Advanced Methods of Physiological System Modeling: Volume 3

✍ Scribed by Vasilis Z. Marmarelis (auth.), Vasilis Z. Marmarelis (eds.)

• Publisher: Springer US
• Year: 1994
• Tongue: English
• Leaves: 276
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xii
Nonlinear Modeling of Physiological Systems Using Principal Dynamic Modes....Pages 1-27
Experimental Basis for an Input/Output Model of the Hippocampal Formation....Pages 29-53
Computational Methods of Neuronal Network Decomposition....Pages 55-86
An Extension of the M-Sequence Technique for the Analysis of Multi-Input Nonlinear Systems....Pages 87-110
Examples of the Investigation of Neural Information Processing by Point Process Analysis....Pages 111-127
Testing a Nonlinear Model of Sensory Adaptation with a Range of Step Input Functions....Pages 129-138
Identification of Nonlinear System with Feedback Structure....Pages 139-162
Identification of Multiple-Input Nonlinear Systems Using Non-White Test Signals....Pages 163-178
Nonlinear System Identification of Hippocampal Neurons....Pages 179-194
Parametric and Nonparametric Nonlinear Modeling of Renal Autoregulation Dynamics....Pages 195-210
Identification of Parametric (NARMAX) Models from Estimated Volterra Kernels....Pages 211-217
Equivalence between Nonlinear Differential and Difference Equation Models Using Kernel Invariance Methods....Pages 219-227
On Kernel Estimation Using Non-Gaussian and/or Non-White Input Data....Pages 229-242
On the Relation between Volterra Models and Feedforward Artificial Neural Networks....Pages 243-259
Three Conjectures on Neural Network Implementations of Volterra Models (Mappings)....Pages 261-267
Back Matter....Pages 269-272

✦ Subjects

Biomedical Engineering