically distinct, electrically coupled neurons. In the pyloric network, a small intrinsically bursting neuron, through gap junctions, drives 2 larger, tonically spiking neurons to reliably burst in-phase with it. Each model neuron has 2 compartments, one responsible for spike generation and the other for producing a slow, large-amplitude oscillation. We illustrate how these compartments interact and determine the dynamics of the model neurons. Our model captures the dynamic oscillation range measured from the isolated and coupled biological neurons. At the network level, we explore the range of coupling strengths for which synchronous bursting oscillations are possible. The spatial segregation of ionic currents significantly enhances the ability of the 2 neurons to burst synchronously, and the oscillation range of the model pacemaker network depends not only on the strength of the electrical synapse but also on the identity of the neuron receiving inputs. We also compare the activity of the electrically coupled, distinct neurons with that of a network of coupled identical bursting neurons. For small to moderate coupling strengths, the network of identical elements, when receiving asymmetrical inputs, can have a smaller dynamic range of oscillation than that of its constituent neurons in isolation.

Shaping Neuronal Network Activity by Presynaptic Mechanisms

PubMed Central

Ashery, Uri

2015-01-01

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

Connexin-Dependent Neuroglial Networking as a New Therapeutic Target.

PubMed

Charvériat, Mathieu; Naus, Christian C; Leybaert, Luc; Sáez, Juan C; Giaume, Christian

2017-01-01

Astrocytes and neurons dynamically interact during physiological processes, and it is now widely accepted that they are both organized in plastic and tightly regulated networks. Astrocytes are connected through connexin-based gap junction channels, with brain region specificities, and those networks modulate neuronal activities, such as those involved in sleep-wake cycle, cognitive, or sensory functions. Additionally, astrocyte domains have been involved in neurogenesis and neuronal differentiation during development; they participate in the "tripartite synapse" with both pre-synaptic and post-synaptic neurons by tuning down or up neuronal activities through the control of neuronal synaptic strength. Connexin-based hemichannels are also involved in those regulations of neuronal activities, however, this feature will not be considered in the present review. Furthermore, neuronal processes, transmitting electrical signals to chemical synapses, stringently control astroglial connexin expression, and channel functions. Long-range energy trafficking toward neurons through connexin-coupled astrocytes and plasticity of those networks are hence largely dependent on neuronal activity. Such reciprocal interactions between neurons and astrocyte networks involve neurotransmitters, cytokines, endogenous lipids, and peptides released by neurons but also other brain cell types, including microglial and endothelial cells. Over the past 10 years, knowledge about neuroglial interactions has widened and now includes effects of CNS-targeting drugs such as antidepressants, antipsychotics, psychostimulants, or sedatives drugs as potential modulators of connexin function and thus astrocyte networking activity. In physiological situations, neuroglial networking is consequently resulting from a two-way interaction between astrocyte gap junction-mediated networks and those made by neurons. As both cell types are modulated by CNS drugs we postulate that neuroglial networking may emerge as new therapeutic targets in neurological and psychiatric disorders.

Can simple rules control development of a pioneer vertebrate neuronal network generating behavior?

PubMed

Roberts, Alan; Conte, Deborah; Hull, Mike; Merrison-Hort, Robert; al Azad, Abul Kalam; Buhl, Edgar; Borisyuk, Roman; Soffe, Stephen R

2014-01-08

How do the pioneer networks in the axial core of the vertebrate nervous system first develop? Fundamental to understanding any full-scale neuronal network is knowledge of the constituent neurons, their properties, synaptic interconnections, and normal activity. Our novel strategy uses basic developmental rules to generate model networks that retain individual neuron and synapse resolution and are capable of reproducing correct, whole animal responses. We apply our developmental strategy to young Xenopus tadpoles, whose brainstem and spinal cord share a core vertebrate plan, but at a tractable complexity. Following detailed anatomical and physiological measurements to complete a descriptive library of each type of spinal neuron, we build models of their axon growth controlled by simple chemical gradients and physical barriers. By adding dendrites and allowing probabilistic formation of synaptic connections, we reconstruct network connectivity among up to 2000 neurons. When the resulting "network" is populated by model neurons and synapses, with properties based on physiology, it can respond to sensory stimulation by mimicking tadpole swimming behavior. This functioning model represents the most complete reconstruction of a vertebrate neuronal network that can reproduce the complex, rhythmic behavior of a whole animal. The findings validate our novel developmental strategy for generating realistic networks with individual neuron- and synapse-level resolution. We use it to demonstrate how early functional neuronal connectivity and behavior may in life result from simple developmental "rules," which lay out a scaffold for the vertebrate CNS without specific neuron-to-neuron recognition.

Constrained synaptic connectivity in functional mammalian neuronal networks grown on patterned surfaces.

PubMed

Wyart, Claire; Ybert, Christophe; Bourdieu, Laurent; Herr, Catherine; Prinz, Christelle; Chatenay, Didier

2002-06-30

The use of ordered neuronal networks in vitro is a promising approach to study the development and the activity of small neuronal assemblies. However, in previous attempts, sufficient growth control and physiological maturation of neurons could not be achieved. Here we describe an original protocol in which polylysine patterns confine the adhesion of cellular bodies to prescribed spots and the neuritic growth to thin lines. Hippocampal neurons in these networks are maintained healthy in serum free medium up to 5 weeks in vitro. Electrophysiology and immunochemistry show that neurons exhibit mature excitatory and inhibitory synapses and calcium imaging reveals spontaneous activity of neurons in isolated networks. We demonstrate that neurons in these geometrical networks form functional synapses preferentially to their first neighbors. We have, therefore, established a simple and robust protocol to constrain both the location of neuronal cell bodies and their pattern of connectivity. Moreover, the long term maintenance of the geometry and the physiology of the networks raises the possibility of new applications for systematic screening of pharmacological agents and for electronic to neuron devices.

Layer-specific chromatin accessibility landscapes reveal regulatory networks in adult mouse visual cortex

PubMed Central

Gray, Lucas T; Yao, Zizhen; Nguyen, Thuc Nghi; Kim, Tae Kyung; Zeng, Hongkui; Tasic, Bosiljka

2017-01-01

Mammalian cortex is a laminar structure, with each layer composed of a characteristic set of cell types with different morphological, electrophysiological, and connectional properties. Here, we define chromatin accessibility landscapes of major, layer-specific excitatory classes of neurons, and compare them to each other and to inhibitory cortical neurons using the Assay for Transposase-Accessible Chromatin with high-throughput sequencing (ATAC-seq). We identify a large number of layer-specific accessible sites, and significant association with genes that are expressed in specific cortical layers. Integration of these data with layer-specific transcriptomic profiles and transcription factor binding motifs enabled us to construct a regulatory network revealing potential key layer-specific regulators, including Cux1/2, Foxp2, Nfia, Pou3f2, and Rorb. This dataset is a valuable resource for identifying candidate layer-specific cis-regulatory elements in adult mouse cortex. DOI: http://dx.doi.org/10.7554/eLife.21883.001 PMID:28112643

TOPICAL REVIEW: Prosthetic interfaces with the visual system: biological issues

NASA Astrophysics Data System (ADS)

Cohen, Ethan D.

2007-06-01

The design of effective visual prostheses for the blind represents a challenge for biomedical engineers and neuroscientists. Significant progress has been made in the miniaturization and processing power of prosthesis electronics; however development lags in the design and construction of effective machine brain interfaces with visual system neurons. This review summarizes what has been learned about stimulating neurons in the human and primate retina, lateral geniculate nucleus and visual cortex. Each level of the visual system presents unique challenges for neural interface design. Blind patients with the retinal degenerative disease retinitis pigmentosa (RP) are a common population in clinical trials of visual prostheses. The visual performance abilities of normals and RP patients are compared. To generate pattern vision in blind patients, the visual prosthetic interface must effectively stimulate the retinotopically organized neurons in the central visual field to elicit patterned visual percepts. The development of more biologically compatible methods of stimulating visual system neurons is critical to the development of finer spatial percepts. Prosthesis electrode arrays need to adapt to different optimal stimulus locations, stimulus patterns, and patient disease states.

Uncovering Neuronal Networks Defined by Consistent Between-Neuron Spike Timing from Neuronal Spike Recordings

PubMed Central

2018-01-01

Abstract It is widely assumed that distributed neuronal networks are fundamental to the functioning of the brain. Consistent spike timing between neurons is thought to be one of the key principles for the formation of these networks. This can involve synchronous spiking or spiking with time delays, forming spike sequences when the order of spiking is consistent. Finding networks defined by their sequence of time-shifted spikes, denoted here as spike timing networks, is a tremendous challenge. As neurons can participate in multiple spike sequences at multiple between-spike time delays, the possible complexity of networks is prohibitively large. We present a novel approach that is capable of (1) extracting spike timing networks regardless of their sequence complexity, and (2) that describes their spiking sequences with high temporal precision. We achieve this by decomposing frequency-transformed neuronal spiking into separate networks, characterizing each network’s spike sequence by a time delay per neuron, forming a spike sequence timeline. These networks provide a detailed template for an investigation of the experimental relevance of their spike sequences. Using simulated spike timing networks, we show network extraction is robust to spiking noise, spike timing jitter, and partial occurrences of the involved spike sequences. Using rat multineuron recordings, we demonstrate the approach is capable of revealing real spike timing networks with sub-millisecond temporal precision. By uncovering spike timing networks, the prevalence, structure, and function of complex spike sequences can be investigated in greater detail, allowing us to gain a better understanding of their role in neuronal functioning. PMID:29789811

Linking express saccade occurance to stimulus properties and sensorimotor integration in the superior colliculus.

PubMed

Marino, Robert A; Levy, Ron; Munoz, Douglas P

2015-08-01

Express saccades represent the fastest possible eye movements to visual targets with reaction times that approach minimum sensory-motor conduction delays. Previous work in monkeys has identified two specific neural signals in the superior colliculus (SC: a midbrain sensorimotor integration structure involved in gaze control) that are required to execute express saccades: 1) previsual activity consisting of a low-frequency increase in action potentials in sensory-motor neurons immediately before the arrival of a visual response; and 2) a transient visual-sensory response consisting of a high-frequency burst of action potentials in visually responsive neurons resulting from the appearance of a visual target stimulus. To better understand how these two neural signals interact to produce express saccades, we manipulated the arrival time and magnitude of visual responses in the SC by altering target luminance and we examined the corresponding influences on SC activity and express saccade generation. We recorded from saccade neurons with visual-, motor-, and previsual-related activity in the SC of monkeys performing the gap saccade task while target luminance was systematically varied between 0.001 and 42.5 cd/m(2) against a black background (∼0.0001 cd/m(2)). Our results demonstrated that 1) express saccade latencies were linked directly to the arrival time in the SC of visual responses produced by abruptly appearing visual stimuli; 2) express saccades were generated toward both dim and bright targets whenever sufficient previsual activity was present; and 3) target luminance altered the likelihood of producing an express saccade. When an express saccade was generated, visuomotor neurons increased their activity immediately before the arrival of the visual response in the SC and saccade initiation. Furthermore, the visual and motor responses of visuomotor neurons merged into a single burst of action potentials, while the visual response of visual-only neurons was unaffected. A linear combination model was used to test which SC signals best predicted the likelihood of producing an express saccade. In addition to visual response magnitude and previsual activity of saccade neurons, the model identified presaccadic activity (activity occurring during the 30-ms epoch immediately before saccade initiation) as a third important signal for predicting express saccades. We conclude that express saccades can be predicted by visual, previsual, and presaccadic signals recorded from visuomotor neurons in the intermediate layers of the SC. Copyright © 2015 the American Physiological Society.

Linking express saccade occurance to stimulus properties and sensorimotor integration in the superior colliculus

PubMed Central

Levy, Ron; Munoz, Douglas P.

2015-01-01

Express saccades represent the fastest possible eye movements to visual targets with reaction times that approach minimum sensory-motor conduction delays. Previous work in monkeys has identified two specific neural signals in the superior colliculus (SC: a midbrain sensorimotor integration structure involved in gaze control) that are required to execute express saccades: 1) previsual activity consisting of a low-frequency increase in action potentials in sensory-motor neurons immediately before the arrival of a visual response; and 2) a transient visual-sensory response consisting of a high-frequency burst of action potentials in visually responsive neurons resulting from the appearance of a visual target stimulus. To better understand how these two neural signals interact to produce express saccades, we manipulated the arrival time and magnitude of visual responses in the SC by altering target luminance and we examined the corresponding influences on SC activity and express saccade generation. We recorded from saccade neurons with visual-, motor-, and previsual-related activity in the SC of monkeys performing the gap saccade task while target luminance was systematically varied between 0.001 and 42.5 cd/m2 against a black background (∼0.0001 cd/m2). Our results demonstrated that 1) express saccade latencies were linked directly to the arrival time in the SC of visual responses produced by abruptly appearing visual stimuli; 2) express saccades were generated toward both dim and bright targets whenever sufficient previsual activity was present; and 3) target luminance altered the likelihood of producing an express saccade. When an express saccade was generated, visuomotor neurons increased their activity immediately before the arrival of the visual response in the SC and saccade initiation. Furthermore, the visual and motor responses of visuomotor neurons merged into a single burst of action potentials, while the visual response of visual-only neurons was unaffected. A linear combination model was used to test which SC signals best predicted the likelihood of producing an express saccade. In addition to visual response magnitude and previsual activity of saccade neurons, the model identified presaccadic activity (activity occurring during the 30-ms epoch immediately before saccade initiation) as a third important signal for predicting express saccades. We conclude that express saccades can be predicted by visual, previsual, and presaccadic signals recorded from visuomotor neurons in the intermediate layers of the SC. PMID:26063770

Most superficial sublamina of rat superior colliculus: neuronal response properties and correlates with perceptual figure-ground segregation.

PubMed

Girman, S V; Lund, R D

2007-07-01

The uppermost layer (stratum griseum superficiale, SGS) of the superior colliculus (SC) provides an important gateway from the retina to the visual extrastriate and visuomotor systems. The majority of attention has been given to the role of this "visual" SC in saccade generation and target selection and it is generally considered to be less important in visual perception. We have found, however, that in the rat SGS1, the most superficial division of the SGS, the neurons perform very sophisticated analysis of visual information. First, in studying their responses with a variety of flashing stimuli we found that the neurons respond not to brightness changes per se, but to the appearance and/or disappearance of visual shapes in their receptive fields (RFs). Contrary to conventional RFs of neurons at the early stages of visual processing, the RFs in SGS1 cannot be described in terms of fixed spatial distribution of excitatory and inhibitory inputs. Second, SGS1 neurons showed robust orientation tuning to drifting gratings and orientation-specific modulation of the center response from surround. These are features previously seen only in visual cortical neurons and are considered to be involved in "contour" perception and figure-ground segregation. Third, responses of SGS1 neurons showed complex dynamics; typically the response tuning became progressively sharpened with repetitive grating periods. We conclude that SGS1 neurons are involved in considerably more complex analysis of retinal input than was previously thought. SGS1 may participate in early stages of figure-ground segregation and have a role in low-resolution nonconscious vision as encountered after visual decortication.

A Simple Network Architecture Accounts for Diverse Reward Time Responses in Primary Visual Cortex.

PubMed

Huertas, Marco A; Hussain Shuler, Marshall G; Shouval, Harel Z

2015-09-16

Many actions performed by animals and humans depend on an ability to learn, estimate, and produce temporal intervals of behavioral relevance. Exemplifying such learning of cued expectancies is the observation of reward-timing activity in the primary visual cortex (V1) of rodents, wherein neural responses to visual cues come to predict the time of future reward as behaviorally experienced in the past. These reward-timing responses exhibit significant heterogeneity in at least three qualitatively distinct classes: sustained increase or sustained decrease in firing rate until the time of expected reward, and a class of cells that reach a peak in firing at the expected delay. We elaborate upon our existing model by including inhibitory and excitatory units while imposing simple connectivity rules to demonstrate what role these inhibitory elements and the simple architectures play in sculpting the response dynamics of the network. We find that simply adding inhibition is not sufficient for obtaining the different distinct response classes, and that a broad distribution of inhibitory projections is necessary for obtaining peak-type responses. Furthermore, although changes in connection strength that modulate the effects of inhibition onto excitatory units have a strong impact on the firing rate profile of these peaked responses, the network exhibits robustness in its overall ability to predict the expected time of reward. Finally, we demonstrate how the magnitude of expected reward can be encoded at the expected delay in the network and how peaked responses express this reward expectancy. Heterogeneity in single-neuron responses is a common feature of neuronal systems, although sometimes, in theoretical approaches, it is treated as a nuisance and seldom considered as conveying a different aspect of a signal. In this study, we focus on the heterogeneous responses in the primary visual cortex of rodents trained with a predictable delayed reward time. We describe under what conditions this heterogeneity can arise by self-organization, and what information it can convey. This study, while focusing on a specific system, provides insight onto how heterogeneity can arise in general while also shedding light onto mechanisms of reinforcement learning using realistic biological assumptions. Copyright © 2015 the authors 0270-6474/15/3512659-14$15.00/0.

Computational model for perception of objects and motions.

PubMed

Yang, WenLu; Zhang, LiQing; Ma, LiBo

2008-06-01

Perception of objects and motions in the visual scene is one of the basic problems in the visual system. There exist 'What' and 'Where' pathways in the superior visual cortex, starting from the simple cells in the primary visual cortex. The former is able to perceive objects such as forms, color, and texture, and the latter perceives 'where', for example, velocity and direction of spatial movement of objects. This paper explores brain-like computational architectures of visual information processing. We propose a visual perceptual model and computational mechanism for training the perceptual model. The computational model is a three-layer network. The first layer is the input layer which is used to receive the stimuli from natural environments. The second layer is designed for representing the internal neural information. The connections between the first layer and the second layer, called the receptive fields of neurons, are self-adaptively learned based on principle of sparse neural representation. To this end, we introduce Kullback-Leibler divergence as the measure of independence between neural responses and derive the learning algorithm based on minimizing the cost function. The proposed algorithm is applied to train the basis functions, namely receptive fields, which are localized, oriented, and bandpassed. The resultant receptive fields of neurons in the second layer have the characteristics resembling that of simple cells in the primary visual cortex. Based on these basis functions, we further construct the third layer for perception of what and where in the superior visual cortex. The proposed model is able to perceive objects and their motions with a high accuracy and strong robustness against additive noise. Computer simulation results in the final section show the feasibility of the proposed perceptual model and high efficiency of the learning algorithm.

Sensory Regulation of Network Components Underlying Ciliary Locomotion in Hermissenda

PubMed Central

Crow, Terry; Tian, Lian-Ming

2008-01-01

Ciliary locomotion in the nudibranch mollusk Hermissenda is modulated by the visual and graviceptive systems. Components of the neural network mediating ciliary locomotion have been identified including aggregates of polysensory interneurons that receive monosynaptic input from identified photoreceptors and efferent neurons that activate cilia. Illumination produces an inhibition of type Ii (off-cell) spike activity, excitation of type Ie (on-cell) spike activity, decreased spike activity in type IIIi inhibitory interneurons, and increased spike activity of ciliary efferent neurons. Here we show that pairs of type Ii interneurons and pairs of type Ie interneurons are electrically coupled. Neither electrical coupling or synaptic connections were observed between Ie and Ii interneurons. Coupling is effective in synchronizing dark-adapted spontaneous firing between pairs of Ie and pairs of Ii interneurons. Out-of-phase burst activity, occasionally observed in dark-adapted and light-adapted pairs of Ie and Ii interneurons, suggests that they receive synaptic input from a common presynaptic source or sources. Rhythmic activity is typically not a characteristic of dark-adapted, light-adapted, or light-evoked firing of type I interneurons. However, burst activity in Ie and Ii interneurons may be elicited by electrical stimulation of pedal nerves or generated at the offset of light. Our results indicate that type I interneurons can support the generation of both rhythmic activity and changes in tonic firing depending on sensory input. This suggests that the neural network supporting ciliary locomotion may be multifunctional. However, consistent with the nonmuscular and nonrhythmic characteristics of visually modulated ciliary locomotion, type I interneurons exhibit changes in tonic activity evoked by illumination. PMID:18768639

Simplicity and efficiency of integrate-and-fire neuron models.

PubMed

Plesser, Hans E; Diesmann, Markus

2009-02-01

Lovelace and Cios (2008) recently proposed a very simple spiking neuron (VSSN) model for simulations of large neuronal networks as an efficient replacement for the integrate-and-fire neuron model. We argue that the VSSN model falls behind key advances in neuronal network modeling over the past 20 years, in particular, techniques that permit simulators to compute the state of the neuron without repeated summation over the history of input spikes and to integrate the subthreshold dynamics exactly. State-of-the-art solvers for networks of integrate-and-fire model neurons are substantially more efficient than the VSSN simulator and allow routine simulations of networks of some 10(5) neurons and 10(9) connections on moderate computer clusters.

Neurons in the pigeon caudolateral nidopallium differentiate Pavlovian conditioned stimuli but not their associated reward value in a sign-tracking paradigm

PubMed Central

Kasties, Nils; Starosta, Sarah; Güntürkün, Onur; Stüttgen, Maik C.

2016-01-01

Animals exploit visual information to identify objects, form stimulus-reward associations, and prepare appropriate behavioral responses. The nidopallium caudolaterale (NCL), an associative region of the avian endbrain, contains neurons exhibiting prominent response modulation during presentation of reward-predicting visual stimuli, but it is unclear whether neural activity represents valuation signals, stimulus properties, or sensorimotor contingencies. To test the hypothesis that NCL neurons represent stimulus value, we subjected pigeons to a Pavlovian sign-tracking paradigm in which visual cues predicted rewards differing in magnitude (large vs. small) and delay to presentation (short vs. long). Subjects’ strength of conditioned responding to visual cues reliably differentiated between predicted reward types and thus indexed valuation. The majority of NCL neurons discriminated between visual cues, with discriminability peaking shortly after stimulus onset and being maintained at lower levels throughout the stimulus presentation period. However, while some cells’ firing rates correlated with reward value, such neurons were not more frequent than expected by chance. Instead, neurons formed discernible clusters which differed in their preferred visual cue. We propose that this activity pattern constitutes a prerequisite for using visual information in more complex situations e.g. requiring value-based choices. PMID:27762287

Recurrent network dynamics reconciles visual motion segmentation and integration.

PubMed

Medathati, N V Kartheek; Rankin, James; Meso, Andrew I; Kornprobst, Pierre; Masson, Guillaume S

2017-09-12

In sensory systems, a range of computational rules are presumed to be implemented by neuronal subpopulations with different tuning functions. For instance, in primate cortical area MT, different classes of direction-selective cells have been identified and related either to motion integration, segmentation or transparency. Still, how such different tuning properties are constructed is unclear. The dominant theoretical viewpoint based on a linear-nonlinear feed-forward cascade does not account for their complex temporal dynamics and their versatility when facing different input statistics. Here, we demonstrate that a recurrent network model of visual motion processing can reconcile these different properties. Using a ring network, we show how excitatory and inhibitory interactions can implement different computational rules such as vector averaging, winner-take-all or superposition. The model also captures ordered temporal transitions between these behaviors. In particular, depending on the inhibition regime the network can switch from motion integration to segmentation, thus being able to compute either a single pattern motion or to superpose multiple inputs as in motion transparency. We thus demonstrate that recurrent architectures can adaptively give rise to different cortical computational regimes depending upon the input statistics, from sensory flow integration to segmentation.

On the Structure of Cortical Microcircuits Inferred from Small Sample Sizes.

PubMed

Vegué, Marina; Perin, Rodrigo; Roxin, Alex

2017-08-30

The structure in cortical microcircuits deviates from what would be expected in a purely random network, which has been seen as evidence of clustering. To address this issue, we sought to reproduce the nonrandom features of cortical circuits by considering several distinct classes of network topology, including clustered networks, networks with distance-dependent connectivity, and those with broad degree distributions. To our surprise, we found that all of these qualitatively distinct topologies could account equally well for all reported nonrandom features despite being easily distinguishable from one another at the network level. This apparent paradox was a consequence of estimating network properties given only small sample sizes. In other words, networks that differ markedly in their global structure can look quite similar locally. This makes inferring network structure from small sample sizes, a necessity given the technical difficulty inherent in simultaneous intracellular recordings, problematic. We found that a network statistic called the sample degree correlation (SDC) overcomes this difficulty. The SDC depends only on parameters that can be estimated reliably given small sample sizes and is an accurate fingerprint of every topological family. We applied the SDC criterion to data from rat visual and somatosensory cortex and discovered that the connectivity was not consistent with any of these main topological classes. However, we were able to fit the experimental data with a more general network class, of which all previous topologies were special cases. The resulting network topology could be interpreted as a combination of physical spatial dependence and nonspatial, hierarchical clustering. SIGNIFICANCE STATEMENT The connectivity of cortical microcircuits exhibits features that are inconsistent with a simple random network. Here, we show that several classes of network models can account for this nonrandom structure despite qualitative differences in their global properties. This apparent paradox is a consequence of the small numbers of simultaneously recorded neurons in experiment: when inferred via small sample sizes, many networks may be indistinguishable despite being globally distinct. We develop a connectivity measure that successfully classifies networks even when estimated locally with a few neurons at a time. We show that data from rat cortex is consistent with a network in which the likelihood of a connection between neurons depends on spatial distance and on nonspatial, asymmetric clustering. Copyright © 2017 the authors 0270-6474/17/378498-13$15.00/0.

A FPGA-Based, Granularity-Variable Neuromorphic Processor and Its Application in a MIMO Real-Time Control System.

PubMed

Zhang, Zhen; Ma, Cheng; Zhu, Rong

2017-08-23

Artificial Neural Networks (ANNs), including Deep Neural Networks (DNNs), have become the state-of-the-art methods in machine learning and achieved amazing success in speech recognition, visual object recognition, and many other domains. There are several hardware platforms for developing accelerated implementation of ANN models. Since Field Programmable Gate Array (FPGA) architectures are flexible and can provide high performance per watt of power consumption, they have drawn a number of applications from scientists. In this paper, we propose a FPGA-based, granularity-variable neuromorphic processor (FBGVNP). The traits of FBGVNP can be summarized as granularity variability, scalability, integrated computing, and addressing ability: first, the number of neurons is variable rather than constant in one core; second, the multi-core network scale can be extended in various forms; third, the neuron addressing and computing processes are executed simultaneously. These make the processor more flexible and better suited for different applications. Moreover, a neural network-based controller is mapped to FBGVNP and applied in a multi-input, multi-output, (MIMO) real-time, temperature-sensing and control system. Experiments validate the effectiveness of the neuromorphic processor. The FBGVNP provides a new scheme for building ANNs, which is flexible, highly energy-efficient, and can be applied in many areas.

A FPGA-Based, Granularity-Variable Neuromorphic Processor and Its Application in a MIMO Real-Time Control System

PubMed Central

Zhang, Zhen; Zhu, Rong

2017-01-01

Artificial Neural Networks (ANNs), including Deep Neural Networks (DNNs), have become the state-of-the-art methods in machine learning and achieved amazing success in speech recognition, visual object recognition, and many other domains. There are several hardware platforms for developing accelerated implementation of ANN models. Since Field Programmable Gate Array (FPGA) architectures are flexible and can provide high performance per watt of power consumption, they have drawn a number of applications from scientists. In this paper, we propose a FPGA-based, granularity-variable neuromorphic processor (FBGVNP). The traits of FBGVNP can be summarized as granularity variability, scalability, integrated computing, and addressing ability: first, the number of neurons is variable rather than constant in one core; second, the multi-core network scale can be extended in various forms; third, the neuron addressing and computing processes are executed simultaneously. These make the processor more flexible and better suited for different applications. Moreover, a neural network-based controller is mapped to FBGVNP and applied in a multi-input, multi-output, (MIMO) real-time, temperature-sensing and control system. Experiments validate the effectiveness of the neuromorphic processor. The FBGVNP provides a new scheme for building ANNs, which is flexible, highly energy-efficient, and can be applied in many areas. PMID:28832522

An analog silicon retina with multichip configuration.

PubMed

Kameda, Seiji; Yagi, Tetsuya

2006-01-01

The neuromorphic silicon retina is a novel analog very large scale integrated circuit that emulates the structure and the function of the retinal neuronal circuit. We fabricated a neuromorphic silicon retina, in which sample/hold circuits were embedded to generate fluctuation-suppressed outputs in the previous study [1]. The applications of this silicon retina, however, are limited because of a low spatial resolution and computational variability. In this paper, we have fabricated a multichip silicon retina in which the functional network circuits are divided into two chips: the photoreceptor network chip (P chip) and the horizontal cell network chip (H chip). The output images of the P chip are transferred to the H chip with analog voltages through the line-parallel transfer bus. The sample/hold circuits embedded in the P and H chips compensate for the pattern noise generated on the circuits, including the analog communication pathway. Using the multichip silicon retina together with an off-chip differential amplifier, spatial filtering of the image with an odd- and an even-symmetric orientation selective receptive fields was carried out in real time. The analog data transfer method in the present multichip silicon retina is useful to design analog neuromorphic multichip systems that mimic the hierarchical structure of neuronal networks in the visual system.

Steady-state visually evoked potential correlates of human body perception.

PubMed

Giabbiconi, Claire-Marie; Jurilj, Verena; Gruber, Thomas; Vocks, Silja

2016-11-01

In cognitive neuroscience, interest in the neuronal basis underlying the processing of human bodies is steadily increasing. Based on functional magnetic resonance imaging studies, it is assumed that the processing of pictures of human bodies is anchored in a network of specialized brain areas comprising the extrastriate and the fusiform body area (EBA, FBA). An alternative to examine the dynamics within these networks is electroencephalography, more specifically so-called steady-state visually evoked potentials (SSVEPs). In SSVEP tasks, a visual stimulus is presented repetitively at a predefined flickering rate and typically elicits a continuous oscillatory brain response at this frequency. This brain response is characterized by an excellent signal-to-noise ratio-a major advantage for source reconstructions. The main goal of present study was to demonstrate the feasibility of this method to study human body perception. To that end, we presented pictures of bodies and contrasted the resulting SSVEPs to two control conditions, i.e., non-objects and pictures of everyday objects (chairs). We found specific SSVEPs amplitude differences between bodies and both control conditions. Source reconstructions localized the SSVEP generators to a network of temporal, occipital and parietal areas. Interestingly, only body perception resulted in activity differences in middle temporal and lateral occipitotemporal areas, most likely reflecting the EBA/FBA.

Network and neuronal membrane properties in hybrid networks reciprocally regulate selectivity to rapid thalamocortical inputs.

PubMed

Pesavento, Michael J; Pinto, David J

2012-11-01

Rapidly changing environments require rapid processing from sensory inputs. Varying deflection velocities of a rodent's primary facial vibrissa cause varying temporal neuronal activity profiles within the ventral posteromedial thalamic nucleus. Local neuron populations in a single somatosensory layer 4 barrel transform sparsely coded input into a spike count based on the input's temporal profile. We investigate this transformation by creating a barrel-like hybrid network with whole cell recordings of in vitro neurons from a cortical slice preparation, embedding the biological neuron in the simulated network by presenting virtual synaptic conductances via a conductance clamp. Utilizing the hybrid network, we examine the reciprocal network properties (local excitatory and inhibitory synaptic convergence) and neuronal membrane properties (input resistance) by altering the barrel population response to diverse thalamic input. In the presence of local network input, neurons are more selective to thalamic input timing; this arises from strong feedforward inhibition. Strongly inhibitory (damping) network regimes are more selective to timing and less selective to the magnitude of input but require stronger initial input. Input selectivity relies heavily on the different membrane properties of excitatory and inhibitory neurons. When inhibitory and excitatory neurons had identical membrane properties, the sensitivity of in vitro neurons to temporal vs. magnitude features of input was substantially reduced. Increasing the mean leak conductance of the inhibitory cells decreased the network's temporal sensitivity, whereas increasing excitatory leak conductance enhanced magnitude sensitivity. Local network synapses are essential in shaping thalamic input, and differing membrane properties of functional classes reciprocally modulate this effect.

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.

Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

PubMed

Schwemmer, Michael A; Fairhall, Adrienne L; Denéve, Sophie; Shea-Brown, Eric T

2015-07-15

While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks including irregular, Poisson-like spike times, and a tight balance between excitation and inhibition. These results significantly increase the biological plausibility of the spike-based approach to network computation, and uncover how several components of biological networks may work together to efficiently carry out computation. Copyright © 2015 the authors 0270-6474/15/3510112-23$15.00/0.

Stimulus-induced dissociation of neuronal firing rates and local field potential gamma power and its relationship to the blood oxygen level-dependent signal in macaque primary visual cortex

PubMed Central

Bartolo, M J; Gieselmann, M A; Vuksanovic, V; Hunter, D; Sun, L; Chen, X; Delicato, L S; Thiele, A

2011-01-01

The functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent (BOLD) signal is regularly used to assign neuronal activity to cognitive function. Recent analyses have shown that the local field potential (LFP) gamma power is a better predictor of the fMRI BOLD signal than spiking activity. However, LFP gamma power and spiking activity are usually correlated, clouding the analysis of the neural basis of the BOLD signal. We show that changes in LFP gamma power and spiking activity in the primary visual cortex (V1) of the awake primate can be dissociated by using grating and plaid pattern stimuli, which differentially engage surround suppression and cross-orientation inhibition/facilitation within and between cortical columns. Grating presentation yielded substantial V1 LFP gamma frequency oscillations and significant multi-unit activity. Plaid pattern presentation significantly reduced the LFP gamma power while increasing population multi-unit activity. The fMRI BOLD activity followed the LFP gamma power changes, not the multi-unit activity. Inference of neuronal activity from the fMRI BOLD signal thus requires detailed a priori knowledge of how different stimuli or tasks activate the cortical network. PMID:22081989

Results on a binding neuron model and their implications for modified hourglass model for neuronal network.

PubMed

Arunachalam, Viswanathan; Akhavan-Tabatabaei, Raha; Lopez, Cristina

2013-01-01

The classical models of single neuron like Hodgkin-Huxley point neuron or leaky integrate and fire neuron assume the influence of postsynaptic potentials to last till the neuron fires. Vidybida (2008) in a refreshing departure has proposed models for binding neurons in which the trace of an input is remembered only for a finite fixed period of time after which it is forgotten. The binding neurons conform to the behaviour of real neurons and are applicable in constructing fast recurrent networks for computer modeling. This paper develops explicitly several useful results for a binding neuron like the firing time distribution and other statistical characteristics. We also discuss the applicability of the developed results in constructing a modified hourglass network model in which there are interconnected neurons with excitatory as well as inhibitory inputs. Limited simulation results of the hourglass network are presented.

Role of GABAA-Mediated Inhibition and Functional Assortment of Synapses onto Individual Layer 4 Neurons in Regulating Plasticity Expression in Visual Cortex.

PubMed

Saez, Ignacio; Friedlander, Michael J

2016-01-01

Layer 4 (L4) of primary visual cortex (V1) is the main recipient of thalamocortical fibers from the dorsal lateral geniculate nucleus (LGNd). Thus, it is considered the main entry point of visual information into the neocortex and the first anatomical opportunity for intracortical visual processing before information leaves L4 and reaches supra- and infragranular cortical layers. The strength of monosynaptic connections from individual L4 excitatory cells onto adjacent L4 cells (unitary connections) is highly malleable, demonstrating that the initial stage of intracortical synaptic transmission of thalamocortical information can be altered by previous activity. However, the inhibitory network within L4 of V1 may act as an internal gate for induction of excitatory synaptic plasticity, thus providing either high fidelity throughput to supragranular layers or transmittal of a modified signal subject to recent activity-dependent plasticity. To evaluate this possibility, we compared the induction of synaptic plasticity using classical extracellular stimulation protocols that recruit a combination of excitatory and inhibitory synapses with stimulation of a single excitatory neuron onto a L4 cell. In order to induce plasticity, we paired pre- and postsynaptic activity (with the onset of postsynaptic spiking leading the presynaptic activation by 10ms) using extracellular stimulation (ECS) in acute slices of primary visual cortex and comparing the outcomes with our previously published results in which an identical protocol was used to induce synaptic plasticity between individual pre- and postsynaptic L4 excitatory neurons. Our results indicate that pairing of ECS with spiking in a L4 neuron fails to induce plasticity in L4-L4 connections if synaptic inhibition is intact. However, application of a similar pairing protocol under GABAARs inhibition by bath application of 2μM bicuculline does induce robust synaptic plasticity, long term potentiation (LTP) or long term depression (LTD), similar to our results with pairing of pre- and postsynaptic activation between individual excitatory L4 neurons in which inhibitory connections are not activated. These results are consistent with the well-established observation that inhibition limits the capacity for induction of plasticity at excitatory synapses and that pre- and postsynaptic activation at a fixed time interval can result in a variable range of plasticity outcomes. However, in the current study by virtue of having two sets of experimental data, we have provided a new insight into these processes. By randomly mixing the assorting of individual L4 neurons according to the frequency distribution of the experimentally determined plasticity outcome distribution based on the calculated convergence of multiple individual L4 neurons onto a single postsynaptic L4 neuron, we were able to compare then actual ECS plasticity outcomes to those predicted by randomly mixing individual pairs of neurons. Interestingly, the observed plasticity profiles with ECS cannot account for the random assortment of plasticity behaviors of synaptic connections between individual cell pairs. These results suggest that connections impinging onto a single postsynaptic cell may be grouped according to plasticity states.

Neuron Learning to Network Organization.

DTIC Science & Technology

1983-12-20

02912 N 0-8 1t COTOLIGOF 1HV AflRS 12. REPORT OATE Pesne an ann Research Program December 20, 1983 Office of Naval Research , Code 442PT 13. NUMBER...visual cortc\\ from R. Cajal, Histologie du Systete Nerveux. mostly hard-wired and perform a great variety of control functions took hundreds of millions of...certain sense there is much that is known. A set of coupled non -linear differential equations. including time delays, can be written down that in

Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity

PubMed Central

Sahasranamam, Ajith; Vlachos, Ioannis; Aertsen, Ad; Kumar, Arvind

2016-01-01

Spike patterns are among the most common electrophysiological descriptors of neuron types. Surprisingly, it is not clear how the diversity in firing patterns of the neurons in a network affects its activity dynamics. Here, we introduce the state-dependent stochastic bursting neuron model allowing for a change in its firing patterns independent of changes in its input-output firing rate relationship. Using this model, we show that the effect of single neuron spiking on the network dynamics is contingent on the network activity state. While spike bursting can both generate and disrupt oscillations, these patterns are ineffective in large regions of the network state space in changing the network activity qualitatively. Finally, we show that when single-neuron properties are made dependent on the population activity, a hysteresis like dynamics emerges. This novel phenomenon has important implications for determining the network response to time-varying inputs and for the network sensitivity at different operating points. PMID:27212008

Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity.

PubMed

Sahasranamam, Ajith; Vlachos, Ioannis; Aertsen, Ad; Kumar, Arvind

2016-05-23

Spike patterns are among the most common electrophysiological descriptors of neuron types. Surprisingly, it is not clear how the diversity in firing patterns of the neurons in a network affects its activity dynamics. Here, we introduce the state-dependent stochastic bursting neuron model allowing for a change in its firing patterns independent of changes in its input-output firing rate relationship. Using this model, we show that the effect of single neuron spiking on the network dynamics is contingent on the network activity state. While spike bursting can both generate and disrupt oscillations, these patterns are ineffective in large regions of the network state space in changing the network activity qualitatively. Finally, we show that when single-neuron properties are made dependent on the population activity, a hysteresis like dynamics emerges. This novel phenomenon has important implications for determining the network response to time-varying inputs and for the network sensitivity at different operating points.

Network adaptation improves temporal representation of naturalistic stimuli in Drosophila eye: II mechanisms.

PubMed

Nikolaev, Anton; Zheng, Lei; Wardill, Trevor J; O'Kane, Cahir J; de Polavieja, Gonzalo G; Juusola, Mikko

2009-01-01

Retinal networks must adapt constantly to best present the ever changing visual world to the brain. Here we test the hypothesis that adaptation is a result of different mechanisms at several synaptic connections within the network. In a companion paper (Part I), we showed that adaptation in the photoreceptors (R1-R6) and large monopolar cells (LMC) of the Drosophila eye improves sensitivity to under-represented signals in seconds by enhancing both the amplitude and frequency distribution of LMCs' voltage responses to repeated naturalistic contrast series. In this paper, we show that such adaptation needs both the light-mediated conductance and feedback-mediated synaptic conductance. A faulty feedforward pathway in histamine receptor mutant flies speeds up the LMC output, mimicking extreme light adaptation. A faulty feedback pathway from L2 LMCs to photoreceptors slows down the LMC output, mimicking dark adaptation. These results underline the importance of network adaptation for efficient coding, and as a mechanism for selectively regulating the size and speed of signals in neurons. We suggest that concert action of many different mechanisms and neural connections are responsible for adaptation to visual stimuli. Further, our results demonstrate the need for detailed circuit reconstructions like that of the Drosophila lamina, to understand how networks process information.

Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems

PubMed Central

Giulioni, Massimiliano; Corradi, Federico; Dante, Vittorio; del Giudice, Paolo

2015-01-01

Neuromorphic chips embody computational principles operating in the nervous system, into microelectronic devices. In this domain it is important to identify computational primitives that theory and experiments suggest as generic and reusable cognitive elements. One such element is provided by attractor dynamics in recurrent networks. Point attractors are equilibrium states of the dynamics (up to fluctuations), determined by the synaptic structure of the network; a ‘basin’ of attraction comprises all initial states leading to a given attractor upon relaxation, hence making attractor dynamics suitable to implement robust associative memory. The initial network state is dictated by the stimulus, and relaxation to the attractor state implements the retrieval of the corresponding memorized prototypical pattern. In a previous work we demonstrated that a neuromorphic recurrent network of spiking neurons and suitably chosen, fixed synapses supports attractor dynamics. Here we focus on learning: activating on-chip synaptic plasticity and using a theory-driven strategy for choosing network parameters, we show that autonomous learning, following repeated presentation of simple visual stimuli, shapes a synaptic connectivity supporting stimulus-selective attractors. Associative memory develops on chip as the result of the coupled stimulus-driven neural activity and ensuing synaptic dynamics, with no artificial separation between learning and retrieval phases. PMID:26463272

Spatial reference frames of visual, vestibular, and multimodal heading signals in the dorsal subdivision of the medial superior temporal area.

PubMed

Fetsch, Christopher R; Wang, Sentao; Gu, Yong; Deangelis, Gregory C; Angelaki, Dora E

2007-01-17

Heading perception is a complex task that generally requires the integration of visual and vestibular cues. This sensory integration is complicated by the fact that these two modalities encode motion in distinct spatial reference frames (visual, eye-centered; vestibular, head-centered). Visual and vestibular heading signals converge in the primate dorsal subdivision of the medial superior temporal area (MSTd), a region thought to contribute to heading perception, but the reference frames of these signals remain unknown. We measured the heading tuning of MSTd neurons by presenting optic flow (visual condition), inertial motion (vestibular condition), or a congruent combination of both cues (combined condition). Static eye position was varied from trial to trial to determine the reference frame of tuning (eye-centered, head-centered, or intermediate). We found that tuning for optic flow was predominantly eye-centered, whereas tuning for inertial motion was intermediate but closer to head-centered. Reference frames in the two unimodal conditions were rarely matched in single neurons and uncorrelated across the population. Notably, reference frames in the combined condition varied as a function of the relative strength and spatial congruency of visual and vestibular tuning. This represents the first investigation of spatial reference frames in a naturalistic, multimodal condition in which cues may be integrated to improve perceptual performance. Our results compare favorably with the predictions of a recent neural network model that uses a recurrent architecture to perform optimal cue integration, suggesting that the brain could use a similar computational strategy to integrate sensory signals expressed in distinct frames of reference.

NeuroTessMesh: A Tool for the Generation and Visualization of Neuron Meshes and Adaptive On-the-Fly Refinement

PubMed Central

Garcia-Cantero, Juan J.; Brito, Juan P.; Mata, Susana; Bayona, Sofia; Pastor, Luis

2017-01-01

Gaining a better understanding of the human brain continues to be one of the greatest challenges for science, largely because of the overwhelming complexity of the brain and the difficulty of analyzing the features and behavior of dense neural networks. Regarding analysis, 3D visualization has proven to be a useful tool for the evaluation of complex systems. However, the large number of neurons in non-trivial circuits, together with their intricate geometry, makes the visualization of a neuronal scenario an extremely challenging computational problem. Previous work in this area dealt with the generation of 3D polygonal meshes that approximated the cells’ overall anatomy but did not attempt to deal with the extremely high storage and computational cost required to manage a complex scene. This paper presents NeuroTessMesh, a tool specifically designed to cope with many of the problems associated with the visualization of neural circuits that are comprised of large numbers of cells. In addition, this method facilitates the recovery and visualization of the 3D geometry of cells included in databases, such as NeuroMorpho, and provides the tools needed to approximate missing information such as the soma’s morphology. This method takes as its only input the available compact, yet incomplete, morphological tracings of the cells as acquired by neuroscientists. It uses a multiresolution approach that combines an initial, coarse mesh generation with subsequent on-the-fly adaptive mesh refinement stages using tessellation shaders. For the coarse mesh generation, a novel approach, based on the Finite Element Method, allows approximation of the 3D shape of the soma from its incomplete description. Subsequently, the adaptive refinement process performed in the graphic card generates meshes that provide good visual quality geometries at a reasonable computational cost, both in terms of memory and rendering time. All the described techniques have been integrated into NeuroTessMesh, available to the scientific community, to generate, visualize, and save the adaptive resolution meshes. PMID:28690511

Rapid Long-Range Disynaptic Inhibition Explains the Formation of Cortical Orientation Maps

PubMed Central

Antolík, Ján

2017-01-01

Competitive interactions are believed to underlie many types of cortical processing, ranging from memory formation, attention and development of cortical functional organization (e.g., development of orientation maps in primary visual cortex). In the latter case, the competitive interactions happen along the cortical surface, with local populations of neurons reinforcing each other, while competing with those displaced more distally. This specific configuration of lateral interactions is however in stark contrast with the known properties of the anatomical substrate, i.e., excitatory connections (mediating reinforcement) having longer reach than inhibitory ones (mediating competition). No satisfactory biologically plausible resolution of this conflict between anatomical measures, and assumed cortical function has been proposed. Recently a specific pattern of delays between different types of neurons in cat cortex has been discovered, where direct mono-synaptic excitation has approximately the same delay, as the combined delays of the disynaptic inhibitory interactions between excitatory neurons (i.e., the sum of delays from excitatory to inhibitory and from inhibitory to excitatory neurons). Here we show that this specific pattern of delays represents a biologically plausible explanation for how short-range inhibition can support competitive interactions that underlie the development of orientation maps in primary visual cortex. We demonstrate this statement analytically under simplifying conditions, and subsequently show using network simulations that development of orientation maps is preserved when long-range excitation, direct inhibitory to inhibitory interactions, and moderate inequality in the delays between excitatory and inhibitory pathways is added. PMID:28408869

Variability and Correlations in Primary Visual Cortical Neurons Driven by Fixational Eye Movements

PubMed Central

McFarland, James M.; Cumming, Bruce G.

2016-01-01

The ability to distinguish between elements of a sensory neuron's activity that are stimulus independent versus driven by the stimulus is critical for addressing many questions in systems neuroscience. This is typically accomplished by measuring neural responses to repeated presentations of identical stimuli and identifying the trial-variable components of the response as noise. In awake primates, however, small “fixational” eye movements (FEMs) introduce uncontrolled trial-to-trial differences in the visual stimulus itself, potentially confounding this distinction. Here, we describe novel analytical methods that directly quantify the stimulus-driven and stimulus-independent components of visual neuron responses in the presence of FEMs. We apply this approach, combined with precise model-based eye tracking, to recordings from primary visual cortex (V1), finding that standard approaches that ignore FEMs typically miss more than half of the stimulus-driven neural response variance, creating substantial biases in measures of response reliability. We show that these effects are likely not isolated to the particular experimental conditions used here, such as the choice of visual stimulus or spike measurement time window, and thus will be a more general problem for V1 recordings in awake primates. We also demonstrate that measurements of the stimulus-driven and stimulus-independent correlations among pairs of V1 neurons can be greatly biased by FEMs. These results thus illustrate the potentially dramatic impact of FEMs on measures of signal and noise in visual neuron activity and also demonstrate a novel approach for controlling for these eye-movement-induced effects. SIGNIFICANCE STATEMENT Distinguishing between the signal and noise in a sensory neuron's activity is typically accomplished by measuring neural responses to repeated presentations of an identical stimulus. For recordings from the visual cortex of awake animals, small “fixational” eye movements (FEMs) inevitably introduce trial-to-trial variability in the visual stimulus, potentially confounding such measures. Here, we show that FEMs often have a dramatic impact on several important measures of response variability for neurons in primary visual cortex. We also present an analytical approach for quantifying signal and noise in visual neuron activity in the presence of FEMs. These results thus highlight the importance of controlling for FEMs in studies of visual neuron function, and demonstrate novel methods for doing so. PMID:27277801

Synchronization in a chaotic neural network with time delay depending on the spatial distance between neurons

NASA Astrophysics Data System (ADS)

Tang, Guoning; Xu, Kesheng; Jiang, Luoluo

2011-10-01

The synchronization is investigated in a two-dimensional Hindmarsh-Rose neuronal network by introducing a global coupling scheme with time delay, where the length of time delay is proportional to the spatial distance between neurons. We find that the time delay always disturbs synchronization of the neuronal network. When both the coupling strength and length of time delay per unit distance (i.e., enlargement factor) are large enough, the time delay induces the abnormal membrane potential oscillations in neurons. Specifically, the abnormal membrane potential oscillations for the symmetrically placed neurons form an antiphase, so that the large coupling strength and enlargement factor lead to the desynchronization of the neuronal network. The complete and intermittently complete synchronization of the neuronal network are observed for the right choice of parameters. The physical mechanism underlying these phenomena is analyzed.

A Scalable Multicore Architecture With Heterogeneous Memory Structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs).

PubMed

Moradi, Saber; Qiao, Ning; Stefanini, Fabio; Indiveri, Giacomo

2018-02-01

Neuromorphic computing systems comprise networks of neurons that use asynchronous events for both computation and communication. This type of representation offers several advantages in terms of bandwidth and power consumption in neuromorphic electronic systems. However, managing the traffic of asynchronous events in large scale systems is a daunting task, both in terms of circuit complexity and memory requirements. Here, we present a novel routing methodology that employs both hierarchical and mesh routing strategies and combines heterogeneous memory structures for minimizing both memory requirements and latency, while maximizing programming flexibility to support a wide range of event-based neural network architectures, through parameter configuration. We validated the proposed scheme in a prototype multicore neuromorphic processor chip that employs hybrid analog/digital circuits for emulating synapse and neuron dynamics together with asynchronous digital circuits for managing the address-event traffic. We present a theoretical analysis of the proposed connectivity scheme, describe the methods and circuits used to implement such scheme, and characterize the prototype chip. Finally, we demonstrate the use of the neuromorphic processor with a convolutional neural network for the real-time classification of visual symbols being flashed to a dynamic vision sensor (DVS) at high speed.

Dynamic binding of visual features by neuronal/stimulus synchrony.

PubMed

Iwabuchi, A

1998-05-01

When people see a visual scene, certain parts of the visual scene are treated as belonging together and we regard them as a perceptual unit, which is called a "figure". People focus on figures, and the remaining parts of the scene are disregarded as "ground". In Gestalt psychology this process is called "figure-ground segregation". According to current perceptual psychology, a figure is formed by binding various visual features in a scene, and developments in neuroscience have revealed that there are many feature-encoding neurons, which respond to such features specifically. It is not known, however, how the brain binds different features of an object into a coherent visual object representation. Recently, the theory of binding by neuronal synchrony, which argues that feature binding is dynamically mediated by neuronal synchrony of feature-encoding neurons, has been proposed. This review article portrays the problem of figure-ground segregation and features binding, summarizes neurophysiological and psychophysical experiments and theory relevant to feature binding by neuronal/stimulus synchrony, and suggests possible directions for future research on this topic.

Transition to subthreshold activity with the use of phase shifting in a model thalamic network

NASA Astrophysics Data System (ADS)

Thomas, Elizabeth; Grisar, Thierry

1997-05-01

Absence epilepsy involves a state of low frequency synchronous oscillations by the involved neuronal networks. These oscillations may be either above or subthreshold. In this investigation, we studied the methods which could be utilized to transform the threshold activity of neurons in the network to a subthreshold state. A model thalamic network was constructed using the Hodgkin Huxley framework. Subthreshold activity was achieved by the application of stimuli to the network which caused phase shifts in the oscillatory activity of selected neurons in the network. In some instances the stimulus was a periodic pulse train of low frequency to the reticular thalamic neurons of the network while in others, it was a constant hyperpolarizing current applied to the thalamocortical neurons.

Combining Microdialysis and Electrophysiology in Cerebral Cortex to Delineate Functional Implications of Acetylcholine Gradients

NASA Astrophysics Data System (ADS)

Nelson, Kari L.

The neuronal network in cerebral cortex is a dynamic system that can undergo changes in collective neural activity as the organism changes its behavior. For example, during sleep and quiet restful awake state, many neurons tend to fire together in synchrony. In contrast, during alert awake states, firing patterns of neurons tend to be more asynchronous, firing more independently. These changes in population-level synchrony are defined as changes in cortical state. Response to sensory input is state-dependent, i.e., change in cortical state can impact the sensory information processing in cortex and introduce trial-to-trial variability in response to the same repeated stimuli. How the brain maintains reliable perception in spite of such trial-to-trial variability is a longstanding important question in neuroscience research. This dissertation is centered on two hypotheses. The first hypothesis is that different parts of the cortex can be in different states simultaneously. The second hypothesis is that inhomogeneity in cortical states can benefit the system by enabling the cortical network to maintain reliable sensory detection. If one part of the system is in a state that is not good for detection, then another part of the system could be in a different state that is good for detection, thus compensating and maintaining good detection for the system as a whole. These hypotheses were tested on anesthetized rats and awake mice. In anesthetized rats, cholinergic neuromodulation via microdialysis (muD) probes was used to induce cortical state changes in the somatosensory barrel cortex. Changes in cortical state and response to whisker stimulus was recorded with a microelectrode array (MEA). In awake mice, nucleus basalis was optogenetically stimulated by inserting an optic fiber in basal forebrain and response to visual stimulus was analyzed. The results demonstrated heterogeneity in cortical state across the spatial extent of cortical network. Changes in sensory response followed this heterogeneity and sensory detection was not reliable at the level of single neurons or small regions of cortex. The greater population of neurons, on the other hand, maintained reliable sensory detection, suggesting that heterogeneous state can be functionally beneficial for the cortical network.

Toward Understanding How Early-Life Stress Reprograms Cognitive and Emotional Brain Networks.

PubMed

Chen, Yuncai; Baram, Tallie Z

2016-01-01

Vulnerability to emotional disorders including depression derives from interactions between genes and environment, especially during sensitive developmental periods. Adverse early-life experiences provoke the release and modify the expression of several stress mediators and neurotransmitters within specific brain regions. The interaction of these mediators with developing neurons and neuronal networks may lead to long-lasting structural and functional alterations associated with cognitive and emotional consequences. Although a vast body of work has linked quantitative and qualitative aspects of stress to adolescent and adult outcomes, a number of questions are unclear. What distinguishes 'normal' from pathologic or toxic stress? How are the effects of stress transformed into structural and functional changes in individual neurons and neuronal networks? Which ones are affected? We review these questions in the context of established and emerging studies. We introduce a novel concept regarding the origin of toxic early-life stress, stating that it may derive from specific patterns of environmental signals, especially those derived from the mother or caretaker. Fragmented and unpredictable patterns of maternal care behaviors induce a profound chronic stress. The aberrant patterns and rhythms of early-life sensory input might also directly and adversely influence the maturation of cognitive and emotional brain circuits, in analogy to visual and auditory brain systems. Thus, unpredictable, stress-provoking early-life experiences may influence adolescent cognitive and emotional outcomes by disrupting the maturation of the underlying brain networks. Comprehensive approaches and multiple levels of analysis are required to probe the protean consequences of early-life adversity on the developing brain. These involve integrated human and animal-model studies, and approaches ranging from in vivo imaging to novel neuroanatomical, molecular, epigenomic, and computational methodologies. Because early-life adversity is a powerful determinant of subsequent vulnerabilities to emotional and cognitive pathologies, understanding the underlying processes will have profound implications for the world's current and future children.

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks.

PubMed

Pena, Rodrigo F O; Vellmer, Sebastian; Bernardi, Davide; Roque, Antonio C; Lindner, Benjamin

2018-01-01

Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdős-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as indicated by comparison with simulation results of large recurrent networks. Our method can help to elucidate how network heterogeneity shapes the asynchronous state in recurrent neural networks.

Multiplex Networks of Cortical and Hippocampal Neurons Revealed at Different Timescales

PubMed Central

Timme, Nicholas; Ito, Shinya; Myroshnychenko, Maxym; Yeh, Fang-Chin; Hiolski, Emma; Hottowy, Pawel; Beggs, John M.

2014-01-01

Recent studies have emphasized the importance of multiplex networks – interdependent networks with shared nodes and different types of connections – in systems primarily outside of neuroscience. Though the multiplex properties of networks are frequently not considered, most networks are actually multiplex networks and the multiplex specific features of networks can greatly affect network behavior (e.g. fault tolerance). Thus, the study of networks of neurons could potentially be greatly enhanced using a multiplex perspective. Given the wide range of temporally dependent rhythms and phenomena present in neural systems, we chose to examine multiplex networks of individual neurons with time scale dependent connections. To study these networks, we used transfer entropy – an information theoretic quantity that can be used to measure linear and nonlinear interactions – to systematically measure the connectivity between individual neurons at different time scales in cortical and hippocampal slice cultures. We recorded the spiking activity of almost 12,000 neurons across 60 tissue samples using a 512-electrode array with 60 micrometer inter-electrode spacing and 50 microsecond temporal resolution. To the best of our knowledge, this preparation and recording method represents a superior combination of number of recorded neurons and temporal and spatial recording resolutions to any currently available in vivo system. We found that highly connected neurons (“hubs”) were localized to certain time scales, which, we hypothesize, increases the fault tolerance of the network. Conversely, a large proportion of non-hub neurons were not localized to certain time scales. In addition, we found that long and short time scale connectivity was uncorrelated. Finally, we found that long time scale networks were significantly less modular and more disassortative than short time scale networks in both tissue types. As far as we are aware, this analysis represents the first systematic study of temporally dependent multiplex networks among individual neurons. PMID:25536059

Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

PubMed Central

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480

Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks.

PubMed

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem.

A modeling comparison of projection neuron- and neuromodulator-elicited oscillations in a central pattern generating network.

PubMed

Kintos, Nickolas; Nusbaum, Michael P; Nadim, Farzan

2008-06-01

Many central pattern generating networks are influenced by synaptic input from modulatory projection neurons. The network response to a projection neuron is sometimes mimicked by bath applying the neuronally-released modulator, despite the absence of network interactions with the projection neuron. One interesting example occurs in the crab stomatogastric ganglion (STG), where bath applying the neuropeptide pyrokinin (PK) elicits a gastric mill rhythm which is similar to that elicited by the projection neuron modulatory commissural neuron 1 (MCN1), despite the absence of PK in MCN1 and the fact that MCN1 is not active during the PK-elicited rhythm. MCN1 terminals have fast and slow synaptic actions on the gastric mill network and are presynaptically inhibited by this network in the STG. These local connections are inactive in the PK-elicited rhythm, and the mechanism underlying this rhythm is unknown. We use mathematical and biophysically-realistic modeling to propose potential mechanisms by which PK can elicit a gastric mill rhythm that is similar to the MCN1-elicited rhythm. We analyze slow-wave network oscillations using simplified mathematical models and, in parallel, develop biophysically-realistic models that account for fast, action potential-driven oscillations and some spatial structure of the network neurons. Our results illustrate how the actions of bath-applied neuromodulators can mimic those of descending projection neurons through mathematically similar but physiologically distinct mechanisms.

Application of heterogeneous pulse coupled neural network in image quantization

NASA Astrophysics Data System (ADS)

Huang, Yi; Ma, Yide; Li, Shouliang; Zhan, Kun

2016-11-01

On the basis of the different strengths of synaptic connections between actual neurons, this paper proposes a heterogeneous pulse coupled neural network (HPCNN) algorithm to perform quantization on images. HPCNNs are developed from traditional pulse coupled neural network (PCNN) models, which have different parameters corresponding to different image regions. This allows pixels of different gray levels to be classified broadly into two categories: background regional and object regional. Moreover, an HPCNN also satisfies human visual characteristics. The parameters of the HPCNN model are calculated automatically according to these categories, and quantized results will be optimal and more suitable for humans to observe. At the same time, the experimental results of natural images from the standard image library show the validity and efficiency of our proposed quantization method.

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.

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

NASA Astrophysics Data System (ADS)

Yan, Hao; Sun, Xiaojuan

2017-06-01

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

Emergent Oscillations in Networks of Stochastic Spiking Neurons

PubMed Central

van Drongelen, Wim; Cowan, Jack D.

2011-01-01

Networks of neurons produce diverse patterns of oscillations, arising from the network's global properties, the propensity of individual neurons to oscillate, or a mixture of the two. Here we describe noisy limit cycles and quasi-cycles, two related mechanisms underlying emergent oscillations in neuronal networks whose individual components, stochastic spiking neurons, do not themselves oscillate. Both mechanisms are shown to produce gamma band oscillations at the population level while individual neurons fire at a rate much lower than the population frequency. Spike trains in a network undergoing noisy limit cycles display a preferred period which is not found in the case of quasi-cycles, due to the even faster decay of phase information in quasi-cycles. These oscillations persist in sparsely connected networks, and variation of the network's connectivity results in variation of the oscillation frequency. A network of such neurons behaves as a stochastic perturbation of the deterministic Wilson-Cowan equations, and the network undergoes noisy limit cycles or quasi-cycles depending on whether these have limit cycles or a weakly stable focus. These mechanisms provide a new perspective on the emergence of rhythmic firing in neural networks, showing the coexistence of population-level oscillations with very irregular individual spike trains in a simple and general framework. PMID:21573105

Identified Serotonergic Modulatory Neurons Have Heterogeneous Synaptic Connectivity within the Olfactory System of Drosophila.

PubMed

Coates, Kaylynn E; Majot, Adam T; Zhang, Xiaonan; Michael, Cole T; Spitzer, Stacy L; Gaudry, Quentin; Dacks, Andrew M

2017-08-02

Modulatory neurons project widely throughout the brain, dynamically altering network processing based on an animal's physiological state. The connectivity of individual modulatory neurons can be complex, as they often receive input from a variety of sources and are diverse in their physiology, structure, and gene expression profiles. To establish basic principles about the connectivity of individual modulatory neurons, we examined a pair of identified neurons, the "contralaterally projecting, serotonin-immunoreactive deutocerebral neurons" (CSDns), within the olfactory system of Drosophila Specifically, we determined the neuronal classes providing synaptic input to the CSDns within the antennal lobe (AL), an olfactory network targeted by the CSDns, and the degree to which CSDn active zones are uniformly distributed across the AL. Using anatomical techniques, we found that the CSDns received glomerulus-specific input from olfactory receptor neurons (ORNs) and projection neurons (PNs), and networkwide input from local interneurons (LNs). Furthermore, we quantified the number of CSDn active zones in each glomerulus and found that CSDn output is not uniform, but rather heterogeneous, across glomeruli and stereotyped from animal to animal. Finally, we demonstrate that the CSDns synapse broadly onto LNs and PNs throughout the AL but do not synapse upon ORNs. Our results demonstrate that modulatory neurons do not necessarily provide purely top-down input but rather receive neuron class-specific input from the networks that they target, and that even a two cell modulatory network has highly heterogeneous, yet stereotyped, pattern of connectivity. SIGNIFICANCE STATEMENT Modulatory neurons often project broadly throughout the brain to alter processing based on physiological state. However, the connectivity of individual modulatory neurons to their target networks is not well understood, as modulatory neuron populations are heterogeneous in their physiology, morphology, and gene expression. In this study, we use a pair of identified serotonergic neurons within the Drosophila olfactory system as a model to establish a framework for modulatory neuron connectivity. We demonstrate that individual modulatory neurons can integrate neuron class-specific input from their target network, which is often nonreciprocal. Additionally, modulatory neuron output can be stereotyped, yet nonuniform, across network regions. Our results provide new insight into the synaptic relationships that underlie network function of modulatory neurons. Copyright © 2017 the authors 0270-6474/17/377318-14$15.00/0.

Distinct populations of neurons respond to emotional valence and arousal in the human subthalamic nucleus.

PubMed

Sieger, Tomáš; Serranová, Tereza; Růžička, Filip; Vostatek, Pavel; Wild, Jiří; Štastná, Daniela; Bonnet, Cecilia; Novák, Daniel; Růžička, Evžen; Urgošík, Dušan; Jech, Robert

2015-03-10

Both animal studies and studies using deep brain stimulation in humans have demonstrated the involvement of the subthalamic nucleus (STN) in motivational and emotional processes; however, participation of this nucleus in processing human emotion has not been investigated directly at the single-neuron level. We analyzed the relationship between the neuronal firing from intraoperative microrecordings from the STN during affective picture presentation in patients with Parkinson's disease (PD) and the affective ratings of emotional valence and arousal performed subsequently. We observed that 17% of neurons responded to emotional valence and arousal of visual stimuli according to individual ratings. The activity of some neurons was related to emotional valence, whereas different neurons responded to arousal. In addition, 14% of neurons responded to visual stimuli. Our results suggest the existence of neurons involved in processing or transmission of visual and emotional information in the human STN, and provide evidence of separate processing of the affective dimensions of valence and arousal at the level of single neurons as well.

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.

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

Visual experience and subsequent sleep induce sequential plastic changes in putative inhibitory and excitatory cortical neurons

PubMed Central

Aton, Sara J.; Broussard, Christopher; Dumoulin, Michelle; Seibt, Julie; Watson, Adam; Coleman, Tammi; Frank, Marcos G.

2013-01-01

Ocular dominance plasticity in the developing primary visual cortex is initiated by monocular deprivation (MD) and consolidated during subsequent sleep. To clarify how visual experience and sleep affect neuronal activity and plasticity, we continuously recorded extragranular visual cortex fast-spiking (FS) interneurons and putative principal (i.e., excitatory) neurons in freely behaving cats across periods of waking MD and post-MD sleep. Consistent with previous reports in mice, MD induces two related changes in FS interneurons: a response shift in favor of the closed eye and depression of firing. Spike-timing–dependent depression of open-eye–biased principal neuron inputs to FS interneurons may mediate these effects. During post-MD nonrapid eye movement sleep, principal neuron firing increases and becomes more phase-locked to slow wave and spindle oscillations. Ocular dominance (OD) shifts in favor of open-eye stimulation—evident only after post-MD sleep—are proportional to MD-induced changes in FS interneuron activity and to subsequent sleep-associated changes in principal neuron activity. OD shifts are greatest in principal neurons that fire 40–300 ms after neighboring FS interneurons during post-MD slow waves. Based on these data, we propose that MD-induced changes in FS interneurons play an instructive role in ocular dominance plasticity, causing disinhibition among open-eye–biased principal neurons, which drive plasticity throughout the visual cortex during subsequent sleep. PMID:23300282

Continuous Dropout.

PubMed

Shen, Xu; Tian, Xinmei; Liu, Tongliang; Xu, Fang; Tao, Dacheng

2017-10-03

Dropout has been proven to be an effective algorithm for training robust deep networks because of its ability to prevent overfitting by avoiding the co-adaptation of feature detectors. Current explanations of dropout include bagging, naive Bayes, regularization, and sex in evolution. According to the activation patterns of neurons in the human brain, when faced with different situations, the firing rates of neurons are random and continuous, not binary as current dropout does. Inspired by this phenomenon, we extend the traditional binary dropout to continuous dropout. On the one hand, continuous dropout is considerably closer to the activation characteristics of neurons in the human brain than traditional binary dropout. On the other hand, we demonstrate that continuous dropout has the property of avoiding the co-adaptation of feature detectors, which suggests that we can extract more independent feature detectors for model averaging in the test stage. We introduce the proposed continuous dropout to a feedforward neural network and comprehensively compare it with binary dropout, adaptive dropout, and DropConnect on Modified National Institute of Standards and Technology, Canadian Institute for Advanced Research-10, Street View House Numbers, NORB, and ImageNet large scale visual recognition competition-12. Thorough experiments demonstrate that our method performs better in preventing the co-adaptation of feature detectors and improves test performance.

Electronic approaches to restoration of sight

NASA Astrophysics Data System (ADS)

Goetz, G. A.; Palanker, D. V.

2016-09-01

Retinal prostheses are a promising means for restoring sight to patients blinded by the gradual atrophy of photoreceptors due to retinal degeneration. They are designed to reintroduce information into the visual system by electrically stimulating surviving neurons in the retina. This review outlines the concepts and technologies behind two major approaches to retinal prosthetics: epiretinal and subretinal. We describe how the visual system responds to electrical stimulation. We highlight major differences between direct encoding of the retinal output with epiretinal stimulation, and network-mediated response with subretinal stimulation. We summarize results of pre-clinical evaluation of prosthetic visual functions in- and ex vivo, as well as the outcomes of current clinical trials of various retinal implants. We also briefly review alternative, non-electronic, approaches to restoration of sight to the blind, and conclude by suggesting some perspectives for future advancement in the field.

Electronic Approaches to Restoration of Sight

PubMed Central

Goetz, G A; Palanker, D V

2016-01-01

Retinal prostheses are a promising means for restoring sight to patients blinded by the gradual atrophy of photoreceptors due to retinal degeneration. They are designed to reintroduce information into the visual system by electrically stimulating surviving neurons in the retina. This review outlines the concepts and technologies behind two major approaches to retinal prosthetics: epiretinal and subretinal. We describe how the visual system responds to electrical stimulation. We highlight major differences between direct encoding of the retinal output with epiretinal stimulation, and network-mediated response with subretinal stimulation. We summarize results of pre-clinical evaluation of prosthetic visual functions in- and ex-vivo, as well as the outcomes of current clinical trials of various retinal implants. We also briefly review alternative, non-electronic, approaches to restoration of sight to the blind, and conclude by suggesting some perspectives for future advancement in the field. PMID:27502748

Intrinsically active and pacemaker neurons in pluripotent stem cell-derived neuronal populations.

PubMed

Illes, Sebastian; Jakab, Martin; Beyer, Felix; Gelfert, Renate; Couillard-Despres, Sébastien; Schnitzler, Alfons; Ritter, Markus; Aigner, Ludwig

2014-03-11

Neurons generated from pluripotent stem cells (PSCs) self-organize into functional neuronal assemblies in vitro, generating synchronous network activities. Intriguingly, PSC-derived neuronal assemblies develop spontaneous activities that are independent of external stimulation, suggesting the presence of thus far undetected intrinsically active neurons (IANs). Here, by using mouse embryonic stem cells, we provide evidence for the existence of IANs in PSC-neuronal networks based on extracellular multielectrode array and intracellular patch-clamp recordings. IANs remain active after pharmacological inhibition of fast synaptic communication and possess intrinsic mechanisms required for autonomous neuronal activity. PSC-derived IANs are functionally integrated in PSC-neuronal populations, contribute to synchronous network bursting, and exhibit pacemaker properties. The intrinsic activity and pacemaker properties of the neuronal subpopulation identified herein may be particularly relevant for interventions involving transplantation of neural tissues. IANs may be a key element in the regulation of the functional activity of grafted as well as preexisting host neuronal networks.

Intrinsically Active and Pacemaker Neurons in Pluripotent Stem Cell-Derived Neuronal Populations

PubMed Central

Illes, Sebastian; Jakab, Martin; Beyer, Felix; Gelfert, Renate; Couillard-Despres, Sébastien; Schnitzler, Alfons; Ritter, Markus; Aigner, Ludwig

2014-01-01

Summary Neurons generated from pluripotent stem cells (PSCs) self-organize into functional neuronal assemblies in vitro, generating synchronous network activities. Intriguingly, PSC-derived neuronal assemblies develop spontaneous activities that are independent of external stimulation, suggesting the presence of thus far undetected intrinsically active neurons (IANs). Here, by using mouse embryonic stem cells, we provide evidence for the existence of IANs in PSC-neuronal networks based on extracellular multielectrode array and intracellular patch-clamp recordings. IANs remain active after pharmacological inhibition of fast synaptic communication and possess intrinsic mechanisms required for autonomous neuronal activity. PSC-derived IANs are functionally integrated in PSC-neuronal populations, contribute to synchronous network bursting, and exhibit pacemaker properties. The intrinsic activity and pacemaker properties of the neuronal subpopulation identified herein may be particularly relevant for interventions involving transplantation of neural tissues. IANs may be a key element in the regulation of the functional activity of grafted as well as preexisting host neuronal networks. PMID:24672755

A spatially resolved network spike in model neuronal cultures reveals nucleation centers, circular traveling waves and drifting spiral waves.

PubMed

Paraskevov, A V; Zendrikov, D K

2017-03-23

We show that in model neuronal cultures, where the probability of interneuronal connection formation decreases exponentially with increasing distance between the neurons, there exists a small number of spatial nucleation centers of a network spike, from where the synchronous spiking activity starts propagating in the network typically in the form of circular traveling waves. The number of nucleation centers and their spatial locations are unique and unchanged for a given realization of neuronal network but are different for different networks. In contrast, if the probability of interneuronal connection formation is independent of the distance between neurons, then the nucleation centers do not arise and the synchronization of spiking activity during a network spike occurs spatially uniform throughout the network. Therefore one can conclude that spatial proximity of connections between neurons is important for the formation of nucleation centers. It is also shown that fluctuations of the spatial density of neurons at their random homogeneous distribution typical for the experiments in vitro do not determine the locations of the nucleation centers. The simulation results are qualitatively consistent with the experimental observations.

A spatially resolved network spike in model neuronal cultures reveals nucleation centers, circular traveling waves and drifting spiral waves

NASA Astrophysics Data System (ADS)

Paraskevov, A. V.; Zendrikov, D. K.

2017-04-01

We show that in model neuronal cultures, where the probability of interneuronal connection formation decreases exponentially with increasing distance between the neurons, there exists a small number of spatial nucleation centers of a network spike, from where the synchronous spiking activity starts propagating in the network typically in the form of circular traveling waves. The number of nucleation centers and their spatial locations are unique and unchanged for a given realization of neuronal network but are different for different networks. In contrast, if the probability of interneuronal connection formation is independent of the distance between neurons, then the nucleation centers do not arise and the synchronization of spiking activity during a network spike occurs spatially uniform throughout the network. Therefore one can conclude that spatial proximity of connections between neurons is important for the formation of nucleation centers. It is also shown that fluctuations of the spatial density of neurons at their random homogeneous distribution typical for the experiments in vitro do not determine the locations of the nucleation centers. The simulation results are qualitatively consistent with the experimental observations.

A new cross-correlation algorithm for the analysis of "in vitro" neuronal network activity aimed at pharmacological studies.

PubMed

Biffi, E; Menegon, A; Regalia, G; Maida, S; Ferrigno, G; Pedrocchi, A

2011-08-15

Modern drug discovery for Central Nervous System pathologies has recently focused its attention to in vitro neuronal networks as models for the study of neuronal activities. Micro Electrode Arrays (MEAs), a widely recognized tool for pharmacological investigations, enable the simultaneous study of the spiking activity of discrete regions of a neuronal culture, providing an insight into the dynamics of networks. Taking advantage of MEAs features and making the most of the cross-correlation analysis to assess internal parameters of a neuronal system, we provide an efficient method for the evaluation of comprehensive neuronal network activity. We developed an intra network burst correlation algorithm, we evaluated its sensitivity and we explored its potential use in pharmacological studies. Our results demonstrate the high sensitivity of this algorithm and the efficacy of this methodology in pharmacological dose-response studies, with the advantage of analyzing the effect of drugs on the comprehensive correlative properties of integrated neuronal networks. Copyright © 2011 Elsevier B.V. All rights reserved.

Neural networks with multiple general neuron models: a hybrid computational intelligence approach using Genetic Programming.

PubMed

Barton, Alan J; Valdés, Julio J; Orchard, Robert

2009-01-01

Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions.

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

Improved contour detection model with spatial summation properties based on nonclassical receptive field

NASA Astrophysics Data System (ADS)

Lin, Chuan; Xu, Guili; Cao, Yijun; Liang, Chenghua; Li, Ya

2016-07-01

The responses of cortical neurons to a stimulus in a classical receptive field (CRF) can be modulated by stimulating the non-CRF (nCRF) of neurons in the primary visual cortex (V1). In the very early stages (at around 40 ms), a neuron in V1 exhibits strong responses to a small set of stimuli. Later, however (after 100 ms), the neurons in V1 become sensitive to the scene's global organization. As per these visual cortical mechanisms, a contour detection model based on the spatial summation properties is proposed. Unlike in previous studies, the responses of the nCRF to the higher visual cortex that results in the inhibition of the neuronal responses in the primary visual cortex by the feedback pathway are considered. In this model, the individual neurons in V1 receive global information from the higher visual cortex to participate in the inhibition process. Computationally, global Gabor energy features are involved, leading to the more coherent physiological characteristics of the nCRF. We conducted an experiment where we compared our model with those proposed by other researchers. Our model explains the role of the mutual inhibition of neurons in V1, together with an approach for object recognition in machine vision.

Spatial updating in area LIP is independent of saccade direction.

PubMed

Heiser, Laura M; Colby, Carol L

2006-05-01

We explore the world around us by making rapid eye movements to objects of interest. Remarkably, these eye movements go unnoticed, and we perceive the world as stable. Spatial updating is one of the neural mechanisms that contributes to this perception of spatial constancy. Previous studies in macaque lateral intraparietal cortex (area LIP) have shown that individual neurons update, or "remap," the locations of salient visual stimuli at the time of an eye movement. The existence of remapping implies that neurons have access to visual information from regions far beyond the classically defined receptive field. We hypothesized that neurons have access to information located anywhere in the visual field. We tested this by recording the activity of LIP neurons while systematically varying the direction in which a stimulus location must be updated. Our primary finding is that individual neurons remap stimulus traces in multiple directions, indicating that LIP neurons have access to information throughout the visual field. At the population level, stimulus traces are updated in conjunction with all saccade directions, even when we consider direction as a function of receptive field location. These results show that spatial updating in LIP is effectively independent of saccade direction. Our findings support the hypothesis that the activity of LIP neurons contributes to the maintenance of spatial constancy throughout the visual field.

Voltage-sensitive dye imaging of transcranial magnetic stimulation-induced intracortical dynamics

PubMed Central

Kozyrev, Vladislav; Eysel, Ulf T.; Jancke, Dirk

2014-01-01

Transcranial magnetic stimulation (TMS) is widely used in clinical interventions and basic neuroscience. Additionally, it has become a powerful tool to drive plastic changes in neuronal networks. However, highly resolved recordings of the immediate TMS effects have remained scarce, because existing recording techniques are limited in spatial or temporal resolution or are interfered with by the strong TMS-induced electric field. To circumvent these constraints, we performed optical imaging with voltage-sensitive dye (VSD) in an animal experimental setting using anaesthetized cats. The dye signals reflect gradual changes in the cells' membrane potential across several square millimeters of cortical tissue, thus enabling direct visualization of TMS-induced neuronal population dynamics. After application of a single TMS pulse across visual cortex, brief focal activation was immediately followed by synchronous suppression of a large pool of neurons. With consecutive magnetic pulses (10 Hz), widespread activity within this “basin of suppression” increased stepwise to suprathreshold levels and spontaneous activity was enhanced. Visual stimulation after repetitive TMS revealed long-term potentiation of evoked activity. Furthermore, loss of the “deceleration–acceleration” notch during the rising phase of the response, as a signature of fast intracortical inhibition detectable with VSD imaging, indicated weakened inhibition as an important driving force of increasing cortical excitability. In summary, our data show that high-frequency TMS changes the balance between excitation and inhibition in favor of an excitatory cortical state. VSD imaging may thus be a promising technique to trace TMS-induced changes in excitability and resulting plastic processes across cortical maps with high spatial and temporal resolutions. PMID:25187557

Self-organization of synchronous activity propagation in neuronal networks driven by local excitation

PubMed Central

Bayati, Mehdi; Valizadeh, Alireza; Abbassian, Abdolhossein; Cheng, Sen

2015-01-01

Many experimental and theoretical studies have suggested that the reliable propagation of synchronous neural activity is crucial for neural information processing. The propagation of synchronous firing activity in so-called synfire chains has been studied extensively in feed-forward networks of spiking neurons. However, it remains unclear how such neural activity could emerge in recurrent neuronal networks through synaptic plasticity. In this study, we investigate whether local excitation, i.e., neurons that fire at a higher frequency than the other, spontaneously active neurons in the network, can shape a network to allow for synchronous activity propagation. We use two-dimensional, locally connected and heterogeneous neuronal networks with spike-timing dependent plasticity (STDP). We find that, in our model, local excitation drives profound network changes within seconds. In the emergent network, neural activity propagates synchronously through the network. This activity originates from the site of the local excitation and propagates through the network. The synchronous activity propagation persists, even when the local excitation is removed, since it derives from the synaptic weight matrix. Importantly, once this connectivity is established it remains stable even in the presence of spontaneous activity. Our results suggest that synfire-chain-like activity can emerge in a relatively simple way in realistic neural networks by locally exciting the desired origin of the neuronal sequence. PMID:26089794

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

Contrast adaptation in the Limulus lateral eye.

PubMed

Valtcheva, Tchoudomira M; Passaglia, Christopher L

2015-12-01

Luminance and contrast adaptation are neuronal mechanisms employed by the visual system to adjust our sensitivity to light. They are mediated by an assortment of cellular and network processes distributed across the retina and visual cortex. Both have been demonstrated in the eyes of many vertebrates, but only luminance adaptation has been shown in invertebrate eyes to date. Since the computational benefits of contrast adaptation should apply to all visual systems, we investigated whether this mechanism operates in horseshoe crab eyes, one of the best-understood neural networks in the animal kingdom. The spike trains of optic nerve fibers were recorded in response to light stimuli modulated randomly in time and delivered to single ommatidia or the whole eye. We found that the retina adapts to both the mean luminance and contrast of a white-noise stimulus, that luminance- and contrast-adaptive processes are largely independent, and that they originate within an ommatidium. Network interactions are not involved. A published computer model that simulates existing knowledge of the horseshoe crab eye did not show contrast adaptation, suggesting that a heretofore unknown mechanism may underlie the phenomenon. This mechanism does not appear to reside in photoreceptors because white-noise analysis of electroretinogram recordings did not show contrast adaptation. The likely site of origin is therefore the spike discharge mechanism of optic nerve fibers. The finding of contrast adaption in a retinal network as simple as the horseshoe crab eye underscores the broader importance of this image processing strategy to vision. Copyright © 2015 the American Physiological Society.

Contrast adaptation in the Limulus lateral eye

PubMed Central

Valtcheva, Tchoudomira M.

2015-01-01

Luminance and contrast adaptation are neuronal mechanisms employed by the visual system to adjust our sensitivity to light. They are mediated by an assortment of cellular and network processes distributed across the retina and visual cortex. Both have been demonstrated in the eyes of many vertebrates, but only luminance adaptation has been shown in invertebrate eyes to date. Since the computational benefits of contrast adaptation should apply to all visual systems, we investigated whether this mechanism operates in horseshoe crab eyes, one of the best-understood neural networks in the animal kingdom. The spike trains of optic nerve fibers were recorded in response to light stimuli modulated randomly in time and delivered to single ommatidia or the whole eye. We found that the retina adapts to both the mean luminance and contrast of a white-noise stimulus, that luminance- and contrast-adaptive processes are largely independent, and that they originate within an ommatidium. Network interactions are not involved. A published computer model that simulates existing knowledge of the horseshoe crab eye did not show contrast adaptation, suggesting that a heretofore unknown mechanism may underlie the phenomenon. This mechanism does not appear to reside in photoreceptors because white-noise analysis of electroretinogram recordings did not show contrast adaptation. The likely site of origin is therefore the spike discharge mechanism of optic nerve fibers. The finding of contrast adaption in a retinal network as simple as the horseshoe crab eye underscores the broader importance of this image processing strategy to vision. PMID:26445869

PhotoMEA: an opto-electronic biosensor for monitoring in vitro neuronal network activity.

PubMed

Ghezzi, Diego; Pedrocchi, Alessandra; Menegon, Andrea; Mantero, Sara; Valtorta, Flavia; Ferrigno, Giancarlo

2007-02-01

PhotoMEA is a biosensor useful for the analysis of an in vitro neuronal network, fully based on optical methods. Its function is based on the stimulation of neurons with caged glutamate and the recording of neuronal activity by Voltage-Sensitive fluorescent Dyes (VSD). The main advantage is that it will be possible to stimulate even at sub-single neuron level and to record with high resolution the activity of the entire network in the culture. A large-scale view of neuronal intercommunications offers a unique opportunity for testing the ability of drugs to affect neuronal properties as well as alterations in the behaviour of the entire network. The concept and a prototype for validation is described here in detail.

Synaptic dynamics regulation in response to high frequency stimulation in neuronal networks

NASA Astrophysics Data System (ADS)

Su, Fei; Wang, Jiang; Li, Huiyan; Wei, Xile; Yu, Haitao; Deng, Bin

2018-02-01

High frequency stimulation (HFS) has confirmed its ability in modulating the pathological neural activities. However its detailed mechanism is unclear. This study aims to explore the effects of HFS on neuronal networks dynamics. First, the two-neuron FitzHugh-Nagumo (FHN) networks with static coupling strength and the small-world FHN networks with spike-time-dependent plasticity (STDP) modulated synaptic coupling strength are constructed. Then, the multi-scale method is used to transform the network models into equivalent averaged models, where the HFS intensity is modeled as the ratio between stimulation amplitude and frequency. Results show that in static two-neuron networks, there is still synaptic current projected to the postsynaptic neuron even if the presynaptic neuron is blocked by the HFS. In the small-world networks, the effects of the STDP adjusting rate parameter on the inactivation ratio and synchrony degree increase with the increase of HFS intensity. However, only when the HFS intensity becomes very large can the STDP time window parameter affect the inactivation ratio and synchrony index. Both simulation and numerical analysis demonstrate that the effects of HFS on neuronal network dynamics are realized through the adjustment of synaptic variable and conductance.

Training a Network of Electronic Neurons for Control of a Mobile Robot

NASA Astrophysics Data System (ADS)

Vromen, T. G. M.; Steur, E.; Nijmeijer, H.

An adaptive training procedure is developed for a network of electronic neurons, which controls a mobile robot driving around in an unknown environment while avoiding obstacles. The neuronal network controls the angular velocity of the wheels of the robot based on the sensor readings. The nodes in the neuronal network controller are clusters of neurons rather than single neurons. The adaptive training procedure ensures that the input-output behavior of the clusters is identical, even though the constituting neurons are nonidentical and have, in isolation, nonidentical responses to the same input. In particular, we let the neurons interact via a diffusive coupling, and the proposed training procedure modifies the diffusion interaction weights such that the neurons behave synchronously with a predefined response. The working principle of the training procedure is experimentally validated and results of an experiment with a mobile robot that is completely autonomously driving in an unknown environment with obstacles are presented.

Controlling the Flow of Visual Information through the Lateral Geniculate Nucleus: From Single Cells to Neural Networks.

DTIC Science & Technology

1991-10-31

in my laboratory, Drs. Dan Kammen, Ernst Niebur and Florentin Worg6tter, as well as with three outside collaborators, Prof. John Kulli from the...also for experimentally observed cortical column structures ( Niebur and Worg6tter, 1990a,b). Temporal Dynamics of Interacting Neuronal Populations We...Connection Machine to simulate a 128 by 128 grid of 16,384 cells under a variety of stimulation patterns ( Niebur , Kammen & Koch, 1991). To explore

Establishment of a Human Neuronal Network Assessment System by Using a Human Neuron/Astrocyte Co-Culture Derived from Fetal Neural Stem/Progenitor Cells.

PubMed

Fukushima, Kazuyuki; Miura, Yuji; Sawada, Kohei; Yamazaki, Kazuto; Ito, Masashi

2016-01-01

Using human cell models mimicking the central nervous system (CNS) provides a better understanding of the human CNS, and it is a key strategy to improve success rates in CNS drug development. In the CNS, neurons function as networks in which astrocytes play important roles. Thus, an assessment system of neuronal network functions in a co-culture of human neurons and astrocytes has potential to accelerate CNS drug development. We previously demonstrated that human hippocampus-derived neural stem/progenitor cells (HIP-009 cells) were a novel tool to obtain human neurons and astrocytes in the same culture. In this study, we applied HIP-009 cells to a multielectrode array (MEA) system to detect neuronal signals as neuronal network functions. We observed spontaneous firings of HIP-009 neurons, and validated functional formation of neuronal networks pharmacologically. By using this assay system, we investigated effects of several reference compounds, including agonists and antagonists of glutamate and γ-aminobutyric acid receptors, and sodium, potassium, and calcium channels, on neuronal network functions using firing and burst numbers, and synchrony as readouts. These results indicate that the HIP-009/MEA assay system is applicable to the pharmacological assessment of drug candidates affecting synaptic functions for CNS drug development. © 2015 Society for Laboratory Automation and Screening.

The stimulus selectivity and connectivity of layer six principal cells reveals cortical microcircuits underlying visual processing.

PubMed

Vélez-Fort, Mateo; Rousseau, Charly V; Niedworok, Christian J; Wickersham, Ian R; Rancz, Ede A; Brown, Alexander P Y; Strom, Molly; Margrie, Troy W

2014-09-17

Sensory computations performed in the neocortex involve layer six (L6) cortico-cortical (CC) and cortico-thalamic (CT) signaling pathways. Developing an understanding of the physiological role of these circuits requires dissection of the functional specificity and connectivity of the underlying individual projection neurons. By combining whole-cell recording from identified L6 principal cells in the mouse primary visual cortex (V1) with modified rabies virus-based input mapping, we have determined the sensory response properties and upstream monosynaptic connectivity of cells mediating the CC or CT pathway. We show that CC-projecting cells encompass a broad spectrum of selectivity to stimulus orientation and are predominantly innervated by deep layer V1 neurons. In contrast, CT-projecting cells are ultrasparse firing, exquisitely tuned to orientation and direction information, and receive long-range input from higher cortical areas. This segregation in function and connectivity indicates that L6 microcircuits route specific contextual and stimulus-related information within and outside the cortical network. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Synaptic plasticity and neuronal refractory time cause scaling behaviour of neuronal avalanches

NASA Astrophysics Data System (ADS)

Michiels van Kessenich, L.; de Arcangelis, L.; Herrmann, H. J.

2016-08-01

Neuronal avalanches measured in vitro and in vivo in different cortical networks consistently exhibit power law behaviour for the size and duration distributions with exponents typical for a mean field self-organized branching process. These exponents are also recovered in neuronal network simulations implementing various neuronal dynamics on different network topologies. They can therefore be considered a very robust feature of spontaneous neuronal activity. Interestingly, this scaling behaviour is also observed on regular lattices in finite dimensions, which raises the question about the origin of the mean field behavior observed experimentally. In this study we provide an answer to this open question by investigating the effect of activity dependent plasticity in combination with the neuronal refractory time in a neuronal network. Results show that the refractory time hinders backward avalanches forcing a directed propagation. Hebbian plastic adaptation plays the role of sculpting these directed avalanche patterns into the topology of the network slowly changing it into a branched structure where loops are marginal.

Synaptic plasticity and neuronal refractory time cause scaling behaviour of neuronal avalanches.

PubMed

Michiels van Kessenich, L; de Arcangelis, L; Herrmann, H J

2016-08-18

Neuronal avalanches measured in vitro and in vivo in different cortical networks consistently exhibit power law behaviour for the size and duration distributions with exponents typical for a mean field self-organized branching process. These exponents are also recovered in neuronal network simulations implementing various neuronal dynamics on different network topologies. They can therefore be considered a very robust feature of spontaneous neuronal activity. Interestingly, this scaling behaviour is also observed on regular lattices in finite dimensions, which raises the question about the origin of the mean field behavior observed experimentally. In this study we provide an answer to this open question by investigating the effect of activity dependent plasticity in combination with the neuronal refractory time in a neuronal network. Results show that the refractory time hinders backward avalanches forcing a directed propagation. Hebbian plastic adaptation plays the role of sculpting these directed avalanche patterns into the topology of the network slowly changing it into a branched structure where loops are marginal.

Collision detection in complex dynamic scenes using an LGMD-based visual neural network with feature enhancement.

PubMed

Yue, Shigang; Rind, F Claire

2006-05-01

The lobula giant movement detector (LGMD) is an identified neuron in the locust brain that responds most strongly to the images of an approaching object such as a predator. Its computational model can cope with unpredictable environments without using specific object recognition algorithms. In this paper, an LGMD-based neural network is proposed with a new feature enhancement mechanism to enhance the expanded edges of colliding objects via grouped excitation for collision detection with complex backgrounds. The isolated excitation caused by background detail will be filtered out by the new mechanism. Offline tests demonstrated the advantages of the presented LGMD-based neural network in complex backgrounds. Real time robotics experiments using the LGMD-based neural network as the only sensory system showed that the system worked reliably in a wide range of conditions; in particular, the robot was able to navigate in arenas with structured surrounds and complex backgrounds.

Dynamic range in small-world networks of Hodgkin-Huxley neurons with chemical synapses

NASA Astrophysics Data System (ADS)

Batista, C. A. S.; Viana, R. L.; Lopes, S. R.; Batista, A. M.

2014-09-01

According to Stevens' law the relationship between stimulus and response is a power-law within an interval called the dynamic range. The dynamic range of sensory organs is found to be larger than that of a single neuron, suggesting that the network structure plays a key role in the behavior of both the scaling exponent and the dynamic range of neuron assemblies. In order to verify computationally the relationships between stimulus and response for spiking neurons, we investigate small-world networks of neurons described by the Hodgkin-Huxley equations connected by chemical synapses. We found that the dynamic range increases with the network size, suggesting that the enhancement of the dynamic range observed in sensory organs, with respect to single neurons, is an emergent property of complex network dynamics.

Low-Intensity Repetitive Transcranial Magnetic Stimulation Improves Abnormal Visual Cortical Circuit Topography and Upregulates BDNF in Mice

PubMed Central

Makowiecki, Kalina; Harvey, Alan R.; Sherrard, Rachel M.

2014-01-01

Repetitive transcranial magnetic stimulation (rTMS) is increasingly used as a treatment for neurological and psychiatric disorders. Although the induced field is focused on a target region during rTMS, adjacent areas also receive stimulation at a lower intensity and the contribution of this perifocal stimulation to network-wide effects is poorly defined. Here, we examined low-intensity rTMS (LI-rTMS)-induced changes on a model neural network using the visual systems of normal (C57Bl/6J wild-type, n = 22) and ephrin-A2A5−/− (n = 22) mice, the latter possessing visuotopic anomalies. Mice were treated with LI-rTMS or sham (handling control) daily for 14 d, then fluorojade and fluororuby were injected into visual cortex. The distribution of dorsal LGN (dLGN) neurons and corticotectal terminal zones (TZs) was mapped and disorder defined by comparing their actual location with that predicted by injection sites. In the afferent geniculocortical projection, LI-rTMS decreased the abnormally high dispersion of retrogradely labeled neurons in the dLGN of ephrin-A2A5−/− mice, indicating geniculocortical map refinement. In the corticotectal efferents, LI-rTMS improved topography of the most abnormal TZs in ephrin-A2A5−/− mice without altering topographically normal TZs. To investigate a possible molecular mechanism for LI-rTMS-induced structural plasticity, we measured brain derived neurotrophic factor (BDNF) in the visual cortex and superior colliculus after single and multiple stimulations. BDNF was upregulated after a single stimulation for all groups, but only sustained in the superior colliculus of ephrin-A2A5−/− mice. Our results show that LI-rTMS upregulates BDNF, promoting a plastic environment conducive to beneficial reorganization of abnormal cortical circuits, information that has important implications for clinical rTMS. PMID:25100609

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.

Origin and Function of Tuning Diversity in Macaque Visual Cortex

PubMed Central

Goris, Robbe L.T.; Simoncelli, Eero P.; Movshon, J. Anthony

2016-01-01

SUMMARY Neurons in visual cortex vary in their orientation selectivity. We measured responses of V1 and V2 cells to orientation mixtures and fit them with a model whose stimulus selectivity arises from the combined effects of filtering, suppression, and response nonlinearity. The model explains the diversity of orientation selectivity with neuron-to-neuron variability in all three mechanisms, of which variability in the orientation bandwidth of linear filtering is the most important. The model also accounts for the cells’ diversity of spatial frequency selectivity. Tuning diversity is matched to the needs of visual encoding. The orientation content found in natural scenes is diverse, and neurons with different selectivities are adapted to different stimulus configurations. Single orientations are better encoded by highly selective neurons, while orientation mixtures are better encoded by less selective neurons. A diverse population of neurons therefore provides better overall discrimination capabilities for natural images than any homogeneous population. PMID:26549331

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

PubMed Central

Pecevski, Dejan; Buesing, Lars; Maass, Wolfgang

2011-01-01

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons. PMID:22219717

Synaptic Plasticity and Spike Synchronisation in Neuronal Networks

NASA Astrophysics Data System (ADS)

Borges, Rafael R.; Borges, Fernando S.; Lameu, Ewandson L.; Protachevicz, Paulo R.; Iarosz, Kelly C.; Caldas, Iberê L.; Viana, Ricardo L.; Macau, Elbert E. N.; Baptista, Murilo S.; Grebogi, Celso; Batista, Antonio M.

2017-12-01

Brain plasticity, also known as neuroplasticity, is a fundamental mechanism of neuronal adaptation in response to changes in the environment or due to brain injury. In this review, we show our results about the effects of synaptic plasticity on neuronal networks composed by Hodgkin-Huxley neurons. We show that the final topology of the evolved network depends crucially on the ratio between the strengths of the inhibitory and excitatory synapses. Excitation of the same order of inhibition revels an evolved network that presents the rich-club phenomenon, well known to exist in the brain. For initial networks with considerably larger inhibitory strengths, we observe the emergence of a complex evolved topology, where neurons sparsely connected to other neurons, also a typical topology of the brain. The presence of noise enhances the strength of both types of synapses, but if the initial network has synapses of both natures with similar strengths. Finally, we show how the synchronous behaviour of the evolved network will reflect its evolved topology.

Higher order visual input to the mushroom bodies in the bee, Bombus impatiens.

PubMed

Paulk, Angelique C; Gronenberg, Wulfila

2008-11-01

To produce appropriate behaviors based on biologically relevant associations, sensory pathways conveying different modalities are integrated by higher-order central brain structures, such as insect mushroom bodies. To address this function of sensory integration, we characterized the structure and response of optic lobe (OL) neurons projecting to the calyces of the mushroom bodies in bees. Bees are well known for their visual learning and memory capabilities and their brains possess major direct visual input from the optic lobes to the mushroom bodies. To functionally characterize these visual inputs to the mushroom bodies, we recorded intracellularly from neurons in bumblebees (Apidae: Bombus impatiens) and a single neuron in a honeybee (Apidae: Apis mellifera) while presenting color and motion stimuli. All of the mushroom body input neurons were color sensitive while a subset was motion sensitive. Additionally, most of the mushroom body input neurons would respond to the first, but not to subsequent, presentations of repeated stimuli. In general, the medulla or lobula neurons projecting to the calyx signaled specific chromatic, temporal, and motion features of the visual world to the mushroom bodies, which included sensory information required for the biologically relevant associations bees form during foraging tasks.

Functional differentiation of macaque visual temporal cortical neurons using a parametric action space.

PubMed

Vangeneugden, Joris; Pollick, Frank; Vogels, Rufin

2009-03-01

Neurons in the rostral superior temporal sulcus (STS) are responsive to displays of body movements. We employed a parametric action space to determine how similarities among actions are represented by visual temporal neurons and how form and motion information contributes to their responses. The stimulus space consisted of a stick-plus-point-light figure performing arm actions and their blends. Multidimensional scaling showed that the responses of temporal neurons represented the ordinal similarity between these actions. Further tests distinguished neurons responding equally strongly to static presentations and to actions ("snapshot" neurons), from those responding much less strongly to static presentations, but responding well when motion was present ("motion" neurons). The "motion" neurons were predominantly found in the upper bank/fundus of the STS, and "snapshot" neurons in the lower bank of the STS and inferior temporal convexity. Most "motion" neurons showed strong response modulation during the course of an action, thus responding to action kinematics. "Motion" neurons displayed a greater average selectivity for these simple arm actions than did "snapshot" neurons. We suggest that the "motion" neurons code for visual kinematics, whereas the "snapshot" neurons code for form/posture, and that both can contribute to action recognition, in agreement with computation models of action recognition.

Homeostatic Scaling of Excitability in Recurrent Neural Networks

PubMed Central

Remme, Michiel W. H.; Wadman, Wytse J.

2012-01-01

Neurons adjust their intrinsic excitability when experiencing a persistent change in synaptic drive. This process can prevent neural activity from moving into either a quiescent state or a saturated state in the face of ongoing plasticity, and is thought to promote stability of the network in which neurons reside. However, most neurons are embedded in recurrent networks, which require a delicate balance between excitation and inhibition to maintain network stability. This balance could be disrupted when neurons independently adjust their intrinsic excitability. Here, we study the functioning of activity-dependent homeostatic scaling of intrinsic excitability (HSE) in a recurrent neural network. Using both simulations of a recurrent network consisting of excitatory and inhibitory neurons that implement HSE, and a mean-field description of adapting excitatory and inhibitory populations, we show that the stability of such adapting networks critically depends on the relationship between the adaptation time scales of both neuron populations. In a stable adapting network, HSE can keep all neurons functioning within their dynamic range, while the network is undergoing several (patho)physiologically relevant types of plasticity, such as persistent changes in external drive, changes in connection strengths, or the loss of inhibitory cells from the network. However, HSE cannot prevent the unstable network dynamics that result when, due to such plasticity, recurrent excitation in the network becomes too strong compared to feedback inhibition. This suggests that keeping a neural network in a stable and functional state requires the coordination of distinct homeostatic mechanisms that operate not only by adjusting neural excitability, but also by controlling network connectivity. PMID:22570604

A fish on the hunt, observed neuron by neuron

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

None

2010-01-01

This three-dimensional microscopy image reveals an output neuron of the optic tectum lighting up in response to visual information from the retina. The scientists used this state-of-the-art imaging technology to learn how neurons in the optic tectum take visual information and convert it into an output that drives action. More information: http://newscenter.lbl.gov/feature-stories/2010/10/29/zebrafish-vision/

Visual Processing: Hungry Like the Mouse.

PubMed

Piscopo, Denise M; Niell, Cristopher M

2016-09-07

In this issue of Neuron, Burgess et al. (2016) explore how motivational state interacts with visual processing, by examining hunger modulation of food-associated visual responses in postrhinal cortical neurons and their inputs from amygdala. Copyright © 2016 Elsevier Inc. All rights reserved.

The Mirror Neurons Network in Aging, Mild Cognitive Impairment, and Alzheimer Disease: A functional MRI Study

PubMed Central

Farina, Elisabetta; Baglio, Francesca; Pomati, Simone; D'Amico, Alessandra; Campini, Isabella C.; Di Tella, Sonia; Belloni, Giulia; Pozzo, Thierry

2017-01-01

The aim of the current study is to investigate the integrity of the Mirror Neurons (MN) network in normal aging, Mild Cognitive Impairment (MCI), and Alzheimer disease (AD). Although AD and MCI are considered “cognitive” diseases, there has been increasing recognition of a link between motor function and AD. More recently the embodied cognition hypothesis has also been developed: it postulates that a part of cognition results from the coupling between action and perception representations. MN represent a neuronal population which links perception, action, and cognition, therefore we decided to characterize MN functioning in neurodegenerative cognitive decline. Three matched groups of 16 subjects (normal elderly-NE, amnesic MCI with hippocampal atrophy and AD) were evaluated with a focused neuropsychological battery and an fMRI task specifically created to test MN: that comprised of an observation run, where subjects were shown movies of a right hand grasping different objects, and of a motor run, where subjects observed visual pictures of objects oriented to be grasped with the right hand. In NE subjects, the conjunction analysis (comparing fMRI activation during observation and execution), showed the activation of a bilateral fronto-parietal network in “classical” MN areas, and of the superior temporal gyrus (STG). The MCI group showed the activation of areas belonging to the same network, however, parietal areas were activated to a lesser extent and the STG was not activated, while the opposite was true for the right Broca's area. We did not observe any activation of the fronto-parietal network in AD participants. They did not perform as well as the NE subjects in all the neuropsychological tests (including tests of functions attributed to MN) whereas the MCI subjects were significantly different from the NE subjects only in episodic memory and semantic fluency. Here we show that the MN network is largely preserved in aging, while it appears involved following an anterior-posterior gradient in neurodegenerative decline. In AD, task performance decays and the MN network appears clearly deficient. The preservation of the anterior part of the MN network in MCI could possibly supplement the initial decay of the posterior part, preserving cognitive performance. PMID:29249956

Detection of 5-hydroxytryptamine (5-HT) in vitro using a hippocampal neuronal network-based biosensor with extracellular potential analysis of neurons.

PubMed

Hu, Liang; Wang, Qin; Qin, Zhen; Su, Kaiqi; Huang, Liquan; Hu, Ning; Wang, Ping

2015-04-15

5-hydroxytryptamine (5-HT) is an important neurotransmitter in regulating emotions and related behaviors in mammals. To detect and monitor the 5-HT, effective and convenient methods are demanded in investigation of neuronal network. In this study, hippocampal neuronal networks (HNNs) endogenously expressing 5-HT receptors were employed as sensing elements to build an in vitro neuronal network-based biosensor. The electrophysiological characteristics were analyzed in both neuron and network levels. The firing rates and amplitudes were derived from signal to determine the biosensor response characteristics. The experimental results demonstrate a dose-dependent inhibitory effect of 5-HT on hippocampal neuron activities, indicating the effectiveness of this hybrid biosensor in detecting 5-HT with a response range from 0.01μmol/L to 10μmol/L. In addition, the cross-correlation analysis of HNNs activities suggests 5-HT could weaken HNN connectivity reversibly, providing more specificity of this biosensor in detecting 5-HT. Moreover, 5-HT induced spatiotemporal firing pattern alterations could be monitored in neuron and network levels simultaneously by this hybrid biosensor in a convenient and direct way. With those merits, this neuronal network-based biosensor will be promising to be a valuable and utility platform for the study of neurotransmitter in vitro. Copyright © 2014 Elsevier B.V. All rights reserved.

Extensive excitatory network interactions shape temporal processing of communication signals in a model sensory system.

PubMed

Ma, Xiaofeng; Kohashi, Tsunehiko; Carlson, Bruce A

2013-07-01

Many sensory brain regions are characterized by extensive local network interactions. However, we know relatively little about the contribution of this microcircuitry to sensory coding. Detailed analyses of neuronal microcircuitry are usually performed in vitro, whereas sensory processing is typically studied by recording from individual neurons in vivo. The electrosensory pathway of mormyrid fish provides a unique opportunity to link in vitro studies of synaptic physiology with in vivo studies of sensory processing. These fish communicate by actively varying the intervals between pulses of electricity. Within the midbrain posterior exterolateral nucleus (ELp), the temporal filtering of afferent spike trains establishes interval tuning by single neurons. We characterized pairwise neuronal connectivity among ELp neurons with dual whole cell recording in an in vitro whole brain preparation. We found a densely connected network in which single neurons influenced the responses of other neurons throughout the network. Similarly tuned neurons were more likely to share an excitatory synaptic connection than differently tuned neurons, and synaptic connections between similarly tuned neurons were stronger than connections between differently tuned neurons. We propose a general model for excitatory network interactions in which strong excitatory connections both reinforce and adjust tuning and weak excitatory connections make smaller modifications to tuning. The diversity of interval tuning observed among this population of neurons can be explained, in part, by each individual neuron receiving a different complement of local excitatory inputs.

Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.

PubMed

Spoerer, Courtney J; McClure, Patrick; Kriegeskorte, Nikolaus

2017-01-01

Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and non-human primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognizing objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognize objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance.

Neural control of visual search by frontal eye field: effects of unexpected target displacement on visual selection and saccade preparation.

PubMed

Murthy, Aditya; Ray, Supriya; Shorter, Stephanie M; Schall, Jeffrey D; Thompson, Kirk G

2009-05-01

The dynamics of visual selection and saccade preparation by the frontal eye field was investigated in macaque monkeys performing a search-step task combining the classic double-step saccade task with visual search. Reward was earned for producing a saccade to a color singleton. On random trials the target and one distractor swapped locations before the saccade and monkeys were rewarded for shifting gaze to the new singleton location. A race model accounts for the probabilities and latencies of saccades to the initial and final singleton locations and provides a measure of the duration of a covert compensation process-target-step reaction time. When the target stepped out of a movement field, noncompensated saccades to the original location were produced when movement-related activity grew rapidly to a threshold. Compensated saccades to the final location were produced when the growth of the original movement-related activity was interrupted within target-step reaction time and was replaced by activation of other neurons producing the compensated saccade. When the target stepped into a receptive field, visual neurons selected the new target location regardless of the monkeys' response. When the target stepped out of a receptive field most visual neurons maintained the representation of the original target location, but a minority of visual neurons showed reduced activity. Chronometric analyses of the neural responses to the target step revealed that the modulation of visually responsive neurons and movement-related neurons occurred early enough to shift attention and saccade preparation from the old to the new target location. These findings indicate that visual activity in the frontal eye field signals the location of targets for orienting, whereas movement-related activity instantiates saccade preparation.

In search of a recognition memory engram

PubMed Central

Brown, M.W.; Banks, P.J.

2015-01-01

A large body of data from human and animal studies using psychological, recording, imaging, and lesion techniques indicates that recognition memory involves at least two separable processes: familiarity discrimination and recollection. Familiarity discrimination for individual visual stimuli seems to be effected by a system centred on the perirhinal cortex of the temporal lobe. The fundamental change that encodes prior occurrence within the perirhinal cortex is a reduction in the responses of neurones when a stimulus is repeated. Neuronal network modelling indicates that a system based on such a change in responsiveness is potentially highly efficient in information theoretic terms. A review is given of findings indicating that perirhinal cortex acts as a storage site for recognition memory of objects and that such storage depends upon processes producing synaptic weakening. PMID:25280908

An Attractive Reelin Gradient Establishes Synaptic Lamination in the Vertebrate Visual System.

PubMed

Di Donato, Vincenzo; De Santis, Flavia; Albadri, Shahad; Auer, Thomas Oliver; Duroure, Karine; Charpentier, Marine; Concordet, Jean-Paul; Gebhardt, Christoph; Del Bene, Filippo

2018-03-07

A conserved organizational and functional principle of neural networks is the segregation of axon-dendritic synaptic connections into laminae. Here we report that targeting of synaptic laminae by retinal ganglion cell (RGC) arbors in the vertebrate visual system is regulated by a signaling system relying on target-derived Reelin and VLDLR/Dab1a on the projecting neurons. Furthermore, we find that Reelin is distributed as a gradient on the target tissue and stabilized by heparan sulfate proteoglycans (HSPGs) in the extracellular matrix (ECM). Through genetic manipulations, we show that this Reelin gradient is important for laminar targeting and that it is attractive for RGC axons. Finally, we suggest a comprehensive model of synaptic lamina formation in which attractive Reelin counter-balances repulsive Slit1, thereby guiding RGC axons toward single synaptic laminae. We establish a mechanism that may represent a general principle for neural network assembly in vertebrate species and across different brain areas. Copyright © 2018 Elsevier Inc. All rights reserved.

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks

PubMed Central

Pena, Rodrigo F. O.; Vellmer, Sebastian; Bernardi, Davide; Roque, Antonio C.; Lindner, Benjamin

2018-01-01

Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdős-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as indicated by comparison with simulation results of large recurrent networks. Our method can help to elucidate how network heterogeneity shapes the asynchronous state in recurrent neural networks. PMID:29551968

Nanostructured superhydrophobic substrates trigger the development of 3D neuronal networks.

PubMed

Limongi, Tania; Cesca, Fabrizia; Gentile, Francesco; Marotta, Roberto; Ruffilli, Roberta; Barberis, Andrea; Dal Maschio, Marco; Petrini, Enrica Maria; Santoriello, Stefania; Benfenati, Fabio; Di Fabrizio, Enzo

2013-02-11

The generation of 3D networks of primary neurons is a big challenge in neuroscience. Here, a novel method is presented for a 3D neuronal culture on superhydrophobic (SH) substrates. How nano-patterned SH devices stimulate neurons to build 3D networks is investigated. Scanning electron microscopy and confocal imaging show that soon after plating neurites adhere to the nanopatterned pillar sidewalls and they are subsequently pulled between pillars in a suspended position. These neurons display an enhanced survival rate compared to standard cultures and develop mature networks with physiological excitability. These findings underline the importance of using nanostructured SH surfaces for directing 3D neuronal growth, as well as for the design of biomaterials for neuronal regeneration. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Self-Organized Supercriticality and Oscillations in Networks of Stochastic Spiking Neurons

NASA Astrophysics Data System (ADS)

Costa, Ariadne; Brochini, Ludmila; Kinouchi, Osame

2017-08-01

Networks of stochastic spiking neurons are interesting models in the area of Theoretical Neuroscience, presenting both continuous and discontinuous phase transitions. Here we study fully connected networks analytically, numerically and by computational simulations. The neurons have dynamic gains that enable the network to converge to a stationary slightly supercritical state (self-organized supercriticality or SOSC) in the presence of the continuous transition. We show that SOSC, which presents power laws for neuronal avalanches plus some large events, is robust as a function of the main parameter of the neuronal gain dynamics. We discuss the possible applications of the idea of SOSC to biological phenomena like epilepsy and dragon king avalanches. We also find that neuronal gains can produce collective oscillations that coexists with neuronal avalanches, with frequencies compatible with characteristic brain rhythms.

Dietary Restriction Affects Neuronal Response Property and GABA Synthesis in the Primary Visual Cortex.

PubMed

Yang, Jinfang; Wang, Qian; He, Fenfen; Ding, Yanxia; Sun, Qingyan; Hua, Tianmiao; Xi, Minmin

2016-01-01

Previous studies have reported inconsistent effects of dietary restriction (DR) on cortical inhibition. To clarify this issue, we examined the response properties of neurons in the primary visual cortex (V1) of DR and control groups of cats using in vivo extracellular single-unit recording techniques, and assessed the synthesis of inhibitory neurotransmitter GABA in the V1 of cats from both groups using immunohistochemical and Western blot techniques. Our results showed that the response of V1 neurons to visual stimuli was significantly modified by DR, as indicated by an enhanced selectivity for stimulus orientations and motion directions, decreased visually-evoked response, lowered spontaneous activity and increased signal-to-noise ratio in DR cats relative to control cats. Further, it was shown that, accompanied with these changes of neuronal responsiveness, GABA immunoreactivity and the expression of a key GABA-synthesizing enzyme GAD67 in the V1 were significantly increased by DR. These results demonstrate that DR may retard brain aging by increasing the intracortical inhibition effect and improve the function of visual cortical neurons in visual information processing. This DR-induced elevation of cortical inhibition may favor the brain in modulating energy expenditure based on food availability.

Dietary Restriction Affects Neuronal Response Property and GABA Synthesis in the Primary Visual Cortex

PubMed Central

Sun, Qingyan; Hua, Tianmiao; Xi, Minmin

2016-01-01

Previous studies have reported inconsistent effects of dietary restriction (DR) on cortical inhibition. To clarify this issue, we examined the response properties of neurons in the primary visual cortex (V1) of DR and control groups of cats using in vivo extracellular single-unit recording techniques, and assessed the synthesis of inhibitory neurotransmitter GABA in the V1 of cats from both groups using immunohistochemical and Western blot techniques. Our results showed that the response of V1 neurons to visual stimuli was significantly modified by DR, as indicated by an enhanced selectivity for stimulus orientations and motion directions, decreased visually-evoked response, lowered spontaneous activity and increased signal-to-noise ratio in DR cats relative to control cats. Further, it was shown that, accompanied with these changes of neuronal responsiveness, GABA immunoreactivity and the expression of a key GABA-synthesizing enzyme GAD67 in the V1 were significantly increased by DR. These results demonstrate that DR may retard brain aging by increasing the intracortical inhibition effect and improve the function of visual cortical neurons in visual information processing. This DR-induced elevation of cortical inhibition may favor the brain in modulating energy expenditure based on food availability. PMID:26863207

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.

Distinct GABAergic targets of feedforward and feedback connections between lower and higher areas of rat visual cortex.

PubMed

Gonchar, Yuri; Burkhalter, Andreas

2003-11-26

Processing of visual information is performed in different cortical areas that are interconnected by feedforward (FF) and feedback (FB) pathways. Although FF and FB inputs are excitatory, their influences on pyramidal neurons also depend on the outputs of GABAergic neurons, which receive FF and FB inputs. Rat visual cortex contains at least three different families of GABAergic neurons that express parvalbumin (PV), calretinin (CR), and somatostatin (SOM) (Gonchar and Burkhalter, 1997). To examine whether pathway-specific inhibition (Shao and Burkhalter, 1996) is attributable to distinct connections with GABAergic neurons, we traced FF and FB inputs to PV, CR, and SOM neurons in layers 1-2/3 of area 17 and the secondary lateromedial area in rat visual cortex. We found that in layer 2/3 maximally 2% of FF and FB inputs go to CR and SOM neurons. This contrasts with 12-13% of FF and FB inputs onto layer 2/3 PV neurons. Unlike inputs to layer 2/3, connections to layer 1, which contains CR but lacks SOM and PV somata, are pathway-specific: 21% of FB inputs go to CR neurons, whereas FF inputs to layer 1 and its CR neurons are absent. These findings suggest that FF and FB influences on layer 2/3 pyramidal neurons mainly involve disynaptic connections via PV neurons that control the spike outputs to axons and proximal dendrites. Unlike FF input, FB input in addition makes a disynaptic link via CR neurons, which may influence the excitability of distal pyramidal cell dendrites in layer 1.

Emergence of Orientation Selectivity in the Mammalian Visual Pathway

PubMed Central

Scholl, Benjamin; Tan, Andrew Y. Y.; Corey, Joseph

2013-01-01

Orientation selectivity is a property of mammalian primary visual cortex (V1) neurons, yet its emergence along the visual pathway varies across species. In carnivores and primates, elongated receptive fields first appear in V1, whereas in lagomorphs such receptive fields emerge earlier, in the retina. Here we examine the mouse visual pathway and reveal the existence of orientation selectivity in lateral geniculate nucleus (LGN) relay cells. Cortical inactivation does not reduce this orientation selectivity, indicating that cortical feedback is not its source. Orientation selectivity is similar for LGN relay cells spiking and subthreshold input to V1 neurons, suggesting that cortical orientation selectivity is inherited from the LGN in mouse. In contrast, orientation selectivity of cat LGN relay cells is small relative to subthreshold inputs onto V1 simple cells. Together, these differences show that although orientation selectivity exists in visual neurons of both rodents and carnivores, its emergence along the visual pathway, and thus its underlying neuronal circuitry, is fundamentally different. PMID:23804085

High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels†

PubMed Central

Müller, Jan; Ballini, Marco; Livi, Paolo; Chen, Yihui; Radivojevic, Milos; Shadmani, Amir; Viswam, Vijay; Jones, Ian L.; Fiscella, Michele; Diggelmann, Roland; Stettler, Alexander; Frey, Urs; Bakkum, Douglas J.; Hierlemann, Andreas

2017-01-01

Studies on information processing and learning properties of neuronal networks would benefit from simultaneous and parallel access to the activity of a large fraction of all neurons in such networks. Here, we present a CMOS-based device, capable of simultaneously recording the electrical activity of over a thousand cells in in vitro neuronal networks. The device provides sufficiently high spatiotemporal resolution to enable, at the same time, access to neuronal preparations on subcellular, cellular, and network level. The key feature is a rapidly reconfigurable array of 26 400 microelectrodes arranged at low pitch (17.5 μm) within a large overall sensing area (3.85 × 2.10 mm2). An arbitrary subset of the electrodes can be simultaneously connected to 1024 low-noise readout channels as well as 32 stimulation units. Each electrode or electrode subset can be used to electrically stimulate or record the signals of virtually any neuron on the array. We demonstrate the applicability and potential of this device for various different experimental paradigms: large-scale recordings from whole networks of neurons as well as investigations of axonal properties of individual neurons. PMID:25973786

High-resolution CMOS MEA platform to study neurons at subcellular, cellular, and network levels.

PubMed

Müller, Jan; Ballini, Marco; Livi, Paolo; Chen, Yihui; Radivojevic, Milos; Shadmani, Amir; Viswam, Vijay; Jones, Ian L; Fiscella, Michele; Diggelmann, Roland; Stettler, Alexander; Frey, Urs; Bakkum, Douglas J; Hierlemann, Andreas

2015-07-07

Studies on information processing and learning properties of neuronal networks would benefit from simultaneous and parallel access to the activity of a large fraction of all neurons in such networks. Here, we present a CMOS-based device, capable of simultaneously recording the electrical activity of over a thousand cells in in vitro neuronal networks. The device provides sufficiently high spatiotemporal resolution to enable, at the same time, access to neuronal preparations on subcellular, cellular, and network level. The key feature is a rapidly reconfigurable array of 26 400 microelectrodes arranged at low pitch (17.5 μm) within a large overall sensing area (3.85 × 2.10 mm(2)). An arbitrary subset of the electrodes can be simultaneously connected to 1024 low-noise readout channels as well as 32 stimulation units. Each electrode or electrode subset can be used to electrically stimulate or record the signals of virtually any neuron on the array. We demonstrate the applicability and potential of this device for various different experimental paradigms: large-scale recordings from whole networks of neurons as well as investigations of axonal properties of individual neurons.

The TINS Lecture. The parietal association cortex in depth perception and visual control of hand action.

PubMed

Sakata, H; Taira, M; Kusunoki, M; Murata, A; Tanaka, Y

1997-08-01

Recent neurophysiological studies in alert monkeys have revealed that the parietal association cortex plays a crucial role in depth perception and visually guided hand movement. The following five classes of parietal neurons covering various aspects of these functions have been identified: (1) depth-selective visual-fixation (VF) neurons of the inferior parietal lobule (IPL), representing egocentric distance; (2) depth-movement sensitive (DMS) neurons of V5A and the ventral intraparietal (VIP) area representing direction of linear movement in 3-D space; (3) depth-rotation-sensitive (RS) neurons of V5A and the posterior parietal (PP) area representing direction of rotary movement in space; (4) visually responsive manipulation-related neurons (visual-dominant or visual-and-motor type) of the anterior intraparietal (AIP) area, representing 3-D shape or orientation (or both) of objects for manipulation; and (5) axis-orientation-selective (AOS) and surface-orientation-selective (SOS) neurons in the caudal intraparietal sulcus (cIPS) sensitive to binocular disparity and representing the 3-D orientation of the longitudinal axes and flat surfaces, respectively. Some AOS and SOS neurons are selective in both orientation and shape. Thus the dorsal visual pathway is divided into at least two subsystems, V5A, PP and VIP areas for motion vision and V6, LIP and cIPS areas for coding position and 3-D features. The cIPS sends the signals of 3-D features of objects to the AIP area, which is reciprocally connected to the ventral premotor (F5) area and plays an essential role in matching hand orientation and shaping with 3-D objects for manipulation.

Phase synchronization of bursting neurons in clustered small-world networks

NASA Astrophysics Data System (ADS)

Batista, C. A. S.; Lameu, E. L.; Batista, A. M.; Lopes, S. R.; Pereira, T.; Zamora-López, G.; Kurths, J.; Viana, R. L.

2012-07-01

We investigate the collective dynamics of bursting neurons on clustered networks. The clustered network model is composed of subnetworks, each of them presenting the so-called small-world property. This model can also be regarded as a network of networks. In each subnetwork a neuron is connected to other ones with regular as well as random connections, the latter with a given intracluster probability. Moreover, in a given subnetwork each neuron has an intercluster probability to be connected to the other subnetworks. The local neuron dynamics has two time scales (fast and slow) and is modeled by a two-dimensional map. In such small-world network the neuron parameters are chosen to be slightly different such that, if the coupling strength is large enough, there may be synchronization of the bursting (slow) activity. We give bounds for the critical coupling strength to obtain global burst synchronization in terms of the network structure, that is, the probabilities of intracluster and intercluster connections. We find that, as the heterogeneity in the network is reduced, the network global synchronizability is improved. We show that the transitions to global synchrony may be abrupt or smooth depending on the intercluster probability.

Visuomotor signals for reaching movements in the rostro-dorsal sector of the monkey thalamic reticular nucleus.

PubMed

Saga, Yosuke; Nakayama, Yoshihisa; Inoue, Ken-Ichi; Yamagata, Tomoko; Hashimoto, Masashi; Tremblay, Léon; Takada, Masahiko; Hoshi, Eiji

2017-05-01

The thalamic reticular nucleus (TRN) collects inputs from the cerebral cortex and thalamus and, in turn, sends inhibitory outputs to the thalamic relay nuclei. This unique connectivity suggests that the TRN plays a pivotal role in regulating information flow through the thalamus. Here, we analyzed the roles of TRN neurons in visually guided reaching movements. We first used retrograde transneuronal labeling with rabies virus, and showed that the rostro-dorsal sector of the TRN (TRNrd) projected disynaptically to the ventral premotor cortex (PMv). In other experiments, we recorded neurons from the TRNrd or PMv while monkeys performed a visuomotor task. We found that neurons in the TRNrd and PMv showed visual-, set-, and movement-related activity modulation. These results indicate that the TRNrd, as well as the PMv, is involved in the reception of visual signals and in the preparation and execution of reaching movements. The fraction of neurons that were non-selective for the location of visual signals or the direction of reaching movements was greater in the TRNrd than in the PMv. Furthermore, the fraction of neurons whose activity increased from the baseline was greater in the TRNrd than in the PMv. The timing of activity modulation of visual-related and movement-related neurons was similar in TRNrd and PMv neurons. Overall, our data suggest that TRNrd neurons provide motor thalamic nuclei with inhibitory inputs that are predominantly devoid of spatial selectivity, and that these signals modulate how these nuclei engage in both sensory processing and motor output during visually guided reaching behavior. © 2016 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Effects of partial time delays on phase synchronization in Watts-Strogatz small-world neuronal networks

NASA Astrophysics Data System (ADS)

Sun, Xiaojuan; Perc, Matjaž; Kurths, Jürgen

2017-05-01

In this paper, we study effects of partial time delays on phase synchronization in Watts-Strogatz small-world neuronal networks. Our focus is on the impact of two parameters, namely the time delay τ and the probability of partial time delay pdelay, whereby the latter determines the probability with which a connection between two neurons is delayed. Our research reveals that partial time delays significantly affect phase synchronization in this system. In particular, partial time delays can either enhance or decrease phase synchronization and induce synchronization transitions with changes in the mean firing rate of neurons, as well as induce switching between synchronized neurons with period-1 firing to synchronized neurons with period-2 firing. Moreover, in comparison to a neuronal network where all connections are delayed, we show that small partial time delay probabilities have especially different influences on phase synchronization of neuronal networks.

Effects of partial time delays on phase synchronization in Watts-Strogatz small-world neuronal networks.

PubMed

Sun, Xiaojuan; Perc, Matjaž; Kurths, Jürgen

2017-05-01

In this paper, we study effects of partial time delays on phase synchronization in Watts-Strogatz small-world neuronal networks. Our focus is on the impact of two parameters, namely the time delay τ and the probability of partial time delay p delay , whereby the latter determines the probability with which a connection between two neurons is delayed. Our research reveals that partial time delays significantly affect phase synchronization in this system. In particular, partial time delays can either enhance or decrease phase synchronization and induce synchronization transitions with changes in the mean firing rate of neurons, as well as induce switching between synchronized neurons with period-1 firing to synchronized neurons with period-2 firing. Moreover, in comparison to a neuronal network where all connections are delayed, we show that small partial time delay probabilities have especially different influences on phase synchronization of neuronal networks.

Cellular computational generalized neuron network for frequency situational intelligence in a multi-machine power system.

PubMed

Wei, Yawei; Venayagamoorthy, Ganesh Kumar

2017-09-01

To prevent large interconnected power system from a cascading failure, brownout or even blackout, grid operators require access to faster than real-time information to make appropriate just-in-time control decisions. However, the communication and computational system limitations of currently used supervisory control and data acquisition (SCADA) system can only deliver delayed information. However, the deployment of synchrophasor measurement devices makes it possible to capture and visualize, in near-real-time, grid operational data with extra granularity. In this paper, a cellular computational network (CCN) approach for frequency situational intelligence (FSI) in a power system is presented. The distributed and scalable computing unit of the CCN framework makes it particularly flexible for customization for a particular set of prediction requirements. Two soft-computing algorithms have been implemented in the CCN framework: a cellular generalized neuron network (CCGNN) and a cellular multi-layer perceptron network (CCMLPN), for purposes of providing multi-timescale frequency predictions, ranging from 16.67 ms to 2 s. These two developed CCGNN and CCMLPN systems were then implemented on two different scales of power systems, one of which installed a large photovoltaic plant. A real-time power system simulator at weather station within the Real-Time Power and Intelligent Systems (RTPIS) laboratory at Clemson, SC, was then used to derive typical FSI results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Distinct populations of neurons respond to emotional valence and arousal in the human subthalamic nucleus

PubMed Central

Sieger, Tomáš; Serranová, Tereza; Růžička, Filip; Vostatek, Pavel; Wild, Jiří; Šťastná, Daniela; Bonnet, Cecilia; Novák, Daniel; Růžička, Evžen; Urgošík, Dušan; Jech, Robert

2015-01-01

Both animal studies and studies using deep brain stimulation in humans have demonstrated the involvement of the subthalamic nucleus (STN) in motivational and emotional processes; however, participation of this nucleus in processing human emotion has not been investigated directly at the single-neuron level. We analyzed the relationship between the neuronal firing from intraoperative microrecordings from the STN during affective picture presentation in patients with Parkinson’s disease (PD) and the affective ratings of emotional valence and arousal performed subsequently. We observed that 17% of neurons responded to emotional valence and arousal of visual stimuli according to individual ratings. The activity of some neurons was related to emotional valence, whereas different neurons responded to arousal. In addition, 14% of neurons responded to visual stimuli. Our results suggest the existence of neurons involved in processing or transmission of visual and emotional information in the human STN, and provide evidence of separate processing of the affective dimensions of valence and arousal at the level of single neurons as well. PMID:25713375

Long-term optical stimulation of channelrhodopsin-expressing neurons to study network plasticity

PubMed Central

Lignani, Gabriele; Ferrea, Enrico; Difato, Francesco; Amarù, Jessica; Ferroni, Eleonora; Lugarà, Eleonora; Espinoza, Stefano; Gainetdinov, Raul R.; Baldelli, Pietro; Benfenati, Fabio

2013-01-01

Neuronal plasticity produces changes in excitability, synaptic transmission, and network architecture in response to external stimuli. Network adaptation to environmental conditions takes place in time scales ranging from few seconds to days, and modulates the entire network dynamics. To study the network response to defined long-term experimental protocols, we setup a system that combines optical and electrophysiological tools embedded in a cell incubator. Primary hippocampal neurons transduced with lentiviruses expressing channelrhodopsin-2/H134R were subjected to various photostimulation protocols in a time window in the order of days. To monitor the effects of light-induced gating of network activity, stimulated transduced neurons were simultaneously recorded using multi-electrode arrays (MEAs). The developed experimental model allows discerning short-term, long-lasting, and adaptive plasticity responses of the same neuronal network to distinct stimulation frequencies applied over different temporal windows. PMID:23970852

Long-term optical stimulation of channelrhodopsin-expressing neurons to study network plasticity.

PubMed

Lignani, Gabriele; Ferrea, Enrico; Difato, Francesco; Amarù, Jessica; Ferroni, Eleonora; Lugarà, Eleonora; Espinoza, Stefano; Gainetdinov, Raul R; Baldelli, Pietro; Benfenati, Fabio

2013-01-01

Neuronal plasticity produces changes in excitability, synaptic transmission, and network architecture in response to external stimuli. Network adaptation to environmental conditions takes place in time scales ranging from few seconds to days, and modulates the entire network dynamics. To study the network response to defined long-term experimental protocols, we setup a system that combines optical and electrophysiological tools embedded in a cell incubator. Primary hippocampal neurons transduced with lentiviruses expressing channelrhodopsin-2/H134R were subjected to various photostimulation protocols in a time window in the order of days. To monitor the effects of light-induced gating of network activity, stimulated transduced neurons were simultaneously recorded using multi-electrode arrays (MEAs). The developed experimental model allows discerning short-term, long-lasting, and adaptive plasticity responses of the same neuronal network to distinct stimulation frequencies applied over different temporal windows.

Improved Autoassociative Neural Networks

NASA Technical Reports Server (NTRS)

Hand, Charles

2003-01-01

Improved autoassociative neural networks, denoted nexi, have been proposed for use in controlling autonomous robots, including mobile exploratory robots of the biomorphic type. In comparison with conventional autoassociative neural networks, nexi would be more complex but more capable in that they could be trained to do more complex tasks. A nexus would use bit weights and simple arithmetic in a manner that would enable training and operation without a central processing unit, programs, weight registers, or large amounts of memory. Only a relatively small amount of memory (to hold the bit weights) and a simple logic application- specific integrated circuit would be needed. A description of autoassociative neural networks is prerequisite to a meaningful description of a nexus. An autoassociative network is a set of neurons that are completely connected in the sense that each neuron receives input from, and sends output to, all the other neurons. (In some instantiations, a neuron could also send output back to its own input terminal.) The state of a neuron is completely determined by the inner product of its inputs with weights associated with its input channel. Setting the weights sets the behavior of the network. The neurons of an autoassociative network are usually regarded as comprising a row or vector. Time is a quantized phenomenon for most autoassociative networks in the sense that time proceeds in discrete steps. At each time step, the row of neurons forms a pattern: some neurons are firing, some are not. Hence, the current state of an autoassociative network can be described with a single binary vector. As time goes by, the network changes the vector. Autoassociative networks move vectors over hyperspace landscapes of possibilities.

Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks

PubMed Central

Burroni, Javier; Taylor, P.; Corey, Cassian; Vachnadze, Tengiz; Siegelmann, Hava T.

2017-01-01

Overview: We model energy constraints in a network of spiking neurons, while exploring general questions of resource limitation on network function abstractly. Background: Metabolic states like dietary ketosis or hypoglycemia have a large impact on brain function and disease outcomes. Glia provide metabolic support for neurons, among other functions. Yet, in computational models of glia-neuron cooperation, there have been no previous attempts to explore the effects of direct realistic energy costs on network activity in spiking neurons. Currently, biologically realistic spiking neural networks assume that membrane potential is the main driving factor for neural spiking, and do not take into consideration energetic costs. Methods: We define local energy pools to constrain a neuron model, termed Spiking Neuron Energy Pool (SNEP), which explicitly incorporates energy limitations. Each neuron requires energy to spike, and resources in the pool regenerate over time. Our simulation displays an easy-to-use GUI, which can be run locally in a web browser, and is freely available. Results: Energy dependence drastically changes behavior of these neural networks, causing emergent oscillations similar to those in networks of biological neurons. We analyze the system via Lotka-Volterra equations, producing several observations: (1) energy can drive self-sustained oscillations, (2) the energetic cost of spiking modulates the degree and type of oscillations, (3) harmonics emerge with frequencies determined by energy parameters, and (4) varying energetic costs have non-linear effects on energy consumption and firing rates. Conclusions: Models of neuron function which attempt biological realism may benefit from including energy constraints. Further, we assert that observed oscillatory effects of energy limitations exist in networks of many kinds, and that these findings generalize to abstract graphs and technological applications. PMID:28289370

Adaptation of velocity encoding in synaptically coupled neurons in the fly visual system.

PubMed

Kalb, Julia; Egelhaaf, Martin; Kurtz, Rafael

2008-09-10

Although many adaptation-induced effects on neuronal response properties have been described, it is often unknown at what processing stages in the nervous system they are generated. We focused on fly visual motion-sensitive neurons to identify changes in response characteristics during prolonged visual motion stimulation. By simultaneous recordings of synaptically coupled neurons, we were able to directly compare adaptation-induced effects at two consecutive processing stages in the fly visual motion pathway. This allowed us to narrow the potential sites of adaptation effects within the visual system and to relate them to the properties of signal transfer between neurons. Motion adaptation was accompanied by a response reduction, which was somewhat stronger in postsynaptic than in presynaptic cells. We found that the linear representation of motion velocity degrades during adaptation to a white-noise velocity-modulated stimulus. This effect is caused by an increasingly nonlinear velocity representation rather than by an increase of noise and is similarly strong in presynaptic and postsynaptic neurons. In accordance with this similarity, the dynamics and the reliability of interneuronal signal transfer remained nearly constant. Thus, adaptation is mainly based on processes located in the presynaptic neuron or in more peripheral processing stages. In contrast, changes of transfer properties at the analyzed synapse or in postsynaptic spike generation contribute little to changes in velocity coding during motion adaptation.

Supranormal orientation selectivity of visual neurons in orientation-restricted animals.

PubMed

Sasaki, Kota S; Kimura, Rui; Ninomiya, Taihei; Tabuchi, Yuka; Tanaka, Hiroki; Fukui, Masayuki; Asada, Yusuke C; Arai, Toshiya; Inagaki, Mikio; Nakazono, Takayuki; Baba, Mika; Kato, Daisuke; Nishimoto, Shinji; Sanada, Takahisa M; Tani, Toshiki; Imamura, Kazuyuki; Tanaka, Shigeru; Ohzawa, Izumi

2015-11-16

Altered sensory experience in early life often leads to remarkable adaptations so that humans and animals can make the best use of the available information in a particular environment. By restricting visual input to a limited range of orientations in young animals, this investigation shows that stimulus selectivity, e.g., the sharpness of tuning of single neurons in the primary visual cortex, is modified to match a particular environment. Specifically, neurons tuned to an experienced orientation in orientation-restricted animals show sharper orientation tuning than neurons in normal animals, whereas the opposite was true for neurons tuned to non-experienced orientations. This sharpened tuning appears to be due to elongated receptive fields. Our results demonstrate that restricted sensory experiences can sculpt the supranormal functions of single neurons tailored for a particular environment. The above findings, in addition to the minimal population response to orientations close to the experienced one, agree with the predictions of a sparse coding hypothesis in which information is represented efficiently by a small number of activated neurons. This suggests that early brain areas adopt an efficient strategy for coding information even when animals are raised in a severely limited visual environment where sensory inputs have an unnatural statistical structure.

Supranormal orientation selectivity of visual neurons in orientation-restricted animals

PubMed Central

Sasaki, Kota S.; Kimura, Rui; Ninomiya, Taihei; Tabuchi, Yuka; Tanaka, Hiroki; Fukui, Masayuki; Asada, Yusuke C.; Arai, Toshiya; Inagaki, Mikio; Nakazono, Takayuki; Baba, Mika; Kato, Daisuke; Nishimoto, Shinji; Sanada, Takahisa M.; Tani, Toshiki; Imamura, Kazuyuki; Tanaka, Shigeru; Ohzawa, Izumi

2015-01-01

Altered sensory experience in early life often leads to remarkable adaptations so that humans and animals can make the best use of the available information in a particular environment. By restricting visual input to a limited range of orientations in young animals, this investigation shows that stimulus selectivity, e.g., the sharpness of tuning of single neurons in the primary visual cortex, is modified to match a particular environment. Specifically, neurons tuned to an experienced orientation in orientation-restricted animals show sharper orientation tuning than neurons in normal animals, whereas the opposite was true for neurons tuned to non-experienced orientations. This sharpened tuning appears to be due to elongated receptive fields. Our results demonstrate that restricted sensory experiences can sculpt the supranormal functions of single neurons tailored for a particular environment. The above findings, in addition to the minimal population response to orientations close to the experienced one, agree with the predictions of a sparse coding hypothesis in which information is represented efficiently by a small number of activated neurons. This suggests that early brain areas adopt an efficient strategy for coding information even when animals are raised in a severely limited visual environment where sensory inputs have an unnatural statistical structure. PMID:26567927

Developing a tissue-engineered neural-electrical relay using encapsulated neuronal constructs on conducting polymer fibers.

PubMed

Cullen, D Kacy; R Patel, Ankur; Doorish, John F; Smith, Douglas H; Pfister, Bryan J

2008-12-01

Neural-electrical interface platforms are being developed to extracellularly monitor neuronal population activity. Polyaniline-based electrically conducting polymer fibers are attractive substrates for sustained functional interfaces with neurons due to their flexibility, tailored geometry and controlled electro-conductive properties. In this study, we addressed the neurobiological considerations of utilizing small diameter (<400 microm) fibers consisting of a blend of electrically conductive polyaniline and polypropylene (PA-PP) as the backbone of encapsulated tissue-engineered neural-electrical relays. We devised new approaches to promote survival, adhesion and neurite outgrowth of primary dorsal root ganglion neurons on PA-PP fibers. We attained a greater than ten-fold increase in the density of viable neurons on fiber surfaces to approximately 700 neurons mm(-2) by manipulating surrounding surface charges to bias settling neuronal suspensions toward fibers coated with cell-adhesive ligands. This stark increase in neuronal density resulted in robust neuritic extension and network formation directly along the fibers. Additionally, we encapsulated these neuronal networks on PA-PP fibers using agarose to form a protective barrier while potentially facilitating network stability. Following encapsulation, the neuronal networks maintained integrity, high viability (>85%) and intimate adhesion to PA-PP fibers. These efforts accomplished key prerequisites for the establishment of functional electrical interfaces with neuronal populations using small diameter PA-PP fibers-specifically, improved neurocompatibility, high-density neuronal adhesion and neuritic network development directly on fiber surfaces.

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

Altered modulation of gamma oscillation frequency by speed of visual motion in children with autism spectrum disorders.

PubMed

Stroganova, Tatiana A; Butorina, Anna V; Sysoeva, Olga V; Prokofyev, Andrey O; Nikolaeva, Anastasia Yu; Tsetlin, Marina M; Orekhova, Elena V

2015-01-01

Recent studies link autism spectrum disorders (ASD) with an altered balance between excitation and inhibition (E/I balance) in cortical networks. The brain oscillations in high gamma-band (50-120 Hz) are sensitive to the E/I balance and may appear useful biomarkers of certain ASD subtypes. The frequency of gamma oscillations is mediated by level of excitation of the fast-spiking inhibitory basket cells recruited by increasing strength of excitatory input. Therefore, the experimental manipulations affecting gamma frequency may throw light on inhibitory networks dysfunction in ASD. Here, we used magnetoencephalography (MEG) to investigate modulation of visual gamma oscillation frequency by speed of drifting annular gratings (1.2, 3.6, 6.0 °/s) in 21 boys with ASD and 26 typically developing boys aged 7-15 years. Multitaper method was used for analysis of spectra of gamma power change upon stimulus presentation and permutation test was applied for statistical comparisons. We also assessed in our participants visual orientation discrimination thresholds, which are thought to depend on excitability of inhibitory networks in the visual cortex. Although frequency of the oscillatory gamma response increased with increasing velocity of visual motion in both groups of participants, the velocity effect was reduced in a substantial proportion of children with ASD. The range of velocity-related gamma frequency modulation correlated inversely with the ability to discriminate oblique line orientation in the ASD group, while no such correlation has been observed in the group of typically developing participants. Our findings suggest that abnormal velocity-related gamma frequency modulation in ASD may constitute a potential biomarker for reduced excitability of fast-spiking inhibitory neurons in a subset of children with ASD.

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

PubMed

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

2016-01-01

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

SPICODYN: A Toolbox for the Analysis of Neuronal Network Dynamics and Connectivity from Multi-Site Spike Signal Recordings.

PubMed

Pastore, Vito Paolo; Godjoski, Aleksandar; Martinoia, Sergio; Massobrio, Paolo

2018-01-01

We implemented an automated and efficient open-source software for the analysis of multi-site neuronal spike signals. The software package, named SPICODYN, has been developed as a standalone windows GUI application, using C# programming language with Microsoft Visual Studio based on .NET framework 4.5 development environment. Accepted input data formats are HDF5, level 5 MAT and text files, containing recorded or generated time series spike signals data. SPICODYN processes such electrophysiological signals focusing on: spiking and bursting dynamics and functional-effective connectivity analysis. In particular, for inferring network connectivity, a new implementation of the transfer entropy method is presented dealing with multiple time delays (temporal extension) and with multiple binary patterns (high order extension). SPICODYN is specifically tailored to process data coming from different Multi-Electrode Arrays setups, guarantying, in those specific cases, automated processing. The optimized implementation of the Delayed Transfer Entropy and the High-Order Transfer Entropy algorithms, allows performing accurate and rapid analysis on multiple spike trains from thousands of electrodes.

Cultured neuronal networks as environmental biosensors.

PubMed

O'Shaughnessy, Thomas J; Gray, Samuel A; Pancrazio, Joseph J

2004-01-01

Contamination of water by toxins, either intentionally or unintentionally, is a growing concern for both military and civilian agencies and thus there is a need for systems capable of monitoring a wide range of natural and industrial toxicants. The EILATox-Oregon Workshop held in September 2002 provided an opportunity to test the capabilities of a prototype neuronal network-based biosensor with unknown contaminants in water samples. The biosensor is a portable device capable of recording the action potential activity from a network of mammalian neurons grown on glass microelectrode arrays. Changes in the action potential fi ring rate across the network are monitored to determine exposure to toxicants. A series of three neuronal networks derived from mice was used to test seven unknown samples. Two of these unknowns later were revealed to be blanks, to which the neuronal networks did not respond. Of the five remaining unknowns, a significant change in network activity was detected for four of the compounds at concentrations below a lethal level for humans: mercuric chloride, sodium arsenite, phosdrin and chlordimeform. These compounds--two heavy metals, an organophosphate and an insecticide--demonstrate the breadth of detection possible with neuronal networks. The results generated at the workshop show the promise of the neuronal network biosensor as an environmental detector but there is still considerable effort needed to produce a device suitable for routine environmental threat monitoring.

Neurons from the adult human dentate nucleus: neural networks in the neuron classification.

PubMed

Grbatinić, Ivan; Marić, Dušica L; Milošević, Nebojša T

2015-04-07

Topological (central vs. border neuron type) and morphological classification of adult human dentate nucleus neurons according to their quantified histomorphological properties using neural networks on real and virtual neuron samples. In the real sample 53.1% and 14.1% of central and border neurons, respectively, are classified correctly with total of 32.8% of misclassified neurons. The most important result present 62.2% of misclassified neurons in border neurons group which is even greater than number of correctly classified neurons (37.8%) in that group, showing obvious failure of network to classify neurons correctly based on computational parameters used in our study. On the virtual sample 97.3% of misclassified neurons in border neurons group which is much greater than number of correctly classified neurons (2.7%) in that group, again confirms obvious failure of network to classify neurons correctly. Statistical analysis shows that there is no statistically significant difference in between central and border neurons for each measured parameter (p>0.05). Total of 96.74% neurons are morphologically classified correctly by neural networks and each one belongs to one of the four histomorphological types: (a) neurons with small soma and short dendrites, (b) neurons with small soma and long dendrites, (c) neuron with large soma and short dendrites, (d) neurons with large soma and long dendrites. Statistical analysis supports these results (p<0.05). Human dentate nucleus neurons can be classified in four neuron types according to their quantitative histomorphological properties. These neuron types consist of two neuron sets, small and large ones with respect to their perykarions with subtypes differing in dendrite length i.e. neurons with short vs. long dendrites. Besides confirmation of neuron classification on small and large ones, already shown in literature, we found two new subtypes i.e. neurons with small soma and long dendrites and with large soma and short dendrites. These neurons are most probably equally distributed throughout the dentate nucleus as no significant difference in their topological distribution is observed. Copyright © 2015 Elsevier Ltd. All rights reserved.

Connectomic constraints on computation in feedforward networks of spiking neurons.

PubMed

Ramaswamy, Venkatakrishnan; Banerjee, Arunava

2014-10-01

Several efforts are currently underway to decipher the connectome or parts thereof in a variety of organisms. Ascertaining the detailed physiological properties of all the neurons in these connectomes, however, is out of the scope of such projects. It is therefore unclear to what extent knowledge of the connectome alone will advance a mechanistic understanding of computation occurring in these neural circuits, especially when the high-level function of the said circuit is unknown. We consider, here, the question of how the wiring diagram of neurons imposes constraints on what neural circuits can compute, when we cannot assume detailed information on the physiological response properties of the neurons. We call such constraints-that arise by virtue of the connectome-connectomic constraints on computation. For feedforward networks equipped with neurons that obey a deterministic spiking neuron model which satisfies a small number of properties, we ask if just by knowing the architecture of a network, we can rule out computations that it could be doing, no matter what response properties each of its neurons may have. We show results of this form, for certain classes of network architectures. On the other hand, we also prove that with the limited set of properties assumed for our model neurons, there are fundamental limits to the constraints imposed by network structure. Thus, our theory suggests that while connectomic constraints might restrict the computational ability of certain classes of network architectures, we may require more elaborate information on the properties of neurons in the network, before we can discern such results for other classes of networks.

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

PubMed Central

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

2014-01-01

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

Modulation of Neuronal Responses by Exogenous Attention in Macaque Primary Visual Cortex.

PubMed

Wang, Feng; Chen, Minggui; Yan, Yin; Zhaoping, Li; Li, Wu

2015-09-30

Visual perception is influenced by attention deployed voluntarily or triggered involuntarily by salient stimuli. Modulation of visual cortical processing by voluntary or endogenous attention has been extensively studied, but much less is known about how involuntary or exogenous attention affects responses of visual cortical neurons. Using implanted microelectrode arrays, we examined the effects of exogenous attention on neuronal responses in the primary visual cortex (V1) of awake monkeys. A bright annular cue was flashed either around the receptive fields of recorded neurons or in the opposite visual field to capture attention. A subsequent grating stimulus probed the cue-induced effects. In a fixation task, when the cue-to-probe stimulus onset asynchrony (SOA) was <240 ms, the cue induced a transient increase of neuronal responses to the probe at the cued location during 40-100 ms after the onset of neuronal responses to the probe. This facilitation diminished and disappeared after repeated presentations of the same cue but recurred for a new cue of a different color. In another task to detect the probe, relative shortening of monkey's reaction times for the validly cued probe depended on the SOA in a way similar to the cue-induced V1 facilitation, and the behavioral and physiological cueing effects remained after repeated practice. Flashing two cues simultaneously in the two opposite visual fields weakened or diminished both the physiological and behavioral cueing effects. Our findings indicate that exogenous attention significantly modulates V1 responses and that the modulation strength depends on both novelty and task relevance of the stimulus. Significance statement: Visual attention can be involuntarily captured by a sudden appearance of a conspicuous object, allowing rapid reactions to unexpected events of significance. The current study discovered a correlate of this effect in monkey primary visual cortex. An abrupt, salient, flash enhanced neuronal responses, and shortened the animal's reaction time, to a subsequent visual probe stimulus at the same location. However, the enhancement of the neural responses diminished after repeated exposures to this flash if the animal was not required to react to the probe. Moreover, a second, simultaneous, flash at another location weakened the neuronal and behavioral effects of the first one. These findings revealed, beyond the observations reported so far, the effects of exogenous attention in the brain. Copyright © 2015 the authors 0270-6474/15/3513419-11$15.00/0.

Revealing degree distribution of bursting neuron networks.

PubMed

Shen, Yu; Hou, Zhonghuai; Xin, Houwen

2010-03-01

We present a method to infer the degree distribution of a bursting neuron network from its dynamics. Burst synchronization (BS) of coupled Morris-Lecar neurons has been studied under the weak coupling condition. In the BS state, all the neurons start and end bursting almost simultaneously, while the spikes inside the burst are incoherent among the neurons. Interestingly, we find that the spike amplitude of a given neuron shows an excellent linear relationship with its degree, which makes it possible to estimate the degree distribution of the network by simple statistics of the spike amplitudes. We demonstrate the validity of this scheme on scale-free as well as small-world networks. The underlying mechanism of such a method is also briefly discussed.

Recording axonal conduction to evaluate the integration of pluripotent cell-derived neurons into a neuronal network.

PubMed

Shimba, Kenta; Sakai, Koji; Takayama, Yuzo; Kotani, Kiyoshi; Jimbo, Yasuhiko

2015-10-01

Stem cell transplantation is a promising therapy to treat neurodegenerative disorders, and a number of in vitro models have been developed for studying interactions between grafted neurons and the host neuronal network to promote drug discovery. However, methods capable of evaluating the process by which stem cells integrate into the host neuronal network are lacking. In this study, we applied an axonal conduction-based analysis to a co-culture study of primary and differentiated neurons. Mouse cortical neurons and neuronal cells differentiated from P19 embryonal carcinoma cells, a model for early neural differentiation of pluripotent stem cells, were co-cultured in a microfabricated device. The somata of these cells were separated by the co-culture device, but their axons were able to elongate through microtunnels and then form synaptic contacts. Propagating action potentials were recorded from these axons by microelectrodes embedded at the bottom of the microtunnels and sorted into clusters representing individual axons. While the number of axons of cortical neurons increased until 14 days in vitro and then decreased, those of P19 neurons increased throughout the culture period. Network burst analysis showed that P19 neurons participated in approximately 80% of the bursting activity after 14 days in vitro. Interestingly, the axonal conduction delay of P19 neurons was significantly greater than that of cortical neurons, suggesting that there are some physiological differences in their axons. These results suggest that our method is feasible to evaluate the process by which stem cell-derived neurons integrate into a host neuronal network.

Speed and segmentation control mechanisms characterized in rhythmically-active circuits created from spinal neurons produced from genetically-tagged embryonic stem cells

PubMed Central

Sternfeld, Matthew J; Hinckley, Christopher A; Moore, Niall J; Pankratz, Matthew T; Hilde, Kathryn L; Driscoll, Shawn P; Hayashi, Marito; Amin, Neal D; Bonanomi, Dario; Gifford, Wesley D; Sharma, Kamal; Goulding, Martyn; Pfaff, Samuel L

2017-01-01

Flexible neural networks, such as the interconnected spinal neurons that control distinct motor actions, can switch their activity to produce different behaviors. Both excitatory (E) and inhibitory (I) spinal neurons are necessary for motor behavior, but the influence of recruiting different ratios of E-to-I cells remains unclear. We constructed synthetic microphysical neural networks, called circuitoids, using precise combinations of spinal neuron subtypes derived from mouse stem cells. Circuitoids of purified excitatory interneurons were sufficient to generate oscillatory bursts with properties similar to in vivo central pattern generators. Inhibitory V1 neurons provided dual layers of regulation within excitatory rhythmogenic networks - they increased the rhythmic burst frequency of excitatory V3 neurons, and segmented excitatory motor neuron activity into sub-networks. Accordingly, the speed and pattern of spinal circuits that underlie complex motor behaviors may be regulated by quantitatively gating the intra-network cellular activity ratio of E-to-I neurons. DOI: http://dx.doi.org/10.7554/eLife.21540.001 PMID:28195039

Widespread receptivity to neuropeptide PDF throughout the neuronal circadian clock network of Drosophila revealed by real-time cyclic AMP imaging.

PubMed

Shafer, Orie T; Kim, Dong Jo; Dunbar-Yaffe, Richard; Nikolaev, Viacheslav O; Lohse, Martin J; Taghert, Paul H

2008-04-24

The neuropeptide PDF is released by sixteen clock neurons in Drosophila and helps maintain circadian activity rhythms by coordinating a network of approximately 150 neuronal clocks. Whether PDF acts directly on elements of this neural network remains unknown. We address this question by adapting Epac1-camps, a genetically encoded cAMP FRET sensor, for use in the living brain. We find that a subset of the PDF-expressing neurons respond to PDF with long-lasting cAMP increases and confirm that such responses require the PDF receptor. In contrast, an unrelated Drosophila neuropeptide, DH31, stimulates large cAMP increases in all PDF-expressing clock neurons. Thus, the network of approximately 150 clock neurons displays widespread, though not uniform, PDF receptivity. This work introduces a sensitive means of measuring cAMP changes in a living brain with subcellular resolution. Specifically, it experimentally confirms the longstanding hypothesis that PDF is a direct modulator of most neurons in the Drosophila clock network.

Dynamical analysis of Parkinsonian state emulated by hybrid Izhikevich neuron models

NASA Astrophysics Data System (ADS)

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

2015-11-01

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

An emergentist perspective on the origin of number sense

PubMed Central

2018-01-01

The finding that human infants and many other animal species are sensitive to numerical quantity has been widely interpreted as evidence for evolved, biologically determined numerical capacities across unrelated species, thereby supporting a ‘nativist’ stance on the origin of number sense. Here, we tackle this issue within the ‘emergentist’ perspective provided by artificial neural network models, and we build on computer simulations to discuss two different approaches to think about the innateness of number sense. The first, illustrated by artificial life simulations, shows that numerical abilities can be supported by domain-specific representations emerging from evolutionary pressure. The second assumes that numerical representations need not be genetically pre-determined but can emerge from the interplay between innate architectural constraints and domain-general learning mechanisms, instantiated in deep learning simulations. We show that deep neural networks endowed with basic visuospatial processing exhibit a remarkable performance in numerosity discrimination before any experience-dependent learning, whereas unsupervised sensory experience with visual sets leads to subsequent improvement of number acuity and reduces the influence of continuous visual cues. The emergent neuronal code for numbers in the model includes both numerosity-sensitive (summation coding) and numerosity-selective response profiles, closely mirroring those found in monkey intraparietal neurons. We conclude that a form of innatism based on architectural and learning biases is a fruitful approach to understanding the origin and development of number sense. This article is part of a discussion meeting issue ‘The origins of numerical abilities'. PMID:29292348

Qualitative validation of the reduction from two reciprocally coupled neurons to one self-coupled neuron in a respiratory network model.

PubMed

Dunmyre, Justin R

2011-06-01

The pre-Bötzinger complex of the mammalian brainstem is a heterogeneous neuronal network, and individual neurons within the network have varying strengths of the persistent sodium and calcium-activated nonspecific cationic currents. Individually, these currents have been the focus of modeling efforts. Previously, Dunmyre et al. (J Comput Neurosci 1-24, 2011) proposed a model and studied the interactions of these currents within one self-coupled neuron. In this work, I consider two identical, reciprocally coupled model neurons and validate the reduction to the self-coupled case. I find that all of the dynamics of the two model neuron network and the regions of parameter space where these distinct dynamics are found are qualitatively preserved in the reduction to the self-coupled case.

Motor-visual neurons and action recognition in social interactions.

PubMed

de la Rosa, Stephan; Bülthoff, Heinrich H

2014-04-01

Cook et al. suggest that motor-visual neurons originate from associative learning. This suggestion has interesting implications for the processing of socially relevant visual information in social interactions. Here, we discuss two aspects of the associative learning account that seem to have particular relevance for visual recognition of social information in social interactions - namely, context-specific and contingency based learning.

Encoding of Target Detection during Visual Search by Single Neurons in the Human Brain.

PubMed

Wang, Shuo; Mamelak, Adam N; Adolphs, Ralph; Rutishauser, Ueli

2018-06-08

Neurons in the primate medial temporal lobe (MTL) respond selectively to visual categories such as faces, contributing to how the brain represents stimulus meaning. However, it remains unknown whether MTL neurons continue to encode stimulus meaning when it changes flexibly as a function of variable task demands imposed by goal-directed behavior. While classically associated with long-term memory, recent lesion and neuroimaging studies show that the MTL also contributes critically to the online guidance of goal-directed behaviors such as visual search. Do such tasks modulate responses of neurons in the MTL, and if so, do their responses mirror bottom-up input from visual cortices or do they reflect more abstract goal-directed properties? To answer these questions, we performed concurrent recordings of eye movements and single neurons in the MTL and medial frontal cortex (MFC) in human neurosurgical patients performing a memory-guided visual search task. We identified a distinct population of target-selective neurons in both the MTL and MFC whose response signaled whether the currently fixated stimulus was a target or distractor. This target-selective response was invariant to visual category and predicted whether a target was detected or missed behaviorally during a given fixation. The response latencies, relative to fixation onset, of MFC target-selective neurons preceded those in the MTL by ∼200 ms, suggesting a frontal origin for the target signal. The human MTL thus represents not only fixed stimulus identity, but also task-specified stimulus relevance due to top-down goal relevance. Copyright © 2018 Elsevier Ltd. All rights reserved.

Developmental time windows for axon growth influence neuronal network topology.

PubMed

Lim, Sol; Kaiser, Marcus

2015-04-01

Early brain connectivity development consists of multiple stages: birth of neurons, their migration and the subsequent growth of axons and dendrites. Each stage occurs within a certain period of time depending on types of neurons and cortical layers. Forming synapses between neurons either by growing axons starting at similar times for all neurons (much-overlapped time windows) or at different time points (less-overlapped) may affect the topological and spatial properties of neuronal networks. Here, we explore the extreme cases of axon formation during early development, either starting at the same time for all neurons (parallel, i.e., maximally overlapped time windows) or occurring for each neuron separately one neuron after another (serial, i.e., no overlaps in time windows). For both cases, the number of potential and established synapses remained comparable. Topological and spatial properties, however, differed: Neurons that started axon growth early on in serial growth achieved higher out-degrees, higher local efficiency and longer axon lengths while neurons demonstrated more homogeneous connectivity patterns for parallel growth. Second, connection probability decreased more rapidly with distance between neurons for parallel growth than for serial growth. Third, bidirectional connections were more numerous for parallel growth. Finally, we tested our predictions with C. elegans data. Together, this indicates that time windows for axon growth influence the topological and spatial properties of neuronal networks opening up the possibility to a posteriori estimate developmental mechanisms based on network properties of a developed network.

Spiking Neurons for Analysis of Patterns

NASA Technical Reports Server (NTRS)

Huntsberger, Terrance

2008-01-01

Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern-analysis and pattern-recognition computational systems. These neurons are represented by a mathematical model denoted the state-variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets. Spiking neurons imitate biological neurons more closely than do the neurons of traditional artificial neural networks. A spiking neuron includes a central cell body (soma) surrounded by a tree-like interconnection network (dendrites). Spiking neurons are so named because they generate trains of output pulses (spikes) in response to inputs received from sensors or from other neurons. They gain their speed advantage over traditional neural networks by using the timing of individual spikes for computation, whereas traditional artificial neurons use averages of activity levels over time. Moreover, spiking neurons use the delays inherent in dendritic processing in order to efficiently encode the information content of incoming signals. Because traditional artificial neurons fail to capture this encoding, they have less processing capability, and so it is necessary to use more gates when implementing traditional artificial neurons in electronic circuitry. Such higher-order functions as dynamic tasking are effected by use of pools (collections) of spiking neurons interconnected by spike-transmitting fibers. The SVM includes adaptive thresholds and submodels of transport of ions (in imitation of such transport in biological neurons). These features enable the neurons to adapt their responses to high-rate inputs from sensors, and to adapt their firing thresholds to mitigate noise or effects of potential sensor failure. The mathematical derivation of the SVM starts from a prior model, known in the art as the point soma model, which captures all of the salient properties of neuronal response while keeping the computational cost low. The point-soma latency time is modified to be an exponentially decaying function of the strength of the applied potential. Choosing computational efficiency over biological fidelity, the dendrites surrounding a neuron are represented by simplified compartmental submodels and there are no dendritic spines. Updates to the dendritic potential, calcium-ion concentrations and conductances, and potassium-ion conductances are done by use of equations similar to those of the point soma. Diffusion processes in dendrites are modeled by averaging among nearest-neighbor compartments. Inputs to each of the dendritic compartments come from sensors. Alternatively or in addition, when an affected neuron is part of a pool, inputs can come from other spiking neurons. At present, SVM neural networks are implemented by computational simulation, using algorithms that encode the SVM and its submodels. However, it should be possible to implement these neural networks in hardware: The differential equations for the dendritic and cellular processes in the SVM model of spiking neurons map to equivalent circuits that can be implemented directly in analog very-large-scale integrated (VLSI) circuits.

Reliability and synchronization in a delay-coupled neuronal network with synaptic plasticity

NASA Astrophysics Data System (ADS)

Pérez, Toni; Uchida, Atsushi

2011-06-01

We investigate the characteristics of reliability and synchronization of a neuronal network of delay-coupled integrate and fire neurons. Reliability and synchronization appear in separated regions of the phase space of the parameters considered. The effect of including synaptic plasticity and different delay values between the connections are also considered. We found that plasticity strongly changes the characteristics of reliability and synchronization in the parameter space of the coupling strength and the drive amplitude for the neuronal network. We also found that delay does not affect the reliability of the network but has a determinant influence on the synchronization of the neurons.

Reducing Neuronal Networks to Discrete Dynamics

PubMed Central

Terman, David; Ahn, Sungwoo; Wang, Xueying; Just, Winfried

2008-01-01

We consider a general class of purely inhibitory and excitatory-inhibitory neuronal networks, with a general class of network architectures, and characterize the complex firing patterns that emerge. Our strategy for studying these networks is to first reduce them to a discrete model. In the discrete model, each neuron is represented as a finite number of states and there are rules for how a neuron transitions from one state to another. In this paper, we rigorously demonstrate that the continuous neuronal model can be reduced to the discrete model if the intrinsic and synaptic properties of the cells are chosen appropriately. In a companion paper [1], we analyze the discrete model. PMID:18443649

Large-scale functional models of visual cortex for remote sensing

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

Brumby, Steven P; Kenyon, Garrett; Rasmussen, Craig E

Neuroscience has revealed many properties of neurons and of the functional organization of visual cortex that are believed to be essential to human vision, but are missing in standard artificial neural networks. Equally important may be the sheer scale of visual cortex requiring {approx}1 petaflop of computation. In a year, the retina delivers {approx}1 petapixel to the brain, leading to massively large opportunities for learning at many levels of the cortical system. We describe work at Los Alamos National Laboratory (LANL) to develop large-scale functional models of visual cortex on LANL's Roadrunner petaflop supercomputer. An initial run of a simplemore » region VI code achieved 1.144 petaflops during trials at the IBM facility in Poughkeepsie, NY (June 2008). Here, we present criteria for assessing when a set of learned local representations is 'complete' along with general criteria for assessing computer vision models based on their projected scaling behavior. Finally, we extend one class of biologically-inspired learning models to problems of remote sensing imagery.« less

Axonal Conduction Delays, Brain State, and Corticogeniculate Communication.

PubMed

Stoelzel, Carl R; Bereshpolova, Yulia; Alonso, Jose-Manuel; Swadlow, Harvey A

2017-06-28

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. Copyright © 2017 the authors 0270-6474/17/376342-17$15.00/0.

Effects of channel noise on firing coherence of small-world Hodgkin-Huxley neuronal networks

NASA Astrophysics Data System (ADS)

Sun, X. J.; Lei, J. Z.; Perc, M.; Lu, Q. S.; Lv, S. J.

2011-01-01

We investigate the effects of channel noise on firing coherence of Watts-Strogatz small-world networks consisting of biophysically realistic HH neurons having a fraction of blocked voltage-gated sodium and potassium ion channels embedded in their neuronal membranes. The intensity of channel noise is determined by the number of non-blocked ion channels, which depends on the fraction of working ion channels and the membrane patch size with the assumption of homogeneous ion channel density. We find that firing coherence of the neuronal network can be either enhanced or reduced depending on the source of channel noise. As shown in this paper, sodium channel noise reduces firing coherence of neuronal networks; in contrast, potassium channel noise enhances it. Furthermore, compared with potassium channel noise, sodium channel noise plays a dominant role in affecting firing coherence of the neuronal network. Moreover, we declare that the observed phenomena are independent of the rewiring probability.

Study on algorithm of process neural network for soft sensing in sewage disposal system

NASA Astrophysics Data System (ADS)

Liu, Zaiwen; Xue, Hong; Wang, Xiaoyi; Yang, Bin; Lu, Siying

2006-11-01

A new method of soft sensing based on process neural network (PNN) for sewage disposal system is represented in the paper. PNN is an extension of traditional neural network, in which the inputs and outputs are time-variation. An aggregation operator is introduced to process neuron, and it makes the neuron network has the ability to deal with the information of space-time two dimensions at the same time, so the data processing enginery of biological neuron is imitated better than traditional neuron. Process neural network with the structure of three layers in which hidden layer is process neuron and input and output are common neurons for soft sensing is discussed. The intelligent soft sensing based on PNN may be used to fulfill measurement of the effluent BOD (Biochemical Oxygen Demand) from sewage disposal system, and a good training result of soft sensing was obtained by the method.

The neocortex of cetartiodactyls. II. Neuronal morphology of the visual and motor cortices in the giraffe (Giraffa camelopardalis).

PubMed

Jacobs, Bob; Harland, Tessa; Kennedy, Deborah; Schall, Matthew; Wicinski, Bridget; Butti, Camilla; Hof, Patrick R; Sherwood, Chet C; Manger, Paul R

2015-09-01

The present quantitative study extends our investigation of cetartiodactyls by exploring the neuronal morphology in the giraffe (Giraffa camelopardalis) neocortex. Here, we investigate giraffe primary visual and motor cortices from perfusion-fixed brains of three subadults stained with a modified rapid Golgi technique. Neurons (n = 244) were quantified on a computer-assisted microscopy system. Qualitatively, the giraffe neocortex contained an array of complex spiny neurons that included both "typical" pyramidal neuron morphology and "atypical" spiny neurons in terms of morphology and/or orientation. In general, the neocortex exhibited a vertical columnar organization of apical dendrites. Although there was no significant quantitative difference in dendritic complexity for pyramidal neurons between primary visual (n = 78) and motor cortices (n = 65), there was a significant difference in dendritic spine density (motor cortex > visual cortex). The morphology of aspiny neurons in giraffes appeared to be similar to that of other eutherian mammals. For cross-species comparison of neuron morphology, giraffe pyramidal neurons were compared to those quantified with the same methodology in African elephants and some cetaceans (e.g., bottlenose dolphin, minke whale, humpback whale). Across species, the giraffe (and cetaceans) exhibited less widely bifurcating apical dendrites compared to elephants. Quantitative dendritic measures revealed that the elephant and humpback whale had more extensive dendrites than giraffes, whereas the minke whale and bottlenose dolphin had less extensive dendritic arbors. Spine measures were highest in the giraffe, perhaps due to the high quality, perfusion fixation. The neuronal morphology in giraffe neocortex is thus generally consistent with what is known about other cetartiodactyls.

A combined Bodian-Nissl stain for improved network analysis in neuronal cell culture.

PubMed

Hightower, M; Gross, G W

1985-11-01

Bodian and Nissl procedures were combined to stain dissociated mouse spinal cord cells cultured on coverslips. The Bodian technique stains fine neuronal processes in great detail as well as an intracellular fibrillar network concentrated around the nucleus and in proximal neurites. The Nissl stain clearly delimits neuronal cytoplasm in somata and in large dendrites. A combination of these techniques allows the simultaneous depiction of neuronal perikarya and all afferent and efferent processes. Costaining with little background staining by either procedure suggests high specificity for neurons. This procedure could be exploited for routine network analysis of cultured neurons.

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

The Role of Adult-Born Neurons in the Constantly Changing Olfactory Bulb Network

PubMed Central

Malvaut, Sarah; Saghatelyan, Armen

2016-01-01

The adult mammalian brain is remarkably plastic and constantly undergoes structurofunctional modifications in response to environmental stimuli. In many regions plasticity is manifested by modifications in the efficacy of existing synaptic connections or synapse formation and elimination. In a few regions, however, plasticity is brought by the addition of new neurons that integrate into established neuronal networks. This type of neuronal plasticity is particularly prominent in the olfactory bulb (OB) where thousands of neuronal progenitors are produced on a daily basis in the subventricular zone (SVZ) and migrate along the rostral migratory stream (RMS) towards the OB. In the OB, these neuronal precursors differentiate into local interneurons, mature, and functionally integrate into the bulbar network by establishing output synapses with principal neurons. Despite continuous progress, it is still not well understood how normal functioning of the OB is preserved in the constantly remodelling bulbar network and what role adult-born neurons play in odor behaviour. In this review we will discuss different levels of morphofunctional plasticity effected by adult-born neurons and their functional role in the adult OB and also highlight the possibility that different subpopulations of adult-born cells may fulfill distinct functions in the OB neuronal network and odor behaviour. PMID:26839709

The frequency preference of neurons and synapses in a recurrent oscillatory network.

PubMed

Tseng, Hua-an; Martinez, Diana; Nadim, Farzan

2014-09-17

A variety of neurons and synapses shows a maximal response at a preferred frequency, generally considered to be important in shaping network activity. We are interested in whether all neurons and synapses in a recurrent oscillatory network can have preferred frequencies and, if so, whether these frequencies are the same or correlated, and whether they influence the network activity. We address this question using identified neurons in the pyloric network of the crab Cancer borealis. Previous work has shown that the pyloric pacemaker neurons exhibit membrane potential resonance whose resonance frequency is correlated with the network frequency. The follower lateral pyloric (LP) neuron makes reciprocally inhibitory synapses with the pacemakers. We find that LP shows resonance at a higher frequency than the pacemakers and the network frequency falls between the two. We also find that the reciprocal synapses between the pacemakers and LP have preferred frequencies but at significantly lower values. The preferred frequency of the LP to pacemaker synapse is correlated with the presynaptic preferred frequency, which is most pronounced when the peak voltage of the LP waveform is within the dynamic range of the synaptic activation curve and a shift in the activation curve by the modulatory neuropeptide proctolin shifts the frequency preference. Proctolin also changes the power of the LP neuron resonance without significantly changing the resonance frequency. These results indicate that different neuron types and synapses in a network may have distinct preferred frequencies, which are subject to neuromodulation and may interact to shape network oscillations. Copyright © 2014 the authors 0270-6474/14/3412933-13$15.00/0.

Mechanisms of inhibition in cat visual cortex.

PubMed Central

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

1991-01-01

1. Neurones from layers 2-6 of the cat primary visual cortex were studied using extracellular and intracellular recordings made in vivo. The aim was to identify inhibitory events and determine whether they were associated with small or large (shunting) changes in the input conductance of the neurones. 2. Visual stimulation of subfields of simple receptive fields produced depolarizing or hyperpolarizing potentials that were associated with increased or decreased firing rates respectively. Hyperpolarizing potentials were small, 5 mV or less. In the same neurones, brief electrical stimulation of cortical afferents produced a characteristic sequence of a brief depolarization followed by a long-lasting (200-400 ms) hyperpolarization. 3. During the response to a stationary flashed bar, the synaptic activation increased the input conductance of the neurone by about 5-20%. Conductance changes of similar magnitude were obtained by electrically stimulating the neurone. Neurones stimulated with non-optimal orientations or directions of motion showed little change in input conductance. 4. These data indicate that while visually or electrically induced inhibition can be readily demonstrated in visual cortex, the inhibition is not associated with large sustained conductance changes. Thus a shunting or multiplicative inhibitory mechanism is not the principal mechanism of inhibition. Images Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 PMID:1804983

Modeling gravity-dependent plasticity of the angular vestibuloocular reflex with a physiologically based neural network.

PubMed

Xiang, Yongqing; Yakushin, Sergei B; Cohen, Bernard; Raphan, Theodore

2006-12-01

A neural network model was developed to explain the gravity-dependent properties of gain adaptation of the angular vestibuloocular reflex (aVOR). Gain changes are maximal at the head orientation where the gain is adapted and decrease as the head is tilted away from that position and can be described by the sum of gravity-independent and gravity-dependent components. The adaptation process was modeled by modifying the weights and bias values of a three-dimensional physiologically based neural network of canal-otolith-convergent neurons that drive the aVOR. Model parameters were trained using experimental vertical aVOR gain values. The learning rule aimed to reduce the error between eye velocities obtained from experimental gain values and model output in the position of adaptation. Although the model was trained only at specific head positions, the model predicted the experimental data at all head positions in three dimensions. Altering the relative learning rates of the weights and bias improved the model-data fits. Model predictions in three dimensions compared favorably with those of a double-sinusoid function, which is a fit that minimized the mean square error at every head position and served as the standard by which we compared the model predictions. The model supports the hypothesis that gravity-dependent adaptation of the aVOR is realized in three dimensions by a direct otolith input to canal-otolith neurons, whose canal sensitivities are adapted by the visual-vestibular mismatch. The adaptation is tuned by how the weights from otolith input to the canal-otolith-convergent neurons are adapted for a given head orientation.

On the Dynamics of the Spontaneous Activity in Neuronal Networks

PubMed Central

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

2007-01-01

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

Discovery regarding visual neuron adaptation applicable to robot use

NASA Astrophysics Data System (ADS)

Korepanov, S.

1985-06-01

Scientists of the USSR Academy of Sciences' Institute of Higher Nervous Activity and Neurophysiology discovered a mechanism of light adaptation by organs of vision to changes in the brightness of light. Studies of the reaction of the visual center of the cerebral cortex showed that neurons in it are arranged in different ways: some, which are call classic neurons, have a fairly stable spatial orientation, while that of others is variable. It was found that vision operates chiefly on the basis of classic neurons in all conditions of illumination. Neurons of the second type are activated during sharp fluctuations of illumination. These neurons momentarily assume the orientation of the classic ones, thus serving as a kind of back-up for the primary system of the brain's visual center. Results of these studies will aid medical specialists in their practical work, as well as developers of image-recognition systems for new-generation robots.

Numbers And Gains Of Neurons In Winner-Take-All Networks

NASA Technical Reports Server (NTRS)

Brown, Timothy X.

1993-01-01

Report presents theoretical study of gains required in neurons to implement winner-take-all electronic neural network of given size and related question of maximum size of winner-take-all network in which neurons have specified sigmoid transfer or response function with specified gain.

Simultaneous stability and sensitivity in model cortical networks is achieved through anti-correlations between the in- and out-degree of connectivity

PubMed Central

Vasquez, Juan C.; Houweling, Arthur R.; Tiesinga, Paul

2013-01-01

Neuronal networks in rodent barrel cortex are characterized by stable low baseline firing rates. However, they are sensitive to the action potentials of single neurons as suggested by recent single-cell stimulation experiments that reported quantifiable behavioral responses in response to short spike trains elicited in single neurons. Hence, these networks are stable against internally generated fluctuations in firing rate but at the same time remain sensitive to similarly-sized externally induced perturbations. We investigated stability and sensitivity in a simple recurrent network of stochastic binary neurons and determined numerically the effects of correlation between the number of afferent (“in-degree”) and efferent (“out-degree”) connections in neurons. The key advance reported in this work is that anti-correlation between in-/out-degree distributions increased the stability of the network in comparison to networks with no correlation or positive correlations, while being able to achieve the same level of sensitivity. The experimental characterization of degree distributions is difficult because all pre-synaptic and post-synaptic neurons have to be identified and counted. We explored whether the statistics of network motifs, which requires the characterization of connections between small subsets of neurons, could be used to detect evidence for degree anti-correlations. We find that the sample frequency of the 3-neuron “ring” motif (1→2→3→1), can be used to detect degree anti-correlation for sub-networks of size 30 using about 50 samples, which is of significance because the necessary measurements are achievable experimentally in the near future. Taken together, we hypothesize that barrel cortex networks exhibit degree anti-correlations and specific network motif statistics. PMID:24223550

Super-pixel extraction based on multi-channel pulse coupled neural network

NASA Astrophysics Data System (ADS)

Xu, GuangZhu; Hu, Song; Zhang, Liu; Zhao, JingJing; Fu, YunXia; Lei, BangJun

2018-04-01

Super-pixel extraction techniques group pixels to form over-segmented image blocks according to the similarity among pixels. Compared with the traditional pixel-based methods, the image descripting method based on super-pixel has advantages of less calculation, being easy to perceive, and has been widely used in image processing and computer vision applications. Pulse coupled neural network (PCNN) is a biologically inspired model, which stems from the phenomenon of synchronous pulse release in the visual cortex of cats. Each PCNN neuron can correspond to a pixel of an input image, and the dynamic firing pattern of each neuron contains both the pixel feature information and its context spatial structural information. In this paper, a new color super-pixel extraction algorithm based on multi-channel pulse coupled neural network (MPCNN) was proposed. The algorithm adopted the block dividing idea of SLIC algorithm, and the image was divided into blocks with same size first. Then, for each image block, the adjacent pixels of each seed with similar color were classified as a group, named a super-pixel. At last, post-processing was adopted for those pixels or pixel blocks which had not been grouped. Experiments show that the proposed method can adjust the number of superpixel and segmentation precision by setting parameters, and has good potential for super-pixel extraction.

Recombinant probes for visualizing endogenous synaptic proteins in living neurons

PubMed Central

Gross, Garrett G.; Junge, Jason A.; Mora, Rudy J.; Kwon, Hyung-Bae; Olson, C. Anders; Takahashi, Terry T.; Liman, Emily R.; Ellis-Davies, Graham C.R.; McGee, Aaron W.; Sabatini, Bernardo L.; Roberts, Richard W.; Arnold, Don B.

2013-01-01

Summary The ability to visualize endogenous proteins in living neurons provides a powerful means to interrogate neuronal structure and function. Here we generate recombinant antibody-like proteins, termed FingRs (Fibronectin intrabodies generated with mRNA display), that bind endogenous neuronal proteins PSD-95 and Gephyrin with high affinity and which, when fused to GFP, allow excitatory and inhibitory synapses to be visualized in living neurons. Design of the FingR incorporates a novel transcriptional regulation system that ties FingR expression to the level of the target and reduces background fluorescence. In dissociated neurons and brain slices FingRs generated against PSD-95 and Gephyrin did not affect the expression patterns of their endogenous target proteins or the number or strength of synapses. Together, our data indicate that PSD-95 and Gephyrin FingRs can report the localization and amount of endogenous synaptic proteins in living neurons and thus may be used to study changes in synaptic strength in vivo. PMID:23791193

Origin and Function of Tuning Diversity in Macaque Visual Cortex.

PubMed

Goris, Robbe L T; Simoncelli, Eero P; Movshon, J Anthony

2015-11-18

Neurons in visual cortex vary in their orientation selectivity. We measured responses of V1 and V2 cells to orientation mixtures and fit them with a model whose stimulus selectivity arises from the combined effects of filtering, suppression, and response nonlinearity. The model explains the diversity of orientation selectivity with neuron-to-neuron variability in all three mechanisms, of which variability in the orientation bandwidth of linear filtering is the most important. The model also accounts for the cells' diversity of spatial frequency selectivity. Tuning diversity is matched to the needs of visual encoding. The orientation content found in natural scenes is diverse, and neurons with different selectivities are adapted to different stimulus configurations. Single orientations are better encoded by highly selective neurons, while orientation mixtures are better encoded by less selective neurons. A diverse population of neurons therefore provides better overall discrimination capabilities for natural images than any homogeneous population. Copyright © 2015 Elsevier Inc. All rights reserved.

Visual adaptation and novelty responses in the superior colliculus

PubMed Central

Boehnke, Susan E.; Berg, David J.; Marino, Robert M.; Baldi, Pierre F.; Itti, Laurent; Munoz, Douglas P.

2011-01-01

The brain's ability to ignore repeating, often redundant, information while enhancing novel information processing is paramount to survival. When stimuli are repeatedly presented, the response of visually-sensitive neurons decreases in magnitude, i.e. neurons adapt or habituate, although the mechanism is not yet known. We monitored activity of visual neurons in the superior colliculus (SC) of rhesus monkeys who actively fixated while repeated visual events were presented. We dissociated adaptation from habituation as mechanisms of the response decrement by using a Bayesian model of adaptation, and by employing a paradigm including rare trials that included an oddball stimulus that was either brighter or dimmer. If the mechanism is adaptation, response recovery should be seen only for the brighter stimulus; if habituation, response recovery (‘dishabituation’) should be seen for both the brighter and dimmer stimulus. We observed a reduction in the magnitude of the initial transient response and an increase in response onset latency with stimulus repetition for all visually responsive neurons in the SC. Response decrement was successfully captured by the adaptation model which also predicted the effects of presentation rate and rare luminance changes. However, in a subset of neurons with sustained activity to visual stimuli, a novelty signal akin to dishabituation was observed late in the visual response profile to both brighter and dimmer stimuli and was not captured by the model. This suggests that SC neurons integrate both rapidly discounted information about repeating stimuli and novelty information about oddball events, to support efficient selection in a cluttered dynamic world. PMID:21864319

Dynamics of alpha control: Preparatory suppression of posterior alpha oscillations by frontal modulators revealed with combined EEG and event-related optical signal (EROS)

PubMed Central

Mathewson, Kyle E.; Beck, Diane M.; Ro, Tony; Maclin, Edward L.; Low, Kathy A.; Fabiani, Monica; Gratton, Gabriele

2015-01-01

We investigated the dynamics of brain processes facilitating conscious experience of external stimuli. Previously we proposed that alpha (8-12 Hz) oscillations, which fluctuate with both sustained and directed attention, represent a pulsed inhibition of ongoing sensory brain activity. Here we tested the prediction that inhibitory alpha oscillations in visual cortex are modulated by top-down signals from frontoparietal attention networks. We measured modulations in phase-coherent alpha oscillations from superficial frontal, parietal, and occipital cortices using the event-related optical signal (EROS), a measure of neuronal activity affording high spatiotemporal resolution, along with concurrently-recorded electroencephalogram (EEG), while subjects performed a visual target-detection task. The pre-target alpha oscillations measured with EEG and EROS from posterior areas were larger for subsequently undetected targets, supporting alpha's inhibitory role. Using EROS, we localized brain correlates of these awareness-related alpha oscillations measured at the scalp to the cuneus and precuneus. Crucially, EROS alpha suppression correlated with posterior EEG alpha power across subjects. Sorting the EROS data based on EEG alpha power quartiles to investigate alpha modulators revealed that suppression of posterior alpha was preceded by increased activity in regions of the dorsal attention network, and decreased activity in regions of the cingulo-opercular network. Cross-correlations revealed the temporal dynamics of activity within these preparatory networks prior to posterior alpha modulation. The novel combination of EEG and EROS afforded localization of the sources and correlates of alpha oscillations and their temporal relationships, supporting our proposal that top-down control from attention networks modulates both posterior alpha and awareness of visual stimuli. PMID:24702458

GaAs Optoelectronic Integrated-Circuit Neurons

NASA Technical Reports Server (NTRS)

Lin, Steven H.; Kim, Jae H.; Psaltis, Demetri

1992-01-01

Monolithic GaAs optoelectronic integrated circuits developed for use as artificial neurons. Neural-network computer contains planar arrays of optoelectronic neurons, and variable synaptic connections between neurons effected by diffraction of light from volume hologram in photorefractive material. Basic principles of neural-network computers explained more fully in "Optoelectronic Integrated Circuits For Neural Networks" (NPO-17652). In present circuits, devices replaced by metal/semiconductor field effect transistors (MESFET's), which consume less power.

Monkey pulvinar neurons fire differentially to snake postures.

PubMed

Le, Quan Van; Isbell, Lynne A; Matsumoto, Jumpei; Le, Van Quang; Hori, Etsuro; Tran, Anh Hai; Maior, Rafael S; Tomaz, Carlos; Ono, Taketoshi; Nishijo, Hisao

2014-01-01

There is growing evidence from both behavioral and neurophysiological approaches that primates are able to rapidly discriminate visually between snakes and innocuous stimuli. Recent behavioral evidence suggests that primates are also able to discriminate the level of threat posed by snakes, by responding more intensely to a snake model poised to strike than to snake models in coiled or sinusoidal postures (Etting and Isbell 2014). In the present study, we examine the potential for an underlying neurological basis for this ability. Previous research indicated that the pulvinar is highly sensitive to snake images. We thus recorded pulvinar neurons in Japanese macaques (Macaca fuscata) while they viewed photos of snakes in striking and non-striking postures in a delayed non-matching to sample (DNMS) task. Of 821 neurons recorded, 78 visually responsive neurons were tested with the all snake images. We found that pulvinar neurons in the medial and dorsolateral pulvinar responded more strongly to snakes in threat displays poised to strike than snakes in non-threat-displaying postures with no significant difference in response latencies. A multidimensional scaling analysis of the 78 visually responsive neurons indicated that threat-displaying and non-threat-displaying snakes were separated into two different clusters in the first epoch of 50 ms after stimulus onset, suggesting bottom-up visual information processing. These results indicate that pulvinar neurons in primates discriminate between poised to strike from those in non-threat-displaying postures. This neuronal ability likely facilitates behavioral discrimination and has clear adaptive value. Our results are thus consistent with the Snake Detection Theory, which posits that snakes were instrumental in the evolution of primate visual systems.