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
NOBILE, Amelia Giuseppina;
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 computerFile in questo prodotto:
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