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

Дата
2022
Авторы
Davydov, Y.
Rybka, R.
Vlasov, D.
Serenko, A.
Sboev, A.
Journal Title
Journal ISSN
Volume Title
Издатель
Научные группы
Организационные подразделения
OrgUnit Logo
Организационная единица
Институт ядерной физики и технологий
Цель ИЯФиТ и стратегия развития - создание и развитие научно-образовательного центра мирового уровня в области ядерной физики и технологий, радиационного материаловедения, физики элементарных частиц, астрофизики и космофизики.
Выпуск журнала
Аннотация
© 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.
Описание
Ключевые слова
Цитирование
A 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
URI
https://www.doi.org/10.1007/978-3-030-96993-6_48
 https://www.scopus.com/record/display.uri?eid=2-s2.0-85127618221&origin=resultslist
 http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000833484200048
 https://openrepository.mephi.ru/handle/123456789/28973
Коллекции
Публикации