title: Bridging rate coding and temporal spike coding by effect of noise.
abstract: It is controversial whether temporal spike coding or rate coding is dominant in the information processing of the brain. We show by a two-layered neural network model with noise that, when noise is small, cortical neurons fire synchronously and intervals of synchronous firing robustly encode the signal information, but that the neurons desynchronize with moderately strong noise to encode waveforms of the signal more accurately. Further increase of noise just deteriorates the encoding. A positive role of noise in the brain is suggested in a meaning different from stochastic resonance, coherence resonance, and deterministic chaos.
authors.affiliation: Department of Mathematical Engineering and Information Physics, Graduate School of Engineering, the University of Tokyo, 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan.
authors.forename: Naoki
authors.initials: N
authors.lastname: Masuda
authors.forename: Kazuyuki
authors.initials: K
authors.lastname: Aihara
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D002540:Cerebral Cortex / Q000166:cytology / Q000502:physiology; D008564:Membrane Potentials; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009475:Neurons, Afferent / Q000502:physiology*