Comparing Evolutionary Strategy Algorithms for Training Spiking Neural Networks

Spiking Neural Networks are considered as the third generation of Artificial Neural Networks, these neural networks naturally process spatio-temporal information. Spiking Neural Networks have been used in several fields and application areas; pattern recognition among them. For dealing with supervised pattern recognition task a gradientdescent based learning rule (Spike-prop) has been developed, however it has some problems like no convergence. To overcome these problems, metaheuristic algorithms such as Evolutionary Strategy have been used. In this work, three variants of the Evolutionary Strategy algorithm are compared for training Spiking Neural Networks. Several well-known benchmark dataset are used to test the capabilities of the algorithms.