Abstract
We consider how correlated inputs affect the variability of cellular output of the integrate-and-fire model with or without reversal potentials. For both models the variability efferent spike trains measured by coefficient of variation of the interspike interval (abbreviated to CV in the remainder of the paper) is a nondecreasing function of input correlation. For the set of physiologicla parameters used in our simulations: when the correlation coefficient is greater than 0.09, the CV of the integrate-and-fire model without reversal potentials is always above 0.5, no matter how strong the inhibitory inputs; when the correlation coefficient is greater than 0.06, CV for the integrate-and-fire model with reversal potentials is always above 0.5, independent of the strength of the inhibitory inputs. A novel method to estimate the distribution density of the first passage time of the integrate-and-fire model is developed and under a given condition on correlation coefficients we find that correlated Poisson processes can be decomposed into independent Poisson processes.
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Feng, J. (1999). Integrate-and-fire model with correlated inputs. In: Mira, J., Sánchez-Andrés, J.V. (eds) Foundations and Tools for Neural Modeling. IWANN 1999. Lecture Notes in Computer Science, vol 1606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0098181
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DOI: https://doi.org/10.1007/BFb0098181
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