Practical Hardware Implementation of Self-configuring Neural Networks

Authors:
Josep L. Rosselló;Vincent Canals;Antoni Morro;Ivan Paúl
Affiliations:
Electronic Systems Group, Physics Department, Universitat de les Illes Balears (UIB), Palma de Mallorca, Spain 07122;Electronic Systems Group, Physics Department, Universitat de les Illes Balears (UIB), Palma de Mallorca, Spain 07122;Electronic Systems Group, Physics Department, Universitat de les Illes Balears (UIB), Palma de Mallorca, Spain 07122;Electronic Systems Group, Physics Department, Universitat de les Illes Balears (UIB), Palma de Mallorca, Spain 07122
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Year:
2009

Citing 2
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Spiking Neuron Models: An Introduction

Spiking Neuron Models: An Introduction
Solving graph algorithms with networks of spiking neurons

IEEE Transactions on Neural Networks

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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.