ERMPD: An efficient and robustness membrane potential driven supervised learning in spiking neural networks

Recent advances in neurosciences have revealed that neural information in the brain is encoded through precisely timed spike trains, not only through the neural firing rate. Due to the inherent complexity of processing spike sequences, the formulation of efficient supervised learning algorithm is difficult and remains an important problem in the research area. 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 with firing threshold only at some special time points. For robust, the dynamic-threshold can be applied by the existing supervised learning methods to improve the noise tolerance of them. Experimental results show that the proposed ERMPD method has higher learning efficiency and accuracy.