Parameterized phase response curves for characterizing neuronal behaviors under transient conditions

Oscar Miranda Dominguez, Theoden I. Netoff

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Phase response curves (PRCs) are a simple model of how a neuron's spike time is affected by synaptic inputs. PRCs are useful in predicting how networks of neurons behave when connected. One challenge in estimating a neuron's PRCs experimentally is that many neurons do not have stationary firing rates. In this article we introduce a new method to estimate PRCs as a function of firing rate of the neuron. We call the resulting model a parameterized PRC (pPRC). Experimentally, we perturb the neuron applying a current with two parts: 1) a current held constant between spikes but changed at the onset of a spike, used to make the neuron fire at different rates, and 2) a pulse to emulate a synaptic input. A model of the applied constant current and the history is made to predict the interspike interval (ISI). A second model is then made to fit the modulation of the spike time from the expected ISI by the pulsatile stimulus. A polynomial with two independent variables, the stimulus phase and the expected ISI, is used to model the pPRC. The pPRC is validated in a computational model and applied to pyramidal neurons from the CA1 region of the hippocampal slices from rat. The pPRC can be used to model the effect of changing firing rates on network synchrony. It can also be used to characterize the effects of neuro modulators and genetic mutations (among other manipulations) on network synchrony. It can also easily be extended to account for more variables.

Original languageEnglish (US)
Pages (from-to)2306-2316
Number of pages11
JournalJournal of Neurophysiology
Volume109
Issue number9
DOIs
StatePublished - Jun 7 2013
Externally publishedYes

Fingerprint

Neurons
Hippocampal CA1 Region
Pyramidal Cells
History
Mutation

Keywords

  • ARMAX models
  • ARX models
  • Patch clamp
  • Phase response curves

ASJC Scopus subject areas

  • Physiology
  • Neuroscience(all)

Cite this

Parameterized phase response curves for characterizing neuronal behaviors under transient conditions. / Miranda Dominguez, Oscar; Netoff, Theoden I.

In: Journal of Neurophysiology, Vol. 109, No. 9, 07.06.2013, p. 2306-2316.

Research output: Contribution to journalArticle

@article{5ba6a202fb1d44ffbe827267903bee4c,
title = "Parameterized phase response curves for characterizing neuronal behaviors under transient conditions",
abstract = "Phase response curves (PRCs) are a simple model of how a neuron's spike time is affected by synaptic inputs. PRCs are useful in predicting how networks of neurons behave when connected. One challenge in estimating a neuron's PRCs experimentally is that many neurons do not have stationary firing rates. In this article we introduce a new method to estimate PRCs as a function of firing rate of the neuron. We call the resulting model a parameterized PRC (pPRC). Experimentally, we perturb the neuron applying a current with two parts: 1) a current held constant between spikes but changed at the onset of a spike, used to make the neuron fire at different rates, and 2) a pulse to emulate a synaptic input. A model of the applied constant current and the history is made to predict the interspike interval (ISI). A second model is then made to fit the modulation of the spike time from the expected ISI by the pulsatile stimulus. A polynomial with two independent variables, the stimulus phase and the expected ISI, is used to model the pPRC. The pPRC is validated in a computational model and applied to pyramidal neurons from the CA1 region of the hippocampal slices from rat. The pPRC can be used to model the effect of changing firing rates on network synchrony. It can also be used to characterize the effects of neuro modulators and genetic mutations (among other manipulations) on network synchrony. It can also easily be extended to account for more variables.",
keywords = "ARMAX models, ARX models, Patch clamp, Phase response curves",
author = "{Miranda Dominguez}, Oscar and Netoff, {Theoden I.}",
year = "2013",
month = "6",
day = "7",
doi = "10.1152/jn.00942.2012",
language = "English (US)",
volume = "109",
pages = "2306--2316",
journal = "Journal of Neurophysiology",
issn = "0022-3077",
publisher = "American Physiological Society",
number = "9",

}

TY - JOUR

T1 - Parameterized phase response curves for characterizing neuronal behaviors under transient conditions

AU - Miranda Dominguez, Oscar

AU - Netoff, Theoden I.

PY - 2013/6/7

Y1 - 2013/6/7

N2 - Phase response curves (PRCs) are a simple model of how a neuron's spike time is affected by synaptic inputs. PRCs are useful in predicting how networks of neurons behave when connected. One challenge in estimating a neuron's PRCs experimentally is that many neurons do not have stationary firing rates. In this article we introduce a new method to estimate PRCs as a function of firing rate of the neuron. We call the resulting model a parameterized PRC (pPRC). Experimentally, we perturb the neuron applying a current with two parts: 1) a current held constant between spikes but changed at the onset of a spike, used to make the neuron fire at different rates, and 2) a pulse to emulate a synaptic input. A model of the applied constant current and the history is made to predict the interspike interval (ISI). A second model is then made to fit the modulation of the spike time from the expected ISI by the pulsatile stimulus. A polynomial with two independent variables, the stimulus phase and the expected ISI, is used to model the pPRC. The pPRC is validated in a computational model and applied to pyramidal neurons from the CA1 region of the hippocampal slices from rat. The pPRC can be used to model the effect of changing firing rates on network synchrony. It can also be used to characterize the effects of neuro modulators and genetic mutations (among other manipulations) on network synchrony. It can also easily be extended to account for more variables.

AB - Phase response curves (PRCs) are a simple model of how a neuron's spike time is affected by synaptic inputs. PRCs are useful in predicting how networks of neurons behave when connected. One challenge in estimating a neuron's PRCs experimentally is that many neurons do not have stationary firing rates. In this article we introduce a new method to estimate PRCs as a function of firing rate of the neuron. We call the resulting model a parameterized PRC (pPRC). Experimentally, we perturb the neuron applying a current with two parts: 1) a current held constant between spikes but changed at the onset of a spike, used to make the neuron fire at different rates, and 2) a pulse to emulate a synaptic input. A model of the applied constant current and the history is made to predict the interspike interval (ISI). A second model is then made to fit the modulation of the spike time from the expected ISI by the pulsatile stimulus. A polynomial with two independent variables, the stimulus phase and the expected ISI, is used to model the pPRC. The pPRC is validated in a computational model and applied to pyramidal neurons from the CA1 region of the hippocampal slices from rat. The pPRC can be used to model the effect of changing firing rates on network synchrony. It can also be used to characterize the effects of neuro modulators and genetic mutations (among other manipulations) on network synchrony. It can also easily be extended to account for more variables.

KW - ARMAX models

KW - ARX models

KW - Patch clamp

KW - Phase response curves

UR - http://www.scopus.com/inward/record.url?scp=84878518790&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84878518790&partnerID=8YFLogxK

U2 - 10.1152/jn.00942.2012

DO - 10.1152/jn.00942.2012

M3 - Article

C2 - 23365188

AN - SCOPUS:84878518790

VL - 109

SP - 2306

EP - 2316

JO - Journal of Neurophysiology

JF - Journal of Neurophysiology

SN - 0022-3077

IS - 9

ER -