In the present paper we propose a new computationally simpIe, speedy and accurate method to construct first passage time probability density functions, through time-dependent boundaries, both for fixed and for random initial states. The considered processes underlying the neurons model are assumed to be of a rather generally kind, falling however within the class of Gauss-Markov processes.
On some computational methods for single neurons' activity modeling (Abstract)
DI CRESCENZO, Antonio;NOBILE, Amelia Giuseppina;
1999-01-01
Abstract
In the present paper we propose a new computationally simpIe, speedy and accurate method to construct first passage time probability density functions, through time-dependent boundaries, both for fixed and for random initial states. The considered processes underlying the neurons model are assumed to be of a rather generally kind, falling however within the class of Gauss-Markov processes.File in questo prodotto:
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