Synaptic "noise": Experiments, computational consequences and methods to analyze experimental data.

Alain Destexhe and Michelle Rudolph-Lilith

In: Stochastic Methods in Neuroscience, Edited by Laing C and Lord GJ, Oxford University Press, Oxford UK, pp. 242-271 (2010).

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Abstract

In the cerebral cortex of awake animals, neurons are subject to a tremendous fluctuating activity mostly of synaptic origin and termed "synaptic noise". Synaptic noise is the dominant source of membrane potential fluctuations in neurons and can have a strong influence on their integrative properties. We review here the experimental measurements of synaptic noise, and its modeling by conductance-based stochastic processes. We next review the consequences of synaptic noise on neuronal integrative properties, as predicted by computational models and investigated experimentally using dynamic-clamp. We also review analysis methods, such as spike-triggered average or conductance analysis, which are derived from the modeling of synaptic noise by stochastic processes. These different approaches aim at understanding the integrative properties of neocortical neurons in the intact brain.
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