A New Supervised Learning Algorithm Based on Genetic Inheritance for Spiking Neural Networks

Spiking neural networks (SNNs) can perform complex spatio-temporal information computations in precise temporal coding. These networks differ from previous models in that spiking neurons convey information by time rather than rate of spikes. Most existing training algorithms are based on gradient descent with inherent defects, such as local optimum and over-fitting. In this paper, we investigate the performance of the Genetic Algorithm Involving Mechanism of Simulated Annealing, as a supervised training algorithm for SNNs. The key idea is to adopt global search, which effectively avoid local optima and over-fitting. According to the experiment results, this approach has higher accuracy than other learning algorithms on well-known classification problems.