title: Efficient estimation of phase-resetting curves in real neurons and its significance for neural-network modeling.
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.
authors.affiliation: Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.
authors.forename: Roberto F
authors.initials: RF
authors.lastname: Galán
authors.forename: G Bard
authors.initials: GB
authors.lastname: Ermentrout
authors.forename: Nathaniel N
authors.initials: NN
authors.lastname: Urban
mesh_terms: D003198:Computer Simulation; D004594:Electrophysiology; D008564:Membrane Potentials / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D013569:Synapses / Q000502:physiology