A Neuroscience-Inspired Approach to Training Spiking Neural Networks

Spiking neural networks (SNNs) have recently gained a lot of attention for use in low-power neuromorphic and edge computing. On their own, SNNs are difficult to train, owing to their lack of a differentiable activation function and their inherent tendency towards chaotic behavior. This work takes a strictly neuroscience-inspired approach to designing and training SNNs. We demonstrate that the use of neuromodulated synaptic time dependent plasticity (STDP) can be used to create a variety of different learning paradigms including unsupervised learning, semi-supervised learning, and reinforcement learning. In order to tackle the highly dynamic and potentially chaotic spiking behavior of SNNs both during training and testing, we discuss a variety of neuroscience-inspired hoemeostatic mechanisms for keeping the network's activity in a healthy range. All of these concepts are brought together in the development of a SNN model that is trained and tested on the MNIST handwritten digits dataset. In order to achieve this, we introduce also introduce a custom Python package called Ganglion that can be used to rapidly design and test SNN architectures.