Measurement of infinitesimal phase response curves from noisy real neurons

Keisuke Ota, Toshiaki Omori, Shigeo Watanabe, Hiroyoshi Miyakawa, Masato Okada, Toru Aonishi

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

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.

Original languageEnglish
Article number041902
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume84
Issue number4
DOIs
Publication statusPublished - 2011 Oct 3

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

Fingerprint

Dive into the research topics of 'Measurement of infinitesimal phase response curves from noisy real neurons'. Together they form a unique fingerprint.

Cite this