A neural network model of spatio-temporal pattern recognition, recall, and timing

The author describes the design of a self-organizing, hierarchical network model of unsupervised serial learning. The model learns to recognize, store, and recall sequences of unitized patterns, using either short-term memory (STM) or both STM and long-term memory (LTM) mechanisms. Timing information is learned and recall both from STM and from LTM is performed with a learned rhythmical structure. The network, bearing similarities to ART, learns to map temporal sequences to unitized patterns, which makes it suitable for hierarchical operations. It is therefore capable of self-organizing codes for sequences of sequences. The capacity is only limited by the number of nodes provided. Selected simulation results are reported to illustrate system properties.<
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