STDP and STDP variations with memristors for spiking neuromorphic learning systems

In this paper we review several ways of realizing asynchronous Spike-Timing-Dependent-Plasticity (STDP) using memristors as synapses. Our focus is on how to use individual memristors to implement synaptic weight multiplications, in a way such that it is not necessary to (a) introduce global synchron...

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Detalhes bibliográficos
Autores: Serrano-Gotarredona, Teresa, Masquelier, Timothée, Prodromakis, Themis, Indiveri, Giacomo, Linares-Barranco, Bernabe
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2013
País:España
Recursos:Universitat Pompeu Fabra
Repositório:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/69465
Acesso em linha:http://hdl.handle.net/10230/69465
http://dx.doi.org/10.3389/fnins.2013.00002
Access Level:Acceso aberto
Palavra-chave:Memristor/cmos
Artificial-learning-synapses
Spike-timing-dependent-plasticity
Spiking-neural-networks
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repository_id_str
spelling STDP and STDP variations with memristors for spiking neuromorphic learning systemsSerrano-Gotarredona, Teresa Masquelier, TimothéeProdromakis, ThemisIndiveri, GiacomoLinares-Barranco, BernabeMemristor/cmosArtificial-learning-synapsesSpike-timing-dependent-plasticitySpiking-neural-networksIn this paper we review several ways of realizing asynchronous Spike-Timing-Dependent-Plasticity (STDP) using memristors as synapses. Our focus is on how to use individual memristors to implement synaptic weight multiplications, in a way such that it is not necessary to (a) introduce global synchronization and (b) to separate memristor learning phases from memristor performing phases. In the approaches described, neurons fire spikes asynchronously when they wish and memristive synapses perform computation and learn at their own pace, as it happens in biological neural systems. We distinguish between two different memristor physics, depending on whether they respond to the original “moving wall” or to the “filament creation and annihilation” models. Independent of the memristor physics, we discuss two different types of STDP rules that can be implemented with memristors: either the pure timing-based rule that takes into account the arrival time of the spikes from the pre- and the post-synaptic neurons, or a hybrid rule that takes into account only the timing of pre-synaptic spikes and the membrane potential and other state variables of the post-synaptic neuron. We show how to implement these rules in cross-bar architectures that comprise massive arrays of memristors, and we discuss applications for artificial vision.This work was supported by Spanish grants from the Ministry of Economy and Competitivity TEC200-106039-C04-01/02 (VULCANO) (with support from the European Regional Development Fund) and PRI-PIMCHI-2011-0768 (PNEUMA) coordinated with the European CHIST-ERA program, and Andalusian grant TIC6091 (NANONEURO). T. Masquelier was supported by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement 269459 (CORONET).Frontiers202520252013info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/69465http://dx.doi.org/10.3389/fnins.2013.00002reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésFrontiers in Neuroscience. 2013;7:2info:eu-repo/grantAgreement/EC/FP7/269459© 2013 Serrano-Gotarredona, Masquelier, Prodromakis, Indiveri and Linares-Barranco. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.http://creativecommons.org/licenses/by/3.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/694652026-06-12T07:21:37Z
dc.title.none.fl_str_mv STDP and STDP variations with memristors for spiking neuromorphic learning systems
title STDP and STDP variations with memristors for spiking neuromorphic learning systems
spellingShingle STDP and STDP variations with memristors for spiking neuromorphic learning systems
Serrano-Gotarredona, Teresa
Memristor/cmos
Artificial-learning-synapses
Spike-timing-dependent-plasticity
Spiking-neural-networks
title_short STDP and STDP variations with memristors for spiking neuromorphic learning systems
title_full STDP and STDP variations with memristors for spiking neuromorphic learning systems
title_fullStr STDP and STDP variations with memristors for spiking neuromorphic learning systems
title_full_unstemmed STDP and STDP variations with memristors for spiking neuromorphic learning systems
title_sort STDP and STDP variations with memristors for spiking neuromorphic learning systems
dc.creator.none.fl_str_mv Serrano-Gotarredona, Teresa
Masquelier, Timothée
Prodromakis, Themis
Indiveri, Giacomo
Linares-Barranco, Bernabe
author Serrano-Gotarredona, Teresa
author_facet Serrano-Gotarredona, Teresa
Masquelier, Timothée
Prodromakis, Themis
Indiveri, Giacomo
Linares-Barranco, Bernabe
author_role author
author2 Masquelier, Timothée
Prodromakis, Themis
Indiveri, Giacomo
Linares-Barranco, Bernabe
author2_role author
author
author
author
dc.subject.none.fl_str_mv Memristor/cmos
Artificial-learning-synapses
Spike-timing-dependent-plasticity
Spiking-neural-networks
topic Memristor/cmos
Artificial-learning-synapses
Spike-timing-dependent-plasticity
Spiking-neural-networks
description In this paper we review several ways of realizing asynchronous Spike-Timing-Dependent-Plasticity (STDP) using memristors as synapses. Our focus is on how to use individual memristors to implement synaptic weight multiplications, in a way such that it is not necessary to (a) introduce global synchronization and (b) to separate memristor learning phases from memristor performing phases. In the approaches described, neurons fire spikes asynchronously when they wish and memristive synapses perform computation and learn at their own pace, as it happens in biological neural systems. We distinguish between two different memristor physics, depending on whether they respond to the original “moving wall” or to the “filament creation and annihilation” models. Independent of the memristor physics, we discuss two different types of STDP rules that can be implemented with memristors: either the pure timing-based rule that takes into account the arrival time of the spikes from the pre- and the post-synaptic neurons, or a hybrid rule that takes into account only the timing of pre-synaptic spikes and the membrane potential and other state variables of the post-synaptic neuron. We show how to implement these rules in cross-bar architectures that comprise massive arrays of memristors, and we discuss applications for artificial vision.
publishDate 2013
dc.date.none.fl_str_mv 2013
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/69465
http://dx.doi.org/10.3389/fnins.2013.00002
url http://hdl.handle.net/10230/69465
http://dx.doi.org/10.3389/fnins.2013.00002
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Frontiers in Neuroscience. 2013;7:2
info:eu-repo/grantAgreement/EC/FP7/269459
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/3.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Frontiers
publisher.none.fl_str_mv Frontiers
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812455

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