A new class of neural networks are 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...

. Two types of artificial neural networks are introduced for the robust classification of spatio-temporal sequences. The first network is the Adaptive Spatio-Temporal Recognizer (ASTER), which adaptively estimates the confidence that a (variable length) signal of a known class is present by continuously monitoring a sequence of feature vectors. If the confidence for any class exceeds a threshold value at some moment, the signal is considered to be detected and classified. The nonlinear behavior of ASTER provides more robust performance than the related dynamic time warping algorithm. ASTER is compared with a more common approach wherein a self-organizing feature map is first used to map a sequence of extracted feature vectors onto a lower dimensional trajectory, which is then identified using a variant of the feedforward time delay neural network. The performance of these two networks is compared using artificial sonograms as well as feature vectors strings obtained from short-duration...