Model-based analysis of excitatory lateral connections in the visual cortex

A comprehensive data-driven model of cat primary visual cortex

Knowledge integration based on the relationship between structure and function of the neural substrate is one of the main targets of neuroinformatics and data-driven computational modeling. However, the multiplicity of data sources, the diversity of benchmarks, the mixing of observables of different natures, and the necessity of a long-term, systematic approach make such a task challenging. Here we present a first snapshot of a long-term integrative modeling program designed to address this issue in the domain of the visual system: a comprehensive spiking model of cat primary visual cortex. The presented model satisfies an extensive range of anatomical, statistical and functional constraints under a wide range of visual input statistics. In the presence of physiological levels of tonic stochastic bombardment by spontaneous thalamic activity, the modeled cortical reverberations self-generate a sparse asynchronous ongoing activity that quantitatively matches a range of experimentally measured statistics. When integrating feed-forward drive elicited by a high diversity of visual contexts, the simulated network produces a realistic, quantitatively accurate interplay between visually evoked excitatory and inhibitory conductances; contrast-invariant orientation-tuning width; center surround interactions; and stimulus-dependent changes in the precision of the neural code. This integrative model offers insights into how the studied properties interact, contributing to a better understanding of visual cortical dynamics. It provides a basis for future development towards a comprehensive model of low-level perception.

A large-scale neuronal network modelling study: Stimulus size modulates gamma oscillations in the primary visual cortex by long-range connections

Stimulus size modulation of neuronal firing activity is a fundamental property of the primary visual cortex. Numerous biological experiments have shown that stimulus size modulation is affected by multiple factors at different spatiotemporal scales, but the exact pathways and mechanisms remain incompletely understood. In this paper, we establish a large-scale neuronal network model of primary visual cortex with layer 2/3 to study how gamma oscillation properties are modulated by stimulus size and especially how long-range connections affect the modulation as realistic neuronal properties and spatial distributions of synaptic connections are considered. It is shown that long-range horizontal synaptic connections are sufficient to produce dimensional modulation of firing rates and gamma oscillations. In particular, with increasing grating stimulus size, the firing rate increases and then decreases, the peak frequency of gamma oscillations decreases and the spectral power increases. These are consistent with biological experimental observations. Furthermore, we explain in detail how the number and spatial distribution of long-range connections affect the size modulation of gamma oscillations by using the analysis of neuronal firing activity and synaptic current fluctuations. Our results provide a mechanism explanation for size modulation of gamma oscillations in the primary visual cortex and reveal the important and unique role played by long-range connections, which contributes to a deeper understanding of the cognitive function of gamma oscillations in visual cortex.

A CODE model bridging crowding in sparse and dense displays

Visual crowding is arguably the strongest limitation imposed on extrafoveal vision, and is a relatively well-understood phenomenon. However, most investigations and theories are based on sparse displays consisting of a target and at most a handful of flanker objects. Recent findings suggest that the laws thought to govern crowding may not hold for densely cluttered displays, and that grouping and nearest neighbour effects may be more important. Here we present a computational model that accounts for crowding effects in both sparse and dense displays. The model is an adaptation and extension of an earlier model that has previously successfully accounted for spatial clustering, numerosity and object-based attention phenomena. Our model combines grouping by proximity and similarity with a nearest neighbour rule, and defines crowding as the extent to which target and flankers fail to segment. We show that when the model is optimized for explaining crowding phenomena in classic, sparse displays, it also does a good job in capturing novel crowding patterns in dense displays, in both existing and new data sets. The model thus ties together different principles governing crowding, specifically Bouma's law, grouping, and nearest neighbour similarity effects.

Form, synapses and orientation topography of a new cell type in layer 6 of the cat’s primary visual cortex

Here we report the morpho-functional features of a novel type of deep-layer neuron. The neuron was selected from a large pool of intracellularly labelled cells based on the large cell body, numerous spine-free dendrites with an overall interneuron morphology. However, the axon gave off long-range axons up to 2.8 mm from the parent soma in layers 5/6 before entering the white matter. The boutons were uniformly distributed along the axon without forming distinct clusters. Dendritic length, surface area and volume values were at least 3 times larger than any known cortical neuron types with the exception of giant pyramidal cells of layer 5. Electron microscopy of the boutons revealed that they targeted dendritic spines (78%) and less frequently dendritic shafts (22%). Nearly half of the postsynaptic dendrites were immunopositive to GABA. Superimposing the axonal field on the orientation map obtained with optical imaging showed a preponderance of boutons to cross-orientations (38%) and an equal representation of iso- and oblique orientations (31%). The results suggest an integrating role for the layer 6 stellate neuron which projects to a functionally broad range of neurons in the deep cortical layers and to other cortical and/or subcortical regions.

Modular Organization of Signal Transmission in Primate Somatosensory Cortex

Axonal patches are known as the major sites of synaptic connections in the cerebral cortex of higher order mammals. However, the functional role of these patches is highly debated. Patches are formed by populations of nearby neurons in a topographic manner and are recognized as the termination fields of long-distance lateral connections within and between cortical areas. In addition, axons form numerous boutons that lie outside the patches, whose function is also unknown. To better understand the functional roles of these two distinct populations of boutons, we compared individual and collective morphological features of axons within and outside the patches of intra-areal, feedforward, and feedback pathways by way of tract tracing in the somatosensory cortex of New World monkeys. We found that, with the exception of tortuosity, which is an invariant property, bouton spacing and axonal convergence properties differ significantly between axons within patch and no-patch domains. Principal component analyses corroborated the clustering of axons according to patch formation without any additional effect by the type of pathway or laminar distribution. Stepwise logistic regression identified convergence and bouton density as the best predictors of patch formation. These findings support that patches are specific sites of axonal convergence that promote the synchronous activity of neuronal populations. On the other hand, no-patch domains could form a neuroanatomical substrate to diversify the responses of cortical neurons.

Revisiting horizontal connectivity rules in V1: from like-to-like towards like-to-all

Horizontal connections in the primary visual cortex of carnivores, ungulates and primates organize on a near-regular lattice. Given the similar length scale for the regularity found in cortical orientation maps, the currently accepted theoretical standpoint is that these maps are underpinned by a like-to-like connectivity rule: horizontal axons connect preferentially to neurons with similar preferred orientation. However, there is reason to doubt the rule's explanatory power, since a growing number of quantitative studies show that the like-to-like connectivity preference and bias mostly observed at short-range scale, are highly variable on a neuron-to-neuron level and depend on the origin of the presynaptic neuron. Despite the wide availability of published data, the accepted model of visual processing has never been revised. Here, we review three lines of independent evidence supporting a much-needed revision of the like-to-like connectivity rule, ranging from anatomy to population functional measures, computational models and to theoretical approaches. We advocate an alternative, distance-dependent connectivity rule that is consistent with new structural and functional evidence: from like-to-like bias at short horizontal distance to like-to-all at long horizontal distance. This generic rule accounts for the observed high heterogeneity in interactions between the orientation and retinotopic domains, that we argue is necessary to process non-trivial stimuli in a task-dependent manner.

Computational Modeling of Information Propagation during the Sleep–Waking Cycle

Simple Summary

During the deep phases of sleep we do not normally wake up by a thunder, but we nevertheless notice it when awake. The exact same sound gets to our ears and cortex through the thalamus and still, it triggers two very different responses. There is growing experimental evidence that these two states of the brain—sleep and wakefulness—distribute sensory information in different ways across the cortex. In particular, during sleep, neuronal responses remain local and do not spread out across distant synaptically connected regions. On the contrary, during wakefulness, stimuli are able to elicit a wider spatial response. We have used a computational model of coupled cortical columns to study how these two propagation modes arise. Moreover, the transition from sleep-like to waking-like dynamics occurs in agreement with the synaptic homeostasis hypothesis and only requires the increase of excitatory conductances. We have found that, in order to reproduce the aforementioned observations, this parameter change has to be selectively applied: synaptic conductances between distinct columns have to be potentiated over local ones.

Abstract

Non-threatening familiar sounds can go unnoticed during sleep despite the fact that they enter our brain by exciting the auditory nerves. Extracellular cortical recordings in the primary auditory cortex of rodents show that an increase in firing rate in response to pure tones during deep phases of sleep is comparable to those evoked during wakefulness. This result challenges the hypothesis that during sleep cortical responses are weakened through thalamic gating. An alternative explanation comes from the observation that the spatiotemporal spread of the evoked activity by transcranial magnetic stimulation in humans is reduced during non-rapid eye movement (NREM) sleep as compared to the wider propagation to other cortical regions during wakefulness. Thus, cortical responses during NREM sleep remain local and the stimulus only reaches nearby neuronal populations. We aim at understanding how this behavior emerges in the brain as it spontaneously shifts between NREM sleep and wakefulness. To do so, we have used a computational neural-mass model to reproduce the dynamics of the sensory auditory cortex and corresponding local field potentials in these two brain states. Following the synaptic homeostasis hypothesis, an increase in a single parameter, namely the excitatory conductance g¯AMPA, allows us to place the model from NREM sleep into wakefulness. In agreement with the experimental results, the endogenous dynamics during NREM sleep produces a comparable, even higher, response to excitatory inputs to the ones during wakefulness. We have extended the model to two bidirectionally connected cortical columns and have quantified the propagation of an excitatory input as a function of their coupling. We have found that the general increase in all conductances of the cortical excitatory synapses that drive the system from NREM sleep to wakefulness does not boost the effective connectivity between cortical columns. Instead, it is the inter-/intra-conductance ratio of cortical excitatory synapses that should raise to facilitate information propagation across the brain.

Optimal responsiveness and information flow in networks of heterogeneous neurons

Cerebral cortex is characterized by a strong neuron-to-neuron heterogeneity, but it is unclear what consequences this may have for cortical computations, while most computational models consider networks of identical units. Here, we study network models of spiking neurons endowed with heterogeneity, that we treat independently for excitatory and inhibitory neurons. We find that heterogeneous networks are generally more responsive, with an optimal responsiveness occurring for levels of heterogeneity found experimentally in different published datasets, for both excitatory and inhibitory neurons. To investigate the underlying mechanisms, we introduce a mean-field model of heterogeneous networks. This mean-field model captures optimal responsiveness and suggests that it is related to the stability of the spontaneous asynchronous state. The mean-field model also predicts that new dynamical states can emerge from heterogeneity, a prediction which is confirmed by network simulations. Finally we show that heterogeneous networks maximise the information flow in large-scale networks, through recurrent connections. We conclude that neuronal heterogeneity confers different responsiveness to neural networks, which should be taken into account to investigate their information processing capabilities.

Assessment of optogenetically-driven strategies for prosthetic restoration of cortical vision in large-scale neural simulation of V1

The neural encoding of visual features in primary visual cortex (V1) is well understood, with strong correlates to low-level perception, making V1 a strong candidate for vision restoration through neuroprosthetics. However, the functional relevance of neural dynamics evoked through external stimulation directly imposed at the cortical level is poorly understood. Furthermore, protocols for designing cortical stimulation patterns that would induce a naturalistic perception of the encoded stimuli have not yet been established. Here, we demonstrate a proof of concept by solving these issues through a computational model, combining (1) a large-scale spiking neural network model of cat V1 and (2) a virtual prosthetic system transcoding the visual input into tailored light-stimulation patterns which drive in situ the optogenetically modified cortical tissue. Using such virtual experiments, we design a protocol for translating simple Fourier contrasted stimuli (gratings) into activation patterns of the optogenetic matrix stimulator. We then quantify the relationship between spatial configuration of the imposed light pattern and the induced cortical activity. Our simulations in the absence of visual drive (simulated blindness) show that optogenetic stimulation with a spatial resolution as low as 100 [Formula: see text]m, and light intensity as weak as [Formula: see text] photons/s/cm[Formula: see text] is sufficient to evoke activity patterns in V1 close to those evoked by normal vision.

The growth of cognition: Free energy minimization and the embryogenesis of cortical computation

The assumption that during cortical embryogenesis neurons and synaptic connections are selected to form an ensemble maximising synchronous oscillation explains mesoscopic cortical development, and a mechanism for cortical information processing is implied by consistency with the Free Energy Principle and Dynamic Logic. A heteroclinic network emerges, with stable and unstable fixed points of oscillation corresponding to activity in symmetrically connected, versus asymmetrically connected, sets of neurons. Simulations of growth explain a wide range of anatomical observations for columnar and non-columnar cortex, superficial patch connections, and the organization and dynamic interactions of neurone response properties. An antenatal scaffold is created, upon which postnatal learning can establish continuously ordered neuronal representations, permitting matching of co-synchronous fields in multiple cortical areas to solve optimization problems as in Dynamic Logic. Fast synaptic competition partitions equilibria, minimizing "the curse of dimensionality", while perturbations between imperfectly partitioned synchronous fields, under internal reinforcement, enable the cortex to become adaptively self-directed. As learning progresses variational free energy is minimized and entropy bounded.

Dynamical patterns underlying response properties of cortical circuits

Recent experimental studies show cortical circuit responses to external stimuli display varied dynamical properties. These include stimulus strength-dependent population response patterns, a shift from synchronous to asynchronous states and a decline in neural variability. To elucidate the mechanisms underlying these response properties and explore how they are mechanistically related, we develop a neural circuit model that incorporates two essential features widely observed in the cerebral cortex. The first feature is a balance between excitatory and inhibitory inputs to individual neurons; the second feature is distance-dependent connectivity. We show that applying a weak external stimulus to the model evokes a wave pattern propagating along lateral connections, but a strong external stimulus triggers a localized pattern; these stimulus strength-dependent population response patterns are quantitatively comparable with those measured in experimental studies. We identify network mechanisms underlying this population response, and demonstrate that the dynamics of population-level response patterns can explain a range of prominent features in neural responses, including changes to the dynamics of neurons' membrane potentials and synaptic inputs that characterize the shift of cortical states, and the stimulus-evoked decline in neuron response variability. Our study provides a unified population activity pattern-based view of diverse cortical response properties, thus shedding new insights into cortical processing.

Suppressive Traveling Waves Shape Representations of Illusory Motion in Primary Visual Cortex of Awake Primate

How does the brain link visual stimuli across space and time? Visual illusions provide an experimental paradigm to study these processes. When two stationary dots are flashed in close spatial and temporal succession, human observers experience a percept of apparent motion. Large spatiotemporal separation challenges the visual system to keep track of object identity along the apparent motion path, the so-called "correspondence problem." Here, we use voltage-sensitive dye imaging in primary visual cortex (V1) of awake monkeys to show that intracortical connections within V1 can solve this issue by shaping cortical dynamics to represent the illusory motion. We find that the appearance of the second stimulus in V1 creates a systematic suppressive wave traveling toward the retinotopic representation of the first. Using a computational model, we show that the suppressive wave is the emergent property of a recurrent gain control fed by the intracortical network. This suppressive wave acts to explain away ambiguous correspondence problems and contributes to precisely encode the expected motion velocity at the surface of V1. Together, these results demonstrate that the nonlinear dynamics within retinotopic maps can shape cortical representations of illusory motion. Understanding these dynamics will shed light on how the brain links sensory stimuli across space and time, by preformatting population responses for a straightforward read-out by downstream areas.SIGNIFICANCE STATEMENT Traveling waves have recently been observed in different animal species, brain areas, and behavioral states. However, it is still unclear what are their functional roles. In the case of cortical visual processing, waves propagate across retinotopic maps and can hereby generate interactions between spatially and temporally separated instances of feedforward driven activity. Such interactions could participate in processing long-range apparent motion stimuli, an illusion for which no clear neuronal mechanisms have yet been proposed. Using this paradigm in awake monkeys, we show that suppressive traveling waves produce a spatiotemporal normalization of apparent motion stimuli. Our study suggests that cortical waves shape the representation of illusory moving stimulus within retinotopic maps for a straightforward read-out by downstream areas.

Connectivity of neuronal populations within and between areas of primate somatosensory cortex

Functions of the cerebral cortex emerge via interactions of horizontally distributed neuronal populations within and across areas. However, the connectional underpinning of these interactions is not well understood. The present study explores the circuitry of column-size cortical domains within the hierarchically organized somatosensory cortical areas 3b and 1 using tract tracing and optical intrinsic signal imaging (OIS). The anatomical findings reveal that feedforward connections exhibit high topographic specificity, while intrinsic and feedback connections have a more widespread distribution. Both intrinsic and inter-areal connections are topographically oriented across the finger representations. Compared to area 3b, the low clustering of connections and small cortical magnification factor supports that the circuitry of area 1 scaffolds a sparse functional representation that integrates peripheral information from a large area that is fed back to area 3b. Fast information exchange between areas is ensured by thick axons forming a topographically organized, reciprocal pathway. Moreover, the highest density of projecting neurons and groups of axon arborization patches corresponds well with the size and locations of the functional population response reported by OIS. The findings establish connectional motifs at the mesoscopic level that underpin the functional organization of the cerebral cortex.

Neural field model to reconcile structure with function in primary visual cortex

Voltage-sensitive dye imaging experiments in primary visual cortex (V1) have shown that local, oriented visual stimuli elicit stable orientation-selective activation within the stimulus retinotopic footprint. The cortical activation dynamically extends far beyond the retinotopic footprint, but the peripheral spread stays non-selective-a surprising finding given a number of anatomo-functional studies showing the orientation specificity of long-range connections. Here we use a computational model to investigate this apparent discrepancy by studying the expected population response using known published anatomical constraints. The dynamics of input-driven localized states were simulated in a planar neural field model with multiple sub-populations encoding orientation. The realistic connectivity profile has parameters controlling the clustering of long-range connections and their orientation bias. We found substantial overlap between the anatomically relevant parameter range and a steep decay in orientation selective activation that is consistent with the imaging experiments. In this way our study reconciles the reported orientation bias of long-range connections with the functional expression of orientation selective neural activity. Our results demonstrate this sharp decay is contingent on three factors, that long-range connections are sufficiently diffuse, that the orientation bias of these connections is in an intermediate range (consistent with anatomy) and that excitation is sufficiently balanced by inhibition. Conversely, our modelling results predict that, for reduced inhibition strength, spurious orientation selective activation could be generated through long-range lateral connections. Furthermore, if the orientation bias of lateral connections is very strong, or if inhibition is particularly weak, the network operates close to an instability leading to unbounded cortical activation.

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

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.

A biological blueprint for the axons of superficial layer pyramidal cells in cat primary visual cortex

Pyramidal cells in the superficial layers of neocortex of higher mammals form a lateral network of axon clusters known as the 'daisy' network. The role of these axon clusters remains speculative and we still lack a comprehensive quantitative description of the single neurons forming the daisy or their heterogeneity. We filled intracellularly 50 superficial layer pyramidal neurons in the cat primary visual cortex and reconstructed the axonal tree and their synaptic boutons in 3D. Individual bouton clusters were identified using an objective mean-shift algorithm. By parameterizing the morphology of these 50 axonal trees and the 217 bouton clusters they formed, we were able to extract one set of relatively constant parameters and another set of variable parameters. Both sets combined allowed us to outline a comprehensive biological blueprint of superficial layer pyramidal neurons. Overall, our detailed analysis supports the hypothesis that pyramidal neurons use their lateral clusters to combine differential contextual cues, required for context-dependent processing of natural scenes.

Synaptic connections formed by patchy projections of pyramidal cells in the superficial layers of cat visual cortex

The present study is the first to describe quantitatively the patterns of synaptic connections made by the patchy network of pyramidal cell axons in the superficial layers of cat V1 in relation to the orientation map. Intrinsic signal imaging of the orientation map was combined with 3D morphological reconstructions of physiologically-characterized neurons at light and electron microscope levels. A Similarity Index (SI) expressed the similarity of the orientation domain of a given bouton cluster to that of its parent dendritic tree. Six pyramidal cells whose axons had a wide range of SIs were examined. Boutons were sampled from five local and five distal clusters, and from the linear segments that link the clusters. The synaptic targets were reconstructed by serial section electron microscopy. Of the 233 synapses examined, 182 synapses were formed with spiny neurons, the remainder with smooth neurons. The proportion of smooth neurons that were synaptic targets varied greatly (from 0 to 50%) between the cluster samples, but was not correlated with the SI. The postsynaptic density sizes were similar for synapses in local and distal clusters, regardless of their SI. This heterogeneity in the synaptic targets of single cells within the superficial layers is a network feature well-suited for context-dependent processing.

Further Work on the Shaping of Cortical Development and Function by Synchrony and Metabolic Competition

This paper furthers our attempts to resolve two major controversies-whether gamma synchrony plays a role in cognition, and whether cortical columns are functionally important. We have previously argued that the configuration of cortical cells that emerges in development is that which maximizes the magnitude of synchronous oscillation and minimizes metabolic cost. Here we analyze the separate effects in development of minimization of axonal lengths, and of early Hebbian learning and selective distribution of resources to growing synapses, by showing in simulations that these effects are partially antagonistic, but their interaction during development produces accurate anatomical and functional properties for both columnar and non-columnar cortex. The resulting embryonic anatomical order can provide a cortex-wide scaffold for postnatal learning that is dimensionally consistent with the representation of moving sensory objects, and, as learning progressively overwrites the embryonic order, further associations also occur in a dimensionally consistent framework. The role ascribed to cortical synchrony does not demand specific frequency, amplitude or phase variation of pulses to mediate "feature linking." Instead, the concerted interactions of pulse synchrony with short-term synaptic dynamics, and synaptic resource competition can further explain cortical information processing in analogy to Hopfield networks and quantum computation.

Axon topography of layer 6 spiny cells to orientation map in the primary visual cortex of the cat (area 18)

To uncover the functional topography of layer 6 neurons, optical imaging was combined with three-dimensional neuronal reconstruction. Apical dendrite morphology of 23 neurons revealed three distinct types. Type Aa possessed a short apical dendrite with many oblique branches, Type Ab was characterized by a short and less branched apical dendrite, whereas Type B had a long apical dendrite with tufts in layer 2. Each type had a similar number of boutons, yet their spatial distribution differed from each other in both radial and horizontal extent. Boutons of Type Aa and Ab were almost restricted to the column of the parent soma with a laminar preference to layer 4 and 5/6, respectively. Only Type B contributed to long horizontal connections (up to 1.5 mm) mostly in deep layers. For all types, bouton distribution on orientation map showed an almost equal occurrence at iso- (52.6 ± 18.8 %) and non-iso-orientation (oblique, 27.7 ± 14.9 % and cross-orientation 19.7 ± 10.9 %) sites. Spatial convergence of axons of nearby layer 6 spiny neurons depended on soma separation of the parent cells, but only weakly on orientation preference, contrary to orientation dependence of converging axons of layer 4 spiny cells. The results show that layer 6 connections have only a weak dependence on orientation preference compared with those of layers 2/3 (Buzás et al., J Comp Neurol 499:861–881, 2006) and 4 (Karube and Kisvárday, Cereb Cortex 21:1443–1458, 2011).

A biologically plausible mechanism for neuronal coding organized by the phase of alpha oscillations

The visual system receives a wealth of sensory information of which only little is relevant for behaviour. We present a mechanism in which alpha oscillations serve to prioritize different components of visual information. By way of simulated neuronal networks, we show that inhibitory modulation in the alpha range (~ 10 Hz) can serve to temporally segment the visual information to prevent information overload. Coupled excitatory and inhibitory neurons generate a gamma rhythm in which information is segmented and sorted according to excitability in each alpha cycle. Further details are coded by distributed neuronal firing patterns within each gamma cycle. The network model produces coupling between alpha phase and gamma (40–100 Hz) amplitude in the simulated local field potential similar to that observed experimentally in human and animal recordings.

Effects of GABAA kinetics on cortical population activity: computational studies and physiological confirmations

Voltage-sensitive dye (VSD) imaging produces an unprecedented real-time and high-resolution mesoscopic signal to measure the cortical population activity. We have previously shown that the neuronal compartments contributions to the signal are dynamic and stimulus-dependent (Chemla S, Chavane F. Neuroimage 53: 420-438, 2010). Moreover, the VSD signal can also be strongly affected by the network state, such as in anesthetized vs. awake preparations. Here, we investigated the impact of the network state, through GABAA receptors modulation, on the VSD signal using a computational approach. We therefore systematically measured the effect of the GABAA-mediated inhibitory postsynaptic potentials (IPSPs) decay time constant (τG) on our modeled VSD response to an input stimulus of increasing strength. Our simulations suggest that τG strongly modulates the dynamics of the VSD signal, affecting the amplitude, input response function, and the transient balance of excitation and inhibition. We confirmed these predictions experimentally on awake and anesthetized monkeys, comparing VSD responses to drifting gratings stimuli of various contrasts. Lastly, one in vitro study has suggested that GABAA receptors may also be directly affected by the VSDs themselves (Mennerick S, Chisari M, Shu H, Taylor A, Vasek M, Eisenman L, Zorumski C. J Neurosci 30: 2871-2879, 2010). Our modeling approach suggests that the type of modulation described in this study would actually have a negligible influence on the population response. This study highlights that functional results acquired with different techniques and network states must be compared with caution. Biophysical models are proposed here as an adequate tool to delineate the domain of VSD data interpretation.

Physiology and Impact of Horizontal Connections in Rat Neocortex

Cortical information processing at the cellular level has predominantly been studied in local networks, which are dominated by strong vertical connectivity between layers. However, recent studies suggest that the bulk of axons targeting pyramidal neurons most likely originate from outside this local range, emphasizing the importance of horizontal connections. We mapped a subset of these connections to L5B pyramidal neurons in rat somatosensory cortex with photostimulation, identifying intact projections up to a lateral distance of 2 mm. Our estimates of the spatial distribution of cells presynaptic to L5B pyramids support the idea that the majority is located outside the local volume. The synaptic physiology of horizontal connections does not differ markedly from that of local connections, whereas the layer and cell-type-dependent pattern of innervation does. Apart from L2/3, L6A provides a strong source of horizontal connections. Implementing our data into a spiking neuronal network model shows that more horizontal connections promote robust asynchronous ongoing activity states and reduce noise correlations in stimulus-induced activity.

Superficial layer pyramidal cells communicate heterogeneously between multiple functional domains of cat primary visual cortex

The axons of pyramidal neurons in the superficial layers of the neocortex of higher mammals form lateral networks of discrete clusters of synaptic boutons. In primary visual cortex the clusters are reported to link domains that share the same orientation preferences, but how individual neurons contribute to this network is unknown. Here we performed optical imaging to record the intrinsic signal, which is an indirect measure of neuronal firing, and determined the global map of orientation preferences in the cat primary visual system. In the same experiment, single cells were recorded and labelled intracellularly. We found that individual axons arborise within the retinotopic representation of the classical receptive field, but their bouton clusters were not aligned along their preferred axis of orientation along the retinotopic map. Axon clusters formed in a variety of different orientation domains, not just the like-orientation domains. This topography and heterogeneity of single-cell connectivity provides circuits for normalization and context-dependent feature processing of visual scenes.

Development and matching of binocular orientation preference in mouse V1

Eye-specific thalamic inputs converge in the primary visual cortex (V1) and form the basis of binocular vision. For normal binocular perceptions, such as depth and stereopsis, binocularly matched orientation preference between the two eyes is required. A critical period of binocular matching of orientation preference in mice during normal development is reported in literature. Using a reaction diffusion model we present the development of RF and orientation selectivity in mouse V1 and investigate the binocular orientation preference matching during the critical period. At the onset of the critical period the preferred orientations of the modeled cells are mostly mismatched in the two eyes and the mismatch decreases and reaches levels reported in juvenile mouse by the end of the critical period. At the end of critical period 39% of cells in binocular zone in our model cortex is orientation selective. In literature around 40% cortical cells are reported as orientation selective in mouse V1. The starting and the closing time for critical period determine the orientation preference alignment between the two eyes and orientation tuning in cortical cells. The absence of near neighbor interaction among cortical cells during the development of thalamo-cortical wiring causes a salt and pepper organization in the orientation preference map in mice. It also results in much lower % of orientation selective cells in mice as compared to ferrets and cats having organized orientation maps with pinwheels.

The shape of dendritic arbors in different functional domains of the cortical orientation map

The neocortex is organized into macroscopic functional maps. However, at the microscopic scale, the functional preference and degree of feature selectivity between neighboring neurons can vary considerably. In the primary visual cortex, adjacent neurons in iso-orientation domains share the same orientation preference, whereas neighboring neurons near pinwheel centers are tuned to different stimulus orientations. Moreover, several studies have found greater orientation selectivity in iso-orientation domains than in pinwheel centers. These differences suggest that neurons sample local inputs in a spatially homogenous fashion and independently of the location of their soma on the orientation map. Here we determine whether dendritic geometry is affected by neuronal position on the orientation map. We labeled individual layer 2/3 pyramidal neurons with fluorescent dyes in specific domains of the orientation map in cat primary visual cortex and imaged their dendritic trees in vivo by two-photon microscopy. We found that the circularity and uniformity of dendritic trees is independent of somatic position on the orientation map. Moreover, the dendrites of neurons located close to pinwheel centers extend across all orientation domains in an unbiased fashion. Thus, unbiased dendritic trees appear to provide an anatomical substrate for the systematic variations in feature selectivity across the orientation map.

Developmental origin of patchy axonal connectivity in the neocortex: a computational model

Injections of neural tracers into many mammalian neocortical areas reveal a common patchy motif of clustered axonal projections. We studied in simulation a mathematical model for neuronal development in order to investigate how this patchy connectivity could arise in layer II/III of the neocortex. In our model, individual neurons of this layer expressed the activator-inhibitor components of a Gierer-Meinhardt reaction-diffusion system. The resultant steady-state reaction-diffusion pattern across the neuronal population was approximately hexagonal. Growth cones at the tips of extending axons used the various morphogens secreted by intrapatch neurons as guidance cues to direct their growth and invoke axonal arborization, so yielding a patchy distribution of arborization across the entire layer II/III. We found that adjustment of a single parameter yields the intriguing linear relationship between average patch diameter and interpatch spacing that has been observed experimentally over many cortical areas and species. We conclude that a simple Gierer-Meinhardt system expressed by the neurons of the developing neocortex is sufficient to explain the patterns of clustered connectivity observed experimentally.

Intrinsic horizontal connections process global tactile features in the primary somatosensory cortex: neuroanatomical evidence

To understand manual tactile functions in primates, it is essential to explore the interactions between the finger pad representations in somatosensory cortex. To this end, we used optical imaging and electrophysiological mapping to guide neuroanatomical tracer injections into distal digit tip representations of Brodmann area 3b in the squirrel monkey. Retrogradely labeled cell densities and anterogradely labeled fibers and terminal patches in somatosensory areas were plotted and quantified with respect to tangential distribution. Within area 3b, reciprocal patchy distribution of anterograde and retrograde labeling spanned the representation of the distal pad of multiple digits, indicating strong cross-digit connectivity. Inter-areal connections revealed bundles of long-range fibers projecting anteroposteriorly, connecting area 3b with clusters of labeled neurons and terminal axon arborizations in area 1. Inter-areal linkage appeared to be largely confined to the representation of the injected finger. These findings provide the neuroanatomical basis for the interaction between distal finger pad representations observed by recent electrophysiological studies. We propose that intra-areal connectivity may be heavily involved in interdigit integration such as shape discrimination, whereas long-range inter-areal connections may subserve active touch in a digit-specific manner.

Neural maps versus salt-and-pepper organization in visual cortex

Theoretical neuroscientists have long been intrigued by the spatial patterns of neuronal selectivities observed in the visual cortices of many mammals, including primates. While theoretical studies have contributed significantly to our understanding of how the brain learns to see, recent experimental discoveries of the spatial irregularity of visual response properties in the rodent visual cortex have prompted new questions about the origin and functional significance of cortical maps. Characterizing the marked differences of cortical design principles among species and comparing them may provide us with a deeper understanding of primate and non-primate vision.

Tracing inputs to inhibitory or excitatory neurons of mouse and cat visual cortex with a targeted rabies virus

BACKGROUND

Cortical inhibition plays a critical role in controlling and modulating cortical excitation, and a more detailed understanding of the neuronal circuits contributing to each will provide more insight into their roles in complex cortical computations. Traditional neuronal tracers lack a means for easily distinguishing between circuits of inhibitory and excitatory neurons. To overcome this limitation, we have developed a technique for retrogradely labeling inputs to local clusters of inhibitory or excitatory neurons, but not both, using neurotropic adenoassociated and lentiviral vectors, cell-type-specific promoters, and a modified rabies virus.

RESULTS

Applied to primary visual cortex (V1) in mouse, the cell-type-specific tracing technique labeled thousands of presynaptically connected neurons and revealed that the dominant source of input to inhibitory and excitatory neurons is local in origin. Neurons in other visual areas are also labeled; the percentage of these intercortical inputs to excitatory neurons is somewhat higher (~20%) than to inhibitory neurons (<10%), suggesting that intercortical connections have less direct control over inhibition. The inputs to inhibitory neurons were also traced in cat V1, and when aligned with the orientation preference map revealed for the first time that long-range inputs to inhibitory neurons are well tuned to orientation.

CONCLUSIONS

These novel findings for inhibitory and excitatory circuits in the visual cortex demonstrate the efficacy of our new technique and its ability to work across species, including larger-brained mammals such as the cat. This paves the way for a better understanding of the roles of specific cell types in higher-order perceptual and cognitive processes.

Laminar firing and membrane dynamics in four visual areas exposed to two objects moving to occlusion

It is not known how visual cortical neurons react to several moving objects and how their firing to the motion of one object is affected by neurons firing to another moving object. Here we combine imaging of voltage sensitive dye (VSD) signals, reflecting the population membrane potential from ferret visual areas 17, 18, 19, and 21, with laminar recordings of multiunit activity, (MUA), when two bars moved toward each other in the visual field, occluded one another, and continued on in opposite directions. Two zones of peak MUA, mapping the bars' motion, moved toward each other along the area 17/18 border, which in the ferret maps the vertical meridian of the field of view. This was reflected also in the VSD signal, at both the 17/18 border as well as at the 19/21 border with a short delay. After some 125 ms at the area 19/21 border, the VSD signal increased and became elongated in the direction of motion in front of both of the moving representations. This was directly followed by the phase of the signal reversing and travelling back from the 19/21 border toward the 17/18 border, seemingly without respect for retinotopic boundaries, where it arrived at 150 ms after stimulus onset. At this point the VSD signal in front of the moving bar representations along the 17/18 border also increased and became elongated in the direction of object motion; the signal now being the linear sum of what has been observed in response to single moving bars. When the neuronal populations representing the bars were some 600 μm apart on the cortex, the dye signal and laminar MUA decreased strongly, with the MUA scaling to that of a single bar during occlusion. Despite a short rebound of the dye signal and MUA, the MUA after the occlusion was significantly depressed. The interactions between the neuronal populations mapping the bars' position, and the neurons in between these populations were, apart from 19/21 to 17/18 interaction, mainly lateral-horizontal; first excitatory and inducing firing at the site of future occlusion, then inhibitory just prior to occlusion. After occlusion the neurons that had fired already to the first bar showed delayed and prolonged inhibition in response to the second bar. Thus, the interactions that were particular to the occlusion condition in these experiments were local and inhibitory at short cortical range, and delayed and inhibitory after the occlusion when the bars moved further apart.

On the dynamics of cortical development: synchrony and synaptic self-organization

We describe a model for cortical development that resolves long-standing difficulties of earlier models. It is proposed that, during embryonic development, synchronous firing of neurons and their competition for limited metabolic resources leads to selection of an array of neurons with ultra-small-world characteristics. Consequently, in the visual cortex, macrocolumns linked by superficial patchy connections emerge in anatomically realistic patterns, with an ante-natal arrangement which projects signals from the surrounding cortex onto each macrocolumn in a form analogous to the projection of a Euclidean plane onto a Möbius strip. This configuration reproduces typical cortical response maps, and simulations of signal flow explain cortical responses to moving lines as functions of stimulus velocity, length, and orientation. With the introduction of direct visual inputs, under the operation of Hebbian learning, development of mature selective response “tuning” to stimuli of given orientation, spatial frequency, and temporal frequency would then take place, overwriting the earlier ante-natal configuration. The model is provisionally extended to hierarchical interactions of the visual cortex with higher centers, and a general principle for cortical processing of spatio-temporal images is sketched.

The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model

In the past decade, the cell-type specific connectivity and activity of local cortical networks have been characterized experimentally to some detail. In parallel, modeling has been established as a tool to relate network structure to activity dynamics. While available comprehensive connectivity maps ( Thomson, West, et al. 2002; Binzegger et al. 2004) have been used in various computational studies, prominent features of the simulated activity such as the spontaneous firing rates do not match the experimental findings. Here, we analyze the properties of these maps to compile an integrated connectivity map, which additionally incorporates insights on the specific selection of target types. Based on this integrated map, we build a full-scale spiking network model of the local cortical microcircuit. The simulated spontaneous activity is asynchronous irregular and cell-type specific firing rates are in agreement with in vivo recordings in awake animals, including the low rate of layer 2/3 excitatory cells. The interplay of excitation and inhibition captures the flow of activity through cortical layers after transient thalamic stimulation. In conclusion, the integration of a large body of the available connectivity data enables us to expose the dynamical consequences of the cortical microcircuitry.

Sharper orientation tuning of the extraclassical suppressive-surround due to a neuron's location in the V1 orientation map emerges late in time

Neuronal responses in primary visual cortex (V1) can be suppressed by a stimulus presented to the extraclassical surround, and such interactions are thought to be critical for figure ground segregation and form perception. While surround suppression likely originates from both feedforward afferents and multiple cortical circuits, it is unclear what role each circuit plays in the surround's orientation tuning. To investigate this we recorded from single units in V1 of anesthetized cat and analyzed the orientation tuning of the suppressive-surround over time. In addition, based on orientation maps derived through optical imaging prior to recording, neurons were classified as being located in domains or pinwheels. For both types of neurons, shortly after response onset (10 ms) the suppressive-surround is broadly tuned to orientation, but this is followed by a steep improvement in tuning over the next ∼30 ms. While the tuning of the pinwheel cells plateaus at this point, tuning is enhanced further for domain cells, especially those located superficially in the cortex, reaching a peak at 80 ms from response onset. This relatively slow evolution of the orientation tuning of the suppressive surround suggests that fast-arriving feedforward circuits (10 ms) likely only provide broadly tuned suppression, but that feedback from higher visual areas which is likely to arrive over the next 30 ms and can cover both the receptive field center and the extraclassical surround contributes to the initial steep rise in tuning for both cell types. Moreover, we speculate that the even later enhancement in tuning for domain neurons could mean the involvement of inputs from relatively long-range lateral connections, which not only propagate slowly but also link like-oriented domains corresponding to the receptive field of only the extraclassical surround.

Communication and wiring in the cortical connectome

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

Effects of contrast and contrast adaptation on static receptive field features in macaque area V1

The spatiotemporal features of the “static” receptive field (RF), as revealed with flashing bars or spots, determine other RF properties. We examined how some of these static RF features vary with contrast and contrast adaptation in area V1 of the anesthetized macaque monkey. RFs were mapped with light and dark flashing bars presented at three different contrasts, with the low and medium contrasts eliciting approximately 1/3 and 2/3 of the high-contrast response amplitude. The main results are as follows: 1) RF widths decreased when contrast decreased; however, the amount of decrease was less than that expected from an iceberg model and closer to the expectation of a contrast invariance of the RF width. 2) Area tuning experiments with drifting gratings showed an opposite effect of contrast: an increase in preferred stimulus diameter when contrast decreased. This implies that the effect of contrast on preferred stimulus size is not predictable from the static RF. 3) Contrast adaptation attenuated the effect of contrast on RF amplitude but did not significantly modify RF width. 4) RF subregion overlap was only marginally affected by changes in contrast and contrast adaptation; the classification of cells as simple and complex, when established from subregion overlap, appears to be robust with respect to changes in contrast and adaptation state. Previous studies have shown that the spatiotemporal features of the RF depend largely on the stimuli used to map the RF. This study shows that contrast is one elemental feature that contributes to the dynamics of the RF.

Information coding in a laminar computational model of cat primary visual cortex

Neural populations across cortical layers perform different computational tasks. However, it is not known whether information in different layers is encoded using a common neural code or whether it depends on the specific layer. Here we studied the laminar distribution of information in a large-scale computational model of cat primary visual cortex. We analyzed the amount of information about the input stimulus conveyed by the different representations of the cortical responses. In particular, we compared the information encoded in four possible neural codes: (1) the information carried by the firing rate of individual neurons; (2) the information carried by spike patterns within a time window; (3) the rate-and-phase information carried by the firing rate labelled by the phase of the Local Field Potentials (LFP); (4) the pattern-and-phase information carried by the spike patterns tagged with the LFP phase. We found that there is substantially more information in the rate-and-phase code compared with the firing rate alone for low LFP frequency bands (less than 30 Hz). When comparing how information is encoded across layers, we found that the extra information contained in a rate-and-phase code may reach 90 % in Layer 4, while in other layers it reaches only 60 %, compared to the information carried by the firing rate alone. These results suggest that information processing in primary sensory cortices could rely on different coding strategies across different layers.

Complex dynamics in recurrent cortical networks based on spatially realistic connectivities

Most studies on the dynamics of recurrent cortical networks are either based on purely random wiring or neighborhood couplings. Neuronal cortical connectivity, however, shows a complex spatial pattern composed of local and remote patchy connections. We ask to what extent such geometric traits influence the “idle” dynamics of two-dimensional (2d) cortical network models composed of conductance-based integrate-and-fire (iaf) neurons. In contrast to the typical 1 mm² used in most studies, we employ an enlarged spatial set-up of 25 mm² to provide for long-range connections. Our models range from purely random to distance-dependent connectivities including patchy projections, i.e., spatially clustered synapses. Analyzing the characteristic measures for synchronicity and regularity in neuronal spiking, we explore and compare the phase spaces and activity patterns of our simulation results. Depending on the input parameters, different dynamical states appear, similar to the known synchronous regular “SR” or asynchronous irregular “AI” firing in random networks. Our structured networks, however, exhibit shifted and sharper transitions, as well as more complex activity patterns. Distance-dependent connectivity structures induce a spatio-temporal spread of activity, e.g., propagating waves, that random networks cannot account for. Spatially and temporally restricted activity injections reveal that a high amount of local coupling induces rather unstable AI dynamics. We find that the amount of local versus long-range connections is an important parameter, whereas the structurally advantageous wiring cost optimization of patchy networks has little bearing on the phase space.

Orientation tuning of the suppressive extraclassical surround depends on intrinsic organization of V1

The intrinsic functional architecture of early cortical areas in highly visual mammals is characterized by the presence of domains and pinwheels, with orientation preference of the inputs to these regions being more and less selective, respectively. We exploited this organizational feature to investigate mechanisms supporting extraclassical surround suppression, a process thought to be critical for figure ground segregation and form vision. Combining intrinsic signal optical imaging and single-unit recording in V1 of anesthetized cats, we show for the first time that the orientation tuning of the suppressive surround is sharper for domain than for pinwheel neurons. This difference depends on high center gain and is more pronounced in superficial cortex. In addition, when we remove the near component of the surround stimulus, the strength of suppression induced by the iso-oriented surround is significantly reduced for domain neurons but is unchanged for orthogonal oriented surrounds. This leads to broader orientation tuning of suppression that renders domain cells indistinguishable from pinwheel cells. Because the limited receptive field of the near surround can be accounted for by the lateral spread of long-range connections in V1, our findings suggest that intrinsic V1 circuits play a key role in the orientation tuning of extraclassical surround suppression.

From neural arbors to daisies

Pyramidal neurons in layers 2 and 3 of the neocortex collectively form an horizontal lattice of long-range, periodic axonal projections, known as the superficial patch system. The precise pattern of projections varies between cortical areas, but the patch system has nevertheless been observed in every area of cortex in which it has been sought, in many higher mammals. Although the clustered axonal arbors of single pyramidal cells have been examined in detail, the precise rules by which these neurons collectively merge their arbors remain unknown. To discover these rules, we generated models of clustered axonal arbors following simple geometric patterns. We found that models assuming spatially aligned but independent formation of each axonal arbor do not produce patchy labeling patterns for large simulated injections into populations of generated axonal arbors. In contrast, a model that used information distributed across the cortical sheet to generate axonal projections reproduced every observed quality of cortical labeling patterns. We conclude that the patch system cannot be built during development using only information intrinsic to single neurons. Information shared across the population of patch-projecting neurons is required for the patch system to reach its adult state.

Development of maps of simple and complex cells in the primary visual cortex

Hubel and Wiesel (1962) classified primary visual cortex (V1) neurons as either simple, with responses modulated by the spatial phase of a sine grating, or complex, i.e., largely phase invariant. Much progress has been made in understanding how simple-cells develop, and there are now detailed computational models establishing how they can form topographic maps ordered by orientation preference. There are also models of how complex cells can develop using outputs from simple cells with different phase preferences, but no model of how a topographic orientation map of complex cells could be formed based on the actual connectivity patterns found in V1. Addressing this question is important, because the majority of existing developmental models of simple-cell maps group neurons selective to similar spatial phases together, which is contrary to experimental evidence, and makes it difficult to construct complex cells. Overcoming this limitation is not trivial, because mechanisms responsible for map development drive receptive fields (RF) of nearby neurons to be highly correlated, while co-oriented RFs of opposite phases are anti-correlated. In this work, we model V1 as two topographically organized sheets representing cortical layer 4 and 2/3. Only layer 4 receives direct thalamic input. Both sheets are connected with narrow feed-forward and feedback connectivity. Only layer 2/3 contains strong long-range lateral connectivity, in line with current anatomical findings. Initially all weights in the model are random, and each is modified via a Hebbian learning rule. The model develops smooth, matching, orientation preference maps in both sheets. Layer 4 units become simple cells, with phase preference arranged randomly, while those in layer 2/3 are primarily complex cells. To our knowledge this model is the first explaining how simple cells can develop with random phase preference, and how maps of complex cells can develop, using only realistic patterns of connectivity.

Beyond the cortical column: abundance and physiology of horizontal connections imply a strong role for inputs from the surround

Current concepts of cortical information processing and most cortical network models largely rest on the assumption that well-studied properties of local synaptic connectivity are sufficient to understand the generic properties of cortical networks. This view seems to be justified by the observation that the vertical connectivity within local volumes is strong, whereas horizontally, the connection probability between pairs of neurons drops sharply with distance. Recent neuroanatomical studies, however, have emphasized that a substantial fraction of synapses onto neocortical pyramidal neurons stems from cells outside the local volume. Here, we discuss recent findings on the signal integration from horizontal inputs, showing that they could serve as a substrate for reliable and temporally precise signal propagation. Quantification of connection probabilities and parameters of synaptic physiology as a function of lateral distance indicates that horizontal projections constitute a considerable fraction, if not the majority, of inputs from within the cortical network. Taking these non-local horizontal inputs into account may dramatically change our current view on cortical information processing.

Dynamics of extraclassical surround modulation in three types of V1 neurons

Visual stimuli outside of the classical receptive field (CRF) can influence the response of neurons in primary visual cortex (V1). While recording single units in cat, we presented drifting sinusoidal gratings in circular apertures of different sizes to investigate this extraclassical surround modulation over time. For the full 2-s stimulus time course, three types of neurons were found: 1) 68% of the cells were “suppressive,” 2) 25% were “plateau” cells that showed response saturation with no suppression, and 3) the remaining 6% of cells were “facilitative.” Analysis of the response dynamics revealed that at response onset, activity of one-half of facilitative cells, 70% of plateau cells, and all suppressive cells is suppressed by the surround. However, over the next 20–30 ms, surround modulation changes to stronger suppression for suppressive cells, substantial facilitation for facilitative cells, and weak facilitation for plateau cells. For all three cell types, these modulatory effects then stabilize between 100 and 200 ms from stimulus onset. Thus our findings illustrate two stages of surround modulation. Early modulation is mainly suppressive regardless of cell type and, because of rapid onset, may rely on feedforward mechanisms. Surround modulation that evolves later in time is not always suppressive, depending on cell type, and may be generated through different combinations of cortical circuits. Additional analysis of modulation throughout the cortical column suggests the possibility that the larger excitatory fields of facilitative cells, primarily found in infragranular layers, may contribute to the second stage of suppression through intracolumnar circuitry.

Lateral Spread of Orientation Selectivity in V1 is Controlled by Intracortical Cooperativity

Neurons in the primary visual cortex receive subliminal information originating from the periphery of their receptive fields (RF) through a variety of cortical connections. In the cat primary visual cortex, long-range horizontal axons have been reported to preferentially bind to distant columns of similar orientation preferences, whereas feedback connections from higher visual areas provide a more diverse functional input. To understand the role of these lateral interactions, it is crucial to characterize their effective functional connectivity and tuning properties. However, the overall functional impact of cortical lateral connections, whatever their anatomical origin, is unknown since it has never been directly characterized. Using direct measurements of postsynaptic integration in cat areas 17 and 18, we performed multi-scale assessments of the functional impact of visually driven lateral networks. Voltage-sensitive dye imaging showed that local oriented stimuli evoke an orientation-selective activity that remains confined to the cortical feedforward imprint of the stimulus. Beyond a distance of one hypercolumn, the lateral spread of cortical activity gradually lost its orientation preference approximated as an exponential with a space constant of about 1 mm. Intracellular recordings showed that this loss of orientation selectivity arises from the diversity of converging synaptic input patterns originating from outside the classical RF. In contrast, when the stimulus size was increased, we observed orientation-selective spread of activation beyond the feedforward imprint. We conclude that stimulus-induced cooperativity enhances the long-range orientation-selective spread.

Statistical comparison of spike responses to natural stimuli in monkey area V1 with simulated responses of a detailed laminar network model for a patch of V1

A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characterization and comparison of the statistical response properties of the primary visual cortex (V1) in vivo and in silico in response to natural stimuli. We recorded from multiple electrodes in area V1 of 4 macaque monkeys and developed a large state-of-the-art network model for a 5 × 5-mm patch of V1 composed of 35,000 neurons and 3.9 million synapses that integrates previously published anatomical and physiological details. By quantitative comparison of the model response to the "statistical fingerprint" of responses in vivo, we find that our model for a patch of V1 responds to the same movie in a way which matches the statistical structure of the recorded data surprisingly well. The deviation between the firing regimen of the model and the in vivo data are on the same level as deviations among monkeys and sessions. This suggests that, despite strong simplifications and abstractions of cortical network models, they are nevertheless capable of generating realistic spiking activity. To reach a realistic firing state, it was not only necessary to include both N-methyl-d-aspartate and GABA(B) synaptic conductances in our model, but also to markedly increase the strength of excitatory synapses onto inhibitory neurons (>2-fold) in comparison to literature values, hinting at the importance to carefully adjust the effect of inhibition for achieving realistic dynamics in current network models.