On the Future of Training Spiking Neural Networks

Abstract

Spiking Neural Networks have obtained a lot of attention in recent years due to their close depiction of brain functionality as well as their energy efficiency. However, the training of Spiking Neural Networks in order to reach state-of-the-art accuracy in complex tasks remains a challenge. This is caused by the inherent nonlinearity and sparsity of spikes. The most promising approaches either train Spiking Neural Networks directly or convert existing artificial neural networks into a spike setting. In this work, we will express our view on the future of Spiking Neural Networks and on which training method is the most promising for recent deep architectures.