Self-configuring spiking neural networks

Abstract

We present a simple architecture for Spiking Neural Networks self-configuration. It consists in the hardware implementation of a simple Genetic Algorithm that may be used to obtain optimum network configurations. The proposed solution is applied to estimate the processing efficiency of different networks. Based on the results we develop a new performance metric to calibrate the processing capacity of SNNs. © IEICE 2008.