A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP

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...

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Autores: Susi, Gianluca, Toro, Luis Antón, Canuet, Leonides, López, María Eugenia, Maestú, Fernando, Mirasso, Claudio R., Pereda, Ernesto
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/188482
Acceso en línea:http://hdl.handle.net/10261/188482
Access Level:acceso abierto
Palabra clave:Coincidence detection
Spiking neurons
Spike latency
Delay
Heterosynaptic plasticity
STDP
Go/NoGo
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spelling A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDPSusi, GianlucaToro, Luis AntónCanuet, LeonidesLópez, María EugeniaMaestú, FernandoMirasso, Claudio R.Pereda, ErnestoCoincidence detectionSpiking neuronsSpike latencyDelayHeterosynaptic plasticitySTDPGo/NoGoHumans 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.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).Peer reviewedFrontiers MediaMinisterio de Economía y Competitividad (España)Universidad de La LagunaEuropean CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]201920192018info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/188482reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/PTA-2015-10395-Iinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/IJCI-2016-30662info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TEC2016-80063-C3-3-Rhttps://doi.org/10.3389/fnins.2018.00780Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1884822026-05-22T06:33:51Z
dc.title.none.fl_str_mv A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP
title A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP
spellingShingle A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP
Susi, Gianluca
Coincidence detection
Spiking neurons
Spike latency
Delay
Heterosynaptic plasticity
STDP
Go/NoGo
title_short A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP
title_full A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP
title_fullStr A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP
title_full_unstemmed A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP
title_sort A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP
dc.creator.none.fl_str_mv Susi, Gianluca
Toro, Luis Antón
Canuet, Leonides
López, María Eugenia
Maestú, Fernando
Mirasso, Claudio R.
Pereda, Ernesto
author Susi, Gianluca
author_facet Susi, Gianluca
Toro, Luis Antón
Canuet, Leonides
López, María Eugenia
Maestú, Fernando
Mirasso, Claudio R.
Pereda, Ernesto
author_role author
author2 Toro, Luis Antón
Canuet, Leonides
López, María Eugenia
Maestú, Fernando
Mirasso, Claudio R.
Pereda, Ernesto
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Economía y Competitividad (España)
Universidad de La Laguna
European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Coincidence detection
Spiking neurons
Spike latency
Delay
Heterosynaptic plasticity
STDP
Go/NoGo
topic Coincidence detection
Spiking neurons
Spike latency
Delay
Heterosynaptic plasticity
STDP
Go/NoGo
description 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.
publishDate 2018
dc.date.none.fl_str_mv 2018
2019
2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/188482
url http://hdl.handle.net/10261/188482
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/PTA-2015-10395-I
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/IJCI-2016-30662
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TEC2016-80063-C3-3-R
https://doi.org/10.3389/fnins.2018.00780
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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