Cross-correlations in high-conductance states of a model cortical network

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Cross-correlations in high-conductance states of a model cortical network. / Hertz, John.

In: Neural Computation, Vol. 22, No. 2, 01.02.2010, p. 427-447.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Hertz, J 2010, 'Cross-correlations in high-conductance states of a model cortical network', Neural Computation, vol. 22, no. 2, pp. 427-447.

APA

Hertz, J. (2010). Cross-correlations in high-conductance states of a model cortical network. Neural Computation, 22(2), 427-447.

Vancouver

Hertz J. Cross-correlations in high-conductance states of a model cortical network. Neural Computation. 2010 Feb 1;22(2):427-447.

Author

Hertz, John. / Cross-correlations in high-conductance states of a model cortical network. In: Neural Computation. 2010 ; Vol. 22, No. 2. pp. 427-447.

Bibtex

@article{bf0f60600cc911df825d000ea68e967b,
title = "Cross-correlations in high-conductance states of a model cortical network",
abstract = "(dansk abstrakt findes ikke)Neuronal firing correlations are studied using simulations of asimple network model for a cortical column in a high-conductancestate with dynamically balanced excitation and inhibition.  Althoughcorrelations between individual pairs of neurons exhibitconsiderable heterogeneity, population averages show systematicbehavior. When the network is in a stationary state, the averagecorrelations are generically small: correlation coefficients are oforder 1/N, where N is the number of neurons in the network.However, when the input to the network varies strongly in time, muchlarger values are found. In this situation, the network is out ofbalance, and the synaptic conductance is low, at times when thestrongest firing occurs.  However, examination of the correlationfunctions of synaptic currents reveals that after these bursts,balance is restored within a few ms by a rapid increase ininhibitory synaptic conductance.  These findings suggest anextension of the notion of the balanced state to include balancedfluctuations of synaptic currents, with a characteristic timescaleof a few ms. Udgivelsesdato: 1. Feb",
author = "John Hertz",
year = "2010",
month = feb,
day = "1",
language = "English",
volume = "22",
pages = "427--447",
journal = "Neural Computation",
issn = "0899-7667",
publisher = "M I T Press",
number = "2",

}

RIS

TY - JOUR

T1 - Cross-correlations in high-conductance states of a model cortical network

AU - Hertz, John

PY - 2010/2/1

Y1 - 2010/2/1

N2 - (dansk abstrakt findes ikke)Neuronal firing correlations are studied using simulations of asimple network model for a cortical column in a high-conductancestate with dynamically balanced excitation and inhibition.  Althoughcorrelations between individual pairs of neurons exhibitconsiderable heterogeneity, population averages show systematicbehavior. When the network is in a stationary state, the averagecorrelations are generically small: correlation coefficients are oforder 1/N, where N is the number of neurons in the network.However, when the input to the network varies strongly in time, muchlarger values are found. In this situation, the network is out ofbalance, and the synaptic conductance is low, at times when thestrongest firing occurs.  However, examination of the correlationfunctions of synaptic currents reveals that after these bursts,balance is restored within a few ms by a rapid increase ininhibitory synaptic conductance.  These findings suggest anextension of the notion of the balanced state to include balancedfluctuations of synaptic currents, with a characteristic timescaleof a few ms. Udgivelsesdato: 1. Feb

AB - (dansk abstrakt findes ikke)Neuronal firing correlations are studied using simulations of asimple network model for a cortical column in a high-conductancestate with dynamically balanced excitation and inhibition.  Althoughcorrelations between individual pairs of neurons exhibitconsiderable heterogeneity, population averages show systematicbehavior. When the network is in a stationary state, the averagecorrelations are generically small: correlation coefficients are oforder 1/N, where N is the number of neurons in the network.However, when the input to the network varies strongly in time, muchlarger values are found. In this situation, the network is out ofbalance, and the synaptic conductance is low, at times when thestrongest firing occurs.  However, examination of the correlationfunctions of synaptic currents reveals that after these bursts,balance is restored within a few ms by a rapid increase ininhibitory synaptic conductance.  These findings suggest anextension of the notion of the balanced state to include balancedfluctuations of synaptic currents, with a characteristic timescaleof a few ms. Udgivelsesdato: 1. Feb

M3 - Journal article

VL - 22

SP - 427

EP - 447

JO - Neural Computation

JF - Neural Computation

SN - 0899-7667

IS - 2

ER -

ID: 17272989