title: Information transmission using non-poisson regular firing.
abstract: In many cortical areas, neural spike trains do not follow a Poisson process. In this study, we investigate a possible benefit of non-Poisson spiking for information transmission by studying the minimal rate fluctuation that can be detected by a Bayesian estimator. The idea is that an inhomogeneous Poisson process may make it difficult for downstream decoders to resolve subtle changes in rate fluctuation, but by using a more regular non-Poisson process, the nervous system can make rate fluctuations easier to detect. We evaluate the degree to which regular firing reduces the rate fluctuation detection threshold. We find that the threshold for detection is reduced in proportion to the coefficient of variation of interspike intervals.
authors: Department of Statistical Modeling, Institute of Statistical Mathematics, Tokyo 190-8562, Japan. skoyama@ism.ac.jp
authors: Shinsuke
authors: S
authors: Koyama
authors: Takahiro
authors: T
authors: Omi
authors: Robert E
authors: RE
authors: Kass
authors: Shigeru
authors: S
authors: Shinomoto
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D001499:Bayes Theorem; D002540:Cerebral Cortex / Q000502:physiology*; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D016012:Poisson Distribution