Predictive Coding in Area V4: Dynamic Shape Discrimination under Partial Occlusion

The primate visual system has an exquisite ability to discriminate partially occluded shapes. Recent electrophysiological recordings suggest that response dynamics in intermediate visual cortical area V4, shaped by feedback from prefrontal cortex (PFC), may play a key role. To probe the algorithms that may underlie these findings, we build and test a model of V4 and PFC interactions based on a hierarchical predictive coding framework. We propose that probabilistic inference occurs in two steps. Initially, V4 responses are driven solely by bottom-up sensory input and are thus strongly influenced by the level of occlusion. After a delay, V4 responses combine both feedforward input and feedback signals from the PFC; the latter reflect predictions made by PFC about the visual stimulus underlying V4 activity. We find that this model captures key features of V4 and PFC dynamics observed in experiments. Specifically, PFC responses are strongest for occluded stimuli and delayed responses in V4 are less sensitive to occlusion, supporting our hypothesis that the feedback signals from PFC underlie robust discrimination of occluded shapes. Thus, our study proposes that area V4 and PFC participate in hierarchical inference, with feedback signals encoding top-down predictions about occluded shapes.

Computational Model Based on Neural Network of Visual Cortex for Human Action Recognition

In this paper, we propose a bioinspired model for human action recognition through modeling neural mechanisms of information processing in two visual cortical areas: the primary visual cortex (V1) and the middle temporal cortex (MT) dedicated to motion. This model, named V1-MT, is composed of V1 and MT models (layers) corresponding to their cortical areas, which are built with layered spiking neural networks (SNNs). Some neuron properties in V1 and MT, such as direction and speed selectivity, spatiotemporal inseparability, and center surround suppression, are integrated into SNNs. Based on speed and direction selectivity, V1 and MT models contain multiple SNN channels, each of which processes motion information in sequences with spatiotemporal tunings of neurons at a certain speed and different directions. Therefore, we propose two operations, input signal perceiving with 3-D Gabor filters and surround inhibition processing with 3-D differences of Gaussian functions, to perform this task according to the spatiotemporal inseparability and center surround suppression of neurons. Then, neurons are modeled with our simplified integrate-and-fire model and motion information is transformed into spike trains. Afterward, we define a new feature vector: a mean motion map computed from spike trains in all channels to represent human actions. Finally, a support vector machine is trained to classify actions represented by the feature vectors. We conducted extensive experiments on public action databases, and the results show that our model outperforms other bioinspired models and rivals the state-of-the-art approaches.

Functional triplet motifs underlie accurate predictions of single-trial responses in populations of tuned and untuned V1 neurons

Visual stimuli evoke activity in visual cortical neuronal populations. Neuronal activity can be selectively modulated by particular visual stimulus parameters, such as the direction of a moving bar of light, resulting in well-defined trial averaged tuning properties. However, given any single stimulus parameter, a large number of neurons in visual cortex remain unmodulated, and the role of this untuned population is not well understood. Here, we use two-photon calcium imaging to record, in an unbiased manner, from large populations of layer 2/3 excitatory neurons in mouse primary visual cortex to describe co-varying activity on single trials in neuronal populations consisting of both tuned and untuned neurons. Specifically, we summarize pairwise covariability with an asymmetric partial correlation coefficient, allowing us to analyze the resultant population correlation structure, or functional network, with graph theory. Using the graph neighbors of a neuron, we find that the local population, including both tuned and untuned neurons, are able to predict individual neuron activity on a moment to moment basis, while also recapitulating tuning properties of tuned neurons. Variance explained in total population activity scales with the number of neurons imaged, demonstrating larger sample sizes are required to fully capture local network interactions. We also find that a specific functional triplet motif in the graph results in the best predictions, suggesting a signature of informative correlations in these populations. In summary, we show that unbiased sampling of the local population can explain single trial response variability as well as trial-averaged tuning properties in V1, and the ability to predict responses is tied to the occurrence of a functional triplet motif.

Systematic generation of biophysically detailed models for diverse cortical neuron types

The cellular components of mammalian neocortical circuits are diverse, and capturing this diversity in computational models is challenging. Here we report an approach for generating biophysically detailed models of 170 individual neurons in the Allen Cell Types Database to link the systematic experimental characterization of cell types to the construction of cortical models. We build models from 3D morphologies and somatic electrophysiological responses measured in the same cells. Densities of active somatic conductances and additional parameters are optimized with a genetic algorithm to match electrophysiological features. We evaluate the models by applying additional stimuli and comparing model responses to experimental data. Applying this technique across a diverse set of neurons from adult mouse primary visual cortex, we verify that models preserve the distinctiveness of intrinsic properties between subsets of cells observed in experiments. The optimized models are accessible online alongside the experimental data. Code for optimization and simulation is also openly distributed.

Simple integration of fast excitation and offset, delayed inhibition computes directional selectivity in Drosophila

A neuron that extracts directionally selective motion information from upstream signals lacking this selectivity must compare visual responses from spatially offset inputs. Distinguishing among prevailing algorithmic models for this computation requires measuring fast neuronal activity and inhibition. In the Drosophila melanogaster visual system, a fourth-order neuron-T4-is the first cell type in the ON pathway to exhibit directionally selective signals. Here we use in vivo whole-cell recordings of T4 to show that directional selectivity originates from simple integration of spatially offset fast excitatory and slow inhibitory inputs, resulting in a suppression of responses to the nonpreferred motion direction. We constructed a passive, conductance-based model of a T4 cell that accurately predicts the neuron's response to moving stimuli. These results connect the known circuit anatomy of the motion pathway to the algorithmic mechanism by which the direction of motion is computed.

[Age-related structural changes in the cells of the primary visual cortex of rats under high-intensity light exposure.]

The experiment on 20 male Wistar rats has demonstrated that under light exposure 3 500 lux for 7 days on different-aged rats morphological changes are observed in the II, IV and V layers of the primary visual cortex. They manifest itself as percentage increase of reversibly and irreversibly altered neurons, mainly in the fourth layer in 18-month-old rats (p≤0,05). Thus, under light exposure in 18-month-old rats the percentage of hyperchromic wrinkled neurons runs up to to 6% (5; 8,5) and the percentage of neurons with total chromatolysis increases up to 10% (8,5; 14) in comparison with 1% (0,5; 14) and 6% (5; 8) in 3 month rats under light exposure, respectively (p≤0,05). The neural damage leads to the glial reaction, which is expressed by the percentage increase of glia with edema and swelling signs, hyperchromia without shrinkage of the nucleus and cytoplasm (p≤0,05), neuronophagy, and the intrusion of gliocytes into the neuron cytoplasm for initiation of intracellular repair. The destructive changes are characterized by hyperchromia of gliocytes with shrinkage of the nucleus and cytoplasm. The percentage of such gliocytes significantly increases in the IV layer in 18 month old rats under light exposure, in comparison with the indexes of young animals (p≤0,05).

MMP‑3 deficiency does not influence the length and number of CA1 dendrites of hippocampus of adult mice

Over the past two decades, metalloproteinases (MMPs), including MMP‑2, MMP‑3, and MMP‑9, have been implicated as important players in mechanisms underlying various forms of neuroplasticity. In particular, MMP‑3 was found to be involved in both cognitive functions and in plasticity phenomena, but the underlying molecular mechanisms remain largely elusive. In general, it is believed that functional plasticity of neurons is associated with morphological alterations. Interestingly, MMP‑9, in addition to playing a key role in synaptic plasticity, was found to affect plasticity‑related spine morphology changes. Whereas the involvement of MMP‑3 in shaping synapse morphology upon induction of synaptic plasticity awaits determination, it has been demostrated that MMP‑3 knockout results in clearly altered apical dendrite morphology in pyramidal neurons in mouse visual cortex. Considering that the involvement of MMP‑3 in synaptic plasticity has been most extensively documented for the CA1 hippocampal region, we decided to investigate whether genetic deletion of MMP‑3 affects neuronal morphology in this area. To this end, we used Golgi staining to compare dendritic morphology of pyramidal neurons in the CA1 region in MMP‑3‑deficient and wild‑type mice. Surprisingly, in contrast to the results obtained in cortex, extensive analysis of dendritic morphology in the CA1 region revealed no significant differences between MMP‑3 knockout and wild‑type groups. These results suggest that the impact of MMP‑3 on neuronal morphology may be region‑specific.

A recurrent neural model for proto-object based contour integration and figure-ground segregation

Visual processing of objects makes use of both feedforward and feedback streams of information. However, the nature of feedback signals is largely unknown, as is the identity of the neuronal populations in lower visual areas that receive them. Here, we develop a recurrent neural model to address these questions in the context of contour integration and figure-ground segregation. A key feature of our model is the use of grouping neurons whose activity represents tentative objects ("proto-objects") based on the integration of local feature information. Grouping neurons receive input from an organized set of local feature neurons, and project modulatory feedback to those same neurons. Additionally, inhibition at both the local feature level and the object representation level biases the interpretation of the visual scene in agreement with principles from Gestalt psychology. Our model explains several sets of neurophysiological results (Zhou et al. Journal of Neuroscience, 20(17), 6594-6611 2000; Qiu et al. Nature Neuroscience, 10(11), 1492-1499 2007; Chen et al. Neuron, 82(3), 682-694 2014), and makes testable predictions about the influence of neuronal feedback and attentional selection on neural responses across different visual areas. Our model also provides a framework for understanding how object-based attention is able to select both objects and the features associated with them.

Temporally precise single-cell-resolution optogenetics

Optogenetic control of individual neurons with high temporal precision within intact mammalian brain circuitry would enable powerful explorations of how neural circuits operate. Two-photon computer-generated holography enables precise sculpting of light and could in principle enable simultaneous illumination of many neurons in a network, with the requisite temporal precision to simulate accurate neural codes. We designed a high-efficacy soma-targeted opsin, finding that fusing the N-terminal 150 residues of kainate receptor subunit 2 (KA2) to the recently discovered high-photocurrent channelrhodopsin CoChR restricted expression of this opsin primarily to the cell body of mammalian cortical neurons. In combination with two-photon holographic stimulation, we found that this somatic CoChR (soCoChR) enabled photostimulation of individual cells in mouse cortical brain slices with single-cell resolution and <1-ms temporal precision. We used soCoChR to perform connectivity mapping on intact cortical circuits.

Specific excitatory connectivity for feature integration in mouse primary visual cortex

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.

Multi-scale account of the network structure of macaque visual cortex

Cortical network structure has been extensively characterized at the level of local circuits and in terms of long-range connectivity, but seldom in a manner that integrates both of these scales. Furthermore, while the connectivity of cortex is known to be related to its architecture, this knowledge has not been used to derive a comprehensive cortical connectivity map. In this study, we integrate data on cortical architecture and axonal tracing data into a consistent multi-scale framework of the structure of one hemisphere of macaque vision-related cortex. The connectivity model predicts the connection probability between any two neurons based on their types and locations within areas and layers. Our analysis reveals regularities of cortical structure. We confirm that cortical thickness decays with cell density. A gradual reduction in neuron density together with the relative constancy of the volume density of synapses across cortical areas yields denser connectivity in visual areas more remote from sensory inputs and of lower structural differentiation. Further, we find a systematic relation between laminar patterns on source and target sides of cortical projections, extending previous findings from combined anterograde and retrograde tracing experiments. Going beyond the classical schemes, we statistically assign synapses to target neurons based on anatomical reconstructions, which suggests that layer 4 neurons receive substantial feedback input. Our derived connectivity exhibits a community structure that corresponds more closely with known functional groupings than previous connectivity maps and identifies layer-specific directional differences in cortico-cortical pathways. The resulting network can form the basis for studies relating structure to neural dynamics in mammalian cortex at multiple scales.

The influence of astrocytes on the width of orientation hypercolumns in visual cortex: A computational perspective

Orientation preference maps (OPMs) are present in carnivores (such as cats and ferrets) and primates but are absent in rodents. In this study we investigate the possible link between astrocyte arbors and presence of OPMs. We simulate the development of orientation maps with varying hypercolumn widths using a variant of the Laterally Interconnected Synergetically Self-Organizing Map (LISSOM) model, the Gain Control Adaptive Laterally connected (GCAL) model, with an additional layer simulating astrocytic activation. The synaptic activity of V1 neurons is given as input to the astrocyte layer. The activity of this astrocyte layer is now used to modulate bidirectional plasticity of lateral excitatory connections in the V1 layer. By simply varying the radius of the astrocytes, the extent of lateral excitatory neuronal connections can be manipulated. An increase in the radius of lateral excitatory connections subsequently increases the size of a single hypercolumn in the OPM. When these lateral excitatory connections become small enough the OPM disappears and a salt-and-pepper organization emerges.

Columns of neurons in the primary visual cortex (V1) are known to be tuned to visual stimuli containing edges of a particular orientation. The arrangement of these cortical columns varies across species. In many species such as in ferrets, cats, and monkeys a topology preserving map is observed, wherein similarly tuned columns are observed in close proximity to each other, resulting in the formation of Orientation Preference Maps (OPMs). The width of the hypercolumns, the fundamental unit of an OPM, also varies across species. However, such an arrangement is not observed in rodents, wherein a more random arrangement of these cortical columns is reported. We explore the role of astrocytes in the arrangement of these cortical columns using a self-organizing computational model. Invoking evidence that astrocytes could influence bidirectional plasticity among effective lateral excitatory connections in V1, we introduce a mechanism by which astrocytic inputs can influence map formation in the neuronal network. In the resulting model-generated OPMs the radius of the hypercolumns is found to be correlated with that of astrocytic arbors, a feature that finds support in experimental studies.

Neural coding in the visual system of Drosophila melanogaster: How do small neural populations support visually guided behaviours?

All organisms wishing to survive and reproduce must be able to respond adaptively to a complex, changing world. Yet the computational power available is constrained by biology and evolution, favouring mechanisms that are parsimonious yet robust. Here we investigate the information carried in small populations of visually responsive neurons in Drosophila melanogaster. These so-called 'ring neurons', projecting to the ellipsoid body of the central complex, are reported to be necessary for complex visual tasks such as pattern recognition and visual navigation. Recently the receptive fields of these neurons have been mapped, allowing us to investigate how well they can support such behaviours. For instance, in a simulation of classic pattern discrimination experiments, we show that the pattern of output from the ring neurons matches observed fly behaviour. However, performance of the neurons (as with flies) is not perfect and can be easily improved with the addition of extra neurons, suggesting the neurons' receptive fields are not optimised for recognising abstract shapes, a conclusion which casts doubt on cognitive explanations of fly behaviour in pattern recognition assays. Using artificial neural networks, we then assess how easy it is to decode more general information about stimulus shape from the ring neuron population codes. We show that these neurons are well suited for encoding information about size, position and orientation, which are more relevant behavioural parameters for a fly than abstract pattern properties. This leads us to suggest that in order to understand the properties of neural systems, one must consider how perceptual circuits put information at the service of behaviour.

Neocortical activity is stimulus- and scale-invariant

Mounting evidence supports the hypothesis that the cortex operates near a critical state, defined as the transition point between order (large-scale activity) and disorder (small-scale activity). This criticality is manifested by power law distribution of the size and duration of spontaneous cascades of activity, which are referred as neuronal avalanches. The existence of such neuronal avalanches has been confirmed by several studies both in vitro and in vivo, among different species and across multiple spatial scales. However, despite the prevalence of scale free activity, still very little is known concerning whether and how the scale-free nature of cortical activity is altered during external stimulation. To address this question, we performed in vivo two-photon population calcium imaging of layer 2/3 neurons in primary visual cortex of behaving mice during visual stimulation and conducted statistical analyses on the inferred spike trains. Our investigation for each mouse and condition revealed power law distributed neuronal avalanches, and irregular spiking individual neurons. Importantly, both the avalanche and the spike train properties remained largely unchanged for different stimuli, while the cross-correlation structure varied with stimuli. Our results establish that microcircuits in the visual cortex operate near the critical regime, while rearranging functional connectivity in response to varying sensory inputs.

Contrast sensitivity, V1 neural activity, and natural vision

Contrast sensitivity is fundamental to natural visual processing and an important tool for characterizing both visual function and clinical disorders. We simultaneously measured contrast sensitivity and neural contrast response functions and compared measurements in common laboratory conditions with naturalistic conditions. In typical experiments, a subject holds fixation and a stimulus is flashed on, whereas in natural vision, saccades bring stimuli into view. Motivated by our previous V1 findings, we tested the hypothesis that perceptual contrast sensitivity is lower in natural vision and that this effect is associated with corresponding changes in V1 activity. We found that contrast sensitivity and V1 activity are correlated and that the relationship is similar in laboratory and naturalistic paradigms. However, in the more natural situation, contrast sensitivity is reduced up to 25% compared with that in a standard fixation paradigm, particularly at lower spatial frequencies, and this effect correlates with significant reductions in V1 responses. Our data suggest that these reductions in natural vision result from fast adaptation on one fixation that lowers the response on a subsequent fixation. This is the first demonstration of rapid, natural-image adaptation that carries across saccades, a process that appears to constantly influence visual sensitivity in natural vision.

NEW & NOTEWORTHY

Visual sensitivity and activity in brain area V1 were studied in a paradigm that included saccadic eye movements and natural visual input. V1 responses and contrast sensitivity were significantly reduced compared with results in common laboratory paradigms. The parallel neural and perceptual effects of eye movements and stimulus complexity appear to be due to a form of rapid adaptation that carries across saccades.

Layer-specificity in the effects of attention and working memory on activity in primary visual cortex

Neuronal activity in early visual cortex depends on attention shifts but the contribution to working memory has remained unclear. Here, we examine neuronal activity in the different layers of the primary visual cortex (V1) in an attention-demanding and a working memory task. A current-source density analysis reveales top-down inputs in the superficial layers and layer 5, and an increase in neuronal firing rates most pronounced in the superficial and deep layers and weaker in input layer 4. This increased activity is strongest in the attention task but it is also highly reliable during working memory delays. A visual mask erases the V1 memory activity, but it reappeares at a later point in time. These results provide new insights in the laminar circuits involved in the top-down modulation of activity in early visual cortex in the presence and absence of visual stimuli.

AAV-Mediated Anterograde Transsynaptic Tagging: Mapping Corticocollicular Input-Defined Neural Pathways for Defense Behaviors

To decipher neural circuits underlying brain functions, viral tracers are widely applied to map input and output connectivity of neuronal populations. Despite the successful application of retrograde transsynaptic viruses for identifying presynaptic neurons of transduced neurons, analogous anterograde transsynaptic tools for tagging postsynaptically targeted neurons remain under development. Here, we discovered that adeno-associated viruses (AAV1 and AAV9) exhibit anterograde transsynaptic spread properties. AAV1-Cre from transduced presynaptic neurons effectively and specifically drives Cre-dependent transgene expression in selected postsynaptic neuronal targets, thus allowing axonal tracing and functional manipulations of the latter input-defined neuronal population. Its application in superior colliculus (SC) reveals that SC neuron subpopulations receiving corticocollicular projections from auditory and visual cortex specifically drive flight and freezing, two different types of defense behavior, respectively. Together with an intersectional approach, AAV-mediated anterograde transsynaptic tagging can categorize neurons by their inputs and molecular identity, and allow forward screening of distinct functional neural pathways embedded in complex brain circuits.

Structural Pathways Supporting Swift Acquisition of New Visuomotor Skills

Human skill learning requires fine-scale coordination of distributed networks of brain regions linked by white matter tracts to allow for effective information transmission. Yet how individual differences in these anatomical pathways may impact individual differences in learning remains far from understood. Here, we test the hypothesis that individual differences in structural organization of networks supporting task performance predict individual differences in the rate at which humans learn a visuomotor skill. Over the course of 6 weeks, 20 healthy adult subjects practiced a discrete sequence production task, learning a sequence of finger movements based on discrete visual cues. We collected structural imaging data, and using deterministic tractography generated structural networks for each participant to identify streamlines connecting cortical and subcortical brain regions. We observed that increased white matter connectivity linking early visual regions was associated with a faster learning rate. Moreover, the strength of multiedge paths between motor and visual modules was also correlated with learning rate, supporting the potential role of extended sets of polysynaptic connections in successful skill acquisition. Our results demonstrate that estimates of anatomical connectivity from white matter microstructure can be used to predict future individual differences in the capacity to learn a new motor-visual skill, and that these predictions are supported both by direct connectivity in visual cortex and indirect connectivity between visual cortex and motor cortex.

The Cytoarchitecture of Domain-specific Regions in Human High-level Visual Cortex

A fundamental hypothesis in neuroscience proposes that underlying cellular architecture (cytoarchitecture) contributes to the functionality of a brain area. However, this hypothesis has not been tested in human ventral temporal cortex (VTC) that contains domain-specific regions causally involved in perception. To fill this gap in knowledge, we used cortex-based alignment to register functional regions from living participants to cytoarchitectonic areas in ex vivo brains. This novel approach reveals 3 findings. First, there is a consistent relationship between domain-specific regions and cytoarchitectonic areas: each functional region is largely restricted to 1 cytoarchitectonic area. Second, extracting cytoarchitectonic profiles from face- and place-selective regions after back-projecting each region to 20-μm thick histological sections indicates that cytoarchitectonic properties distinguish these regions from each other. Third, some cytoarchitectonic areas contain more than 1 domain-specific region. For example, face-, body-, and character-selective regions are located within the same cytoarchitectonic area. We summarize these findings with a parsimonious hypothesis incorporating how cellular properties may contribute to functional specialization in human VTC. Specifically, we link computational principles to correlated axes of functional and cytoarchitectonic segregation in human VTC, in which parallel processing across domains occurs along a lateral-medial axis while transformations of information within domain occur along an anterior-posterior axis.

[CAT’S VISUAL AREA 18 INTERMODULAR INTERACTIONS DEVELOPMENT UNDER DIFFERENT VISUAL ENVIRONMENT]

Primary visual cortex contains a set of modules, and their postnatal development depends on a combination of internal genetic and external (defined by visual environment) factors. In order to examine a development of intermodular interactions in visual cortex of kittens subjected to rhythmic light stimulation (15 Hz and 50 Hz frequencies, groups RLS-15 and RLS-50), we investigate an intermodular signal correlation by mean of optical imaging technique. Data was compared with control kittens and with kittens reared with no visual experience in total darkness (group DARK). A significant reduction of the intermodular correlation coefficient was obtained in the group RLS-15; the correlation coefficient values in the groups RLS-50 and DARK was not affected. Thus 15 Hz rhythmic light stimulation during sensitive periods of development disrupts an efficacy of intermodular neuronal connections.

The Fuzzy Logic of Network Connectivity in Mouse Visual Thalamus

In an attempt to chart parallel sensory streams passing through the visual thalamus, we acquired a 100-trillion-voxel electron microscopy (EM) dataset and identified cohorts of retinal ganglion cell axons (RGCs) that innervated each of a diverse group of postsynaptic thalamocortical neurons (TCs). Tracing branches of these axons revealed the set of TCs innervated by each RGC cohort. Instead of finding separate sensory pathways, we found a single large network that could not be easily subdivided because individual RGCs innervated different kinds of TCs and different kinds of RGCs co-innervated individual TCs. We did find conspicuous network subdivisions organized on the basis of dendritic rather than neuronal properties. This work argues that, in the thalamus, neural circuits are not based on a canonical set of connections between intrinsically different neuronal types but, rather, may arise by experience-based mixing of different kinds of inputs onto individual postsynaptic cells.

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).

Neurons in Macaque Area V4 Are Tuned for Complex Spatio-Temporal Patterns

To deepen our understanding of object recognition, it is critical to understand the nature of transformations that occur in intermediate stages of processing in the ventral visual pathway, such as area V4. Neurons in V4 are selective to local features of global shape, such as extended contours. Previously, we found that V4 neurons selective for curved elements exhibit a high degree of spatial variation in their preference. If spatial variation in curvature selectivity was also marked by distinct temporal response patterns at different spatial locations, then it might be possible to untangle this information in subsequent processing based on temporal responses. Indeed, we find that V4 neurons whose receptive fields exhibit intricate selectivity also show variation in their temporal responses across locations. A computational model that decodes stimulus identity based on population responses benefits from using this temporal information, suggesting that it could provide a multiplexed code for spatio-temporal features.

Efficient Receptive Field Tiling in Primate V1

The primary visual cortex (V1) encodes a diverse set of visual features, including orientation, ocular dominance (OD), and spatial frequency (SF), whose joint organization must be precisely structured to optimize coverage within the retinotopic map. Prior experiments have only identified efficient coverage based on orthogonal maps. Here we used two-photon calcium imaging to reveal an alternative arrangement for OD and SF maps in macaque V1; their gradients run parallel but with unique spatial periods, whereby low-SF regions coincide with monocular regions. Next we mapped receptive fields and found surprisingly precise micro-retinotopy that yields a smaller point-image and requires more efficient inter-map geometry, thus underscoring the significance of map relationships. While smooth retinotopy is constraining, studies suggest that it improves both wiring economy and the V1 population code read downstream. Altogether, these data indicate that connectivity within V1 is finely tuned and precise at the level of individual neurons.

Congenital Anophthalmia and Binocular Neonatal Enucleation Differently Affect the Proteome of Primary and Secondary Visual Cortices in Mice

In blind individuals, visually deprived occipital areas are activated by non-visual stimuli. The extent of this cross-modal activation depends on the age at onset of blindness. Cross-modal inputs have access to several anatomical pathways to reactivate deprived visual areas. Ectopic cross-modal subcortical connections have been shown in anophthalmic animals but not in animals deprived of sight at a later age. Direct and indirect cross-modal cortical connections toward visual areas could also be involved, yet the number of neurons implicated is similar between blind mice and sighted controls. Changes at the axon terminal, dendritic spine or synaptic level are therefore expected upon loss of visual inputs. Here, the proteome of V1, V2M and V2L from P0-enucleated, anophthalmic and sighted mice, sharing a common genetic background (C57BL/6J x ZRDCT/An), was investigated by 2-D DIGE and Western analyses to identify molecular adaptations to enucleation and/or anophthalmia. Few proteins were differentially expressed in enucleated or anophthalmic mice in comparison to sighted mice. The loss of sight affected three pathways: metabolism, synaptic transmission and morphogenesis. Most changes were detected in V1, followed by V2M. Overall, cross-modal adaptations could be promoted in both models of early blindness but not through the exact same molecular strategy. A lower metabolic activity observed in visual areas of blind mice suggests that even if cross-modal inputs reactivate visual areas, they could remain suboptimally processed.

3D meshes of carbon nanotubes guide functional reconnection of segregated spinal explants

In modern neuroscience, significant progress in developing structural scaffolds integrated with the brain is provided by the increasing use of nanomaterials. We show that a multiwalled carbon nanotube self-standing framework, consisting of a three-dimensional (3D) mesh of interconnected, conductive, pure carbon nanotubes, can guide the formation of neural webs in vitro where the spontaneous regrowth of neurite bundles is molded into a dense random net. This morphology of the fiber regrowth shaped by the 3D structure supports the successful reconnection of segregated spinal cord segments. We further observed in vivo the adaptability of these 3D devices in a healthy physiological environment. Our study shows that 3D artificial scaffolds may drive local rewiring in vitro and hold great potential for the development of future in vivo interfaces.

Contour Curvature As an Invariant Code for Objects in Visual Area V4

Size-invariant object recognition-the ability to recognize objects across transformations of scale-is a fundamental feature of biological and artificial vision. To investigate its basis in the primate cerebral cortex, we measured single neuron responses to stimuli of varying size in visual area V4, a cornerstone of the object-processing pathway, in rhesus monkeys (Macaca mulatta). Leveraging two competing models for how neuronal selectivity for the bounding contours of objects may depend on stimulus size, we show that most V4 neurons (∼70%) encode objects in a size-invariant manner, consistent with selectivity for a size-independent parameter of boundary form: for these neurons, "normalized" curvature, rather than "absolute" curvature, provided a better account of responses. Our results demonstrate the suitability of contour curvature as a basis for size-invariant object representation in the visual cortex, and posit V4 as a foundation for behaviorally relevant object codes.

SIGNIFICANCE STATEMENT

Size-invariant object recognition is a bedrock for many perceptual and cognitive functions. Despite growing neurophysiological evidence for invariant object representations in the primate cortex, we still lack a basic understanding of the encoding rules that govern them. Classic work in the field of visual shape theory has long postulated that a representation of objects based on information about their bounding contours is well suited to mediate such an invariant code. In this study, we provide the first empirical support for this hypothesis, and its instantiation in single neurons of visual area V4.

Corticothalamic Axons Are Essential for Retinal Ganglion Cell Axon Targeting to the Mouse Dorsal Lateral Geniculate Nucleus

Retinal ganglion cells (RGCs) relay information about the outside world to multiple subcortical targets within the brain. This information is either used to dictate reflexive behaviors or relayed to the visual cortex for further processing. Many subcortical visual nuclei also receive descending inputs from projection neurons in the visual cortex. Most areas receive inputs from layer 5 cortical neurons in the visual cortex but one exception is the dorsal lateral geniculate nucleus (dLGN), which receives layer 6 inputs and is also the only RGC target that sends direct projections to the cortex. Here we ask how visual system development and function changes in mice that develop without a cortex. We find that the development of a cortex is essential for RGC axons to terminate in the dLGN, but is not required for targeting RGC axons to other subcortical nuclei. RGC axons also fail to target to the dLGN in mice that specifically lack cortical layer 6 projections to the dLGN. Finally, we show that when mice develop without a cortex they can still perform a number of vision-dependent tasks.

SIGNIFICANCE STATEMENT

The dorsal lateral geniculate nucleus (dLGN) is a sensory thalamic relay area that receives feedforward inputs from retinal ganglion cells (RGCs) in the retina, and feed back inputs from layer 6 neurons in the visual cortex. In this study we examined genetically manipulated mice that develop without a cortex or without cortical layer 6 axonal projections, and find that RGC axons fail to project to the dLGN. Other RGC recipient areas, such as the superior colliculus and suprachiasmatic nucleus, are targeted normally. These results provide support for a new mechanism of target selection that may be specific to the thalamus, whereby descending cortical axons provide an activity that promotes feedforward targeting of RGC axons to the dLGN.

Synaptic Correlates of Low-Level Perception in V1

The computational role of primary visual cortex (V1) in low-level perception remains largely debated. A dominant view assumes the prevalence of higher cortical areas and top-down processes in binding information across the visual field. Here, we investigated the role of long-distance intracortical connections in form and motion processing by measuring, with intracellular recordings, their synaptic impact on neurons in area 17 (V1) of the anesthetized cat. By systematically mapping synaptic responses to stimuli presented in the nonspiking surround of V1 receptive fields, we provide the first quantitative characterization of the lateral functional connectivity kernel of V1 neurons. Our results revealed at the population level two structural-functional biases in the synaptic integration and dynamic association properties of V1 neurons. First, subthreshold responses to oriented stimuli flashed in isolation in the nonspiking surround exhibited a geometric organization around the preferred orientation axis mirroring the psychophysical "association field" for collinear contour perception. Second, apparent motion stimuli, for which horizontal and feedforward synaptic inputs summed in-phase, evoked dominantly facilitatory nonlinear interactions, specifically during centripetal collinear activation along the preferred orientation axis, at saccadic-like speeds. This spatiotemporal integration property, which could constitute the neural correlate of a human perceptual bias in speed detection, suggests that local (orientation) and global (motion) information is already linked within V1. We propose the existence of a "dynamic association field" in V1 neurons, whose spatial extent and anisotropy are transiently updated and reshaped as a function of changes in the retinal flow statistics imposed during natural oculomotor exploration.

SIGNIFICANCE STATEMENT

The computational role of primary visual cortex in low-level perception remains debated. The expression of this "pop-out" perception is often assumed to require attention-related processes, such as top-down feedback from higher cortical areas. Using intracellular techniques in the anesthetized cat and novel analysis methods, we reveal unexpected structural-functional biases in the synaptic integration and dynamic association properties of V1 neurons. These structural-functional biases provide a substrate, within V1, for contour detection and, more unexpectedly, global motion flow sensitivity at saccadic speed, even in the absence of attentional processes. We argue for the concept of a "dynamic association field" in V1 neurons, whose spatial extent and anisotropy changes with retinal flow statistics, and more generally for a renewed focus on intracortical computation.

Sparse coding generates curvature selectivity in V4 neurons

The cortical area V4 produces a representation of curvature as the intermediate-level representation of an object's shape. We investigated whether sparse coding is the principle driving the generation of the spatial properties of the receptive field in V4 that exhibit curvature selectivity. To investigate the role of sparseness in the construction of curvature representations, we applied component analysis with a sparseness constraint to the activity of model V2 neurons that were responding to shapes derived from natural images. Our simulation results showed that single basis functions with medium degrees of sparseness (0.7-0.8) produced curvature selectivity, and their population activity produced acute curvature bias. The results support the hypothesis that sparseness plays an essential role in the construction of curvature selectivity in V4.

Developmental Sculpting of Intracortical Circuits by MHC Class I H2-Db and H2-Kb

Synapse pruning is an activity-regulated process needed for proper circuit sculpting in the developing brain. Major histocompatibility class I (MHCI) molecules are regulated by activity, but little is known about their role in the development of connectivity in cortex. Here we show that protein for 2 MHCI molecules H2-Kb and H2-Db is associated with synapses in the visual cortex. Pyramidal neurons in mice lacking H2-Kb and H2-Db (KbDb KO) have more extensive cortical connectivity than normal. Modified rabies virus tracing was used to monitor the extent of pyramidal cell connectivity: Horizontal connectivity is greater in the visual cortex of KbDb KO mice. Basal dendrites of L2/3 pyramids, where many horizontal connections terminate, are more highly branched and have elevated spine density in the KO. Furthermore, the density of axonal boutons is elevated within L2/3 of mutant mice. These increases are accompanied by elevated miniature excitatory postsynaptic current frequency, consistent with an increase in functional synapses. This functional and anatomical increase in intracortical connectivity is also associated with enhanced ocular dominance plasticity that persists into adulthood. Thus, these MHCI proteins regulate sculpting of local cortical circuits and in their absence, the excess connectivity can function as a substrate for cortical plasticity throughout life.

Cross-Modality Sharpening of Visual Cortical Processing through Layer-1-Mediated Inhibition and Disinhibition

Cross-modality interaction in sensory perception is advantageous for animals' survival. How cortical sensory processing is cross-modally modulated and what are the underlying neural circuits remain poorly understood. In mouse primary visual cortex (V1), we discovered that orientation selectivity of layer (L)2/3, but not L4, excitatory neurons was sharpened in the presence of sound or optogenetic activation of projections from primary auditory cortex (A1) to V1. The effect was manifested by decreased average visual responses yet increased responses at the preferred orientation. It was more pronounced at lower visual contrast and was diminished by suppressing L1 activity. L1 neurons were strongly innervated by A1-V1 axons and excited by sound, while visual responses of L2/L3 vasoactive intestinal peptide (VIP) neurons were suppressed by sound, both preferentially at the cell's preferred orientation. These results suggest that the cross-modality modulation is achieved primarily through L1 neuron- and L2/L3 VIP-cell-mediated inhibitory and disinhibitory circuits.

SegEM: Efficient Image Analysis for High-Resolution Connectomics

Progress in electron microscopy-based high-resolution connectomics is limited by data analysis throughput. Here, we present SegEM, a toolset for efficient semi-automated analysis of large-scale fully stained 3D-EM datasets for the reconstruction of neuronal circuits. By combining skeleton reconstructions of neurons with automated volume segmentations, SegEM allows the reconstruction of neuronal circuits at a work hour consumption rate of about 100-fold less than manual analysis and about 10-fold less than existing segmentation tools. SegEM provides a robust classifier selection procedure for finding the best automated image classifier for different types of nerve tissue. We applied these methods to a volume of 44 × 60 × 141 μm(3) SBEM data from mouse retina and a volume of 93 × 60 × 93 μm(3) from mouse cortex, and performed exemplary synaptic circuit reconstruction. SegEM resolves the tradeoff between synapse detection and semi-automated reconstruction performance in high-resolution connectomics and makes efficient circuit reconstruction in fully-stained EM datasets a ready-to-use technique for neuroscience.

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

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.

Effect of Contrast on Visual Spatial Summation in Different Cell Categories in Cat Primary Visual Cortex

Multiple cell classes have been found in the primary visual cortex, but the relationship between cell types and spatial summation has seldom been studied. Parvalbumin-expressing inhibitory interneurons can be distinguished from pyramidal neurons based on their briefer action potential durations. In this study, we classified V1 cells into fast-spiking units (FSUs) and regular-spiking units (RSUs) and then examined spatial summation at high and low contrast. Our results revealed that the excitatory classical receptive field and the suppressive non-classical receptive field expanded at low contrast for both FSUs and RSUs, but the expansion was more marked for the RSUs than for the FSUs. For most V1 neurons, surround suppression varied as the contrast changed from high to low. However, FSUs exhibited no significant difference in the strength of suppression between high and low contrast, although the overall suppression decreased significantly at low contrast for the RSUs. Our results suggest that the modulation of spatial summation by stimulus contrast differs across populations of neurons in the cat primary visual cortex.

Turtle Dorsal Cortex Pyramidal Neurons Comprise Two Distinct Cell Types with Indistinguishable Visual Responses

A detailed inventory of the constituent pieces in cerebral cortex is considered essential to understand the principles underlying cortical signal processing. Specifically, the search for pyramidal neuron subtypes is partly motivated by the hypothesis that a subtype-specific division of labor could create a rich substrate for computation. On the other hand, the extreme integration of individual neurons into the collective cortical circuit promotes the hypothesis that cellular individuality represents a smaller computational role within the context of the larger network. These competing hypotheses raise the important question to what extent the computational function of a neuron is determined by its individual type or by its circuit connections. We created electrophysiological profiles from pyramidal neurons within the sole cellular layer of turtle visual cortex by measuring responses to current injection using whole-cell recordings. A blind clustering algorithm applied to these data revealed the presence of two principle types of pyramidal neurons. Brief diffuse light flashes triggered membrane potential fluctuations in those same cortical neurons. The apparently network driven variability of the visual responses concealed the existence of subtypes. In conclusion, our results support the notion that the importance of diverse intrinsic physiological properties is minimized when neurons are embedded in a synaptic recurrent network.

Requirement of keratan sulfate proteoglycan phosphacan with a specific sulfation pattern for critical period plasticity in the visual cortex

Proteoglycans play important roles in regulating the development and functions of the brain. They consist of a core protein and glycosaminoglycans, which are long sugar chains of repeating disaccharide units with sulfation. A recent study demonstrated that the sulfation pattern of chondroitin sulfate on proteoglycans contributes to regulation of the critical period of experience-dependent plasticity in the mouse visual cortex. In the present study, we investigated the role of keratan sulfate (KS), another glycosaminoglycan, in critical period plasticity in the mouse visual cortex. Immunohistochemical analyses demonstrated the presence of KS containing disaccharide units of N-acetylglucosamine (GlcNAc)-6-sulfate and nonsulfated galactose during the critical period, although KS containing disaccharide units of GlcNAc-6-sulfate and galactose-6-sulfate was already known to disappear before that period. The KS chains were distributed diffusely in the extracellular space and densely around the soma of a large population of excitatory and inhibitory neurons. Electron microscopic analysis revealed that the KS was localized within the perisynaptic spaces and dendrites but not in presynaptic sites. KS was mainly located on phosphacan. In mice deficient in GlcNAc-6-O-sulfotransferase 1, which is one of the enzymes necessary for the synthesis of KS chains, the expression of KS was one half that in wild-type mice. In the knockout mice, monocular deprivation during the critical period resulted in a depression of deprived-eye responses but failed to produce potentiation of nondeprived-eye responses. In addition, T-type Ca(2+) channel-dependent long-term potentiation (LTP), which occurs only during the critical period, was not observed. These results suggest that regulation by KS-phosphacan with a specific sulfation pattern is necessary for the generation of LTP and hence the potentiation of nondeprived-eye responses after monocular deprivation.

Origin and Function of Tuning Diversity in Macaque Visual Cortex

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.

[NEURONS IN VISUAL THALAMIC CENTERS OF TURTLES, PROJECTING UPON THE TELENCEPHALON, EXPRESS DIFFERENT TYPES OF CALCIUM-BINDING PROTEINS: COMBINED IMMUNOHISTOCHEMICAL AND TRACER STUDY]

In turtles (Testudo horsfieldi, Emys orbicularis) the immunoreactivity for calbindin (CB), parvalbumin (PV), calretinin (CR), and colocalization of PV and CB in neurons of visual thalamic nuclei (Rot, GLD) projecting to the telencephalon were studied using combined immunohistochemical and tracer method. The predominance of CB-ir neurons in Rot, CB-ir and CR-ir neurons in GLD and a lower amount of PV-ir neurons in both nuclei were shown. With double labeling fluoroimmunohistochemistry technique the colocalization of PV with CB was revealed in the majority of PV-ir neurons and in a fewer number of CB-ir neurons within both nuclei. After delivery of horseradish peroxidase into the telencephalic projection fields of Rot and GLD, retrograde labeling was found in the corresponding thalamic projection neurons immunoreactive for every protein investigated. After fluorescent tracer (Fluoro Gold) injection into the same telencephalic regions retrograde labeling was observed in Rot and GLD neurons, immunoreactive only for PV or CB, as well in neurons with colocalization of the both proteins. These data support predominance of CB-ir component in the rotundo-telencephalic pathway and CB/CR components in the geniculo-telencephalic pathway in turtles. The role of functional specialization in segregation of neurons expressing different types of calcium-binding proteins is postulated.

Random Wiring, Ganglion Cell Mosaics, and the Functional Architecture of the Visual Cortex

The architecture of iso-orientation domains in the primary visual cortex (V1) of placental carnivores and primates apparently follows species invariant quantitative laws. Dynamical optimization models assuming that neurons coordinate their stimulus preferences throughout cortical circuits linking millions of cells specifically predict these invariants. This might indicate that V1's intrinsic connectome and its functional architecture adhere to a single optimization principle with high precision and robustness. To validate this hypothesis, it is critical to closely examine the quantitative predictions of alternative candidate theories. Random feedforward wiring within the retino-cortical pathway represents a conceptually appealing alternative to dynamical circuit optimization because random dimension-expanding projections are believed to generically exhibit computationally favorable properties for stimulus representations. Here, we ask whether the quantitative invariants of V1 architecture can be explained as a generic emergent property of random wiring. We generalize and examine the stochastic wiring model proposed by Ringach and coworkers, in which iso-orientation domains in the visual cortex arise through random feedforward connections between semi-regular mosaics of retinal ganglion cells (RGCs) and visual cortical neurons. We derive closed-form expressions for cortical receptive fields and domain layouts predicted by the model for perfectly hexagonal RGC mosaics. Including spatial disorder in the RGC positions considerably changes the domain layout properties as a function of disorder parameters such as position scatter and its correlations across the retina. However, independent of parameter choice, we find that the model predictions substantially deviate from the layout laws of iso-orientation domains observed experimentally. Considering random wiring with the currently most realistic model of RGC mosaic layouts, a pairwise interacting point process, the predicted layouts remain distinct from experimental observations and resemble Gaussian random fields. We conclude that V1 layout invariants are specific quantitative signatures of visual cortical optimization, which cannot be explained by generic random feedforward-wiring models.

Modularity in the Organization of Mouse Primary Visual Cortex

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.

A mathematical model of color and orientation processing in V1

Orientation processing in the primary visual cortex (V1) has been experimentally investigated in detail and reproduced in models, while color processing remains unclear. Thus, we have constructed a mathematical model of color and orientation processing in V1. The model is mainly based on the following experimental evidence concerning color blobs: A blob contains overlapping neuronal patches activated by different hues, so that each blob represents a full gamut of hue and might be structured with a loop (Xiao et al. in NeuroImage 35:771-786, 2007). The proposed model describes a set of orientation hypercolumns and color blobs, in which color and orientation preferences are represented by the poloidal and toroidal angles of a torus, correspondingly. The model consists of color-insensitive (CI) and color-sensitive (CS) neuronal populations, which are described by a firing-rate model. The set of CI neurons is described by the classical ring model (Ben-Yishai et al. in Proc Natl Acad Sci USA 92:3844-3848, 1995) with recurrent connections in the orientation space; similarly, the set of CS neurons is described in the color space and also receives input from CI neurons of the same orientation preference. The model predictions are as follows: (1) responses to oriented color stimuli are significantly stronger than those to non-oriented color stimuli; (2) the activity of CS neurons in total is higher than that of CI neurons; (3) a random color can be illusorily perceived in the case of gray oriented stimulus; (4) in response to two-color stimulus in the marginal phase, the network chooses either one of the colors or the intermediate color; (5) input to a blob has rather continual representation of a hue than discrete one (with two narrowly tuned opponent signals).

Invariant visual object recognition: biologically plausible approaches

Key properties of inferior temporal cortex neurons are described, and then, the biological plausibility of two leading approaches to invariant visual object recognition in the ventral visual system is assessed to investigate whether they account for these properties. Experiment 1 shows that VisNet performs object classification with random exemplars comparably to HMAX, except that the final layer C neurons of HMAX have a very non-sparse representation (unlike that in the brain) that provides little information in the single-neuron responses about the object class. Experiment 2 shows that VisNet forms invariant representations when trained with different views of each object, whereas HMAX performs poorly when assessed with a biologically plausible pattern association network, as HMAX has no mechanism to learn view invariance. Experiment 3 shows that VisNet neurons do not respond to scrambled images of faces, and thus encode shape information. HMAX neurons responded with similarly high rates to the unscrambled and scrambled faces, indicating that low-level features including texture may be relevant to HMAX performance. Experiment 4 shows that VisNet can learn to recognize objects even when the view provided by the object changes catastrophically as it transforms, whereas HMAX has no learning mechanism in its S-C hierarchy that provides for view-invariant learning. This highlights some requirements for the neurobiological mechanisms of high-level vision, and how some different approaches perform, in order to help understand the fundamental underlying principles of invariant visual object recognition in the ventral visual stream.

Spatial Attention and Temporal Expectation Under Timed Uncertainty Predictably Modulate Neuronal Responses in Monkey V1

The brain uses attention and expectation as flexible devices for optimizing behavioral responses associated with expected but unpredictably timed events. The neural bases of attention and expectation are thought to engage higher cognitive loci; however, their influence at the level of primary visual cortex (V1) remains unknown. Here, we asked whether single-neuron responses in monkey V1 were influenced by an attention task of unpredictable duration. Monkeys covertly attended to a spot that remained unchanged for a fixed period and then abruptly disappeared at variable times, prompting a lever release for reward. We show that monkeys responded progressively faster and performed better as the trial duration increased. Neural responses also followed monkey's task engagement-there was an early, but short duration, response facilitation, followed by a late but sustained increase during the time monkeys expected the attention spot to disappear. This late attentional modulation was significantly and negatively correlated with the reaction time and was well explained by a modified hazard function. Such bimodal, time-dependent changes were, however, absent in a task that did not require explicit attentional engagement. Thus, V1 neurons carry reliable signals of attention and temporal expectation that correlate with predictable influences on monkeys' behavioral responses.

A model for attentional information routing through coherence predicts biased competition and multistable perception

Selective attention allows to focus on relevant information and to ignore distracting features of a visual scene. These principles of information processing are reflected in response properties of neurons in visual area V4: if a neuron is presented with two stimuli in its receptive field, and one is attended, it responds as if the nonattended stimulus was absent (biased competition). In addition, when the luminance of the two stimuli is temporally and independently varied, local field potentials are correlated with the modulation of the attended stimulus and not, or much less, correlated with the nonattended stimulus (information routing). To explain these results in one coherent framework, we present a two-layer spiking cortical network model with distance-dependent lateral connectivity and converging feed-forward connections. With oscillations arising inherently from the network structure, our model reproduces both experimental observations. Hereby, lateral interactions and shifts of relative phases between sending and receiving layers (communication through coherence) are identified as the main mechanisms underlying both biased competition as well as selective routing. Exploring the parameter space, we show that the effects are robust and prevalent over a broad range of parameters. In addition, we identify the strength of lateral inhibition in the first model layer as crucial for determining the working regime of the system: increasing lateral inhibition allows a transition from a network configuration with mixed representations to one with bistable representations of the competing stimuli. The latter is discussed as a possible neural correlate of multistable perception phenomena such as binocular rivalry.

Pattern and Component Motion Responses in Mouse Visual Cortical Areas

Spanning about 9 mm(2) of the posterior cortex surface, the mouse's small but organized visual cortex has recently gained attention for its surprising sophistication and experimental tractability. Though it lacks the highly ordered orientation columns of primates, mouse visual cortex is organized retinotopically and contains at least ten extrastriate areas that likely integrate more complex visual features via dorsal and ventral streams of processing. Extending our understanding of visual perception to the mouse model is justified by the evolving ability to interrogate specific neural circuits using genetic and molecular techniques. In order to probe the functional properties of the putative mouse dorsal stream, we used moving plaids, which demonstrate differences between cells that identify local motion (component cells) and those that integrate global motion of the plaid (pattern cells; Figure 1A;). In primates, there are sparse pattern cell responses in primate V1, but many more in higher-order regions; 25%-30% of cells in MT and 40%-60% in MST are pattern direction selective. We present evidence that mice have small numbers of pattern cells in areas LM and RL, while V1, AL, and AM are largely component-like. Although the proportion of pattern cells is smaller in mouse visual cortex than in primate MT, this study provides evidence that the organization of the mouse visual system shares important similarities to that of primates and opens the possibility of using mice to probe motion computation mechanisms.

Vision loss shifts the balance of feedforward and intracortical circuits in opposite directions in mouse primary auditory and visual cortices

Loss of a sensory modality leads to widespread changes in synaptic function across sensory cortices, which are thought to be the basis for cross-modal adaptation. Previous studies suggest that experience-dependent cross-modal regulation of the spared sensory cortices may be mediated by changes in cortical circuits. Here, we report that loss of vision, in the form of dark exposure (DE) for 1 week, produces laminar-specific changes in excitatory and inhibitory circuits in the primary auditory cortex (A1) of adult mice to promote feedforward (FF) processing and also strengthens intracortical inputs to primary visual cortex (V1). Specifically, DE potentiated FF excitatory synapses from layer 4 (L4) to L2/3 in A1 and recurrent excitatory inputs in A1-L4 in parallel with a reduction in the strength of lateral intracortical excitatory inputs to A1-L2/3. This suggests a shift in processing in favor of FF information at the expense of intracortical processing. Vision loss also strengthened inhibitory synaptic function in L4 and L2/3 of A1, but via laminar specific mechanisms. In A1-L4, DE specifically potentiated the evoked synaptic transmission from parvalbumin-positive inhibitory interneurons to principal neurons without changes in spontaneous miniature IPSCs (mIPSCs). In contrast, DE specifically increased the frequency of mIPSCs in A1-L2/3. In V1, FF excitatory inputs were unaltered by DE, whereas lateral intracortical connections in L2/3 were strengthened, suggesting a shift toward intracortical processing. Our results suggest that loss of vision produces distinct circuit changes in the spared and deprived sensory cortices to shift between FF and intracortical processing to allow adaptation.

A small-world-based population encoding model of the primary visual cortex

A wide range of evidence has shown that information encoding performed by the visual cortex involves complex activities of neuronal populations. However, the effects of the neuronal connectivity structure on the population's encoding performance remain poorly understood. In this paper, a small-world-based population encoding model of the primary visual cortex (V1) is established on the basis of the generalized linear model (GLM) to describe the computation of the neuronal population. The model mainly consists of three sets of filters, including a spatiotemporal stimulus filter, a post-spike history filter, and a set of coupled filters with the coupling neurons organizing as a small-world network. The parameters of the model were fitted with neuronal data of the rat V1 recorded with a micro-electrode array. Compared to the traditional GLM, without considering the small-world structure of the neuronal population, the proposed model was proved to produce more accurate spiking response to grating stimuli and enhance the capability of the neuronal population to carry information. The comparison results proved the validity of the proposed model and further suggest the role of small-world structure in the encoding performance of local populations in V1, which provides new insights for understanding encoding mechanisms of a small scale population in visual system.

Clock genes control cortical critical period timing

Circadian rhythms control a variety of physiological processes, but whether they may also time brain development remains largely unknown. Here, we show that circadian clock genes control the onset of critical period plasticity in the neocortex. Within visual cortex of Clock-deficient mice, the emergence of circadian gene expression was dampened, and the maturation of inhibitory parvalbumin (PV) cell networks slowed. Loss of visual acuity in response to brief monocular deprivation was concomitantly delayed and rescued by direct enhancement of GABAergic transmission. Conditional deletion of Clock or Bmal1 only within PV cells recapitulated the results of total Clock-deficient mice. Unique downstream gene sets controlling synaptic events and cellular homeostasis for proper maturation and maintenance were found to be mis-regulated by Clock deletion specifically within PV cells. These data demonstrate a developmental role for circadian clock genes outside the suprachiasmatic nucleus, which may contribute mis-timed brain plasticity in associated mental disorders.