Temporally sensitive neural networks

Abstract

The problem of recognizing rhythmic patterns characterized by a periodically repeating sequence of events is addressed. An approach to representing temporal information in neural networks and an application that makes use of this representation are described. The Tempnet rhythm system is a particular instantiation of these ideas. It is used to demonstrate the use of temporal representation in the processing of temporal signals. Decaying node activations are used to represent the timing of specific temporal events. This approach was demonstrated in a system for categorizing periodically repeating patterns, independent of time scale. The network simulator is described, along with the results of some sample training and performance runs.<>