Inferring network activity from synaptic noise
Michael Rudolph and Alain Destexhe
Journal of Physiology (Paris) 98: 452-466, 2004.
During intense network activity in vivo, cortical neurons are in a
high-conductance state, in which the membrane potential (Vm) is subject to a
tremendous fluctuating activity. Clearly, this "synaptic noise" contains
information about the activity of the network, but there are presently no
methods available to extract this information. We focus here on this problem
from a computational neuroscience perspective, with the aim of drawing methods
to analyze experimental data. We start from models of cortical neurons, in which
high-conductance states stem from the random release of thousands of excitatory
and inhibitory synapses. This highly complex system can be simplified by using
global synaptic conductances described by effective stochastic processes. The
advantage of this approach is that one can derive analytically a number of
properties from the statistics of resulting Vm fluctuations. For example, the
global excitatory and inhibitory conductances can be extracted from synaptic
noise, and can be related to the mean activity of presynaptic neurons. We show
here that extracting the variances of excitatory and inhibitory synaptic
conductances can provide estimates of the mean temporal correlation-or level of
synchrony-among thousands of neurons in the network. Thus, "probing the network"
through intracellular Vm activity is possible and constitutes a promising
approach, but it will require a continuous effort combining theory,
computational models and intracellular physiology.
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