Efficient Estimation of Phase-Resetting Curves in Real Neurons and its Significance for Neural-Network Modeling

Roberto F. Galán, G. Bard Ermentrout, and Nathaniel N. Urban
Phys. Rev. Lett. 94, 158101 – Published 19 April 2005

Abstract

The phase-resetting curve (PRC) of a neural oscillator describes the effect of a perturbation on its periodic motion and is therefore useful to study how the neuron responds to stimuli and whether it phase locks to other neurons in a network. Combining theory, computer simulations and electrophysiological experiments we present a simple method for estimating the PRC of real neurons. This allows us to simplify the complex dynamics of a single neuron to a phase model. We also illustrate how to infer the existence of coherent network activity from the estimated PRC.

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  • Received 21 December 2004

DOI:https://doi.org/10.1103/PhysRevLett.94.158101

©2005 American Physical Society

Authors & Affiliations

Roberto F. Galán1,2, G. Bard Ermentrout2,3, and Nathaniel N. Urban1,2

  • 1Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
  • 2Center for the Neural Basis of Cognition, Mellon Institute, Pittsburgh, Pennsylvania 15213, USA
  • 3Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA

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Issue

Vol. 94, Iss. 15 — 22 April 2005

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