Literature DB >> 21207126

The variance of phase-resetting curves.

G Bard Ermentrout1, Bryce Beverlin, Todd Troyer, Theoden I Netoff.   

Abstract

Phase resetting curves (PRCs) provide a measure of the sensitivity of oscillators to perturbations. In a noisy environment, these curves are themselves very noisy. Using perturbation theory, we compute the mean and the variance for PRCs for arbitrary limit cycle oscillators when the noise is small. Phase resetting curves and phase dependent variance are fit to experimental data and the variance is computed using an ad-hoc method. The theoretical curves of this phase dependent method match both simulations and experimental data significantly better than an ad-hoc method. A dual cell network simulation is compared to predictions using the analytical phase dependent variance estimation presented in this paper. We also discuss how entrainment of a neuron to a periodic pulse depends on the noise amplitude.

Mesh:

Year:  2011        PMID: 21207126     DOI: 10.1007/s10827-010-0305-9

Source DB:  PubMed          Journal:  J Comput Neurosci        ISSN: 0929-5313            Impact factor:   1.621


  20 in total

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Authors:  J T Ariaratnam; S H Strogatz
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2.  Starting, stopping, and resetting biological oscillators: in search of optimum perturbations.

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3.  On the phase reduction and response dynamics of neural oscillator populations.

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5.  Propagating neuronal discharges in neocortical slices: computational and experimental study.

Authors:  D Golomb; Y Amitai
Journal:  J Neurophysiol       Date:  1997-09       Impact factor: 2.714

6.  Phase resetting and coupling of noisy neural oscillators.

Authors:  Bard Ermentrout; David Saunders
Journal:  J Comput Neurosci       Date:  2006-04-06       Impact factor: 1.621

7.  The generalisation of student's problems when several different population variances are involved.

Authors:  B L WELCH
Journal:  Biometrika       Date:  1947       Impact factor: 2.445

8.  Type-II phase resetting curve is optimal for stochastic synchrony.

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Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2009-07-16

9.  Effects of transient depolarizing potentials on the firing rate of cat neocortical neurons.

Authors:  A D Reyes; E E Fetz
Journal:  J Neurophysiol       Date:  1993-05       Impact factor: 2.714

10.  Phase locking, period doubling bifurcations and chaos in a mathematical model of a periodically driven oscillator: a theory for the entrainment of biological oscillators and the generation of cardiac dysrhythmias.

Authors:  M R Guevara; L Glass
Journal:  J Math Biol       Date:  1982       Impact factor: 2.259

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  23 in total

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2.  Feedback control of variability in the cycle period of a central pattern generator.

Authors:  Ryan M Hooper; Ruben A Tikidji-Hamburyan; Carmen C Canavier; Astrid A Prinz
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Journal:  J Neurophysiol       Date:  2011-05       Impact factor: 2.714

4.  Frequency-dependent entrainment of striatal fast-spiking interneurons.

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Journal:  J Neurophysiol       Date:  2019-07-17       Impact factor: 2.714

5.  Predicting the response of striatal spiny neurons to sinusoidal input.

Authors:  Charles J Wilson
Journal:  J Neurophysiol       Date:  2017-05-10       Impact factor: 2.714

6.  Intrinsic heterogeneity in oscillatory dynamics limits correlation-induced neural synchronization.

Authors:  Shawn D Burton; G Bard Ermentrout; Nathaniel N Urban
Journal:  J Neurophysiol       Date:  2012-07-18       Impact factor: 2.714

7.  Hippocampal CA1 pyramidal neurons exhibit type 1 phase-response curves and type 1 excitability.

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8.  Dynamical changes in neurons during seizures determine tonic to clonic shift.

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9.  Origins and suppression of oscillations in a computational model of Parkinson's disease.

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Journal:  J Comput Neurosci       Date:  2014-08-07       Impact factor: 1.621

Review 10.  Active decorrelation in the basal ganglia.

Authors:  C J Wilson
Journal:  Neuroscience       Date:  2013-07-24       Impact factor: 3.590

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