Abstract
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 τc is small enough the firing rate can be calculated analytically for small values of the correlation amplitude α2. It is shown that the rate decreases as \( \sqrt {\tau _c } \) from its value at τc = 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 \( \sqrt {\tau _c } \) . Two model examples are discussed.
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Moreno, R., Parga, N. (2002). Firing Rate for a Generic Integrate-and-Fire Neuron with Exponentially Correlated Input. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_37
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DOI: https://doi.org/10.1007/3-540-46084-5_37
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