Habituation based neural networks for spatio-temporal classification1

A new class of neural networks is proposed for the dynamic classification of spatio-temporal signals. These networks are designed to classify signals of different durations, taking into account correlations among different signal segments. Such networks are applicable to SONAR and speech signal classification problems, among others. Network parameters are adapted based on the biologically observed habituation mechanism. This allows the storage of contextual information, without a substantial increase in network complexity. We introduce the concept of a complete memory. We then prove mathematically that a network with a complete memory temporal encoding stage followed by a sufficiently powerful feedforward network is capable of approximating arbitrarily well any continuous, causal, time-invariant discrete-time system with a uniformly bounded input domain. The memory mechanisms of the habituation based networks are complete memories, and involve nonlinear transformations of the input signal. In networks such as the time delay neural network (TDNN) [35] and focused gamma networks [8], nonlinearities are present in the feedforward stage only. This distinction is made important by recent theoretical results concerning the limitations of structures with linear temporal encoding stages. Results are reported on classification of high dimensional feature vectors obtained from Banzhaf sonograms.