dc.creator | Camuñas Mesa, Luis Alejandro | es |
dc.creator | Linares Barranco, Bernabé | es |
dc.creator | Serrano Gotarredona, María Teresa | es |
dc.date.accessioned | 2020-07-07T10:38:13Z | |
dc.date.available | 2020-07-07T10:38:13Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Camuñas 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.issn | 1996-1944 | es |
dc.identifier.uri | https://hdl.handle.net/11441/98922 | |
dc.description.abstract | Inspired 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.sponsorship | EU H2020 grant 687299 ”NEURAM3” | es |
dc.description.sponsorship | EU H2020 grant 824164 ”HERMES” | es |
dc.description.sponsorship | Ministry of Economy and Competitivity (Spain) and European Regional Development Fund TEC2015-63884-C2-1-P (COGNET) | es |
dc.description.sponsorship | VI PPIT through the Universidad de Sevilla. | es |
dc.format | application/pdf | es |
dc.format.extent | 28 p. | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Materials, 12 (17), 2745-. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Neuromorphic systems | es |
dc.subject | Spiking neural networks | es |
dc.subject | Memristors | es |
dc.subject | Spike-timing-dependent plasticity | es |
dc.title | Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores | es |
dc.relation.projectID | 687299 ”NEURAM3” | es |
dc.relation.projectID | 824164 ”HERMES” | es |
dc.relation.projectID | TEC2015-63884-C2-1-P (COGNET) | es |
dc.relation.publisherversion | https://www.mdpi.com/1996-1944/12/17/2745 | es |
dc.identifier.doi | 10.3390/ma12172745 | es |
dc.contributor.group | Universidad de Sevilla. TIC178: Diseño y Test de Circuitos Integrados de Señal Mixta | es |
dc.journaltitle | Materials | es |
dc.publication.volumen | 12 | es |
dc.publication.issue | 17 | es |
dc.publication.initialPage | 2745 | es |