IDENTIFICATION OF A COMPLEX NEUROPHYSIOLOGICAL SYSTEM USING THE MAXIMUM LIKELIHOOD APPROACH
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Publication:4736670
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
generalized linear modelsdeviancestationary point processesneuromuscular receptorstochastic system identification
Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Neural biology (92C20)
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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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