Dynamical encoding by networks of competing neuron groups: winnerless 
competition

Rabinovich, M.; Volkovskii, A.; Lecanda, P.; Huerta, R.; Abarbanel, H. D.; Laurent, G.

doi:10.1103/PhysRevLett.87.068102

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Journal Article

Dynamical encoding by networks of competing neuron groups: winnerless competition

MPS-Authors

Rabinovich, M.
Max Planck Society;

Volkovskii, A.
Max Planck Society;

Lecanda, P.
Max Planck Society;

Huerta, R.
Max Planck Society;

Abarbanel, H. D.
Max Planck Society;

Laurent, G.
Max Planck Society;

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https://www.ncbi.nlm.nih.gov/pubmed/11497865
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Citation

Rabinovich, M., Volkovskii, A., Lecanda, P., Huerta, R., Abarbanel, H. D., & Laurent, G. (2001). Dynamical encoding by networks of competing neuron groups: winnerless competition. Phys Rev Lett, 87(6), 068102. doi:10.1103/PhysRevLett.87.068102.

Cite as: https://hdl.handle.net/21.11116/0000-0008-07DE-7

Abstract

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.