Maximum likelihood estimation for an Ornstein-Uhlenbeck model for neural activity

DOI10.1007/s42519-021-00190-3zbMath1477.62064OpenAlexW3139002094MaRDI QIDQ2241467

Satish Iyengar

Publication date: 9 November 2021

Published in: Journal of Statistical Theory and Practice (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s42519-021-00190-3

zbMATH Keywords

Laplace transformmaximum likelihoodrenewal processfirst-passage timeintegrate-and-fire modelparabolic cylinder function

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Gaussian processes (60G15) Applications of statistics to biology and medical sciences; meta analysis (62P10) Neural biology (92C20)

Uses Software

• DLMF

Cites Work

• The Ornstein-Uhlenbeck process as a model for neuronal activity. I. Mean and variance of the firing time
• First hitting time models for the generalized inverse Gaussian distribution
• The inverse Gaussian distribution. Statistical theory and applications
• Modeling neural activity using the generalized inverse Gaussian distribution
• On the asymptotic normality of the maximum-likelihood estimate when sampling from a stable distribution
• First passage time problem for a drifted Ornstein-Uhlenbeck process
• On the parameter estimation for diffusion models of single neuron's activities. I: Application to spontaneous activities of mesencephalic reticular formation cells in sleep and waking states
• Estimation of the input parameters in the Feller neuronal model
• First-passage-time density and moments of the ornstein-uhlenbeck process
• An Improved Method for Numerical Inversion of Laplace Transforms
• Introduction to Theoretical Neurobiology
• Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model
• The First Passage Problem for a Continuous Markov Process
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