HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Mathayomchan, B. Articles by Beer, R. D. Search for Related Content PubMed PubMed Citation Articles by Mathayomchan, B. Articles by Beer, R. D.

Center-Crossing Recurrent Neural Networks for the Evolution of Rhythmic Behavior

bxm40{at}po.cwru.edu, Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, U.S.A.

Randall D. Beer

beer{at}eecs.cwru.edu, Departments of Electrical Engineering and Computer Science and of Biology, Case Western Reserve University, Cleveland, OH 44106, U.S.A.

A center-crossing recurrent neural network is one in which the null-(hyper)surfaces of each neuron intersect at their exact centers of symmetry, ensuring that each neuron's activation function is centered over the range of net inputs that it receives. We demonstrate that relative to a random initial population, seeding the initial population of an evolutionary search with center-crossing networks significantly improves both the frequency and the speed with which high-fitness oscillatory circuits evolve on a simple walking task. The improvement is especially striking at low mutation variances. Our results suggest that seeding with center-crossing networks may often be beneficial, since a wider range of dynamics is more likely to be easily accessible from a population of center-crossing networks than from a population of random networks.

This article has been cited by other articles:

				R. D. Beer Parameter space structure of continuous-time recurrent neural networks. Neural Comput., December 1, 2006; 18(12): 3009 - 3051. [Abstract] [Full Text] [PDF]

				R. J. Vickerstaff and E. A. Di Paolo Evolving neural models of path integration J. Exp. Biol., September 1, 2005; 208(17): 3349 - 3366. [Abstract] [Full Text] [PDF]

				G. Capi and K. Doya Evolution of Neural Architecture Fitting Environmental Dynamics Adaptive Behavior, March 1, 2005; 13(1): 53 - 66. [Abstract] [PDF]