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 computer
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/1737807
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