A habituation based neural network for spatio-temporal classification

A novel neural network is proposed for the dynamic classification of spatio-temporal signals. The network is designed to classify signals of different durations, taking into account correlations among different signal segments. Such a network is 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. Experiments on classification of high dimensional feature vectors obtained from Banzhaf sonograms, demonstrate that the proposed network performs better than time delay neural networks while using a substantially simpler structure. The mathematical power of the network is discussed, including its ability to realize any function realizable by a TDNN. Additionally, principal component analysis is used to introduce a further improvement to the network design by reducing the dimensionality of the encoded temporal information