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Proceedings Paper

Distributed recognition codes, catastrophic forgetting, and the rules of synaptic transmission
Author(s): Gail A. Carpenter
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Paper Abstract

Analysis of catastrophic forgetting by distributed codes leads to the unexpected conclusion that the standard synaptic transmission rule may not be optimal in certain neural networks. The distributed outstar generalizes the outstar network for spatial pattern learning, replacing the outstar source node with a source field, of arbitrarily many nodes, where the activity pattern may be arbitrarily distributed or compressed. Distributed outstar learning proceeds according to a principle of atrophy due to disuse whereby a path weight decreases in joint proportion to the transmitted path signal and the degree of disuse of the target node. During learning, the total signal to target node converges toward that node's activity level. Weight changes at a node are apportioned according to the distributed pattern of converging signals. Three types of synaptic transmission, the standard product rule, a fuzzy capacity rule, and an adaptive threshold rule, are examined for this system. Only the threshold rule solves the catastrophic forgetting problem of fast learning. Analysis of spatial distributed coding hereby leads to the conjecture that the unit of long-term memory is a spatial pattern learning system may be a subtractive threshold, rather than a multiplicative weight.

Paper Details

Date Published: 10 October 1994
PDF: 10 pages
Proc. SPIE 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, (10 October 1994); doi: 10.1117/12.188902
Show Author Affiliations
Gail A. Carpenter, Boston Univ. (United States)

Published in SPIE Proceedings Vol. 2353:
Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision
David P. Casasent, Editor(s)

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