A Supervised Learning Algorithm for Learning Precise Timing of Multispike in Multilayer Spiking Neural Networks

Rong Xiao, Tianyu Geng

Abstract: Biological evidence shows that precise timing spikes can more accurately describe the activity of the neuron and effectively transmit spatio-temporal patterns. However, it is still a core challenge to trigger multiple precise timing spikes in each layer of multilayer spiking neural network (SNN), since the complexity of the learning targets increases significantly for multispike learning. To address this issue, we propose a novel supervised, multispike learning method for multilayer SNNs, which can accomplish the complex spatio-temporal pattern learning of spike trains. The proposed method derives the synaptic weight update rule from the Widrow-Hoff (WH) rule, and then credits the network error simultaneously to preceding layers using backpropagation. The algorithm has been successfully applied to the benchmark datasets from the UCI dataset. Experimental results show that the proposed method can achieve comparable classification accuracy with classical learning methods and a state-of-the-art supervised algorithm. In addition, the training framework effectively reduces the number of connections, thus improving the computational efficiency of the network.