Abstract
The neural system is believed to have such a capacity for self-organization that it can modify its structures or behavior in adapting to the information structures of the environment. We have constructed a mathematical theory of self-organizing nerve nets, with the aim of elucidating the modes and capabilities of this peculiar information processing in nerve nets.
We first present a unified theoretical framework for analyzing learning and selforganization of a system of neurons with modifiable synapses, which receive signals from a stationary information source. We consider the dynamics of self-organization, which shows how the synaptic weights are modified, together with the dynamics of neural excitation patterns. It is proved that a neural system has the ability automatically to form, by selforganization, detectors or processors for every signal included in the information source of the environment.
A model of self-organization in nerve fields is then presented, and the dynamics of pattern formation is analyzed in nerve fields. The theory is applied to the formation of topographic maps between two nerve fields. It is shown that under certain conditions columnar microstructures are formed in nerve fields by self-organization.
