One-Pass Online Learning Based on Gradient Descent for Multilayer Spiking Neural Networks

Abstract

Online supervised learning algorithms update synaptic weights in real-time during the running process of spiking neural networks (SNNs), which are important for modeling the behavior and cognitive process of the brain. This article proposes an online supervised learning algorithm based on gradient descent for multilayer feedforward SNNs, where precisely timed spike trains are used to represent neural information. The online learning rule is derived from the real-time error function and backpropagation mechanism. The synaptic weights are adjusted when an output neuron fires a spike. Results of spike train learning demonstrate that the proposed online learning algorithm can achieve higher learning accuracy and requires fewer learning epochs than the corresponding offline learning method and other typical supervised learning algorithms. Furthermore, the proposed algorithm is used for solving pattern classification problems, where the one-pass learning approach is employed for training SNNs. Results show that the proposed algorithm can obtain comparable classification accuracy compared with other state-of-the-art algorithms even in the case of only one iteration. It indicates that the proposed algorithm is effective for solving spatio-temporal pattern recognition problems.