Self-organizing neural network for spatio-temporal patterns

A self-organizing neural network for spatiotemporal patterns is proposed which is a nearest neighbor classifier that stores arbitrary-length spatiotemporal patterns. It has at least three layers. Layer-1 and layer-2 classify input spatial patterns one by one and the algorithm is based on the Carpenter/Grossberg net algorithm. Layer-2 and layer-3 classify and memorize the sequence of the patterns classified in layer-2. The connections between layer-2 and layer-3 are adjusted in both weight and time-delay to deal with spatiotemporal patterns. The features of the proposed neural network are its self-organization, classification, and memorization abilities for spatiotemporal patterns. In addition, it can distinguish different sequences composed of the same patterns such as `ACT´ and `CAT´ by unsupervised learning, and can deal with many sequences of different lengths