Extracting information from the power spectrum of synaptic noise
Alain Destexhe and Michael Rudolph
Journal of Computational Neuroscience 17: 327-345 (2004).
In cortical neurons, synaptic "noise" is caused by the nearly-random release of
thousands of synapses. Few methods are presently available to analyze synaptic
noise and deduce properties of the underlying synaptic inputs. We focus here on
the power spectral density (PSD) of several models of synaptic noise. We examine
different classes of analytically-solvable kinetic models for synaptic currents,
such as the "delta kinetic models", which use Dirac delta functions to represent
the activation of the ion channel. We first show that, for this class of kinetic
models, one can obtain an analytic expression for the PSD of the total synaptic
conductance and derive equivalent stochastic models with only a few variables.
This yields a method for constraining models of synaptic currents by analyzing
voltage-clamp recordings of synaptic noise. Second, we show that a similar
approach can be followed for the PSD of the the membrane potential (Vm) through
an effective-leak approximation. Third, we show that this approach is also valid
for inputs distributed in dendrites. In this case, the frequency-scaling of the
Vm PSD is preserved, suggesting that this approach may be applied to
intracellular recordings of real neurons. In conclusion, using simple
mathematical tools, we show that Vm recordings can be used to constrain kinetic
models of synaptic currents, as well as to estimate equivalent stochastic models.
This approach therefore provides a direct link between intracellular recordings
in vivo and the design of models consistent with the dynamics and spectral
structure of synaptic noise.
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