Practical hardware implementation of self-configuring neural networks

Abstract

This work provides practical guidelines for an efficient hardware implementation of Neural Networks. Networks are configured using a practical self-learning architecture that iterates a basic Genetic Algorithm. The learning methodology is based on the generation of random vectors that can be extracted from chaotic signals. The proposed solution is applied to estimate the processing efficiency of Spiking Neural Networks. © 2009 Springer Berlin Heidelberg.