Computation with phase oscillators: An oscillatory perceptron model

Autores
Kaluza, Pablo Federico
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We present a perceptron model with processing units consisting of coupled phase oscillators. The processing units are able to compute the input signals through a high order synapse mechanism. We show how a network of these elements can be used in analogy to the classical multilayer feedforward neural network. The main characteristics of the classical multilayer perceptron model are conserved, as for example, the backpropagation algorithm for learning. This model of coupled phase oscillators can be seen as a generic study in order to use different kind of oscillators for computational tasks.
Fil: Kaluza, Pablo Federico. Fritz-Haber-Institut der Max-Planck Gesellschaft. Abteilung Physikalische Chemie; Alemania. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Mendoza; Argentina
Materia
Phase Oscillators
Neural Network
Computational Abilities
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/3595
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Computation with phase oscillators: An oscillatory perceptron modelKaluza, Pablo FedericoPhase OscillatorsNeural NetworkComputational Abilitieshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1We present a perceptron model with processing units consisting of coupled phase oscillators. The processing units are able to compute the input signals through a high order synapse mechanism. We show how a network of these elements can be used in analogy to the classical multilayer feedforward neural network. The main characteristics of the classical multilayer perceptron model are conserved, as for example, the backpropagation algorithm for learning. This model of coupled phase oscillators can be seen as a generic study in order to use different kind of oscillators for computational tasks.Fil: Kaluza, Pablo Federico. Fritz-Haber-Institut der Max-Planck Gesellschaft. Abteilung Physikalische Chemie; Alemania. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Mendoza; ArgentinaElsevier2013-03-26info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/3595Kaluza, Pablo Federico; Computation with phase oscillators: An oscillatory perceptron model; Elsevier; Neurocomputing; 118; 26-3-2013; 127-1310925-2312enginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0925231213003147info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neucom.2013.02.025info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:41:29Zoai:ri.conicet.gov.ar:11336/3595instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 15:41:29.709CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Computation with phase oscillators: An oscillatory perceptron model
title Computation with phase oscillators: An oscillatory perceptron model
spellingShingle Computation with phase oscillators: An oscillatory perceptron model
Kaluza, Pablo Federico
Phase Oscillators
Neural Network
Computational Abilities
title_short Computation with phase oscillators: An oscillatory perceptron model
title_full Computation with phase oscillators: An oscillatory perceptron model
title_fullStr Computation with phase oscillators: An oscillatory perceptron model
title_full_unstemmed Computation with phase oscillators: An oscillatory perceptron model
title_sort Computation with phase oscillators: An oscillatory perceptron model
dc.creator.none.fl_str_mv Kaluza, Pablo Federico
author Kaluza, Pablo Federico
author_facet Kaluza, Pablo Federico
author_role author
dc.subject.none.fl_str_mv Phase Oscillators
Neural Network
Computational Abilities
topic Phase Oscillators
Neural Network
Computational Abilities
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We present a perceptron model with processing units consisting of coupled phase oscillators. The processing units are able to compute the input signals through a high order synapse mechanism. We show how a network of these elements can be used in analogy to the classical multilayer feedforward neural network. The main characteristics of the classical multilayer perceptron model are conserved, as for example, the backpropagation algorithm for learning. This model of coupled phase oscillators can be seen as a generic study in order to use different kind of oscillators for computational tasks.
Fil: Kaluza, Pablo Federico. Fritz-Haber-Institut der Max-Planck Gesellschaft. Abteilung Physikalische Chemie; Alemania. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Mendoza; Argentina
description We present a perceptron model with processing units consisting of coupled phase oscillators. The processing units are able to compute the input signals through a high order synapse mechanism. We show how a network of these elements can be used in analogy to the classical multilayer feedforward neural network. The main characteristics of the classical multilayer perceptron model are conserved, as for example, the backpropagation algorithm for learning. This model of coupled phase oscillators can be seen as a generic study in order to use different kind of oscillators for computational tasks.
publishDate 2013
dc.date.none.fl_str_mv 2013-03-26
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/3595
Kaluza, Pablo Federico; Computation with phase oscillators: An oscillatory perceptron model; Elsevier; Neurocomputing; 118; 26-3-2013; 127-131
0925-2312
url http://hdl.handle.net/11336/3595
identifier_str_mv Kaluza, Pablo Federico; Computation with phase oscillators: An oscillatory perceptron model; Elsevier; Neurocomputing; 118; 26-3-2013; 127-131
0925-2312
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0925231213003147
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neucom.2013.02.025
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.22299

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