Advanced Methods of Physiological System Modeling

1. Front Matter

   Pages i-xii

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2. Nonlinear Modeling of Physiological Systems Using Principal Dynamic Modes

   • Vasilis Z. Marmarelis

   Pages 1-27

3. Experimental Basis for an Input/Output Model of the Hippocampal Formation

   • Theodore W. Berger, T. Patrick Harty, Choi Choi, Xiaping Xie, German Barrionuevo, Robert J. Sclabassi

   Pages 29-53

4. Computational Methods of Neuronal Network Decomposition

   • Robert J. Sclabassi, Bogdan R. Kosanović, German Barrionuevo, Theodore W. Berger

   Pages 55-86

5. An Extension of the M-Sequence Technique for the Analysis of Multi-Input Nonlinear Systems

   • Ethan A. Benardete, Jonathan D. Victor

   Pages 87-110

6. Examples of the Investigation of Neural Information Processing by Point Process Analysis

   • David R. Brillinger, Alessandro E. P. Villa

   Pages 111-127

7. Testing a Nonlinear Model of Sensory Adaptation with a Range of Step Input Functions

   • Andrew S. French, Susan K. Patrick

   Pages 129-138

8. Identification of Nonlinear System with Feedback Structure

   • Junhao Shi, Hun H. Sun

   Pages 139-162

9. Identification of Multiple-Input Nonlinear Systems Using Non-White Test Signals

   • David T. Westwick, Robert E. Kearney

   Pages 163-178

10. Nonlinear System Identification of Hippocampal Neurons

    • Berj L. Bardakjian, W. Neil Wright, Taufik A. Valiante, Peter L. Carlen

    Pages 179-194

11. Parametric and Nonparametric Nonlinear Modeling of Renal Autoregulation Dynamics

    • Ki H. Chon, Niels H. Holstein-Rathlou, Donald J. Marsh, Vasilis Z. Marmarelis

    Pages 195-210

12. Identification of Parametric (NARMAX) Models from Estimated Volterra Kernels

    • Xiao Zhao, Vasilis Z. Marmarelis

    Pages 211-217

13. Equivalence between Nonlinear Differential and Difference Equation Models Using Kernel Invariance Methods

    • Xiao Zhao, Vasilis Z. Marmarelis

    Pages 219-227

14. On Kernel Estimation Using Non-Gaussian and/or Non-White Input Data

    • Vasilis Z. Marmarelis

    Pages 229-242

15. On the Relation between Volterra Models and Feedforward Artificial Neural Networks

    • Vasilis Z. Marmarelis, Xiao Zhao

    Pages 243-259

16. Three Conjectures on Neural Network Implementations of Volterra Models (Mappings)

    • Vasilis Z. Marmarelis

    Pages 261-267

17. Back Matter

    Pages 269-272

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This volume is the third in a series entitled" Advanced Methods of Physiological System Modeling" and the fifth in a series of research volumes published by Plenum under the sponsorship of the Biomedical Simulations Resource (BMSR) at the Uni­ versity of Southern California in the context of dissemination activities supported by the Biomedical Research Technology Program of the National Center for Research Resources at the National Institutes of Health under Grant No. P41 RR-OI861. These volumes are edited by BMSR principal scientists and report on recent research de­ velopments in the area of physiological systems modeling, as well as on advanced methods for analysis of physiological signals and data. As in the previous two volumes of this series, the work reported herein is con­ cerned with the development of advanced modeling methodologies and their novel application to problems of biomedical interest, with emphasis on nonlinear aspects of physiological function. The term "advanced methodologies" is used to indicate that the scope of this work extends beyond the ordinary type of analysis, which is confined traditionally to the linear domain. As the importance of nonlinearities in understanding the complex mechanisms of physiological function is increasingly recognized, the need for effective and practical modeling methodologies that address the issue of nonlinear dynamics in life sciences becomes more and more pressing.

• adaptation
• artificial neural network
• dynamics
• information processing
• neurons
• regulation

• University of Southern California, Los Angeles, USA

  Vasilis Z. Marmarelis

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