Meta-Learned Stateful Plasticity: Local Rules for Online Learning

Biology motivates learning rules that modify the connection between neurons using only local information available at the synapse. While hand-designed local plasticity rules are interpretable, they are not generally optimized for task objectives. We present Meta-Learned Stateful Plasticity (MLSP), a framework for discovering local rules in online classification settings. The rules are parameterized by multilayer perceptrons (MLPs) that are shared across synapses and take in only locally available information such as pre- and postsynaptic activations, the current connection weight, and a broadcast reward signal, while also updating a writable neuron-local plasticity state. We apply MLSP to the hidden weight matrices of a feedforward network whose readout weights learn with the delta rule. The meta-trained rules outperform all tested classical rules, generalize to task families not used in meta-training, and are competitive with, though generally below, online backpropagation. Rules meta-trained with plasticity states reach higher average accuracy than rules without them, and we present evidence that the states track progress through the episode at the layer level. Finally, we fit polynomials of local variables to rules meta-trained without plasticity states. Networks trained with these polynomial rules retain much of the advantage over learning with the delta rule alone, but the fits do not reduce to any classical rule. This indicates that effective local learning rules extend beyond the hand-designed forms typically considered.