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Phys. Rev. Lett. 87, 068102 (2001) [4 pages]

Dynamical Encoding by Networks of Competing Neuron Groups: Winnerless Competition

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M. Rabinovich1, A. Volkovskii1, P. Lecanda2,3, R. Huerta1,2, H. D. I. Abarbanel1,4, and G. Laurent5
1Institute for Nonlinear Science, University of California, San Diego, La Jolla, California 92093-0402
2GNB, E.T.S. de Ingeniería Informática, Universidad Autónoma de Madrid, 28049 Madrid, Spain
3Instituto de Ciencia de Materiales de Madrid, CSIC Cantoblanco, 28049 Madrid, Spain
4Department of Physics and Marine Physical Laboratory, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California 93093-0402
5California Institute of Technology, Division of Biology, MC 139-74, Pasadena, California 91125

Received 29 December 2000; published 20 July 2001

Following studies of olfactory processing in insects and fish, we investigate neural networks whose dynamics in phase space is represented by orbits near the heteroclinic connections between saddle regions (fixed points or limit cycles). These networks encode input information as trajectories along the heteroclinic connections. If there are N neurons in the network, the capacity is approximately e(N-1)!, i.e., much larger than that of most traditional network structures. We show that a small winnerless competition network composed of FitzHugh-Nagumo spiking neurons efficiently transforms input information into a spatiotemporal output.

© 2001 The American Physical Society

URL:
http://link.aps.org/doi/10.1103/PhysRevLett.87.068102
DOI:
10.1103/PhysRevLett.87.068102
PACS:
87.10.+e, 05.45.-a, 87.18.Bb
 
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