Neural networks with high-order connections

Detalhes bibliográficos
Autor(a) principal: Arenzon, Jeferson Jacob
Data de Publicação: 1993
Outros Autores: Almeida, Rita Maria Cunha de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRGS
Texto Completo: http://hdl.handle.net/10183/101306
Resumo: We present results for two difFerent kinds of high-order connections between neurons acting as corrections to the Hopfield model. Equilibrium properties are analyzed using the replica mean-field theory and compared with numerical simulations. An optimal learning algorithm for fourth-order connections is given that improves the storage capacity without increasing the weight of the higherorder term. While the behavior of one of the models qualitatively resembles the original Hopfield one, the other presents a new and very rich behavior: depending on the strength of the fourth-order connections and the temperature, the system presents two distinct retrieval regions separated by a gap, as well as several phase transitions. Also, the spin-glass states seems to disappear above a certain value of the load parameter α, αg.
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spelling Arenzon, Jeferson JacobAlmeida, Rita Maria Cunha de2014-08-19T02:10:10Z19931063-651Xhttp://hdl.handle.net/10183/101306000056962We present results for two difFerent kinds of high-order connections between neurons acting as corrections to the Hopfield model. Equilibrium properties are analyzed using the replica mean-field theory and compared with numerical simulations. An optimal learning algorithm for fourth-order connections is given that improves the storage capacity without increasing the weight of the higherorder term. While the behavior of one of the models qualitatively resembles the original Hopfield one, the other presents a new and very rich behavior: depending on the strength of the fourth-order connections and the temperature, the system presents two distinct retrieval regions separated by a gap, as well as several phase transitions. Also, the spin-glass states seems to disappear above a certain value of the load parameter α, αg.application/pdfengPhysical Review. E, Statistical Physics, Plasmas, Fluids and Related Interdisciplinary Topics. New York. Vol. 48, no. 5 (Nov. 1993), p. 4060-4069Física da matéria condensadaRedes neuraisBiofísicaModelos de cerebroNeural networks with high-order connectionsEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSORIGINAL000056962.pdf000056962.pdfTexto completo (inglês)application/pdf534056http://www.lume.ufrgs.br/bitstream/10183/101306/1/000056962.pdfe6299209fe97f5eca6f80d8aea53ed6dMD51TEXT000056962.pdf.txt000056962.pdf.txtExtracted Texttext/plain41291http://www.lume.ufrgs.br/bitstream/10183/101306/2/000056962.pdf.txtf0ff359d5530546b9068749a2abdfca8MD52THUMBNAIL000056962.pdf.jpg000056962.pdf.jpgGenerated Thumbnailimage/jpeg1911http://www.lume.ufrgs.br/bitstream/10183/101306/3/000056962.pdf.jpg74180971338e433ec2299c4511cf91eeMD5310183/1013062024-03-29 06:19:36.086078oai:www.lume.ufrgs.br:10183/101306Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2024-03-29T09:19:36Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false
dc.title.pt_BR.fl_str_mv Neural networks with high-order connections
title Neural networks with high-order connections
spellingShingle Neural networks with high-order connections
Arenzon, Jeferson Jacob
Física da matéria condensada
Redes neurais
Biofísica
Modelos de cerebro
title_short Neural networks with high-order connections
title_full Neural networks with high-order connections
title_fullStr Neural networks with high-order connections
title_full_unstemmed Neural networks with high-order connections
title_sort Neural networks with high-order connections
author Arenzon, Jeferson Jacob
author_facet Arenzon, Jeferson Jacob
Almeida, Rita Maria Cunha de
author_role author
author2 Almeida, Rita Maria Cunha de
author2_role author
dc.contributor.author.fl_str_mv Arenzon, Jeferson Jacob
Almeida, Rita Maria Cunha de
dc.subject.por.fl_str_mv Física da matéria condensada
Redes neurais
Biofísica
Modelos de cerebro
topic Física da matéria condensada
Redes neurais
Biofísica
Modelos de cerebro
description We present results for two difFerent kinds of high-order connections between neurons acting as corrections to the Hopfield model. Equilibrium properties are analyzed using the replica mean-field theory and compared with numerical simulations. An optimal learning algorithm for fourth-order connections is given that improves the storage capacity without increasing the weight of the higherorder term. While the behavior of one of the models qualitatively resembles the original Hopfield one, the other presents a new and very rich behavior: depending on the strength of the fourth-order connections and the temperature, the system presents two distinct retrieval regions separated by a gap, as well as several phase transitions. Also, the spin-glass states seems to disappear above a certain value of the load parameter α, αg.
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dc.date.issued.fl_str_mv 1993
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dc.relation.ispartof.pt_BR.fl_str_mv Physical Review. E, Statistical Physics, Plasmas, Fluids and Related Interdisciplinary Topics. New York. Vol. 48, no. 5 (Nov. 1993), p. 4060-4069
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