Memory by a thousand rules: Automated discovery of multi-type plasticity rules reveals variety and degeneracy at the heart of learning.

Abstract

Synaptic plasticity is the basis of learning and memory, but the link between synaptic changes and neural function remains elusive. Here, we used automated search algorithms to obtain thousands of strikingly diverse quadruplets of excitatory(E)-to-E, E-to-inhibitory(I), IE, and II plasticity rules, cooperating to stabilize recurrent spiking networks. Despite the fact that quadruplets were selected for homeostasis, more than 90% of them performed well in simple and more difficult memory tasks such as novelty detection, contextual novelty and sequence replay. Co-activity was crucial, i.e., most rules failed in isolation. Our purely local, unsupervised plasticity rules could also help solve computer games such as pong. Our work showcases automated discovery augmenting human intuition to find en masse solutions for high dimensional problems.