Topographic Receptive Fields and Patterned Lateral Interaction in a Self-Organizing Model of The Primary Visual Cortex

Joseph Sirosh and Risto Miikkulainen.
Department of Computer Sciences
The University of Texas at Austin
Austin, TX 78712
{sirosh,risto}@cs.utexas.edu

Abstract:

A self-organizing neural network model for the simultaneous development of topographic receptive fields and lateral interactions in cortical maps is presented. Both afferent and lateral connections adapt by the same Hebbian mechanism in a purely local and unsupervised learning process. Afferent input weights of each neuron self-organize into hill-shaped profiles, receptive fields organize topographically across the network, and unique lateral interaction profiles develop for each neuron. The model suggests that precise cortical maps develop only if the initial receptive fields are topographically ordered or if they cover the whole receptive surface. It demonstrates how patterned lateral connections develop based on correlated activity, and explains why lateral connection patterns closely follow receptive field properties such as ocular dominance. The model predicts a dual role for lateral connections: to support self-organization of receptive fields, and to represent low-level Gestalt knowledge acquired during development of the cortex.