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On some computational results for single neurons' activity modeling.

A Di Crescenzo·E Di Nardo·A G Nobile·E Pirozzi·L M Ricciardi
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Abstract

The classical Ornstein-Uhlenbeck diffusion neuronal model is generalized by inclusion of a time-dependent input whose strength exponentially decreases in time. The behavior of the membrane potential is consequently seen to be modeled by a process whose mean and covariance classify, it as Gaussian-Markov. The effect of the input on the neuron's firing characteristics is investigated by comparing the firing probability densities and distributions for such a process with the corresponding ones of the Ornstein-Uhlenbeck model. All numerical results are obtained by implementation of a recently developed computational method.

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Computational BiologyMarkov ChainsMembrane PotentialsModels, NeurologicalNeuronsJournal ArticleResearch Support, Non-U.S. Gov't

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A Di Crescenzo, E Di Nardo, A G Nobile, E Pirozzi, L M Ricciardi. On some computational results for single neurons' activity modeling.. https://doi.org/10.1016/s0303-2647(00)00102-7

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