Advanced Methods of Physiological System Modeling: Volume 3 / Edition 1by V.Z. Marmarelis
Pub. Date: 10/31/1994
Publisher: Springer US
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
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.
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Table of Contents
Nonlinear Modeling of Physiological Systems Using Principal Dynamic Modes (V.Z. Marmarelis). Experimental Basis for an Input/Output Model of the Hippocampal Formation (T.W. Berger et al.). Computational Methods of Neuronal Network Decomposition (R.J. Sclabassi et al.). An Extension of the Msequence Technique for the Analysis of Multiinput Nonlinear Systems (E.A. Benardete, J.D. Victor). Examples of the Investigation of Neural Information Processing by Point Process Analysis (D.R. Brillinger, A.E.P. Villa). Testing a Nonlinear Model of Sensory Adaptation with a Range of Step Input Functions (A.S. French, S.K. Patrick). Identification of Nonlinear System with Feedback Structure (J. Shi, H.H. Sun). Identification of Multipleinput Nonlinear Systems Using Nonwhite Test Signals (D.T. Westwick, R.E. Kearney). Nonlinear System Identification of Hippocampal Neurons (B.L. Bardakjian et al.). Parametric and Nonparametric Nonlinear Modeling of Renal Autoregulation Dynamics (K.H. Chon et al.). 5 additional articles. Index.
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