Title: Discovering heterogeneous synaptic plasticity rules via large-scale neural evolution
Primary area: applications to neuroscience & cognitive science
Keywords: Synaptic plasticity, Evolutionary computation, Computational neuroscience

Abstract: Synaptic plasticity is a fundamental substrate for learning and memory, yet how functional behaviors emerge from heterogeneous synaptic plasticity mechanisms remains poorly understood. Here, we introduce a computational framework that harnesses Darwinian evolutionary principles to discover biologically plausible, heterogeneous synaptic plasticity rules within a biologically realistic model of the mouse primary visual cortex. Specifically, we parameterize several key factors related to synaptic plasticity, including pre- and post-synaptic spikes, their associated eligibility traces, and neuromodulatory signals. By integrating these factors via a truncated Taylor expansion, we construct a large-scale search space of candidate plasticity rules, with each rule containing over 2.6k optimizable parameters. Each rule is subsequently evaluated in a multitask learning setting according to both task performance and biological validity. Leveraging a multi-objective evolutionary algorithm, we effectively navigate this high-dimensional search space to identify plasticity rules that are both biologically plausible and yield high task performance. Experimental results demonstrate that the discovered plasticity rules can attain high task performance with orders of magnitude fewer training samples than the gradient-based rule, while also maintaining biological plausibility and exhibiting scalability across various network sizes. Collectively, these findings offer insights into potential mechanisms by which the brain may implement heterogeneous synaptic plasticity rules and provide a foundation for the development of more efficient and adaptive artificial intelligence systems.