Impact of Correlated Inputs on the Output of the Integrate-and-Fire Model
Neural Computation
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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 驴c is small enough the firing rate can be calculated analytically for small values of the correlation amplitude 驴2. It is shown that the rate decreases as 驴驴c from its value at 驴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 驴驴c. Two model examples are discussed.