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

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 learnin

SUBMITTER: Boscaglia M 

PROVIDER: S-EPMC10766193 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature