DocumentCode :
1338463
Title :
A neural network model of memory under stress
Author :
Cemuschi-Frias, B. ; García, Rafael A. ; Zanutto, Silvano
Author_Institution :
Fac. de Ingenieria, Buenos Aires Univ., Argentina
Volume :
27
Issue :
2
fYear :
1997
fDate :
4/1/1997 12:00:00 AM
Firstpage :
278
Lastpage :
284
Abstract :
A model that attempts to simulate animal memory under stress is presented. For this purpose a model of selectable multiple associative memories is given. We consider two underlying types of memories: stressed and unstressed, implemented on the same neural network. In our model, learning into one or the other type of memory is done according to the stress of the individual at the time of learning. Memory retrieval is obtained according to a continuous function of the stress of the individual at the time of retrieval, who for low stress retrieves unstressed associations and for high stress retrieves stressed associations. Several biological results supporting this model are presented. A mathematical proof on the behaviour of the basins of attraction of the network as a function of stress is presented. Also a generalization to selectable multiple coexisting memories is given, and engineering and other applications of the model are suggested
Keywords :
content-addressable storage; neural nets; biological results; mathematical proof; memory under stress; neural network model; selectable multiple associative memories; selectable multiple coexisting memories; Amino acids; Animal behavior; Animal structures; Associative memory; Biochemistry; Biological system modeling; Hopfield neural networks; Mathematical model; Neural networks; Stress;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/3477.558817
Filename :
558817
Link To Document :
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