On the use of quantum-inspired optimization techniques for training spiking neural networks: A new method proposed

Abstract

Spiking neural networks (SNN) are brain-like connectionist methods, where the output activation function is represented as a train of spikes and not as a potential. This and other reasons make SNN models biologically closer to brain principles than any of the alternative Artificial Neural networks (ANN) models proposed. In fact, they have great potential for solving complicated time-dependent pattern recognition problems defined by time series because of their inherent dynamical representation. A lot of works have been presented in the last decade about SNN which promote these models as third generation ANN. Nevertheless, several still open challenges have been reported in these studies. In this paper we analyze a particular type of SNN, the evolving SNN (eSNN), mainly focusing on their weights, parameters and features optimization using a new evolutionary strategy.