Publication:
A Comparison of Two Variants of Memristive Plasticity for Solving the Classification Problem of Handwritten Digits Recognition

Простая страница элемента
dc.contributor.authorDavydov, Y.
dc.contributor.authorRybka, R.
dc.contributor.authorVlasov, D.
dc.contributor.authorSerenko, A.
dc.contributor.authorSboev, A.
dc.contributor.authorСбоев, Александр Георгиевич
dc.date.accessioned2024-12-26T10:56:39Z
dc.date.available2024-12-26T10:56:39Z
dc.date.issued2022
dc.description.abstract© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Nowadays, the task of creating and training spiking neural networks (SNN) is extremely relevant due to their high energy efficiency achieved by implementing such networks via neuromorphic hardware. Especially interesting is the possibility of building SNNs based on memristors, which have properties that potentially allow them to be used as analog synapses. With that in mind, it seems relevant to study spike networks built upon plasticity rules that correspond to the experimentally observed nonlinear laws of conductivity change in memristors. Earlier it was shown that spiking neural networks trained with a biologically inspired local STDP (Spike-Timing-Dependent Plasticity) rule are capable of solving classification problems successfully. In addition, it was also demonstrated that classification problems can also be solved with spiking neural networks operating with a plasticity rule that models the change in conductivity in nanocomposite (NC) memristors. This paper presents a continuation of the study of the applicability of memristive plasticity rules on the handwritten digit recognition problem. Two types of memristive plasticity are compared: for nanocomposite and PPX memristors. It is shown that both models can successfully solve the classification problem, and the key differences between them are identified.
dc.format.extentС. 438-446
dc.identifier.citationA Comparison of Two Variants of Memristive Plasticity for Solving the Classification Problem of Handwritten Digits Recognition / Davydov, Y. [et al.] // Studies in Computational Intelligence. - 2022. - 1032 SCI. - P. 438-446. - 10.1007/978-3-030-96993-6_48
dc.identifier.doi10.1007/978-3-030-96993-6_48
dc.identifier.urihttps://www.doi.org/10.1007/978-3-030-96993-6_48
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85127618221&origin=resultslist
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000833484200048
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/28973
dc.relation.ispartofStudies in Computational Intelligence
dc.titleA Comparison of Two Variants of Memristive Plasticity for Solving the Classification Problem of Handwritten Digits Recognition
dc.typeConference Paper
dspace.entity.typePublication
oaire.citation.volume1032 SCI
relation.isAuthorOfPublicationfc2d63d7-5260-41ba-a952-0420c8848b13
relation.isAuthorOfPublication.latestForDiscoveryfc2d63d7-5260-41ba-a952-0420c8848b13
relation.isOrgUnitOfPublicationba0b4738-e6bd-4285-bda5-16ab2240dbd1
relation.isOrgUnitOfPublication.latestForDiscoveryba0b4738-e6bd-4285-bda5-16ab2240dbd1
Файлы
Коллекции
Публикации