Cooperative self-organization of afferent and lateral connections in cortical maps

Abstract

A self-organizing neural network model called LISSOM for the synergetic development of afferent and lateral connections in cortical feature maps is presented. The weight adaptation process is purely activity-dependent, unsupervised, and local. The afferent input weights self-organize into a topological map of the input space. At the same time, the lateral interconnection weights adapt, and a unique lateral interaction profile develops for each neuron. Weak lateral connections die off, leaving a pattern of connections that represents the significant long-term correlations of activity on the feature map. LISSOM demonstrates how self-organization can bootstrap based on input information only, without global supervision or predetermined lateral interaction. The model gives rise to a nontopographically organized lateral connectivity similar to that observed in the mammalian neocortex as illustrated by a LISSOM model of ocular dominance column formation in the primary visual cortex. In addition, LISSOM can potentially account for the development of multiple maps of different modalities on the same undifferentiated cortical architecture.