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dc.contributor.authorSusi, Gianlucaes_ES
dc.contributor.authorToro, Luis Antónes_ES
dc.contributor.authorCanuet, Leonideses_ES
dc.contributor.authorLópez, María Eugeniaes_ES
dc.contributor.authorMaestú, Fernandoes_ES
dc.contributor.authorMirasso, Claudio R.es_ES
dc.contributor.authorPereda, Ernestoes_ES
dc.date.accessioned2019-08-19T11:38:38Z-
dc.date.available2019-08-19T11:38:38Z-
dc.date.issued2018-10-31-
dc.identifier.citationFrontiers in Neuroscience 12: 780 (2018)es_ES
dc.identifier.issn1662-4548-
dc.identifier.urihttp://hdl.handle.net/10261/188482-
dc.description.abstractHumans perform remarkably well in many cognitive tasks including pattern recognition. However, the neuronal mechanisms underlying this process are not well understood. Nevertheless, artificial neural networks, inspired in brain circuits, have been designed and used to tackle spatio-temporal pattern recognition tasks. In this paper we present a multi-neuronal spike pattern detection structure able to autonomously implement online learning and recognition of parallel spike sequences (i.e., sequences of pulses belonging to different neurons/neural ensembles). The operating principle of this structure is based on two spiking/synaptic neurocomputational characteristics: spike latency, which enables neurons to fire spikes with a certain delay and heterosynaptic plasticity, which allows the own regulation of synaptic weights. From the perspective of the information representation, the structure allows mapping a spatio-temporal stimulus into a multi-dimensional, temporal, feature space. In this space, the parameter coordinate and the time at which a neuron fires represent one specific feature. In this sense, each feature can be considered to span a single temporal axis. We applied our proposed scheme to experimental data obtained from a motor-inhibitory cognitive task. The results show that out method exhibits similar performance compared with other classification methods, indicating the effectiveness of our approach. In addition, its simplicity and low computational cost suggest a large scale implementation for real time recognition applications in several areas, such as brain computer interface, personal biometrics authentication, or early detection of diseases.es_ES
dc.description.sponsorshipGS acknowledges financial support by the Spanish Ministry of Economy and Competitiveness (PTA-2015-10395-I). Research by author LC is supported by Viera y Clavijo fellowship from Tenerife, Spain. ML is supported by a postdoctoral fellowship from the Spanish Ministry of Economy and Competitiveness (IJCI-2016-30662).CM and EP acknowledge support from the Spanish Ministry of Economy and Competitiveness and Fondo Europeo de Desarrollo Regional (FEDER) through projects TEC2016-80063-C3-3-R (AEI/FEDER, UE). CM acknowledges the Spanish State Research Agency, through the María de Maeztu Program for Units of Excellence in R&D (MDM-2018-2022).es_ES
dc.language.isoenges_ES
dc.publisherFrontiers Mediaes_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/PTA-2015-10395-Ies_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/IJCI-2016-30662es_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TEC2016-80063-C3-3-Res_ES
dc.relation.isversionofPublisher's versiones_ES
dc.rightsopenAccesses_ES
dc.subjectCoincidence detectiones_ES
dc.subjectSpiking neuronses_ES
dc.subjectSpike latencyes_ES
dc.subjectDelayes_ES
dc.subjectHeterosynaptic plasticityes_ES
dc.subjectSTDPes_ES
dc.subjectGo/NoGoes_ES
dc.titleA Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDPes_ES
dc.typeartículoes_ES
dc.identifier.doi10.3389/fnins.2018.00780-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttps://doi.org/10.3389/fnins.2018.00780es_ES
dc.identifier.e-issn1662-453X-
dc.rights.licensehttp://creativecommons.org/licenses/by/4.0/es_ES
dc.contributor.funderMinisterio de Economía y Competitividad (España)es_ES
dc.contributor.funderUniversidad de La Lagunaes_ES
dc.contributor.funderEuropean Commissiones_ES
dc.relation.csices_ES
oprm.item.hasRevisionno ko 0 false*
dc.identifier.funderhttp://dx.doi.org/10.13039/501100003329es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000780es_ES
dc.identifier.pmid30429767-
dc.type.coarhttp://purl.org/coar/resource_type/c_6501es_ES
item.openairetypeartículo-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
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