Topic: familiarity detection

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 where 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 and replicates experim

Synaptic plasticity is thought to underlie learning and memory [1]. Plasticity rules can be active in different synaptic connection types, such as excitatory-excitatory (EE) and inhibitory-excitatory (IE) connections, with in silico and experimental evidence suggesting distinct mechanisms for each type [2,3,4]. While the interaction between connection types in learning and memory is an active research focus [5], studies typically probe only one type of plasticity at a time. Thus, at scale, the contributions of connection types and their co-active plasticity rules to memory processes remain unc