Topic: dynamic attractor network

Memories that are frequently revisited are easy to recall. In line with this observation, experimental evidence shows that familiar items involve larger hippocampal representations than less familiar ones. Here we developed a computational modeling approach, based on a dynamic attractor network model, to show how hippocampal neural assemblies may evolve differently, depending on the frequency of presentation of the stimuli (Boscaglia et al., 2023). Specifically, we built a dynamic attractor network model starting from a standard rate attractor model to which we added an online Hebbian learning