Journal of Computational Neuroscience

, Volume 17, Issue 3, pp 327–345 | Cite as

Extracting Information from the Power Spectrum of Synaptic Noise

  • Alain Destexhe
  • Michael Rudolph


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 (V m ) 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 V m 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 V m 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.

synaptic noise computational models power spectrum cerebral cortex 


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Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Alain Destexhe
    • 1
  • Michael Rudolph
    • 1
  1. 1.Integrative and Computational Neuroscience Unit (UNIC), CNRSGif-sur-YvetteFrance

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