Are the input parameters of white noise driven integrate and fire neurons uniquely determined by rate and CV?

  • Vilela R
  • Lindner B
  • 23


    Mendeley users who have this article in their library.
  • 31


    Citations of this article.


Integrate and fire (IF) neurons have found widespread applications in computational neuroscience. Particularly important are stochastic versions of these models where the driving consists of a synaptic input modeled as white Gaussian noise with mean μ and noise intensity D. Different IF models have been proposed, the firing statistics of which depends nontrivially on the input parameters μ and D. In order to compare these models among each other, one must first specify the correspondence between their parameters. This can be done by determining which set of parameters (μ, D) of each model is associated with a given set of basic firing statistics as, for instance, the firing rate and the coefficient of variation (CV) of the interspike interval (ISI). However, it is not clear a priori whether for a given firing rate and CV there is only one unique choice of input parameters for each model. Here we review the dependence of rate and CV on input parameters for the perfect, leaky, and quadratic IF neuron models and show analytically that indeed in these three models the firing rate and the CV uniquely determine the input parameters. © 2008 Elsevier Ltd. All rights reserved.

Author-supplied keywords

  • Neuronal models

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Rafael D. Vilela

  • Benjamin Lindner

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free