A dynamic attractor network model of memory coding
Experimental evidence suggests that familiar items are represented by larger hippocampal neuronal assemblies than less familiar ones. Memory storage and recall in the hippocampus have been modelled using attractor neural networks, whose design poses stability challenges when dynamic learning rules are implemented. In this talk I will describe a computational modelling approach, based on a dynamic attractor network model, that we used to show how hippocampal neural assemblies can evolve differently, depending on the frequency of presentation of the stimuli (Boscaglia et al., 2023). I will illustrate the design choices that we made in order to have dynamic memory representations and the behaviour of the model in different experimental paradigms involving memory formation, reinforcement and forgetting. It will be discussed how our computational results align with findings from single-cell recordings in the human hippocampus, making this model suitable to explore other memory coding mechanisms.
Date:
9 May 2024, 14:00 (Thursday, 3rd week, Trinity 2024)
Venue:
Tinsley Building, Mansfield Road OX1 3TA
Venue Details:
Seminar room (EP space)
Speaker:
Marta Boscaglia (University of Leicester)
Organising department:
Medical Sciences Division
Organisers:
Vitor Lopes dos Santos (University of Oxford),
Dr Rafal Bogacz (University of Oxford),
Dr Rui Ponte Costa (University of Oxford)
Organiser contact email address:
vitor.lopesdossantos@ndcn.ox.ac.uk
Host:
Vitor Lopes dos Santos (University of Oxford)
Part of:
Oxford Neurotheory Forum
Booking required?:
Not required
Audience:
Members of the University only
Editor:
Rui Costa
