Supervised Learning Algorithm for Spiking Neural Networks Based on Nonlinear Synaptic Interaction

Abstract

In biological nervous systems, the synaptic transmission of information is a complex process through the release of neurotransmitters. The input of multiple signals shows nonlinear interaction characteristics in synapses, so nonlinear synaptic interaction is considered as an important part of biological neural networks. At present, most artificial neural networks simplify synapses into a linear structure. Considering the nonlinear interaction of the input multiple signals of synapse, this paper proposes an online supervised learning algorithm for spiking neural networks based on nonlinear synaptic kernels, which can implement the complex spatio-temporal pattern learning of spike trains. The algorithm is successfully applied to learn sequences of spikes. In addition, different learning parameters are analyzed, such as synaptic kernel. The experimental results show that the proposed algorithm has high learning accuracy.