Article Summary

**Title : ** Network-state modulation of power-law frequency-scaling in visual cortical neurons

**Authors : ** Sami El Boustani, Olivier Marre, Sébastien Béhuret, Pierre Baudot, Pierre Yger, Thierry Bal, Alain Destexhe and Yves Frégnac

**Year : ** 2009

**Journal : ** PLoS Computational Biology

**Volume : ** 5:

**Pages : ** e10005519

Abstract

Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They share in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because frequency-scaling could re?ect the network state, and thus be used to characterize the functional impact of the con- nectivity. In intracellularly-recorded neurons of cat primary visual cortex in vivo, the power spectral density of Vm activity displays a power-law structure at high frequencies, with a frac- tional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex, receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also repli- cated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the Vm reflects stimulus-driven correlations in the cortical network activity. Therefore we propose that the scaling exponent could be used to read-out the ”effective” connectivity responsible for the dynamical signature of the population signals measured at di?erent integration levels, from Vm, to LFP, EEG and fMRI.