
Proceedings Paper
Evolving spiking neural networks: a novel growth algorithm exhibits unintelligent designFormat | Member Price | Non-Member Price |
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Paper Abstract
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.
Paper Details
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)
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
Show Author Affiliations
J. David Schaffer, Binghamton Univ. (United States)
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)
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