Evolutionary Indirect Design of Feed-Forward Spiking Neural Networks

Abstract

The present paper proposes the automatic design of Feed-Forward Spiking Neural Networks by representing several inherent aspects of the neural architecture in a proposed Context-Free Grammar; which is evolved through an Evolutionary Strategy. In the indirect design, the power of the design and the capabilities of the designed neural network are strongly related with the complexity of the grammars. The neural networks designed with the proposed grammar are tested with two well-known benchmark datasets of pattern recognition. Finally, neural networks derived from the proposed grammar are compared with other generated by similar grammars which were designed for the same purposed, the neural network design.