Aiming at the construction of non-Markov models for single neuron’s activity, the asymptotic behavior of the upcrossing first passage time probability density function through certain time-varying boundaries, is established for a class of stationary Gaussian processes. The goodness of the theoretical results is then tested, in specific instances, by means of a simulation method implemented on a large scale parallel computer.
Gaussian Processes and Neural Modeling: an Asymptotic Analysis / E., DI NARDO; A. G., Nobile; Pirozzi, Enrica; Ricciardi, LUIGI MARIA. - STAMPA. - (2002), pp. 313-318.
Gaussian Processes and Neural Modeling: an Asymptotic Analysis
PIROZZI, ENRICA;RICCIARDI, LUIGI MARIA
2002
Abstract
Aiming at the construction of non-Markov models for single neuron’s activity, the asymptotic behavior of the upcrossing first passage time probability density function through certain time-varying boundaries, is established for a class of stationary Gaussian processes. The goodness of the theoretical results is then tested, in specific instances, by means of a simulation method implemented on a large scale parallel computer.File in questo prodotto:
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