Neural fields models of visual areas: principles, successes, and caveats

Description

I discuss how the notion of neural fields, a phenomenological averaged description of spatially distributed populations of neurons, can be used to build models of how visual information is represented and processed in the visual areas of primates. I describe in a pedestrian way one of the basic principles of operation of these neural fields equations which is closely connected to the idea of a bifurcation of their solutions. I then apply this concept to several visual features, edges, textures and motion and show that it can account very simply for a number of experimental facts as well as suggest new experiments. I outline several outstanding open problems and sketch out briefly interesting connections with computer vision and machine learning.

Abstract

International audience

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