Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks.

ABSTRACT: Synaptic plasticity is a key player in the brain's life-long learning abilities. However, due to experimental limitations, the mechanistic link between synaptic plasticity rules and the network-level computations they enable remain opaque. Here we use evolutionary strategies (ES) to meta learn local co-active plasticity rules in large recurrent spiking networks with excitatory (E) and inhibitory (I) neurons, using parameterizations of increasing complexity. We discover rules that robustly stabilize network dynamics for all four synapse types acting in isolation (E-to-E, E-to-I, I-to-E and I-to-I). More complex functions such as familiarity detection can also be included in the search constraints. However, our meta learning strategy begins to fail for co-active rules of increasing complexit

SUBMITTER: Confavreux B 

PROVIDER: S-EPMC12021293 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature