General methodology for nonlinear modeling of neural systems with Poisson point-process inputs.

[1]  N. Wiener,et al.  Nonlinear Problems in Random Theory , 1964 .

[2]  Y. W. Lee,et al.  Measurement of the Wiener Kernels of a Non-linear System by Cross-correlation† , 1965 .

[3]  Hisanao Ogura,et al.  Orthogonal functionals of the Poisson process , 1972, IEEE Trans. Inf. Theory.

[4]  Vasilis Z. Marmarelis,et al.  Advanced Methods of Physiological System Modeling , 1989 .

[5]  G. Barrionuevo,et al.  Modeling of neuronal networks through experimental decomposition , 1991, [1991] Proceedings of the 34th Midwest Symposium on Circuits and Systems.

[6]  Theodore W. Berger,et al.  Experimental Basis for an Input/Output Model of the Hippocampal Formation , 1994 .

[7]  Theodore W. Berger,et al.  Computational Methods of Neuronal Network Decomposition , 1994 .

[8]  Vasilis Z. Marmarelis,et al.  Nonlinear Dynamic Modeling of Physiological Systems , 2004 .

[9]  H. I. Krausz,et al.  Identification of nonlinear systems using random impulse train inputs , 1975, Biological Cybernetics.

[10]  Vasilis Z. Marmarelis.  Modeling of Neuronal Systems , 2004 .

[11]  Wiener analysis of nonlinear systems using Poisson-Charlier crosscorrelation , 1977, Biological Cybernetics.

[12]  Theodore W. Berger,et al.  A systems theoretic approach to the study of CNS function , 2006, Annals of Biomedical Engineering.

[13]  V. Marmarelis.  Identification of nonlinear biological systems using laguerre expansions of kernels , 1993, Annals of Biomedical Engineering.

[14]  V. Marmarelis.  Modeling methology for nonlinear physiological systems , 1997, Annals of Biomedical Engineering.