  In awake animals, the activity of the cerebral cortex is highly complex, with
neurons firing irregularly with apparent Poisson statistics. One way to
characterize this complexity is to take advantage of the high interconnectivity
of cerebral cortex and use intracellular recordings of cortical neurons, which
contain information about the activity of thousands of other cortical neurons.
Identifying the membrane potential (Vm) to a stochastic process enables the
extraction of important statistical signatures of this complex synaptic
activity. Typically, one estimates the total synaptic conductances (excitatory
and inhibitory) but this type of estimation requires at least two Vm levels and
therefore cannot be applied to single Vm traces. We propose here a method to
extract excitatory and inhibitory conductances (mean and variance) from single
Vm traces. This "VmT method" estimates conductance parameters using maximum
likelihood criteria, under the assumption are that synaptic conductances are
described by Gaussian stochastic processes and are integrated by a passive
leaky membrane. The method is illustrated using models and is tested on
guinea-pig visual cortex neurons in vitro using dynamic-clamp experiments. The
VmT method holds promises for extracting conductances from single-trial
measurements, which has a high potential for in vivo applications.
