1 Introduction.
1.1 Purpose of this Book.
1.2 Advocated Approach.
1.3 The Problem of System Modeling in Physiology.
1.4 Types of Nonlinear Models of Physiological Systems.
2 Nonparametric Modeling.
2.1 Volterra Models.
2.2 Wiener Models.
2.3 Efficient Volterra Kernel Estimation.
2.4 Analysis of Estimation Errors.
3 Parametric Modeling.
3.1 Basic Parametric Model Forms and Estimation Procedures.
3.2 Volterra Kernels of Nonlinear Differential Equations.
3.3 Discrete-Time Volterra Kernels of NARMAX Models.
3.4 From Volterra Kernel Measurements to Parametric Models.
3.5 Equivalence Between Continuous and Discrete Parametric Models.
4 Modular and Connectionist Modeling.
4.1 Modular Form of Nonparametric Models.
4.2 Connectionist Models.
4.3 The Laguerre-Volterra Network.
4.4 The VWM Model.
5 A Practitioner’s Guide.
5.1 Practical Considerations and Experimental Requirements.
5.2 Preliminary Tests and Data Preparation.
5.3 Model Specification and Estimation.
5.4 Model Validation and Interpretation.
5.5 Outline of Step-by-Step Procedure.
6 Selected Applications.
6.1 Neurosensory Systems.
6.2 Cardiovascular System.
6.3 Renal System.
6.4 Metabolic-Endocrine System.
7 Modeling of Multiinput/Multioutput Systems.
7.1 The Two-Input Case.
7.2 Applications of Two-Input Modeling to Physiological Systems.
7.3 The Multiinput Case.
7.4 Spatiotemporal and Spectrotemporal Modeling.
8 Modeling of Neuronal Systems.
8.1 A General Model of Membrane and Synaptic Dynamics.
8.2 Functional Integration in the Single Neuron.
8.3 Neuronal Systems with Point-Process Inputs.
8.4 Modeling of Neuronal Ensembles.
9 Modeling of Nonstationary Systems.
9.1 Quasistationary and Recursive Tracking Methods.
9.2 Kernel Expansion Method.
9.3 Network-Based Methods.
9.4 Applications to Nonstationary Physiological Systems.
10 Modeling of Closed-Loop Systems.
10.1 Autoregressive Form of Closed-Loop Model.
10.2 Network Model Form of Closed-Loop Systems.
Appendix I: Function Expansions.
Appendix II: Gaussian White Noise.
Appendix III: Construction of the Wiener Series.
Appendix IV: Stationarity, Ergodicity, and Autocorrelation Functions of Random Processes.
References.
Index.
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