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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/3904

Title: ROBUSTNESS AGAINST RANDOM DILUTION IN ATTRACTOR NEURAL NETWORKS
Authors: KOMODA, A
SERNEELS, Roger
WONG, KYM
BOUTEN, Marcus
Issue Date: 1991
Publisher: IOP PUBLISHING LTD
Citation: JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL, 24(13). p. L743-L749
Abstract: We study the robustness of attractor neural networks against random disruption of a fraction of the synaptic couplings. For the maximally stable network (MSN), we determine the effect of different degrees of dilution on the overall storage capacity, the size of the basins of attraction and the attractor overlap. Comparison with corresponding results for the Hopfield model indicates that, although MSN is more robust at low degrees of dilution, the Hopfield network becomes more robust at high dilution.
Notes: UNIV OXFORD,DEPT THEORET PHYS,OXFORD OX1 3NP,ENGLAND.KOMODA, A, LIMBURGS UNIV CENTRUM,UNIV CAMPUS,B-3610 DIEPENBEEK,BELGIUM.
URI: http://hdl.handle.net/1942/3904
DOI: 10.1088/0305-4470/24/13/008
ISI #: A1991FW63600008
ISSN: 0305-4470
Type: Journal Contribution
Appears in Collections: Research publications

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