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dc.creatorCamuñas Mesa, Luis Alejandroes
dc.creatorLinares Barranco, Bernabées
dc.creatorSerrano Gotarredona, María Teresaes
dc.date.accessioned2020-07-07T10:38:13Z
dc.date.available2020-07-07T10:38:13Z
dc.date.issued2019
dc.identifier.citationCamuñas Mesa, L.A., Linares Barranco, B. y Serrano Gotarredona, M.T. (2019). Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations. Materials, 12 (17), 2745-.
dc.identifier.issn1996-1944es
dc.identifier.urihttps://hdl.handle.net/11441/98922
dc.description.abstractInspired by biology, neuromorphic systems have been trying to emulate the human brain for decades, taking advantage of its massive parallelism and sparse information coding. Recently, several large-scale hardware projects have demonstrated the outstanding capabilities of this paradigm for applications related to sensory information processing. These systems allow for the implementation of massive neural networks with millions of neurons and billions of synapses. However, the realization of learning strategies in these systems consumes an important proportion of resources in terms of area and power. The recent development of nanoscale memristors that can be integrated with Complementary Metal–Oxide–Semiconductor (CMOS) technology opens a very promising solution to emulate the behavior of biological synapses. Therefore, hybrid memristor-CMOS approaches have been proposed to implement large-scale neural networks with learning capabilities, offering a scalable and lower-cost alternative to existing CMOS systems.es
dc.description.sponsorshipEU H2020 grant 687299 ”NEURAM3”es
dc.description.sponsorshipEU H2020 grant 824164 ”HERMES”es
dc.description.sponsorshipMinistry of Economy and Competitivity (Spain) and European Regional Development Fund TEC2015-63884-C2-1-P (COGNET)es
dc.description.sponsorshipVI PPIT through the Universidad de Sevilla.es
dc.formatapplication/pdfes
dc.format.extent28 p.es
dc.language.isoenges
dc.publisherMDPIes
dc.relation.ispartofMaterials, 12 (17), 2745-.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectNeuromorphic systemses
dc.subjectSpiking neural networkses
dc.subjectMemristorses
dc.subjectSpike-timing-dependent plasticityes
dc.titleNeuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementationses
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadoreses
dc.relation.projectID687299 ”NEURAM3”es
dc.relation.projectID824164 ”HERMES”es
dc.relation.projectIDTEC2015-63884-C2-1-P (COGNET)es
dc.relation.publisherversionhttps://www.mdpi.com/1996-1944/12/17/2745es
dc.identifier.doi10.3390/ma12172745es
dc.contributor.groupUniversidad de Sevilla. TIC178: Diseño y Test de Circuitos Integrados de Señal Mixtaes
dc.journaltitleMaterialses
dc.publication.volumen12es
dc.publication.issue17es
dc.publication.initialPage2745es

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