A Generalized Approach For Connectionist Auto-Associative Memories: Interpretation, Implication Illu

XI.1. Introduction Recent years have seen an increasing number of papers in Psychology that attempt to model cognitive and perceptual processes using associative memory models (e. Basically, these models use large sets of neuron-like units. The units are linked by connections of modiiable intensity (e.g., synapses). Learning or information storage occurs by modiication of the connections. The major advantages of associative models over more traditional information processing and artiicial intelligence models are that they make use of computations that are potentially completely parallel and that they employ distributed rather than localized storage of data. Parallel computations are simply those that may, in principle, be done simultaneously and therefore do not depend upon the outcome of the other computations. Distributed storage refers to that fact that discrete locations of memory do not code for individual pieces of data, but rather form parts of the coding of many stimuli. The workings of both of these features have provided useful insights into some psychological and computational issues in cognition and perception that have proven quite diicult to model with logic-based information processing models. Along with the psychological studies that have appeared recently, there have been many papers that have attempted to analyze the various learning The author wishes to thank Alice O'Toole and Raymond Bruyer for help and comments on previous drafts of this paper and Jim Anderson for support. This paper has been written during a visiting professorship in Brown University made possible by a Fullbright scholarship (1986{1988).

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