IDENTIFICATION OF A COMPLEX NEUROPHYSIOLOGICAL SYSTEM USING THE MAXIMUM LIKELIHOOD APPROACH

DOI10.1142/S0218339003000798zbMath1041.62091OpenAlexW2049452820MaRDI QIDQ4736670

V. K. Kotti, Alexandros G. Rigas

Publication date: 6 August 2004

Published in: Journal of Biological Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0218339003000798

zbMATH Keywords

generalized linear modelsdeviancestationary point processesneuromuscular receptorstochastic system identification

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Neural biology (92C20)

Related Items (2)

Statistical methods and software for risk assessment: applications to a neurophysiological data set ⋮ Measuring the association of stationary point processes using spectral analysis techniques

Cites Work

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• Spectra-based estimates of certain time-domain parameters of a bivariate stationary-point process
• Maximum likelihood analysis of spike trains of interacting nerve cells
• An introduction to the theory of point processes
• SPECTRAL ANALYSIS OF STATIONARY POINT PROCESSES USING THE FAST FOURIER TRANSFORM ALGORITHM

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