On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective

Detalhes bibliográficos
Autor(a) principal: Santiago, Rodrigo M. M.
Data de Publicação: 2020
Outros Autores: Tort, Adriano Bretanha Lopes
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRN
Texto Completo: https://repositorio.ufrn.br/jspui/handle/123456789/28960
Resumo: The reconsolidation and extinction of aversive memories and their boundary conditions have been extensively studied. Knowing their network mechanisms may lead to the development of better strategies for the treatment of fear and anxiety-related disorders. In 2011, Osan et al. developed a computational model for exploring such phenomena based on attractor dynamics, Hebbian plasticity and synaptic degradation induced by prediction error. This model was able to explain, in a single formalism, experimental findings regarding the freezing behavior of rodents submitted to contextual fear conditioning. In 2017, through the study of inhibitory avoidance in rats, Radiske et al. showed that the previous knowledge of a context as non-aversive is a boundary condition for the reconsolidation of the shock memory subsequently experienced in that context. In the present work, by adapting the model of Osan et al. (2011) to simulate the experimental protocols of Radiske et al. (2017), we show that such boundary condition is compatible with the dynamics of an attractor network that supports synaptic labilization common to reconsolidation and extinction. Additionally, by varying parameters such as the levels of protein synthesis and degradation, we predict behavioral outcomes, and thus boundary conditions that can be tested experimentally.
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spelling Santiago, Rodrigo M. M.Tort, Adriano Bretanha Lopes2020-05-12T17:21:20Z2020-05-12T17:21:20Z2020-04-18SANTIAGO, R. M. M.; TORT, A. B. L. On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective. Neural Netw., [S. l.], v. 127, p. 96‐109, abr. 2020.https://repositorio.ufrn.br/jspui/handle/123456789/2896010.1016/j.neunet.2020.04.013Attractor networkinhibitory avoidanceboundary conditionsynaptic plasticityOn the boundary conditions of avoidance memory reconsolidation: An attractor network perspectiveinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleThe reconsolidation and extinction of aversive memories and their boundary conditions have been extensively studied. Knowing their network mechanisms may lead to the development of better strategies for the treatment of fear and anxiety-related disorders. In 2011, Osan et al. developed a computational model for exploring such phenomena based on attractor dynamics, Hebbian plasticity and synaptic degradation induced by prediction error. This model was able to explain, in a single formalism, experimental findings regarding the freezing behavior of rodents submitted to contextual fear conditioning. In 2017, through the study of inhibitory avoidance in rats, Radiske et al. showed that the previous knowledge of a context as non-aversive is a boundary condition for the reconsolidation of the shock memory subsequently experienced in that context. In the present work, by adapting the model of Osan et al. (2011) to simulate the experimental protocols of Radiske et al. (2017), we show that such boundary condition is compatible with the dynamics of an attractor network that supports synaptic labilization common to reconsolidation and extinction. 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dc.title.pt_BR.fl_str_mv On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective
title On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective
spellingShingle On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective
Santiago, Rodrigo M. M.
Attractor network
inhibitory avoidance
boundary condition
synaptic plasticity
title_short On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective
title_full On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective
title_fullStr On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective
title_full_unstemmed On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective
title_sort On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective
author Santiago, Rodrigo M. M.
author_facet Santiago, Rodrigo M. M.
Tort, Adriano Bretanha Lopes
author_role author
author2 Tort, Adriano Bretanha Lopes
author2_role author
dc.contributor.author.fl_str_mv Santiago, Rodrigo M. M.
Tort, Adriano Bretanha Lopes
dc.subject.por.fl_str_mv Attractor network
inhibitory avoidance
boundary condition
synaptic plasticity
topic Attractor network
inhibitory avoidance
boundary condition
synaptic plasticity
description The reconsolidation and extinction of aversive memories and their boundary conditions have been extensively studied. Knowing their network mechanisms may lead to the development of better strategies for the treatment of fear and anxiety-related disorders. In 2011, Osan et al. developed a computational model for exploring such phenomena based on attractor dynamics, Hebbian plasticity and synaptic degradation induced by prediction error. This model was able to explain, in a single formalism, experimental findings regarding the freezing behavior of rodents submitted to contextual fear conditioning. In 2017, through the study of inhibitory avoidance in rats, Radiske et al. showed that the previous knowledge of a context as non-aversive is a boundary condition for the reconsolidation of the shock memory subsequently experienced in that context. In the present work, by adapting the model of Osan et al. (2011) to simulate the experimental protocols of Radiske et al. (2017), we show that such boundary condition is compatible with the dynamics of an attractor network that supports synaptic labilization common to reconsolidation and extinction. Additionally, by varying parameters such as the levels of protein synthesis and degradation, we predict behavioral outcomes, and thus boundary conditions that can be tested experimentally.
publishDate 2020
dc.date.accessioned.fl_str_mv 2020-05-12T17:21:20Z
dc.date.available.fl_str_mv 2020-05-12T17:21:20Z
dc.date.issued.fl_str_mv 2020-04-18
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.citation.fl_str_mv SANTIAGO, R. M. M.; TORT, A. B. L. On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective. Neural Netw., [S. l.], v. 127, p. 96‐109, abr. 2020.
dc.identifier.uri.fl_str_mv https://repositorio.ufrn.br/jspui/handle/123456789/28960
dc.identifier.doi.none.fl_str_mv 10.1016/j.neunet.2020.04.013
identifier_str_mv SANTIAGO, R. M. M.; TORT, A. B. L. On the boundary conditions of avoidance memory reconsolidation: An attractor network perspective. Neural Netw., [S. l.], v. 127, p. 96‐109, abr. 2020.
10.1016/j.neunet.2020.04.013
url https://repositorio.ufrn.br/jspui/handle/123456789/28960
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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