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Dynamic-Clamp pp 115-140 | Cite as

Testing Methods for Synaptic Conductance Analysis Using Controlled Conductance Injection with Dynamic Clamp

  • Zuzanna Piwkowska
  • Martin Pospischil
  • Michelle Rudolph-Lilith
  • Thierry Bal
  • Alain Destexhe
Chapter
Part of the Springer Series in Computational Neuroscience book series (NEUROSCI, volume 1)

Abstract

In this chapter, we present different methods to analyze intracellular recordings and the testing of these methods using dynamic-clamp techniques. The methods are derived from a model of synaptic background activity where the synaptic membrane conductances are considered as stochastic processes. Because this fluctuating point-conductance model can be treated analytically, different methods can be outlined to estimate different characteristics of synaptic noise from the membrane potential (V m) activity, such as the mean and variance of the excitatory and inhibitory conductance distributions (the VmD method) or spike-triggered averages of conductances. These analysis methods can be validated in controlled conditions using dynamic-clamp injection of known synaptic conductance patterns, as we illustrate here. Our approach constitutes a novel application of the dynamic clamp, which could be extended to the testing of other methods for extracting conductance information from the recorded V m activity of neurons.

Keywords

Cortical Neuron Power Spectral Density Synaptic Input Synaptic Conductance Leak Conductance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Research supported by CNRS, ANR, ACI, HFSP, and the European Community (FACETS grant FP6 15879). Z.P. gratefully acknowledges the support of the FRM.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Zuzanna Piwkowska
    • 1
  • Martin Pospischil
  • Michelle Rudolph-Lilith
  • Thierry Bal
  • Alain Destexhe
  1. 1.Unité de Neurosciences Intégratives et Computationnelles (UNIC), CNRSFrance

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