Spiking neural networks (SNNs) have drawn considerable excitement because of their computational properties, believed to be superior to conventional von Neumann machines, and sharing properties with living brains. Yet progress building these systems has been limited because we lack a design methodology. We present a gene-driven network growth algorithm that enables a genetic algorithm (evolutionary computation) to generate and test SNNs. The genome for this algorithm grows O(n) where n is the number of neurons; n is also evolved. The genome not only specifies the network topology, but all its parameters as well. Experiments show the algorithm producing SNNs that effectively produce a robust spike bursting behavior given tonic inputs, an application suitable for central pattern generators. Even though evolution did not include perturbations of the input spike trains, the evolved networks showed remarkable robustness to such perturbations. In addition, the output spike patterns retain evidence of the specific perturbation of the inputs, a feature that could be exploited by network additions that could use this information for refined decision making if required. On a second task, a sequence detector, a discriminating design was found that might be considered an example of “unintelligent design”; extra non-functional neurons were included that, while inefficient, did not hamper its proper functioning.

Date Published: 18 June 2015
PDF: 12 pages
Proc. SPIE 9494, Next-Generation Robotics II; and Machine Intelligence and Bio-inspired Computation: Theory and Applications IX, 94940M (18 June 2015); doi: 10.1117/12.2175896

Published in SPIE Proceedings Vol. 9494:
Next-Generation Robotics II; and Machine Intelligence and Bio-inspired Computation: Theory and Applications IX
Misty Blowers; Dan Popa; Muthu B. J. Wijesundara, Editor(s)