  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 $\mu$ and noise intensity $D$. Different IF models
have been proposed, the firing statistics of which depends nontrivially on the
input parameters $\mu$ 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 ($\mu$, $D$) of each model
is associated to 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 {\em 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.

Neurons and Cognition;Adaptation and Self-Organizing Systems