Spiking neural network training using evolutionary algorithms

Pavlidis, Nicos and Tasoulis, DK and Plagianakos, Vassilis P. and Vrahatis, Michael N. and Nikiforidis, G. (2005) Spiking neural network training using evolutionary algorithms. In: International Joint Conference on Neural Networks (IJCNN 2005). IEEE, pp. 2190-2194. ISBN 0-7803-9048-2

Full text not available from this repository.


Networks of spiking neurons can perform complex non-linear computations in fast temporal coding just as well as rate coded networks. These networks differ from previous models in that spiking neurons communicate information by the timing, rather than the rate, of spikes. To apply spiking neural networks on particular tasks, a learning process is required. Most existing training algorithms are based on unsupervised Hebbian learning. In this paper, we investigate the performance of the parallel differential evolution algorithm, as a supervised training algorithm for spiking neural networks. The approach was successfully tested on well-known and widely used classification problems.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
Deposited By:
Deposited On:
09 Nov 2011 14:33
Last Modified:
15 Sep 2023 01:48