Statistical Inference for Stochastic Neuronal Models

Věra Lánská
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Věra Lánská: Institute for Clinical and Experimental Medicine

A chapter in Biomathematics and Related Computational Problems, 1988, pp 47-53 from Springer

Abstract: Abstract One of the main problems in experimental neurophysiology is to decide whether a presented stimuli modifies a discharge pattern of a neuron. To solve the problem the neuron spike train is modelled by doubly stochastic Poisson process and filtering theory is applied to estimate the random intensity functions of the process. Two different models are proposed and the methods of statistical inference for evoked neuronal activity are derived on their basis.

Keywords: Point Process; Quadratic Error; Experimental Neurophysiology; Stochastic Differential System; Nonhomogeneous Poisson Process (search for similar items in EconPapers)
Date: 1988
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-94-009-2975-3_5

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DOI: 10.1007/978-94-009-2975-3_5

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