Meta-learning synaptic plasticity and memory addressing for continual familiarity detection.

ABSTRACT: Over the course of a lifetime, we process a continual stream of information. Extracted from this stream, memories must be efficiently encoded and stored in an addressable manner for retrieval. To explore potential mechanisms, we consider a familiarity detection task in which a subject reports whether an image has been previously encountered. We design a feedforward network endowed with synaptic plasticity and an addressing matrix, meta-learned to optimize familiarity detection over long intervals. We find that anti-Hebbian plasticity leads to better performance than Hebbian plasticity and replicates experimental results such as repetition suppression. A combinatorial addressing function emerges, selecting a unique neuron as an index into the synaptic memory matrix for storage or retrieval.

SUBMITTER: Tyulmankov D 

PROVIDER: S-EPMC8813911 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature