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dc.contributor.authorDavey, N.
dc.contributor.authorHunt, S.
dc.date.accessioned2008-02-05T12:13:21Z
dc.date.available2008-02-05T12:13:21Z
dc.date.issued1999
dc.identifier.citationDavey , N & Hunt , S 1999 , ' The capacity and attractor basins of associative memory models ' , Lecture Notes in Computer Science , vol. 1606 , pp. 330-339 . https://doi.org/10.1007/BFb0098189
dc.identifier.issn0302-9743
dc.identifier.otherPURE: 102876
dc.identifier.otherPURE UUID: 97d33538-2e43-49b5-a115-2c2dd068a5aa
dc.identifier.otherdspace: 2299/1564
dc.identifier.otherScopus: 33745879861
dc.identifier.urihttp://hdl.handle.net/2299/1564
dc.descriptionThe original publication is available at www.springerlink.com . Copyright Springer
dc.description.abstractThe performance characteristics of five variants of the Hopfield network are examined. Two performance metrics are used: memory capacity, and a measure of the size of basins of attraction. We find that the posttraining adjustment of processor thresholds has, at best, little or no effect on performance, and at worst a significant negative effect. The adoption of a local learning rule can, however, give rise to significant performance gains.en
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science
dc.rightsOpen
dc.titleThe capacity and attractor basins of associative memory modelsen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.description.statusPeer reviewed
dcterms.dateAccepted1999
rioxxterms.versionofrecordhttps://doi.org/10.1007/BFb0098189
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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