DISTRIBUTED LEARNING, RECOGNITION, AND PREDICTION BY ART AND ARTMAP NEURAL NETWORKS

A class of adaptive resonance theory (ART) models for learning, recogn ition, and prediction with arbitrarily distributed code representation s is introduced. Distributed ART neural networks combine the stable fa st learning capabilities of winner-take-all ART systems with the noise tolerance and code compression capabilities of multilayer perceptrons . With a winner-take-all code, the unsupervised model dART reduces to fuzzy ART and the supervised model dARTMAP reduces to fuzzy ARTMAP. Wi th a distributed code, these networks automatically apportion learned changes according to the degree of activation of each coding node, whi ch permits fast as well as slow learning without catastrophic forgetti ng. Distributed ART models replace the traditional neural network path weight with a dynamic weight equal to the rectified difference betwee n coding node activation and an adaptive threshold Thresholds increase monotonically during learning according to a principle of atrophy due to disuse. However, monotonic change at the synaptic level manifests itself as bidirectional change at the dynamic level, where the result of adaptation resembles long-term potentiation (LTP) for single-pulse or low frequency test inputs but can resemble long-term depression (LT D) for higher frequency test inputs. This paradoxical behavior is trac ed to dual computational properties of phasic and tonic coding signal components. A parallel distributed match-reset-search process also hel ps stabilize memory. Without the match-reset-search system, dART becom es a type of distributed competitive learning network. (C) 1997 Elsevi er Science Ltd.