Comment on "Characterization of subthreshold voltage fluctuations in neuronal membranes," by M. Rudolph and A. Destexhe

Authors:
Benjamin Lindner;André Longtin
Affiliations:
Department of Physics, University of Ottawa, Ottawa, Canada;Department of Physics, University of Ottawa, Ottawa, Canada
Venue:
Neural Computation
Year:
2006

Citing 5
Cited 4

Diffusion approximation of the neuronal model with synaptic reversal potentials

Biological Cybernetics
Characterization of subthreshold voltage fluctuations in neuronal membranes

Neural Computation
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,

Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Synaptic Shot Noise and Conductance Fluctuations Affect the Membrane Voltage with Equal Significance

Neural Computation
An Extended Analytic Expression for the Membrane Potential Distribution of Conductance-Based Synaptic Noise

Neural Computation

On the use of analytical expressions for the voltage distribution to analyze intracellular recordings

Neural Computation
Mean, variance, and autocorrelation of subthreshold potential fluctuations driven by filtered conductance shot noise

Neural Computation
Estimation of time-dependent input from neuronal membrane potential

Neural Computation
Effects of noise on models of spiny dendrites

Journal of Computational Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

In two recent articles, Rudolph and Destexhe (2003, 2005) studied a leaky integrator model (an RC-circuit) driven by correlated ("colored") gaussian conductance noise and Gaussian current noise. In the first article, they derived an expression for the stationary probability density of the membrane voltage; in the second, they modified this expression to cover a larger parameter regime. Here we show by standard analysis of solvable limit cases (white noise limit of additive and multiplicative noise sources; only slow multiplicative noise; only additive noise) and by numerical simulations that their first result does not hold for the general colored-noise case and uncover the errors made in the derivation of a Fokker-Planck equation for the probability density. Furthermore, we demonstrate analytically (including an exact integral expression for the time-dependent mean value of the voltage) and by comparison to simulation results that the extended expression for the probability density works much better but still does not exactly solve the full colored-noise problem. We also show that at stronger synaptic input, the stationary mean value of the linear voltage model may diverge and give an exact condition relating the system parameters for which this takes place.