A back-propagation training method for multilayer pulsed neural networks using principle of duality

Abstract

Pulsed Neuron (PN) model was proposed as one of the simplest models working by pulse trains. PN model has a membrane potential to deal with the temporal information, and the calculation process is inexpensive. However, as the output function of PN model is an Unit Step function, PN model cannot directly use the back-propagation (BP) method. It would be possible to solve general pattern recognition problems if the PN model could be trained by the BP method. In this paper, we propose a BP method for multilayer pulsed neural networks. The proposed method uses the duality of PN model, in which the desired output of hidden layer neuron is calculated from output layer neurons' weights and output. Experimental results show that the multilayer pulsed neural networks can learn and recognize non-linear problems using the proposed method. © 2009 Springer Berlin Heidelberg.