id: 01928619
dt: a
an: 01928619
au: Moreno, Rubén; Parga, Néstor
ti: Firing rate for a generic integrate-and-fire neuron with exponentially
    correlated input.
so: Dorronsoro, José R. (ed.), Artificial neural networks - ICANN 2002. 12th
    international conference, Madrid, Spain, August 28-30, 2002.
    Proceedings. Berlin: Springer. Lect. Notes Comput. Sci. 2415, 223-228
    (2002).
py: 2002
pu: Berlin: Springer
la: EN
cc: I.2.6
ut: 
ci: 
li: http://link.springer.de/link/service/series/0558/bibs/2415/24150223.htm
ab: Summary: The effect of time correlations in the afferent current on the
    firing rate of a generalized integrate-and-fire neuron model is
    studied. When the correlation time $τ_c$ is small enough the firing
    rate can bbe calculated analytically for small values of the
    correlation amplitude $α^2$. It is shown that the rate decreases as
    $\sqrt{τ_c}$ from its value at $τ_c=0$. This limit behavior is
    universal for integrate-and-fire neurons driven by exponential
    correlated Gaussian input. The details of the model only determine the
    pre-factor multiplying $\sqrt{τ_c}$. Two model examples are discussed.
rv: