Synaptic compensation on Hopfield network: Implications for memory rehabilitation

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dc.contributor.authorMenezes R.A.
dc.contributor.authorMonteiro L.H.A.
dc.date.accessioned2024-03-13T01:11:02Z
dc.date.available2024-03-13T01:11:02Z
dc.date.issued2011
dc.description.abstractThe discrete-time neural network proposed by Hopfield can be used for storing and recognizing binary patterns. Here, we investigate how the performance of this network on pattern recognition task is altered when neurons are removed and the weights of the synapses corresponding to these deleted neurons are divided among the remaining synapses. Five distinct ways of distributing such weights are evaluated. We speculate how this numerical work about synaptic compensation may help to guide experimental studies on memory rehabilitation interventions. © 2010 Springer-Verlag London Limited.
dc.description.firstpage753
dc.description.issuenumber5
dc.description.lastpage757
dc.description.volume20
dc.identifier.doi10.1007/s00521-010-0480-7
dc.identifier.issn0941-0643
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/36974
dc.relation.ispartofNeural Computing and Applications
dc.rightsAcesso Restrito
dc.subject.otherlanguageAlzheimer's disease
dc.subject.otherlanguageHopfield neural network
dc.subject.otherlanguageMemory rehabilitation
dc.subject.otherlanguageSynaptic compensation
dc.titleSynaptic compensation on Hopfield network: Implications for memory rehabilitation
dc.typeArtigo
local.scopus.citations3
local.scopus.eid2-s2.0-79958083985
local.scopus.subjectAlzheimer's disease
local.scopus.subjectBinary patterns
local.scopus.subjectDiscrete-time neural networks
local.scopus.subjectExperimental studies
local.scopus.subjectHopfield Networks
local.scopus.subjectMemory rehabilitation
local.scopus.subjectSynaptic compensation
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79958083985&origin=inward
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