Network-state modulation of power-law frequency-scaling in
visual cortical neurons.
Sami El Boustani, Olivier Marre, Sébastien
Béhuret, Pierre Baudot, Pierre Yger, Thierry Bal, Alain
Destexhe and Yves Frégnac
PLoS Computational Biology 5: e1000519, 2009.
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 have
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 if
frequency-scaling reflects the network state, it could be used to
characterize the functional impact of the connectivity. 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 fractional 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 replicated 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 spike 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 different
integration levels, from Vm to LFP, EEG and fMRI.
Authors' summary
Intracellular recording of neocortical neurons provides an
opportunity of characterizing the statistical signature of the
synaptic bombardment to which it is submitted. Indeed the membrane
potential displays intense fluctuations which reflect the cumulative
activity of thousand of input neurons. In sensory cortical areas,
this measure could be used to estimate the correlational structure of
the external drive. We show that changes in the statistical
properties of network activity, namely the local correlation between
neurons, can be detected by analyzing the power spectrum density
(PSD) of the subthreshold membrane potential. These PSD can be fitted
by a power-law function 1/falpha in the upper temporal
frequency range. In vivo recordings in primary visual cortex
show that the alpha exponent varies with the statistics of the
sensory input. Most remarkably, the exponent observed in the ongoing
activity is indistinguishable from that evoked by natural visual
statistics. These results are emulated by models which demonstrate
that the exponent alpha is determined by the local level of
correlation imposed in the recurrent network activity. Similar
relationships are also reproduced in cortical neurons recorded in
vitro with artificial synaptic inputs by controlling in
computo the level of correlation in real time.
return to
publication list
return to main page