title: Measurement of infinitesimal phase response curves from noisy real neurons.
abstract: We sought to measure infinitesimal phase response curves (iPRCs) from rat hippocampal CA1 pyramidal neurons. It is difficult to measure iPRCs from noisy neurons because of the dilemma that either the linearity or the signal-to-noise ratio of responses to external perturbations must be sacrificed. To overcome this difficulty, we used an iPRC measurement model formulated as the Langevin phase equation (LPE) to extract iPRCs in the Bayesian scheme. We then simultaneously verified the effectiveness of the measurement model and the reliability of the estimated iPRCs by demonstrating that LPEs with the estimated iPRCs could predict the stochastic behaviors of the same neurons, whose iPRCs had been measured, when they were perturbed by periodic stimulus currents. Our results suggest that the LPE is an effective model for real oscillating neurons and that many theoretical frameworks based on it may be applicable to real nerve systems.
authors.affiliation: Brain Science Institute, RIKEN, Saitama 351-0198, Japan.
authors.forename: Keisuke
authors.initials: K
authors.lastname: Ota
authors.forename: Toshiaki
authors.initials: T
authors.lastname: Omori
authors.forename: Shigeo
authors.initials: S
authors.lastname: Watanabe
authors.forename: Hiroyoshi
authors.initials: H
authors.lastname: Miyakawa
authors.forename: Masato
authors.initials: M
authors.lastname: Okada
authors.forename: Toru
authors.initials: T
authors.lastname: Aonishi
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D017966:Pyramidal Cells / Q000502:physiology*; D051381:Rats