Efficient and Robust Supervised Learning Algorithm for Spiking Neural Networks

Abstract

Recent advances in neuroscience have revealed that the brains neural information is encoded via precisely timed spike trains, but not just because of the neural firing rate. Owing to the inherent complexity of processing spike sequences, the formulation of an efficiently supervised learning algorithm is difficult and remains an important problem in research. This paper proposes an efficient and robust membrane potential-driven (ERMPD) supervised learning method capable of training neurons to generate desired sequences of spikes. For efficiency, at undesired output times, ERMPD calculates the membrane potential and makes a comparison to a firing threshold only at specified time points. For robustness, the dynamic threshold can be applied by the existing supervised learning methods to improve noise tolerance. Experimental results show that the proposed method has higher learning accuracy and efficiency over existing learning methods. Thus, it is more powerful for solving complex and real-time problems.