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

Boscaglia, Marta ; Gastaldi, Chiara ; Gerstner, Wulfram ; Quian Quiroga, Rodrigo Kaiser, Marcus

PLoS computational biology, 2023-12, Vol.19 (12), p.e1011727-e1011727 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
A dynamic attractor network model of memory formation, reinforcement and forgetting
Autor: Boscaglia, Marta ; Gastaldi, Chiara ; Gerstner, Wulfram ; Quian Quiroga, Rodrigo
Kaiser, Marcus
Assuntos: Analysis ; Assemblies ; Biology and Life Sciences ; Computer and Information Sciences ; Distance learning ; Dynamical systems ; Firing pattern ; Hippocampus ; Hippocampus - physiology ; Hypotheses ; Learning ; Learning - physiology ; Medicine and Health Sciences ; Memory ; Mental Recall - physiology ; Methods ; Models, Neurological ; Neural networks ; Neural plasticity ; Neuronal Plasticity - physiology ; Neurons ; Neurons - physiology ; Neurophysiology ; Reinforcement, Psychology ; Representations ; Social Sciences
É parte de: PLoS computational biology, 2023-12, Vol.19 (12), p.e1011727-e1011727
Notas: new_version
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The authors have declared that no competing interests exist.
Descrição: 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.
Editor: United States: Public Library of Science
Idioma: Inglês

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

Boscaglia, Marta ; Gastaldi, Chiara ; Gerstner, Wulfram ; Quian Quiroga, Rodrigo Kaiser, Marcus

United States: Public Library of Science

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