A dynamic attractor network model of memory formation, reinforcement and forgetting
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- dc.contributor.author Boscaglia, Marta
- dc.contributor.author Gastaldi, Chiara
- dc.contributor.author Gerstner, Wulfram
- dc.contributor.author Quian Quiroga, Rodrigo
- dc.date.accessioned 2024-11-04T06:49:36Z
- dc.date.available 2024-11-04T06:49:36Z
- dc.date.issued 2023
- dc.description.abstract 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.
- dc.description.sponsorship 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.
- dc.format.mimetype application/pdf
- dc.identifier.citation Boscaglia M, Gastaldi C, Gerstner W, Quian Quiroga R. A dynamic attractor network model of memory formation, reinforcement and forgetting. PLoS Comput Biol. 2023 Dec 20;19(12):e1011727. DOI: 10.1371/journal.pcbi.1011727
- dc.identifier.doi http://dx.doi.org/10.1371/journal.pcbi.1011727
- dc.identifier.issn 1553-734X
- dc.identifier.uri http://hdl.handle.net/10230/68411
- dc.language.iso eng
- dc.publisher Public Library of Science (PLoS)
- dc.relation.ispartof PLoS Comput Biol. 2023 Dec 20;19(12):e1011727
- dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/785907
- dc.rights © 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.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Neurons
- dc.subject.keyword Memory
- dc.subject.keyword Neural networks
- dc.subject.keyword Learning
- dc.subject.keyword Memory recall
- dc.subject.keyword Hippocampus
- dc.subject.keyword Neuronal plasticity
- dc.subject.keyword Synapses
- dc.title A dynamic attractor network model of memory formation, reinforcement and forgetting
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion