Self-organization of the velocity selectivity of a directionally selective neural network.

We first present a mathematical analysis of the relation between the parameters and the behavior of the basic module in the proposed neural network model for visual motion detection. Based on the analytical results, a learning rule is put forth that can develop velocity selectivity of directionally selective cells in the basic module. The learning rule is furthermore introduced into the total model called a 'mass model', which is constructed with many basic modules. Numerical simulation results showed that each basic module in the mass model learned in a self-organizing manner to acquire selectivity for the velocity of an input stimulus. The proposed learning rule would be plausible in the actual nervous system in that it is simple and can be described with only local information.