Extracting synaptic conductances from single membrane potential
traces.
Martin Pospischil, Zuzanna Piwkowska, Thierry Bal and Alain
Destexhe.
Neuroscience 158: 545-552, 2009.
Abstract
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 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.
PYTHON Demo
This PYTHON code implements the method on intracellular
recordings. The code was written by Martin Pospischil.
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