A comparison of learning rules in pulse-based neural networks

With a similar mechanism of processing spikes as biological neural systems, spiking neural networks (SNNs) are expected to perform a better performance than traditional rate based neural networks. Despite the progress in the application of SNNs, how does the brain learn information from spiking based codes remains an open question. To train a spiking neuron output desired spike trains, many learning methods have been proposed, and can be mainly divided into two classes: spike-driven methods and membrane potential-driven methods. Moreover, different learning algorithms have different advantages in different applications. Therefore, in this paper, we analysis and compare the existing learning algorithm for spiking neurons, including learning efficiency, learning accuracy.