Advanced Methods of Physiological System Modeling

Volume 3

  • Vasilis Z. Marmarelis

Table of contents

  1. Front Matter
    Pages i-xii
  2. Theodore W. Berger, T. Patrick Harty, Choi Choi, Xiaping Xie, German Barrionuevo, Robert J. Sclabassi
    Pages 29-53
  3. Robert J. Sclabassi, Bogdan R. Kosanović, German Barrionuevo, Theodore W. Berger
    Pages 55-86
  4. David R. Brillinger, Alessandro E. P. Villa
    Pages 111-127
  5. Junhao Shi, Hun H. Sun
    Pages 139-162
  6. David T. Westwick, Robert E. Kearney
    Pages 163-178
  7. Berj L. Bardakjian, W. Neil Wright, Taufik A. Valiante, Peter L. Carlen
    Pages 179-194
  8. Ki H. Chon, Niels H. Holstein-Rathlou, Donald J. Marsh, Vasilis Z. Marmarelis
    Pages 195-210
  9. Back Matter
    Pages 269-272

About this book


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

Editors and affiliations

  • Vasilis Z. Marmarelis
    • 1
  1. 1.University of Southern CaliforniaLos AngelesUSA

Bibliographic information

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