Temporal learning rule and dynamic neural network model

Abstract

The central nervous system is a highly dynamic network which is constantly being changed by a learning process. A new temporal learning rule, the revised Hebbian rule with synaptic history, was proposed in order to organize the dynamic associative memory. The learning rule was applied to a pulse-driven neural network model, and a temporal associative memory was self-organized by input temporal signals. This result leads to a new concept that the temporal sequence of events is memorized among the asymmetric connections in the network. It was also shown that dynamic neural networks were effectively organized using temporal information. Grouping or isolation for the multi-modal information was performed well by temporal learning processing. These results suggest that temporal information may be an important factor for organizing information processing circuits in the nervous system in addition to spatial information.