Publication:
Correlation Encoding of Input Data for Solving a Classification Task by a Spiking Neural Network with Spike-Timing-Dependent Plasticity

dc.contributor.authorSerenko, A.
dc.contributor.authorRybka, R.
dc.contributor.authorSboev, A.
dc.contributor.authorСбоев, Александр Георгиевич
dc.date.accessioned2024-12-26T10:56:04Z
dc.date.available2024-12-26T10:56:04Z
dc.date.issued2022
dc.description.abstract© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.We propose a new approach for encoding input data into spike sequences presented to the spiking neural network in a classification task: an input vector is represented by mutual correlations of input spike sequences. The accuracy obtained on the benchmark classification tasks of Fisher’s Iris and Wisconsin breast cancer is comparable to the results of other existing approaches to spiking neural network learning on base of Spike-Timing-Dependent Plasticity.
dc.format.extentС. 457-462
dc.identifier.citationSerenko, A. Correlation Encoding of Input Data for Solving a Classification Task by a Spiking Neural Network with Spike-Timing-Dependent Plasticity / Serenko, A., Rybka, R., Sboev, A. // Studies in Computational Intelligence. - 2022. - 1032 SCI. - P. 457-462. - 10.1007/978-3-030-96993-6_50
dc.identifier.doi10.1007/978-3-030-96993-6_50
dc.identifier.urihttps://www.doi.org/10.1007/978-3-030-96993-6_50
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85127619111&origin=resultslist
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000833484200050
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/28972
dc.relation.ispartofStudies in Computational Intelligence
dc.titleCorrelation Encoding of Input Data for Solving a Classification Task by a Spiking Neural Network with Spike-Timing-Dependent Plasticity
dc.typeConference Paper
dspace.entity.typePublication
oaire.citation.volume1032 SCI
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