Differentiation and Integration of Competing Memories: A Neural Network Model.

ABSTRACT: What determines when neural representations of memories move together (integrate) or apart (differentiate)? Classic supervised learning models posit that, when two stimuli predict similar outcomes, their representations should integrate. However, these models have recently been challenged by studies showing that pairing two stimuli with a shared associate can sometimes cause differentiation, depending on the parameters of the study and the brain region being examined. Here, we provide a purely unsupervised neural network model that can explain these and other related findings. The model can exhibit integration or differentiation depending on the amount of activity allowed to spread to competitors - inactive memories are not modified, connections to moderately active competitors are weakene

SUBMITTER: Ritvo VJH 

PROVIDER: S-EPMC10103961 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature