Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/1411
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dc.contributor.authorGreve, Andrea-
dc.contributor.authorSterratt, David C-
dc.contributor.authorDonaldson, David-
dc.contributor.authorWillshaw, David J-
dc.contributor.authorvan, Rossum Mark C W-
dc.date.accessioned2013-06-09T11:51:02Z-
dc.date.available2013-06-09T11:51:02Z-
dc.date.issued2009-01-
dc.identifier.urihttp://hdl.handle.net/1893/1411-
dc.description.abstractIt has been suggested that the mammalian memory system has both familiarity and recollection components. Recently, a high-capacity network to store familiarity has been proposed. Here we derive analytically the optimal learning rule for such a familiarity memory using a signalto- noise ratio analysis. We find that in the limit of large networks the covariance rule, known to be the optimal local, linear learning rule for pattern association, is also the optimal learning rule for familiarity discrimination. The capacity is independent of the sparseness of the patterns, as long as the patterns have a fixed number of bits set. The corresponding information capacity is 0.057 bits per synapse, less than typically found for associative networks.en_UK
dc.language.isoen-
dc.publisherSpringer-
dc.relationGreve A, Sterratt DC, Donaldson D, Willshaw DJ & van Rossum MCW (2009) Optimal learning rules for familiarity detection, Biological Cybernetics, 100 (1), pp. 11-19.-
dc.rightsPublished in Biological Cybernetics by Springer.; The original publication is available at www.springerlink.com-
dc.subjectFamiliarityen_UK
dc.subjectLearningen_UK
dc.subjectMemoryen_UK
dc.subject.lcshMemory Recognition (Psychology)-
dc.subject.lcshMemory Recollection (Psychology)-
dc.subject.lcshMeaning (Psychology)-
dc.titleOptimal learning rules for familiarity detectionen_UK
dc.typeJournal Articleen_UK
dc.identifier.doihttp://dx.doi.org/10.1007/s00422-008-0275-4-
dc.citation.jtitleBiological Cybernetics-
dc.citation.issn0340-1200-
dc.citation.volume100-
dc.citation.issue1-
dc.citation.spage11-
dc.citation.epage19-
dc.citation.publicationstatusPublished-
dc.citation.peerreviewedRefereed-
dc.type.statusPost-print (author final draft post-refereeing)-
dc.author.emaildid1@stir.ac.uk-
dc.contributor.affiliationUniversity of Edinburgh-
dc.contributor.affiliationUniversity of Edinburgh-
dc.contributor.affiliationPsychology-
dc.contributor.affiliationUniversity of Edinburgh-
dc.contributor.affiliationUniversity of Edinburgh-
dc.identifier.isi000263486700003-
Appears in Collections:Psychology Journal Articles

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