A Unified Neural Network Model for the Self-organization of Topographic Receptive Fields and Lateral Interaction

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 resulting self-organized structure remains in a dynamic and continuously-adapting equilibrium with the input. The model can be seen as a generalization of previous self-organizing models of the visual cortex, and provides a general computational framework for experiments on receptive field development and cortical plasticity. The model also serves to point out general limits on activity-dependent self-organization: when multiple inputs are presented simultaneously, the receptive field centers need to be initially ordered for stable self-organization to occur.