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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/265639
Ver los metadatos del registro completo
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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 |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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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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13.13397 |