EMPD: An Efficient Membrane Potential Driven Supervised Learning Algorithm for Spiking Neurons

Abstract

The brain-inspired spiking neurons, considered as the third generation of artificial neurons, are more biologically plausible and computationally powerful than traditional artificial neurons. One of the fundamental research in spiking neurons is to transform streams of incoming spikes into precisely timed spikes. 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 presents an efficient membrane potential driven (EMPD) supervised learning method capable of training neurons to generate desired sequences of spikes. The learning rule of EMPD is composed of two processes: 1) at desired output times, the gradient descent method is implemented to minimize the error function defined as the difference between the membrane potential and the firing threshold and 2) at undesired output time, synaptic weights are adjusted to make the membrane potential below the threshold. For efficiency, at undesired output times, EMPD calculates the membrane potential and makes a comparison with firing threshold only at some special time points when the neuron is most likely to cross the firing threshold. Experimental results show that the proposed EMPD approach has higher learning efficiency and accuracy over the existing learning algorithms.