Por favor, use este identificador para citar o enlazar a este item:
http://hdl.handle.net/10261/188482
COMPARTIR / EXPORTAR:
SHARE CORE BASE | |
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE | |
Campo DC | Valor | Lengua/Idioma |
---|---|---|
dc.contributor.author | Susi, Gianluca | es_ES |
dc.contributor.author | Toro, Luis Antón | es_ES |
dc.contributor.author | Canuet, Leonides | es_ES |
dc.contributor.author | López, María Eugenia | es_ES |
dc.contributor.author | Maestú, Fernando | es_ES |
dc.contributor.author | Mirasso, Claudio R. | es_ES |
dc.contributor.author | Pereda, Ernesto | es_ES |
dc.date.accessioned | 2019-08-19T11:38:38Z | - |
dc.date.available | 2019-08-19T11:38:38Z | - |
dc.date.issued | 2018-10-31 | - |
dc.identifier.citation | Frontiers in Neuroscience 12: 780 (2018) | es_ES |
dc.identifier.issn | 1662-4548 | - |
dc.identifier.uri | http://hdl.handle.net/10261/188482 | - |
dc.description.abstract | Humans 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.sponsorship | GS 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.iso | eng | es_ES |
dc.publisher | Frontiers Media | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/PTA-2015-10395-I | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/IJCI-2016-30662 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TEC2016-80063-C3-3-R | es_ES |
dc.relation.isversionof | Publisher's version | es_ES |
dc.rights | openAccess | es_ES |
dc.subject | Coincidence detection | es_ES |
dc.subject | Spiking neurons | es_ES |
dc.subject | Spike latency | es_ES |
dc.subject | Delay | es_ES |
dc.subject | Heterosynaptic plasticity | es_ES |
dc.subject | STDP | es_ES |
dc.subject | Go/NoGo | es_ES |
dc.title | A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP | es_ES |
dc.type | artículo | es_ES |
dc.identifier.doi | 10.3389/fnins.2018.00780 | - |
dc.description.peerreviewed | Peer reviewed | es_ES |
dc.relation.publisherversion | https://doi.org/10.3389/fnins.2018.00780 | es_ES |
dc.identifier.e-issn | 1662-453X | - |
dc.rights.license | http://creativecommons.org/licenses/by/4.0/ | es_ES |
dc.contributor.funder | Ministerio de Economía y Competitividad (España) | es_ES |
dc.contributor.funder | Universidad de La Laguna | es_ES |
dc.contributor.funder | European Commission | es_ES |
dc.relation.csic | Sí | es_ES |
oprm.item.hasRevision | no ko 0 false | * |
dc.identifier.funder | http://dx.doi.org/10.13039/501100003329 | es_ES |
dc.identifier.funder | http://dx.doi.org/10.13039/501100000780 | es_ES |
dc.identifier.pmid | 30429767 | - |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | es_ES |
item.openairetype | artículo | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.grantfulltext | open | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
Aparece en las colecciones: | (IFISC) Artículos |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
---|---|---|---|---|
fnins-12-00780.pdf | 2,25 MB | Adobe PDF | Visualizar/Abrir |
CORE Recommender
PubMed Central
Citations
4
checked on 17-mar-2024
SCOPUSTM
Citations
19
checked on 24-mar-2024
WEB OF SCIENCETM
Citations
8
checked on 28-feb-2024
Page view(s)
199
checked on 29-mar-2024
Download(s)
64
checked on 29-mar-2024