Neural Networks for Stable Disbributed Coding and Prediction

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9401659 Carpenter The proposed research will design, develop, analyze, and apply neural network architectures for stable recognition coding and prediction. One set of projects will focus on new unsupervised ART (Adaptive Resonance Theory) and supervised ARTMAP systems to support distributed recognition codes that are stable under training regimes with either fast or slow learning. Typically, the pattern of activity of a recognition code is distributed when there is little reason to select one category over another, as might occur early in a learning process. This pattern becomes more compressed as categories are more sharply defined through experience. Generalization and storage capacity of the ART neural networks will be greatly increased by the distributed coding capability. Related work will focus on the computational requirements of distributed search and on spatio-temporal recognition problems.