A dynamic attractor network model of memory formation, reinforcement and forgetting

Empirical evidence shows that memories that are frequently revisited are easy to recall, and that familiar items involve larger hippocampal representations than less familiar ones. In line with these observations, here we develop a modelling approach to provide a mechanistic understanding of how hip...

Descripción completa

Detalles Bibliográficos
Autores: Boscaglia, Marta, Gastaldi, Chiara, Gerstner, Wulfram, Quian Quiroga, Rodrigo
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/68411
Acceso en línea:http://hdl.handle.net/10230/68411
http://dx.doi.org/10.1371/journal.pcbi.1011727
Access Level:acceso abierto
Palabra clave:Neurons
Memory
Neural networks
Learning
Memory recall
Hippocampus
Neuronal plasticity
Synapses
id ES_eb892e077cf36534e7ffcd75c63fde65
oai_identifier_str oai:repositori.upf.edu:10230/68411
network_acronym_str ES
network_name_str España
repository_id_str
spelling A dynamic attractor network model of memory formation, reinforcement and forgettingBoscaglia, MartaGastaldi, ChiaraGerstner, WulframQuian Quiroga, RodrigoNeuronsMemoryNeural networksLearningMemory recallHippocampusNeuronal plasticitySynapsesEmpirical evidence shows that memories that are frequently revisited are easy to recall, and that familiar items involve larger hippocampal representations than less familiar ones. In line with these observations, here we develop a modelling approach to provide a mechanistic understanding of how hippocampal neural assemblies evolve differently, depending on the frequency of presentation of the stimuli. For this, we added an online Hebbian learning rule, background firing activity, neural adaptation and heterosynaptic plasticity to a rate attractor network model, thus creating dynamic memory representations that can persist, increase or fade according to the frequency of presentation of the corresponding memory patterns. Specifically, we show that a dynamic interplay between Hebbian learning and background firing activity can explain the relationship between the memory assembly sizes and their frequency of stimulation. Frequently stimulated assemblies increase their size independently from each other (i.e. creating orthogonal representations that do not share neurons, thus avoiding interference). Importantly, connections between neurons of assemblies that are not further stimulated become labile so that these neurons can be recruited by other assemblies, providing a neuronal mechanism of forgetting.RQQ and MB were supported by the Biotechnology and Biological Sciences Research Council (https://www.ukri.org/councils/bbsrc/), grant reference number BB/T001291/1. WG and CG were supported by the Swiss National Science Foundation (https://www.snf.ch/en), grant agreement 200020_184615 and by the European Union Horizon 2020 Framework Program (https://ec.europa.eu/programmes/horizon2020/) under agreement no. 785907 (HumanBrain Project, SGA2). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Public Library of Science (PLoS)202420242023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/68411http://dx.doi.org/10.1371/journal.pcbi.1011727reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésPLoS Comput Biol. 2023 Dec 20;19(12):e1011727info:eu-repo/grantAgreement/EC/H2020/785907© 2023 Boscaglia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/684112026-06-12T07:21:37Z
dc.title.none.fl_str_mv A dynamic attractor network model of memory formation, reinforcement and forgetting
title A dynamic attractor network model of memory formation, reinforcement and forgetting
spellingShingle A dynamic attractor network model of memory formation, reinforcement and forgetting
Boscaglia, Marta
Neurons
Memory
Neural networks
Learning
Memory recall
Hippocampus
Neuronal plasticity
Synapses
title_short A dynamic attractor network model of memory formation, reinforcement and forgetting
title_full A dynamic attractor network model of memory formation, reinforcement and forgetting
title_fullStr A dynamic attractor network model of memory formation, reinforcement and forgetting
title_full_unstemmed A dynamic attractor network model of memory formation, reinforcement and forgetting
title_sort A dynamic attractor network model of memory formation, reinforcement and forgetting
dc.creator.none.fl_str_mv Boscaglia, Marta
Gastaldi, Chiara
Gerstner, Wulfram
Quian Quiroga, Rodrigo
author Boscaglia, Marta
author_facet Boscaglia, Marta
Gastaldi, Chiara
Gerstner, Wulfram
Quian Quiroga, Rodrigo
author_role author
author2 Gastaldi, Chiara
Gerstner, Wulfram
Quian Quiroga, Rodrigo
author2_role author
author
author
dc.subject.none.fl_str_mv Neurons
Memory
Neural networks
Learning
Memory recall
Hippocampus
Neuronal plasticity
Synapses
topic Neurons
Memory
Neural networks
Learning
Memory recall
Hippocampus
Neuronal plasticity
Synapses
description Empirical evidence shows that memories that are frequently revisited are easy to recall, and that familiar items involve larger hippocampal representations than less familiar ones. In line with these observations, here we develop a modelling approach to provide a mechanistic understanding of how hippocampal neural assemblies evolve differently, depending on the frequency of presentation of the stimuli. For this, we added an online Hebbian learning rule, background firing activity, neural adaptation and heterosynaptic plasticity to a rate attractor network model, thus creating dynamic memory representations that can persist, increase or fade according to the frequency of presentation of the corresponding memory patterns. Specifically, we show that a dynamic interplay between Hebbian learning and background firing activity can explain the relationship between the memory assembly sizes and their frequency of stimulation. Frequently stimulated assemblies increase their size independently from each other (i.e. creating orthogonal representations that do not share neurons, thus avoiding interference). Importantly, connections between neurons of assemblies that are not further stimulated become labile so that these neurons can be recruited by other assemblies, providing a neuronal mechanism of forgetting.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/68411
http://dx.doi.org/10.1371/journal.pcbi.1011727
url http://hdl.handle.net/10230/68411
http://dx.doi.org/10.1371/journal.pcbi.1011727
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv PLoS Comput Biol. 2023 Dec 20;19(12):e1011727
info:eu-repo/grantAgreement/EC/H2020/785907
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Public Library of Science (PLoS)
publisher.none.fl_str_mv Public Library of Science (PLoS)
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869423236442226688
score 15,812429

Similares en LA Referencia