SpikePropamine: Meta-Learning Synaptic Plasticity for Spiking Neural Networks

Abstract

This presentation is an overview of the recently developed SpikePropamine meta-learning framework submitted for publication in the Frontiers In Neurorobotics journal. Material from the original paper as well as more recent developments will be presented. Our goal in developing this framework is to leverage synaptic plasticity in Spiking Neural Networks (SNNs), the learning mechanism found in biological neural networks. Today, most engineered neural network only use fixed-weights despite the superior learning capabilities found in biological plastic neural networks. Therefore, SpikePropamine is a framework that harnesses the power of offline learning of fixed weights along with the online continual learning through synaptic plasticity. Specifically, along with the fixed weights, SpikePropamine additionally learns the rules that govern the online plastic learning and neuromodulation in SNNs through offline gradient descent. The capabilities of the framework are demonstrated through a series of challenging benchmarks. The parameters for variants of BCM, Oja’s and other plasticity and neuromodulated rules are learned. Results show that the SpikePropamine based networks are able to solve temporal learning tasks that fixed-weight SNNs fail to solve. Additional results highlight the robustness of SpikePropamine based SNNs to noise and novel conditions not seen during training. This is seen through minimal degradation of performance in a high-dimensional legged locomotion task governed by a SpikePropamine based SNN.