title: Dynamical encoding by networks of competing neuron groups: winnerless competition.
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.
authors.affiliation: Institute for Nonlinear Science, University of California, San Diego, La Jolla, California 92093-0402, USA.
authors.forename: M
authors.initials: M
authors.lastname: Rabinovich
authors.forename: A
authors.initials: A
authors.lastname: Volkovskii
authors.forename: P
authors.initials: P
authors.lastname: Lecanda
authors.forename: R
authors.initials: R
authors.lastname: Huerta
authors.forename: H D
authors.initials: HD
authors.lastname: Abarbanel
authors.forename: G
authors.initials: G
authors.lastname: Laurent
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D005399:Fishes / Q000502:physiology; D006110:Grasshoppers / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D009833:Olfactory Pathways / Q000502:physiology; D013569:Synapses / Q000502:physiology