Segregation-to-integration transformation model of memory evolution

Autores
Bavassi, Mariana Luz; Fuentemilla, Lluís
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Memories are thought to use coding schemes that dynamically adjust their representational structure to maximize both persistence and efficiency. However, the nature of these coding scheme adjustments and their impact on the temporal evolution of memory after initial encoding is unclear. Here, we introduce the Segregation-to-Integration Transformation (SIT) Model, a network formalization that offers a unified account of how the representational structure of a memory is transformed over time. SIT model asserts that memories initially adopt a highly modular or segregated network structure, functioning as an optimal storage buffer by balancing protection from disruptions and accommodating substantial information.Over time, a repeated combination of neural network reactivations involving activation spreading and synaptic plasticity transforms the initial modular structure into an integrated memory form, facilitating intercommunity spreading and fostering generalization. The SIT Model identifies a non-linear or inverted U-shaped function in memory evolution where memories are most susceptible to changing their representation. This time window, located early during the transformation, is a consequence of memory’s structural configuration, where the activation diffusion across the network is maximized.
Fil: Bavassi, Mariana Luz. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina
Fil: Fuentemilla, Lluís. Universidad de Barcelona. Facultad de Psicologia; España
Materia
NEURAL NETWORK
MODULARITY
CONSOLIDATION
REACTIVATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/265639

id CONICETDig_698eeef91e27de36d2e764776642a797
oai_identifier_str oai:ri.conicet.gov.ar:11336/265639
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Segregation-to-integration transformation model of memory evolutionBavassi, Mariana LuzFuentemilla, LluísNEURAL NETWORKMODULARITYCONSOLIDATIONREACTIVATIONhttps://purl.org/becyt/ford/1.7https://purl.org/becyt/ford/1Memories are thought to use coding schemes that dynamically adjust their representational structure to maximize both persistence and efficiency. However, the nature of these coding scheme adjustments and their impact on the temporal evolution of memory after initial encoding is unclear. Here, we introduce the Segregation-to-Integration Transformation (SIT) Model, a network formalization that offers a unified account of how the representational structure of a memory is transformed over time. SIT model asserts that memories initially adopt a highly modular or segregated network structure, functioning as an optimal storage buffer by balancing protection from disruptions and accommodating substantial information.Over time, a repeated combination of neural network reactivations involving activation spreading and synaptic plasticity transforms the initial modular structure into an integrated memory form, facilitating intercommunity spreading and fostering generalization. The SIT Model identifies a non-linear or inverted U-shaped function in memory evolution where memories are most susceptible to changing their representation. This time window, located early during the transformation, is a consequence of memory’s structural configuration, where the activation diffusion across the network is maximized.Fil: Bavassi, Mariana Luz. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; ArgentinaFil: Fuentemilla, Lluís. Universidad de Barcelona. Facultad de Psicologia; EspañaMIT Press2024-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/265639Bavassi, Mariana Luz; Fuentemilla, Lluís; Segregation-to-integration transformation model of memory evolution; MIT Press; Network Neuroscience; 8; 4; 9-2024; 1529-15442472-1751CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://direct.mit.edu/netn/article/8/4/1529/124254/Segregation-to-integration-transformation-model-ofinfo:eu-repo/semantics/altIdentifier/doi/10.1162/netn_a_00415info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:49:07Zoai:ri.conicet.gov.ar:11336/265639instacron: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:34982025-09-03 09:49:07.309CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Segregation-to-integration transformation model of memory evolution
title Segregation-to-integration transformation model of memory evolution
spellingShingle Segregation-to-integration transformation model of memory evolution
Bavassi, Mariana Luz
NEURAL NETWORK
MODULARITY
CONSOLIDATION
REACTIVATION
title_short Segregation-to-integration transformation model of memory evolution
title_full Segregation-to-integration transformation model of memory evolution
title_fullStr Segregation-to-integration transformation model of memory evolution
title_full_unstemmed Segregation-to-integration transformation model of memory evolution
title_sort Segregation-to-integration transformation model of memory evolution
dc.creator.none.fl_str_mv Bavassi, Mariana Luz
Fuentemilla, Lluís
author Bavassi, Mariana Luz
author_facet Bavassi, Mariana Luz
Fuentemilla, Lluís
author_role author
author2 Fuentemilla, Lluís
author2_role author
dc.subject.none.fl_str_mv NEURAL NETWORK
MODULARITY
CONSOLIDATION
REACTIVATION
topic NEURAL NETWORK
MODULARITY
CONSOLIDATION
REACTIVATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.7
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Memories are thought to use coding schemes that dynamically adjust their representational structure to maximize both persistence and efficiency. However, the nature of these coding scheme adjustments and their impact on the temporal evolution of memory after initial encoding is unclear. Here, we introduce the Segregation-to-Integration Transformation (SIT) Model, a network formalization that offers a unified account of how the representational structure of a memory is transformed over time. SIT model asserts that memories initially adopt a highly modular or segregated network structure, functioning as an optimal storage buffer by balancing protection from disruptions and accommodating substantial information.Over time, a repeated combination of neural network reactivations involving activation spreading and synaptic plasticity transforms the initial modular structure into an integrated memory form, facilitating intercommunity spreading and fostering generalization. The SIT Model identifies a non-linear or inverted U-shaped function in memory evolution where memories are most susceptible to changing their representation. This time window, located early during the transformation, is a consequence of memory’s structural configuration, where the activation diffusion across the network is maximized.
Fil: Bavassi, Mariana Luz. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Fisiología, Biología Molecular y Neurociencias. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Fisiología, Biología Molecular y Neurociencias; Argentina
Fil: Fuentemilla, Lluís. Universidad de Barcelona. Facultad de Psicologia; España
description Memories are thought to use coding schemes that dynamically adjust their representational structure to maximize both persistence and efficiency. However, the nature of these coding scheme adjustments and their impact on the temporal evolution of memory after initial encoding is unclear. Here, we introduce the Segregation-to-Integration Transformation (SIT) Model, a network formalization that offers a unified account of how the representational structure of a memory is transformed over time. SIT model asserts that memories initially adopt a highly modular or segregated network structure, functioning as an optimal storage buffer by balancing protection from disruptions and accommodating substantial information.Over time, a repeated combination of neural network reactivations involving activation spreading and synaptic plasticity transforms the initial modular structure into an integrated memory form, facilitating intercommunity spreading and fostering generalization. The SIT Model identifies a non-linear or inverted U-shaped function in memory evolution where memories are most susceptible to changing their representation. This time window, located early during the transformation, is a consequence of memory’s structural configuration, where the activation diffusion across the network is maximized.
publishDate 2024
dc.date.none.fl_str_mv 2024-09
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/11336/265639
Bavassi, Mariana Luz; Fuentemilla, Lluís; Segregation-to-integration transformation model of memory evolution; MIT Press; Network Neuroscience; 8; 4; 9-2024; 1529-1544
2472-1751
CONICET Digital
CONICET
url http://hdl.handle.net/11336/265639
identifier_str_mv Bavassi, Mariana Luz; Fuentemilla, Lluís; Segregation-to-integration transformation model of memory evolution; MIT Press; Network Neuroscience; 8; 4; 9-2024; 1529-1544
2472-1751
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://direct.mit.edu/netn/article/8/4/1529/124254/Segregation-to-integration-transformation-model-of
info:eu-repo/semantics/altIdentifier/doi/10.1162/netn_a_00415
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv MIT Press
publisher.none.fl_str_mv MIT Press
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
_version_ 1842268953812926464
score 13.13397