Synaptic inhibition and excitation estimated via the time constant of membrane potential fluctuations

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Synaptic inhibition and excitation estimated via the time constant of membrane potential fluctuations. / Berg, Rune W.; Ditlevsen, Susanne.

I: Journal of Neurophysiology, Bind 110, Nr. 4, 2013, s. 1021-1034.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Berg, RW & Ditlevsen, S 2013, 'Synaptic inhibition and excitation estimated via the time constant of membrane potential fluctuations', Journal of Neurophysiology, bind 110, nr. 4, s. 1021-1034. https://doi.org/10.1152/jn.00006.2013

APA

Berg, R. W., & Ditlevsen, S. (2013). Synaptic inhibition and excitation estimated via the time constant of membrane potential fluctuations. Journal of Neurophysiology, 110(4), 1021-1034. https://doi.org/10.1152/jn.00006.2013

Vancouver

Berg RW, Ditlevsen S. Synaptic inhibition and excitation estimated via the time constant of membrane potential fluctuations. Journal of Neurophysiology. 2013;110(4):1021-1034. https://doi.org/10.1152/jn.00006.2013

Author

Berg, Rune W. ; Ditlevsen, Susanne. / Synaptic inhibition and excitation estimated via the time constant of membrane potential fluctuations. I: Journal of Neurophysiology. 2013 ; Bind 110, Nr. 4. s. 1021-1034.

Bibtex

@article{c49e767501724f5b8cd7bec138c8e8a8,
title = "Synaptic inhibition and excitation estimated via the time constant of membrane potential fluctuations",
abstract = "When recording the membrane potential, V, of a neuron it is desirable to be able to extract the synaptic input. Critically, the synaptic input is stochastic and non-reproducible so one is therefore often restricted to single trial data. Here, we introduce means of estimating the inhibition and excitation and their confidence limits from single sweep trials. The estimates are based on the mean membrane potential, (V) , and the membrane time constant,τ. The time constant provides the total conductance (G = capacitance/τ) and is extracted from the autocorrelation of V. The synaptic conductances can then be inferred from (V) when approximating the neuron as a single compartment. We further employ a stochastic model to establish limits of confidence. The method is verified on models and experimental data, where the synaptic input is manipulated pharmacologically or estimated by an alternative method. The method gives best results if the synaptic input is large compared to other conductances, the intrinsic conductances have little or no time dependence or are comparably small, the ligand gated kinetics is faster than the membrane time constant, and the majority of synaptic contacts are electrotonically close to soma (recording site). Though our data is in current-clamp, the method also works in V-clamp recordings, with some minor adaptations. All custom made procedures are provided in Matlab.",
author = "Berg, {Rune W.} and Susanne Ditlevsen",
year = "2013",
doi = "10.1152/jn.00006.2013",
language = "English",
volume = "110",
pages = "1021--1034",
journal = "Journal of Neurophysiology",
issn = "0022-3077",
publisher = "American Physiological Society",
number = "4",

}

RIS

TY - JOUR

T1 - Synaptic inhibition and excitation estimated via the time constant of membrane potential fluctuations

AU - Berg, Rune W.

AU - Ditlevsen, Susanne

PY - 2013

Y1 - 2013

N2 - When recording the membrane potential, V, of a neuron it is desirable to be able to extract the synaptic input. Critically, the synaptic input is stochastic and non-reproducible so one is therefore often restricted to single trial data. Here, we introduce means of estimating the inhibition and excitation and their confidence limits from single sweep trials. The estimates are based on the mean membrane potential, (V) , and the membrane time constant,τ. The time constant provides the total conductance (G = capacitance/τ) and is extracted from the autocorrelation of V. The synaptic conductances can then be inferred from (V) when approximating the neuron as a single compartment. We further employ a stochastic model to establish limits of confidence. The method is verified on models and experimental data, where the synaptic input is manipulated pharmacologically or estimated by an alternative method. The method gives best results if the synaptic input is large compared to other conductances, the intrinsic conductances have little or no time dependence or are comparably small, the ligand gated kinetics is faster than the membrane time constant, and the majority of synaptic contacts are electrotonically close to soma (recording site). Though our data is in current-clamp, the method also works in V-clamp recordings, with some minor adaptations. All custom made procedures are provided in Matlab.

AB - When recording the membrane potential, V, of a neuron it is desirable to be able to extract the synaptic input. Critically, the synaptic input is stochastic and non-reproducible so one is therefore often restricted to single trial data. Here, we introduce means of estimating the inhibition and excitation and their confidence limits from single sweep trials. The estimates are based on the mean membrane potential, (V) , and the membrane time constant,τ. The time constant provides the total conductance (G = capacitance/τ) and is extracted from the autocorrelation of V. The synaptic conductances can then be inferred from (V) when approximating the neuron as a single compartment. We further employ a stochastic model to establish limits of confidence. The method is verified on models and experimental data, where the synaptic input is manipulated pharmacologically or estimated by an alternative method. The method gives best results if the synaptic input is large compared to other conductances, the intrinsic conductances have little or no time dependence or are comparably small, the ligand gated kinetics is faster than the membrane time constant, and the majority of synaptic contacts are electrotonically close to soma (recording site). Though our data is in current-clamp, the method also works in V-clamp recordings, with some minor adaptations. All custom made procedures are provided in Matlab.

U2 - 10.1152/jn.00006.2013

DO - 10.1152/jn.00006.2013

M3 - Journal article

C2 - 23636725

VL - 110

SP - 1021

EP - 1034

JO - Journal of Neurophysiology

JF - Journal of Neurophysiology

SN - 0022-3077

IS - 4

ER -

ID: 45947003