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

Synaptic plasticity is a key player in the brains 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 net-works, 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 complexity, as it is challenging to devise loss functions that effectively constrain net-work dynamics to plausible solutions a priori. Moreover, in line with previous work, we can [fi]nd multiple degenerate solutions with identical network behaviour. As a local optimization strategy, ES provides one solution at a time and makes exploration of this degeneracy cumbersome. Regardless, we can glean the interdependecies of various plasticity parameters by considering the covariance matrix learned alongside the optimal rule with ES. Our work provides a proof of principle for the success of machine-learning-guided discovery of plasticity rules in large spiking networks, and points at the necessity of more elaborate search strategies going forward.