STDP and STDP variations with memristors for spiking neuromorphic learning systems

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• dc.contributor.author Serrano-Gotarredona, Teresa
• dc.contributor.author Masquelier, Timothée
• dc.contributor.author Prodromakis, Themis
• dc.contributor.author Indiveri, Giacomo
• dc.contributor.author Linares-Barranco, Bernabe
• dc.date.accessioned 2025-02-04T07:29:03Z
• dc.date.available 2025-02-04T07:29:03Z
• dc.date.issued 2013
• dc.description.abstract 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.en
• dc.description.sponsorship 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).en
• dc.format.mimetype application/pdf
• dc.identifier.citation Serrano-Gotarredona T, Masquelier T, Prodromakis T, Indiveri G, Linares-Barranco B. STDP and STDP variations with memristors for spiking neuromorphic learning systems. Front Neurosci. 2013;7:2. DOI: 10.3389/fnins.2013.00002
• dc.identifier.doi http://dx.doi.org/10.3389/fnins.2013.00002
• dc.identifier.issn 1662-4548
• dc.identifier.uri http://hdl.handle.net/10230/69465
• dc.language.iso eng
• dc.publisher Frontiers
• dc.relation.ispartof Frontiers in Neuroscience. 2013;7:2
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/269459
• dc.rights © 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.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/3.0/
• dc.subject.keyword Memristor/cmosen
• dc.subject.keyword Artificial-learning-synapsesen
• dc.subject.keyword Spike-timing-dependent-plasticityen
• dc.subject.keyword Spiking-neural-networksen
• dc.title STDP and STDP variations with memristors for spiking neuromorphic learning systemsen
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion