Inference for stochastic neuronal models
type: Item
instance of: scholarly article
title: Inference for stochastic neuronal models
zbMATH Open document ID: 0724.92001
DOI: 10.1016/0096-3003(90)90080-M
published in: Applied Mathematics and Computation
publication date: 1990-01-01T00:00:00Z
review text: Stochastic models are developed to study the subthreshold behavior of the membrane potential of neurons. First, the membrane potential is modeled as a solution of stochastic differential equations driven by various stochastic processes such as Poisson processes, Wiener processes, and a mixture of Poisson and Wiener processes. Maximum-likelihood estimates of the parameters of a diffusion neuronal model are obtained. Then, the authors propose a diffusion neuronal model with time-dependent parameters and a semimartingale neuronal model which extends previously discussed models. The method of optimal estimating functions is applied in this case. A simulation study is conducted to evaluate the performance of the maximum-likelihood estimators for a diffusion neuronal model. For long periods of observation, the estimates of parameters deteriorate as the number of observed trajectories increases. This counter-intuitive phenomenon may require further investigation.
Mathematics Subject Classification ID: 92C20
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 60H10
Mathematics Subject Classification ID: 60H15
zbMATH DE Number: 4195760
zbMATH Keywords: subthreshold behavior
zbMATH Keywords: membrane potential of neurons
zbMATH Keywords: Poisson processes
zbMATH Keywords: Wiener processes
zbMATH Keywords: mixture of Poisson and Wiener processes
zbMATH Keywords: Maximum- likelihood estimates
zbMATH Keywords: diffusion neuronal model
zbMATH Keywords: semimartingale neuronal model
zbMATH Keywords: method of optimal estimating functions
MaRDI profile type: Publication
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full work available at URL: 0096-3003(90)90080-m
OpenAlex ID: W4253771402
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description: scientific article; zbMATH DE number 4195760