An incremental Hebbian learning model of the primary visual cortex with lateral plasticity and real input patterns

We present a simplified binocular neural network model of the primary visual cortex with separate ON/OFF-pathways and modifiable afferent as well as intracortical synaptic couplings. Random as well as natural image stimuli drive the weight adaptation which follows Hebbian learning rules stabilized with constant norm and constant sum constraints. The simulations consider the development of orientation and ocular dominance maps under different conditions concerning stimulus patterns and lateral couplings. With random input patterns realistic orientation maps with +/- 1/2-vortices mostly develop and plastic lateral couplings self-organize into mexican hat type structures on average. Using natural greyscale images as input patterns, realistic orientation maps develop as well and the lateral coupling profiles of the cortical neurons represent the two point correlations of the input image used.     

Neural network model of the primary visual cortex: From functional architecture to lateral connectivity and back

The role of intrinsic cortical dynamics is a debatable issue. A recent optical imaging study (Kenet et al., 2003) found that activity patterns similar to orientation maps (OMs), emerge in the primary visual cortex (V1) even in the absence of sensory input, suggesting an intrinsic mechanism of OM activation. To better understand these results and shed light on the intrinsic V1 processing, we suggest a neural network model in which OMs are encoded by the intrinsic lateral connections. The proposed connectivity pattern depends on the preferred orientation and, unlike previous models, on the degree of orientation selectivity of the interconnected neurons. We prove that the network has a ring attractor composed of an approximated version of the OMs. Consequently, OMs emerge spontaneously when the network is presented with an unstructured noisy input. Simulations show that the model can be applied to experimental data and generate realistic OMs. We study a variation of the model with spatially restricted connections, and show that it gives rise to states composed of several OMs. We hypothesize that these states can represent local properties of the visual scene. Action Editor: Jonathan D. Victor     

Lateral connectivity and contextual interactions in macaque primary visual cortex

Two components of cortical circuits could mediate contour integration in primary visual cortex (V1): intrinsic horizontal connections and feedback from higher cortical areas. To distinguish between these, we combined functional mapping with a new technique for labeling axons, a recombinant adenovirus bearing the gene for green fluorescent protein (GFP), to determine the extent, density, and orientation specificity of V1 intrinsic connections and V2 to V1 feedback. Both connections cover portions of V1 representing regions of visual space up to eight times larger than receptive fields as classically defined, though the intrinsic connections are an order of magnitude denser than the feedback. Whereas the intrinsic connections link similarly oriented domains in V1, V2 to V1 feedback displays no such specificity. These findings suggest that V1 intrinsic horizontal connections provide a more likely substrate for contour integration.     

Extrastriate feedback to primary visual cortex in primates: a quantitative analysis of connectivity.

Knowledge-based or top-down influences on primary visual cortex (area V1) are believed to originate from information conveyed by extrastriate feedback axon connections. Understanding how this information is communicated to area V1 neurons relies in part on elucidating the quantitative as well as the qualitative nature of extrastriate pathway connectivity. A quantitative analysis of the connectivity based on anatomical data regarding the feedback pathway from extrastriate area V2 to area V1 in macaque monkey suggests (i) a total of around ten million or more area V2 axons project to area V1; (ii) the mean number of synaptic inputs from area V2 per upper-layer pyramidal cell in area V1 is less than 6% of all excitatory inputs; and (iii) the mean degree of convergence of area V2 afferents may be high, perhaps more than 100 afferent axons per cell. These results are consistent with empirical observations of the density of radial myelinated axons present in the upper layers in macaque area V1 and the proportion of excitatory extrastriate feedback synaptic inputs onto upper-layer neurons in rat visual cortex. Thus, in primate area V1, extrastriate feedback synapses onto upper-layer cells may, like geniculocortical afferent synapses onto layer IVC neurons, form only a small percentage of the total excitatory synaptic input.     

Conductance-based refractory density model of primary visual cortex

A layered continual population model of primary visual cortex has been constructed, which reproduces a set of experimental data, including postsynaptic responses of single neurons on extracellular electric stimulation and spatially distributed activity patterns in response to visual stimulation. In the model, synaptically interacting excitatory and inhibitory neuronal populations are described by a conductance-based refractory density approach. Populations of two-compartment excitatory and inhibitory neurons in cortical layers 2/3 and 4 are distributed in the 2-d cortical space and connected by AMPA, NMDA and GABA type synapses. The external connections are pinwheel-like, according to the orientation of a stimulus. Intracortical connections are isotropic local and patchy between neurons with similar orientations. The model proposes better temporal resolution and more detailed elaboration than conventional mean-field models. In comparison to large network simulations, it excludes a posteriori statistical data manipulation and provides better computational efficiency and minimal parametrization.     

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.     

A mathematical model of the primary visual cortex and hypercolumn

A mathematical model of the primary visual cortex is presented. Basically, the model comprises two features. Firstly, in analogy with the principle of the computerized tomography (CT), it assumes that simple cells in each hypercolumn are not merely detecting line segments in images as features, but rather that they are as a whole representing the local image with a certain representation. Secondly, it assumes that each hypercolumn is performing spatial frequency analyses of local images using that representation, and that the resultant spectra are represented by complex cells. The model is analyzed using numerical simulations and its advantages are discussed from the viewpoint of visual information processing. It is shown that 1) the proposed processing is tolerant to shifts in position of input images, and that 2) spatial frequency filtering operations can be easily performed in the model.     

Propagating waves in visual cortex: a large-scale model of turtle visual cortex

This article describes a large-scale model of turtle visual cortex that simulates the propagating waves of activity seen in real turtle cortex. The cortex model contains 744 multicompartment models of pyramidal cells, stellate cells, and horizontal cells. Input is provided by an array of 201 geniculate neurons modeled as single compartments with spike-generating mechanisms and axons modeled as delay lines. Diffuse retinal flashes or presentation of spots of light to the retina are simulated by activating groups of geniculate neurons. The model is limited in that it does not have a retina to provide realistic input to the geniculate, and the cortex and does not incorporate all of the biophysical details of real cortical neurons. However, the model does reproduce the fundamental features of planar propagating waves. Activation of geniculate neurons produces a wave of activity that originates at the rostrolateral pole of the cortex at the point where a high density of geniculate afferents enter the cortex. Waves propagate across the cortex with velocities of 4 m/ms to 70 m/ms and occasionally reflect from the caudolateral border of the cortex.     

A reaction-diffusion model to capture disparity selectivity in primary visual cortex

Decades of experimental studies are available on disparity selective cells in visual cortex of macaque and cat. Recently, local disparity map for iso-orientation sites for near-vertical edge preference is reported in area 18 of cat visual cortex. No experiment is yet reported on complete disparity map in V1. Disparity map for layer IV in V1 can provide insight into how disparity selective complex cell receptive field is organized from simple cell subunits. Though substantial amounts of experimental data on disparity selective cells is available, no model on receptive field development of such cells or disparity map development exists in literature. We model disparity selectivity in layer IV of cat V1 using a reaction-diffusion two-eye paradigm. In this model, the wiring between LGN and cortical layer IV is determined by resource an LGN cell has for supporting connections to cortical cells and competition for target space in layer IV. While competing for target space, the same type of LGN cells, irrespective of whether it belongs to left-eye-specific or right-eye-specific LGN layer, cooperate with each other while trying to push off the other type. Our model captures realistic 2D disparity selective simple cell receptive fields, their response properties and disparity map along with orientation and ocular dominance maps. There is lack of correlation between ocular dominance and disparity selectivity at the cell population level. At the map level, disparity selectivity topography is not random but weakly clustered for similar preferred disparities. This is similar to the experimental result reported for macaque. The details of weakly clustered disparity selectivity map in V1 indicate two types of complex cell receptive field organization.     

A multi-stage model for fundamental functional properties in primary visual cortex

Many neurons in mammalian primary visual cortex have properties such as sharp tuning for contour orientation, strong selectivity for motion direction, and insensitivity to stimulus polarity, that are not shared with their sub-cortical counterparts. Successful models have been developed for a number of these properties but in one case, direction selectivity, there is no consensus about underlying mechanisms. We here define a model that accounts for many of the empirical observations concerning direction selectivity. The model describes a single column of cat primary visual cortex and comprises a series of processing stages. Each neuron in the first cortical stage receives input from a small number of on-centre and off-centre relay cells in the lateral geniculate nucleus. Consistent with recent physiological evidence, the off-centre inputs to cortex precede the on-centre inputs by a small (～4 ms) interval, and it is this difference that confers direction selectivity on model neurons. We show that the resulting model successfully matches the following empirical data: the proportion of cells that are direction selective; tilted spatiotemporal receptive fields; phase advance in the response to a stationary contrast-reversing grating stepped across the receptive field. The model also accounts for several other fundamental properties. Receptive fields have elongated subregions, orientation selectivity is strong, and the distribution of orientation tuning bandwidth across neurons is similar to that seen in the laboratory. Finally, neurons in the first stage have properties corresponding to simple cells, and more complex-like cells emerge in later stages. The results therefore show that a simple feed-forward model can account for a number of the fundamental properties of primary visual cortex.     

Studies of functional connectivity in the developing and mature visual cortex

We have investigated several aspects of cortical organization in adult cats and in young kittens. First, we determined receptive field (RF) maps of correlated discharge between pairs of cortical cells. Unique bicellular RFs appear to convey high resolution information. Second, we studied the dynamics of neural interaction between pairs of cells. Using cross-correlation analysis, we studied monosynaptic and polysynaptic interactions in both kittens and cats. A somewhat surprising finding is that there were no cases of monosynaptic excitation from simple to complex cells as would be predicted by a simple hierarchical processing theory. Third, we studied length and side tuning characteristics of cortical cells and worked out the relationships between them. Fourth, we carried out an investigation of binocular processing in which we compared monocular and binocular sensitivity of cortical cells with respect to contrast. Our results are comparable to those found in psychophysical work. Fifth, we examined how stereoscopic depth information is encoded by simple cells in the visual cortex. We show that structural differences in RFs of left and right eyes may be expressed in terms of phase. Phase-based encoding appears to be a very plausible alternative to the standard position-based notion. Sixth, we attempted to induce plastic changes in connections between cell pairs by long-term activation (up to 2 h) in kittens and cats. Although connection strength between some cell pairs was increased during long-term activation, there was no consistent pattern of this effect. Seventh, we attempted to study the functional basis of reported claims of RF expansion following use of an artificial scotoma. However, we found no receptive field size change from this procedure. For some cells, there is an apparent change of gain in the form of base (spontaneous) rates and absolute response levels. Finally, we have examined RF dynamics in the central visual pathways. The standard treatment of RFs is to consider only spatial aspects. But the RF is inherently both temporal and spatial in nature and we have examined the dynamics of spatiotemporal organization of RFs in central visual pathways.     

Emergent dynamics in a model of visual cortex

This paper proposes that the network dynamics of the mammalian visual cortex are highly structured and strongly shaped by temporally localized barrages of excitatory and inhibitory firing we call ‘multiple-firing events’ (MFEs). Our proposal is based on careful study of a network of spiking neurons built to reflect the coarse physiology of a small patch of layer 2/3 of V1. When appropriately benchmarked this network is capable of reproducing the qualitative features of a range of phenomena observed in the real visual cortex, including spontaneous background patterns, orientation-specific responses, surround suppression and gamma-band oscillations. Detailed investigation into the relevant regimes reveals causal relationships among dynamical events driven by a strong competition between the excitatory and inhibitory populations. It suggests that along with firing rates, MFE characteristics can be a powerful signature of a regime. Testable predictions based on model observations and dynamical analysis are proposed.     

Contour saliency in primary visual cortex

Contour integration is an important intermediate stage of object recognition, in which line segments belonging to an object boundary are perceptually linked and segmented from complex backgrounds. Contextual influences observed in primary visual cortex (V1) suggest the involvement of V1 in contour integration. Here, we provide direct evidence that, in monkeys performing a contour detection task, there was a close correlation between the responses of V1 neurons and the perceptual saliency of contours. Receiver operating characteristic analysis showed that single neuronal responses encode the presence or absence of a contour as reliably as the animal's behavioral responses. We also show that the same visual contours elicited significantly weaker neuronal responses when they were not detected in the detection task, or when they were unattended. Our results demonstrate that contextual interactions in V1 play a pivotal role in contour integration and saliency.     

A computational model of vertical signal propagation in the primary visual cortex

A computational model of the flow of activity in a vertically organized slab of cat primary visual cortex (area 17) has been developed. The membrane potential of each cell in the model, as a function of time, is given by the solution of a system of first order, coupled, non-linear differential equations. When firing threshold is exceeded, an action potential waveform is pasted in. The behavior of the model following a brief simulated stimulus to afferents from the dorsal lateral geniculate nucleus (dLGN) is explored. Excitatory and inhibitory post-synaptic potential (E and IPSP) latencies, as a function of cortical depth, were generated by the model. These data were compared with the experimental literature. In general, good agreement was found for EPSPs. Many disynaptic inhibitory inputs were found to be masked by the firing of action potentials in the model. To our knowledge this phenomenon has not been reported in the experimental literature. The model demonstrates that whether a cell exhibits disynaptic or polysynaptic PSP latencies is not a fixed consequence of anatomical connectivity, but rather, can be influenced by connection strengths, and may be influenced by the ongoing pattern of activity in the cortex.Supported by a grant from Cray Research Inc.     

Phase-of-firing coding of natural visual stimuli in primary visual cortex

We investigated the hypothesis that neurons encode rich naturalistic stimuli in terms of their spike times relative to the phase of ongoing network fluctuations rather than only in terms of their spike count. We recorded local field potentials (LFPs) and multiunit spikes from the primary visual cortex of anaesthetized macaques while binocularly presenting a color movie. We found that both the spike counts and the low-frequency LFP phase were reliably modulated by the movie and thus conveyed information about it. Moreover, movie periods eliciting higher firing rates also elicited a higher reliability of LFP phase across trials. To establish whether the LFP phase at which spikes were emitted conveyed visual information that could not be extracted by spike rates alone, we compared the Shannon information about the movie carried by spike counts to that carried by the phase of firing. We found that at low LFP frequencies, the phase of firing conveyed 54% additional information beyond that conveyed by spike counts. The extra information available in the phase of firing was crucial for the disambiguation between stimuli eliciting high spike rates of similar magnitude. Thus, phase coding may allow primary cortical neurons to represent several effective stimuli in an easily decodable format.     

Perceptual and neuronal correspondence in primary visual cortex

Recent findings from the study of primary visual cortex in humans and animals blur the distinction between early and late visual processing. Under some conditions, the activity of neurons in primary visual cortex appears as close or closer to perception than activity in 'higher' visual areas.     

A computational model of the vertical anatomical organization of primary visual cortex

A method for modeling anatomical connectivity for a vertically organized slab of cortical tissue in mammalian primary visual cortex has been developed. The modeled slab covers 500 × 500 m of cortical surface and extends vertically throughout the full depth of the cortex. The model slab was divided into 6 laminae and neuronal somata were distributed in three dimensions through the slab in accordance with experimentally derived cell densities. Axonal and dendritic arborizations were modeled as line segments. A total of 17 morphological types of neurons were included. Connectivity was established based on proximity between axonal and dendritic arbors. There is good general agreement between the vertical distribution of connections generated by the model and the vertical distribution of synapses observed for cat area 17. In all layers, fewer connections were generated in the model than synapses in cat area 17. This is due, at least in part, to the exclusion of long range intracortical projections and sources of afferent input other than the dorsal lateral geniculate nucleus from the model. The connection scheme described here will be used in conjunction with a physiology model to model vertical signal flow, and will be expanded further to model receptive fields of cortical neurons.Supported in part by a grant from Cray Research Inc.