title: Neurons as ideal change-point detectors.
abstract: Every computational unit in the brain monitors incoming signals, instant by instant, for meaningful changes in the face of stochastic fluctuation. Recent studies have suggested that even a single neuron can detect changes in noisy signals. In this paper, we demonstrate that a single leaky integrate-and-fire neuron can achieve change-point detection close to that of theoretical optimal, for uniform-rate process, functions even better than a Bayes-optimal algorithm when the underlying rate deviates from a presumed uniform rate process. Given a reasonable number of synaptic connections (order 10(4)) and the rate of the input spike train, the values of the membrane time constant and the threshold found for optimizing change-point detection are close to those seen in biological neurons. These findings imply that biological neurons could act as sophisticated change-point detectors.
authors.affiliation: Department of Physics, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan.
authors.forename: Hideaki
authors.initials: H
authors.lastname: Kim
authors.forename: Barry J
authors.initials: BJ
authors.lastname: Richmond
authors.forename: Shigeru
authors.initials: S
authors.lastname: Shinomoto
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001921:Brain / Q000166:cytology; D003198:Computer Simulation; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission