area-v1

An adaptive homeostatic algorithm for the unsupervised learning of visual features

The formation of structure in the visual system, that is, of the connections between cells within neural populations, is by large an unsupervised learning process: the emergence of this architecture is mostly self-organized. In the primary visual …

Suppressive waves disambiguate the representation of long-range apparent motion in awake monkey V1

Traveling waves have recently been observed in different animal species, brain areas and behavioral states. However, it is still unclear what are their functional roles. In the case of cortical visual processing, waves propagate across retinotopic maps and can hereby generate interactions between spatially and temporally separated instances of feedforward driven activity. Such interactions could participate in processing long-range apparent motion stimuli, an illusion for which no clear neuronal mechanisms have yet been proposed. Using this paradigm in awake monkeys, we show that suppressive traveling waves produce to a spatio-temporal normalization of apparent motion stimuli. Our study suggests that cortical waves shape the representation of illusory moving stimulus within retinotopic maps for an straightforward read-out by downstream areas.

Orientation selectivity to synthetic natural patterns in a cortical-like model of the cat primary visual cortex

A key property of the neurons in the primary visual cortex (V1) is their selectivity to oriented stimuli in the visual field. Orientation selectivity allows the segmentation of objects in natural visual scenes, which is the first step in building …

The Philosophy and Science of Predictive Processing

Within the central nervous system, visual areas are essential in transforming the raw luminous signal into a representation which efficiently conveys information about the environment. This process is constrained by the necessity of being robust and …

Selectivity to oriented patterns of different precisions

The selectivity of the visual system to oriented patterns is very well documented in a wide range of species, especially in mammals. In particular, neurons of the primary visual cortex are anatomically grouped by their preference to a given oriented …

Push-Pull Receptive Field Organization and Synaptic Depression: Mechanisms for Reliably Encoding Naturalistic Stimuli in V1

Neurons in the primary visual cortex are known for responding vigorously but with high variability to classical stimuli such as drifting bars or gratings. By contrast, natural scenes are encoded more efficiently by sparse and temporal precise spiking …

Phase space analysis of networks based on biologically realistic parameters

We study cortical network dynamics for a spatially embedded network model. It represents, in terms of spatial scale, a large piece of cortex allowing for long-range connections, resulting in a rather sparse connectivity. The spatial embedding also …

On efficient sparse spike coding schemes for learning natural scenes in the primary visual cortex

We describe the theoretical formulation of a learning algorithm in a model of the primary visual cortex (V1) and present results of the efficiency of this algorithm by comparing it to the SparseNet algorithm [1]. As the SparseNet algorithm, it is …

Sparse Gabor wavelets by local operations

Efficient sparse coding of overcomplete transforms remains still anopen problem. Different methods have been proposed in theliterature, but most of them are limited by a heavy computationalcost and by difficulties to find the optimal solutions. We …

Finding Independent Components using spikes : a natural result of hebbian learning in a sparse spike coding scheme

To understand possible strategies of temporal spike coding in the central nervous system, we study functional neuromimetic models of visual processing for static images. We will first present the retinal model which was introduced by Van Rullen and …

Emergence of filters from natural scenes in a sparse spike coding scheme

Sparse Image Coding Using an Asynchronous Spiking Neural Network

Progressive reconstruction of a static image using spikes in a Laplacian pyramid.