Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks

Detalhes bibliográficos
Autor(a) principal: Aguiar,MAD
Data de Publicação: 2017
Outros Autores: Dias,APS, Flora Rocha Ferreira
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/4976
http://dx.doi.org/10.1063/1.4973234
Resumo: We consider feed-forward and auto-regulation feed-forward neural (weighted) coupled cell networks. In feed-forward neural networks, cells are arranged in layers such that the cells of the first layer have empty input set and cells of each other layer receive only inputs from cells of the previous layer. An auto-regulation feed-forward neural coupled cell network is a feed-forward neural network where additionally some cells of the first layer have auto-regulation, that is, they have a self-loop. Given a network structure, a robust pattern of synchrony is a space defined in terms of equalities of cell coordinates that is flow-invariant for any coupled cell system (with additive input structure) associated with the network. In this paper, we describe the robust patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks. Regarding feed-forward neural networks, we show that only cells in the same layer can synchronize. On the other hand, in the presence of auto-regulation, we prove that cells in different layers can synchronize in a robust way and we give a characterization of the possible patterns of synchrony that can occur for auto-regulation feed-forward neural networks. Published by AIP Publishing.
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spelling Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networksWe consider feed-forward and auto-regulation feed-forward neural (weighted) coupled cell networks. In feed-forward neural networks, cells are arranged in layers such that the cells of the first layer have empty input set and cells of each other layer receive only inputs from cells of the previous layer. An auto-regulation feed-forward neural coupled cell network is a feed-forward neural network where additionally some cells of the first layer have auto-regulation, that is, they have a self-loop. Given a network structure, a robust pattern of synchrony is a space defined in terms of equalities of cell coordinates that is flow-invariant for any coupled cell system (with additive input structure) associated with the network. In this paper, we describe the robust patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks. Regarding feed-forward neural networks, we show that only cells in the same layer can synchronize. On the other hand, in the presence of auto-regulation, we prove that cells in different layers can synchronize in a robust way and we give a characterization of the possible patterns of synchrony that can occur for auto-regulation feed-forward neural networks. Published by AIP Publishing.2017-12-27T13:30:44Z2017-01-01T00:00:00Z2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/4976http://dx.doi.org/10.1063/1.4973234engAguiar,MADDias,APSFlora Rocha Ferreirainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:25Zoai:repositorio.inesctec.pt:123456789/4976Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:05.648011Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks
title Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks
spellingShingle Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks
Aguiar,MAD
title_short Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks
title_full Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks
title_fullStr Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks
title_full_unstemmed Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks
title_sort Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks
author Aguiar,MAD
author_facet Aguiar,MAD
Dias,APS
Flora Rocha Ferreira
author_role author
author2 Dias,APS
Flora Rocha Ferreira
author2_role author
author
dc.contributor.author.fl_str_mv Aguiar,MAD
Dias,APS
Flora Rocha Ferreira
description We consider feed-forward and auto-regulation feed-forward neural (weighted) coupled cell networks. In feed-forward neural networks, cells are arranged in layers such that the cells of the first layer have empty input set and cells of each other layer receive only inputs from cells of the previous layer. An auto-regulation feed-forward neural coupled cell network is a feed-forward neural network where additionally some cells of the first layer have auto-regulation, that is, they have a self-loop. Given a network structure, a robust pattern of synchrony is a space defined in terms of equalities of cell coordinates that is flow-invariant for any coupled cell system (with additive input structure) associated with the network. In this paper, we describe the robust patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks. Regarding feed-forward neural networks, we show that only cells in the same layer can synchronize. On the other hand, in the presence of auto-regulation, we prove that cells in different layers can synchronize in a robust way and we give a characterization of the possible patterns of synchrony that can occur for auto-regulation feed-forward neural networks. Published by AIP Publishing.
publishDate 2017
dc.date.none.fl_str_mv 2017-12-27T13:30:44Z
2017-01-01T00:00:00Z
2017
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/4976
http://dx.doi.org/10.1063/1.4973234
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http://dx.doi.org/10.1063/1.4973234
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