A model for the development of neurons selective to visual stimulus size

Abstract

In this work, a neural network model for the development of variable sized receptive fields is presented. The system self-organizes under simple rules such as correlation of activity, signal diffusion, and competitive synaptic growth. The network model has one input and one output layer. They are fully connected by an excitatory weight matrix. In addition, the neurons of the output layer are interconnected by inhibitory weights. The set of differential equations for the time evolution of the system is calculated. Numerical integration shows that according to the set of network parameters the system reaches either a non-organized steady state, where all the connections have the same value, or any of two organized states, one of them having connections that represent mexican hat shaped receptive fields of variable size.