Geometry and dimensionality reduction of feature spaces in primary visual cortex

Some geometric properties of the wavelet analysis performed by visual neurons are discussed and compared with experimental data. In particular, several relationships between the cortical morphologies and the parametric dependencies of extracted features are formalized and considered from a harmonic analysis point of view

The effects of noise on binocular rivalry waves: a stochastic neural field model

We analyse the effects of extrinsic noise on traveling waves of visual perception in a competitive neural field model of binocular rivalry. The model consists of two one-dimensional excitatory neural fields, whose activity variables represent the responses to left-eye and right-eye stimuli, respectively. The two networks mutually inhibit each other, and slow adaptation is incorporated into the model by taking the network connections to exhibit synaptic depression. We first show how, in the absence of any noise, the system supports a propagating composite wave consisting of an invading activity front in one network co-moving with a retreating front in the other network. Using a separation of time scales and perturbation methods previously developed for stochastic reaction-diffusion equations, we then show how multiplicative noise in the activity variables leads to a diffusive–like displacement (wandering) of the composite wave from its uniformly translating position at long time scales, and fluctuations in the wave profile around its instantaneous position at short time scales. The multiplicative noise also renormalizes the mean speed of the wave. We use our analysis to calculate the first passage time distribution for a stochastic rivalry wave to travel a fixed distance, which we find to be given by an inverse Gaussian. Finally, we investigate the effects of noise in the depression variables, which under an adiabatic approximation leads to quenched disorder in the neural fields during propagation of a wave

Attentional modulation of firing rate and synchrony in a model cortical network

When attention is directed into the receptive field of a V4 neuron, its contrast response curve is shifted to lower contrast values (Reynolds et al, 2000, Neuron 26:703). Attention also increases the coherence between neurons responding to the same stimulus (Fries et al, 2001, Science 291:1560). We studied how the firing rate and synchrony of a densely interconnected cortical network varied with contrast and how they were modulated by attention. We found that an increased driving current to the excitatory neurons increased the overall firing rate of the network, whereas variation of the driving current to inhibitory neurons modulated the synchrony of the network. We explain the synchrony modulation in terms of a locking phenomenon during which the ratio of excitatory to inhibitory firing rates is approximately constant for a range of driving current values. We explored the hypothesis that contrast is represented primarily as a drive to the excitatory neurons, whereas attention corresponds to a reduction in driving current to the inhibitory neurons. Using this hypothesis, the model reproduces the following experimental observations: (1) the firing rate of the excitatory neurons increases with contrast; (2) for high contrast stimuli, the firing rate saturates and the network synchronizes; (3) attention shifts the contrast response curve to lower contrast values; (4) attention leads to stronger synchronization that starts at a lower value of the contrast compared with the attend-away condition. In addition, it predicts that attention increases the delay between the inhibitory and excitatory synchronous volleys produced by the network, allowing the stimulus to recruit more downstream neurons.Comment: 36 pages, submitted to Journal of Computational Neuroscienc

Adaptive Scales of Spatial Integration and Response Latencies in a Critically-Balanced Model of the Primary Visual Cortex

The brain processes visual inputs having structure over a large range of spatial scales. The precise mechanisms or algorithms used by the brain to achieve this feat are largely unknown and an open problem in visual neuroscience. In particular, the spatial extent in visual space over which primary visual cortex (V1) performs evidence integration has been shown to change as a function of contrast and other visual parameters, thus adapting scale in visual space in an input-dependent manner. We demonstrate that a simple dynamical mechanism---dynamical criticality---can simultaneously account for the well-documented input-dependence characteristics of three properties of V1: scales of integration in visuotopic space, extents of lateral integration on the cortical surface, and response latencies

Cortical spatio-temporal dimensionality reduction for visual grouping

The visual systems of many mammals, including humans, is able to integrate the geometric information of visual stimuli and to perform cognitive tasks already at the first stages of the cortical processing. This is thought to be the result of a combination of mechanisms, which include feature extraction at single cell level and geometric processing by means of cells connectivity. We present a geometric model of such connectivities in the space of detected features associated to spatio-temporal visual stimuli, and show how they can be used to obtain low-level object segmentation. The main idea is that of defining a spectral clustering procedure with anisotropic affinities over datasets consisting of embeddings of the visual stimuli into higher dimensional spaces. Neural plausibility of the proposed arguments will be discussed

Dynamic and Integrative Properties of the Primary Visual Cortex

The ability to derive meaning from complex, ambiguous sensory input requires the integration of information over both space and time, as well as cognitive mechanisms to dynamically shape that integration. We have studied these processes in the primary visual cortex (V1), where neurons have been proposed to integrate visual inputs along a geometric pattern known as the association field (AF). We first used cortical reorganization as a model to investigate the role that a specific network of V1 connections, the long-range horizontal connections, might play in temporal and spatial integration across the AF. When retinal lesions ablate sensory information from portions of the visual field, V1 undergoes a process of reorganization mediated by compensatory changes in the network of horizontal collaterals. The reorganization accompanies the brain’s amazing ability to perceptually “fill-inâ€, or “seeâ€, the lost visual input. We developed a computational model to simulate cortical reorganization and perceptual fill-in mediated by a plexus of horizontal connections that encode the AF. The model reproduces the major features of the perceptual fill-in reported by human subjects with retinal lesions, and it suggests that V1 neurons, empowered by their horizontal connections, underlie both perceptual fill-in and normal integrative mechanisms that are crucial to our visual perception. These results motivated the second prong of our work, which was to experimentally study the normal integration of information in V1. Since psychophysical and physiological studies suggest that spatial interactions in V1 may be under cognitive control, we investigated the integrative properties of V1 neurons under different cognitive states. We performed extracellular recordings from single V1 neurons in macaques that were trained to perform a delayed-match-to-sample contour detection task. We found that the ability of V1 neurons to summate visual inputs from beyond the classical receptive field (cRF) imbues them with selectivity for complex contour shapes, and that neuronal shape selectivity in V1 changed dynamically according to the shapes monkeys were cued to detect. Over the population, V1 encoded subsets of the AF, predicted by the computational model, that shifted as a function of the monkeys’ expectations. These results support the major conclusions of the theoretical work; even more, they reveal a sophisticated mode of form processing, whereby the selectivity of the whole network in V1 is reshaped by cognitive state

From receptive profiles to a metric model of V1

In this work we show how to construct connectivity kernels induced by the receptive profiles of simple cells of the primary visual cortex (V1). These kernels are directly defined by the shape of such profiles: this provides a metric model for the functional architecture of V1, whose global geometry is determined by the reciprocal interactions between local elements. Our construction adapts to any bank of filters chosen to represent a set of receptive profiles, since it does not require any structure on the parameterization of the family. The connectivity kernel that we define carries a geometrical structure consistent with the well-known properties of long-range horizontal connections in V1, and it is compatible with the perceptual rules synthesized by the concept of association field. These characteristics are still present when the kernel is constructed from a bank of filters arising from an unsupervised learning algorithm.Comment: 25 pages, 18 figures. Added acknowledgement

Mechanisms of spatiotemporal selectivity in cortical area MT

Cortical sensory neurons are characterized by selectivity to stimulation. This selectivity was originally viewed as a part of the fundamental “receptive field” characteristic of neurons. This view was later challenged by evidence that receptive fields are modulated by stimuli outside of the classical receptive field. Here we show that even this modified view of selectivity needs revision. We measured spatial frequency selectivity of neurons in cortical area MT of alert monkeys and found that their selectivity strongly depends on luminance contrast, shifting to higher spatial frequencies as contrast increases. The changes of preferred spatial frequency are large at low temporal frequency and they decrease monotonically as temporal frequency increases. That is, even interactions among basic stimulus dimensions of luminance contrast, spatial frequency and temporal frequency strongly influence neuronal selectivity. This dynamic nature of neuronal selectivity is inconsistent with the notion of stimulus preference as a stable characteristic of cortical neurons