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dc.contributor.authorCarpenter, Gail A.en_US
dc.date.accessioned2011-11-14T18:19:26Z
dc.date.available2011-11-14T18:19:26Z
dc.date.issued1995-03
dc.identifier.urihttps://hdl.handle.net/2144/2035
dc.description.abstractIt is a neural network truth universally acknowledged, that the signal transmitted to a target node must be equal to the product of the path signal times a weight. Analysis of catastrophic forgetting by distributed codes leads to the unexpected conclusion that this universal synaptic transmission rule may not be optimal in certain neural networks. The distributed outstar, a network designed to support stable codes with fast or slow learning, generalizes the outstar network for spatial pattern learning. In the outstar, signals from a source node cause weights to learn and recall arbitrary patterns across a target field of nodes. The distributed outstar replaces the outstar source node with a source field, of arbitrarily many nodes, where the activity pattern may be arbitrarily distributed or compressed. Learning proceeds according to a principle of atrophy due to disuse whereby a path weight decreases in joint proportion to the transmittcd path signal and the degree of disuse of the target node. During learning, the total signal to a 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, a product rule, a capacity rule, and a threshold rule, are examined for this system. The three rules are computationally equivalent when source field activity is maximally compressed, or winner-take-all when source field activity is distributed, catastrophic forgetting may occur. Only the threshold rule solves this problem. Analysis of spatial pattern learning by distributed codes thereby leads to the conjecture that the optimal unit of long-term memory in such a system is a subtractive threshold, rather than a multiplicative weight.en_US
dc.description.sponsorshipAdvanced Research Projects Agency (ONR N00014-92-J-4015); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309)en_US
dc.publisherBoston University Center for Adaptive Systems and Department of Cognitive and Neural Systemsen_US
dc.relation.ispartofseriesBU CAS/CNS Technical Reports;CAS/CNS-TR-1993-058
dc.rightsCopyright 1995 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.en_US
dc.subjectSpatial pattern learningen_US
dc.subjectDistributed codingen_US
dc.subjectOutstaren_US
dc.subjectAdaptive thresholden_US
dc.subjectRectified biasen_US
dc.subjectAtrophy due to disuseen_US
dc.subjectTransmission functionen_US
dc.subjectNeural networksen_US
dc.titleSpatial Pattern Learning, Catastophic Forgetting and Optimal Rules of Synaptic Transmissionen_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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