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.
I: Neural Computation, Bind 22, Nr. 2, 01.02.2010, s. 427-447.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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