Topological Evolution of Spiking Neural Networks

Slade, Samuel and Zhang, Li (2018) Topological Evolution of Spiking Neural Networks. In: IEEE World Congress on Computational Intelligence. IEEE, Piscataway, pp. 3135-3143. ISBN 9781509060153, 9781509060146

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Abstract

Neuro-evolution is often used to generate the parameters, topology, and rules of artificial neural networks. This technique allows for automatic configuration of a neural network. In this paper we propose a method to generate Spiking Neural Networks (SNNs) automatically called NENG (Neuro-Evolutionary Network Generation). The aim was to help alleviate the manual construction and optimization of neural network implementations. The results show the algorithm is successful at generating and improving the design of SNNs for a Classification task. After 812 generations with a population size of 20 the algorithm converges to model the Xor gate with 100% accuracy. The results show improvements to the algorithm execution time and number of neurons over time.

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