Free recall scaling laws and short-term memory effects in a latching attractor network.

ABSTRACT: Despite the complexity of human memory, paradigms like free recall have revealed robust qualitative and quantitative characteristics, such as power laws governing recall capacity. Although abstract random matrix models could explain such laws, the possibility of their implementation in large networks of interacting neurons has so far remained underexplored. We study an attractor network model of long-term memory endowed with firing rate adaptation and global inhibition. Under appropriate conditions, the transitioning behavior of the network from memory to memory is constrained by limit cycles that prevent the network from recalling all memories, with scaling similar to what has been found in experiments. When the model is supplemented with a heteroassociative learning rule, complementing t

SUBMITTER: Boboeva V 

PROVIDER: S-EPMC8670499 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature