Dynamical encoding by networks of competing neuron groups: winnerless competition

Phys Rev Lett. 2001 Aug 6;87(6):068102. doi: 10.1103/PhysRevLett.87.068102. Epub 2001 Jul 20.


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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Action Potentials / physiology
  • Animals
  • Fishes / physiology
  • Grasshoppers / physiology
  • Models, Neurological*
  • Nerve Net / physiology*
  • Neurons / physiology*
  • Olfactory Pathways / physiology
  • Synapses / physiology