nMNSD—A Spiking Neuron-Based Classifier That Combines Weight-Adjustment and Delay-Shift

The recent “multi-neuronal spike sequence detector” (MNSD) architecture integrates the weight- and delay-adjustment methods by combining heterosynaptic plasticity with the neurocomputational feature spike latency, representing a new opportunity to understand the mechanisms underlying biological lear...

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Detalles Bibliográficos
Autores: Susi, Gianluca, Antón Toro, Luis Fernando, Maestu Unturbe, Fernando, Pereda, Ernesto, Mirasso, Claudio
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/116676
Acceso en línea:https://hdl.handle.net/20.500.14352/116676
Access Level:acceso abierto
Palabra clave:616.8
517
classification
delay learning
MNSD
online learning
spike latency
heterosynaptic plasticity
MNIST database
Neurociencias (Biológicas)
Análisis matemático
Física (Física)
12 Matemáticas
3314 Tecnología Médica
33 Ciencias Tecnológicas
22 Física
id ES_b2c2eefa579ec4dd147bcbb354691e23
oai_identifier_str oai:docta.ucm.es:20.500.14352/116676
network_acronym_str ES
network_name_str España
repository_id_str
spelling nMNSD—A Spiking Neuron-Based Classifier That Combines Weight-Adjustment and Delay-ShiftSusi, GianlucaAntón Toro, Luis FernandoMaestu Unturbe, FernandoPereda, ErnestoMirasso, Claudio616.8517classificationdelay learningMNSDonline learningspike latencyheterosynaptic plasticityMNIST databaseNeurociencias (Biológicas)Análisis matemáticoFísica (Física)12 Matemáticas3314 Tecnología Médica33 Ciencias Tecnológicas22 FísicaThe recent “multi-neuronal spike sequence detector” (MNSD) architecture integrates the weight- and delay-adjustment methods by combining heterosynaptic plasticity with the neurocomputational feature spike latency, representing a new opportunity to understand the mechanisms underlying biological learning. Unfortunately, the range of problems to which this topology can be applied is limited because of the low cardinality of the parallel spike trains that it can process, and the lack of a visualization mechanism to understand its internal operation. We present here the nMNSD structure, which is a generalization of the MNSD to any number of inputs. The mathematical framework of the structure is introduced, together with the “trapezoid method,” that is a reduced method to analyze the recognition mechanism operated by the nMNSD in response to a specific input parallel spike train. We apply the nMNSD to a classification problem previously faced with the classical MNSD from the same authors, showing the new possibilities the nMNSD opens, with associated improvement in classification performances. Finally, we benchmark the nMNSD on the classification of static inputs (MNIST database) obtaining state-of-the-art accuracies together with advantageous aspects in terms of time- and energy-efficiency if compared to similar classification methods.Frontiers MediaUniversidad Complutense de Madrid20212021-01-0120212021-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/116676reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)InglésengEuropean Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 899265Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 Not available TEC2016-80063-C3-2-R MEJORANDO LA DESCODIFICACIÓN DE DATOS DE FORMA ÓPTICA EN REDES DE COMUNICACIONES POR FIBRA UTILIZANDO DISPOSITIVOS FOTÓNICOS NEUROINSPIRADOSopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1166762026-06-02T12:44:21Z
dc.title.none.fl_str_mv nMNSD—A Spiking Neuron-Based Classifier That Combines Weight-Adjustment and Delay-Shift
title nMNSD—A Spiking Neuron-Based Classifier That Combines Weight-Adjustment and Delay-Shift
spellingShingle nMNSD—A Spiking Neuron-Based Classifier That Combines Weight-Adjustment and Delay-Shift
Susi, Gianluca
616.8
517
classification
delay learning
MNSD
online learning
spike latency
heterosynaptic plasticity
MNIST database
Neurociencias (Biológicas)
Análisis matemático
Física (Física)
12 Matemáticas
3314 Tecnología Médica
33 Ciencias Tecnológicas
22 Física
title_short nMNSD—A Spiking Neuron-Based Classifier That Combines Weight-Adjustment and Delay-Shift
title_full nMNSD—A Spiking Neuron-Based Classifier That Combines Weight-Adjustment and Delay-Shift
title_fullStr nMNSD—A Spiking Neuron-Based Classifier That Combines Weight-Adjustment and Delay-Shift
title_full_unstemmed nMNSD—A Spiking Neuron-Based Classifier That Combines Weight-Adjustment and Delay-Shift
title_sort nMNSD—A Spiking Neuron-Based Classifier That Combines Weight-Adjustment and Delay-Shift
dc.creator.none.fl_str_mv Susi, Gianluca
Antón Toro, Luis Fernando
Maestu Unturbe, Fernando
Pereda, Ernesto
Mirasso, Claudio
author Susi, Gianluca
author_facet Susi, Gianluca
Antón Toro, Luis Fernando
Maestu Unturbe, Fernando
Pereda, Ernesto
Mirasso, Claudio
author_role author
author2 Antón Toro, Luis Fernando
Maestu Unturbe, Fernando
Pereda, Ernesto
Mirasso, Claudio
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv 616.8
517
classification
delay learning
MNSD
online learning
spike latency
heterosynaptic plasticity
MNIST database
Neurociencias (Biológicas)
Análisis matemático
Física (Física)
12 Matemáticas
3314 Tecnología Médica
33 Ciencias Tecnológicas
22 Física
topic 616.8
517
classification
delay learning
MNSD
online learning
spike latency
heterosynaptic plasticity
MNIST database
Neurociencias (Biológicas)
Análisis matemático
Física (Física)
12 Matemáticas
3314 Tecnología Médica
33 Ciencias Tecnológicas
22 Física
description The recent “multi-neuronal spike sequence detector” (MNSD) architecture integrates the weight- and delay-adjustment methods by combining heterosynaptic plasticity with the neurocomputational feature spike latency, representing a new opportunity to understand the mechanisms underlying biological learning. Unfortunately, the range of problems to which this topology can be applied is limited because of the low cardinality of the parallel spike trains that it can process, and the lack of a visualization mechanism to understand its internal operation. We present here the nMNSD structure, which is a generalization of the MNSD to any number of inputs. The mathematical framework of the structure is introduced, together with the “trapezoid method,” that is a reduced method to analyze the recognition mechanism operated by the nMNSD in response to a specific input parallel spike train. We apply the nMNSD to a classification problem previously faced with the classical MNSD from the same authors, showing the new possibilities the nMNSD opens, with associated improvement in classification performances. Finally, we benchmark the nMNSD on the classification of static inputs (MNIST database) obtaining state-of-the-art accuracies together with advantageous aspects in terms of time- and energy-efficiency if compared to similar classification methods.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-01
2021
2021-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/116676
url https://hdl.handle.net/20.500.14352/116676
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 899265
Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 Not available TEC2016-80063-C3-2-R MEJORANDO LA DESCODIFICACIÓN DE DATOS DE FORMA ÓPTICA EN REDES DE COMUNICACIONES POR FIBRA UTILIZANDO DISPOSITIVOS FOTÓNICOS NEUROINSPIRADOS
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869417082037207040
score 15.811543

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