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Analysis of physiological systems : the white-noise approach

Author: Panos Z Marmarelis; Vasilis Z Marmarelis
Publisher: New York : Plenum Press, ©1978.
Series: Computers in biology and medicine (New York, 1977- )
Edition/Format:   Print book : EnglishView all editions and formats
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Genre/Form: Laboratory Manuals
Additional Physical Format: Online version:
Marmarelis, Panos Z.
Analysis of physiological systems.
New York : Plenum Press, ©1978
(OCoLC)557796044
Online version:
Marmarelis, Panos Z.
Analysis of physiological systems.
New York : Plenum Press, ©1978
(OCoLC)606154990
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Panos Z Marmarelis; Vasilis Z Marmarelis
ISBN: 030631066X 9780306310669
OCLC Number: 3627785
Description: xvi, 487 pages : illustrations ; 24 cm.
Contents: 1. The Problem of System Identification in Physiology.- 1.1. The Problem of Systems Analysis in Physiology.- 1.2. Functional and Structural Identification of Physiological Systems.- 1.3. "Black Box" vs. Parameter Identification in Physiological Systems.- 2. Analysis of Physiological Signals.- 2.1. Physiological Systems Data: Deterministic and Stochastic Descriptions.- 2.2. Some Statistical Tools and Concepts.- 2.2.1. Stationarity and Ergodicity of Signals.- 2.2.2. Certain Statistical Quantities of Interest.- 2.3. Autocorrelation and Crosscorrelation Functions.- 2.3.1. Certain Properties of the Auto- and Crosscorrelation Functions.- 2.3.2. Correlation Measurement from Underlying Probability Distribution.- 2.3.3. Summary of Definitions of Auto- and Crosscorrelation Functions.- 2.3.4. Use of Correlation Functions.- 2.4. Frequency Domain Description of Signals.- 2.4.1. Fourier Series.- 2.4.2. The Fourier Transform.- 2.4.3. Power Spectrum.- 2.5. Certain Properties of Gaussian Signals.- 2.5.1. High-Order Moments of Gaussian Signals.- 2.5.2. Stationarity and Ergodicity of Gaussian Signals.- 2.5.3. Gaussian Signals through Linear Systems.- 2.5.4. Gaussian White Noise.- 2.6. Sampling Considerations.- 2.7. Statistical Estimation from Physiological Signals.- 2.7.1. Variance of the Mean for Sampled Signals.- 2.7.2. Confidence Interval of Estimates.- 2.8. Filtering of Physiological Signals.- 2.8.1. Averaging Responses to Identical Stimuli.- 2.8.2. Low-Frequency Trend Removal.- 2.8.3. Digital Filters.- 2.8.4. Analog Filtering.- 2.9. Considerations in Computing Power Spectra.- 2.9.1. Aliasing.- 2.9.2. Statistical Errors.- 2.9.3. Smoothing.- 2.9.4. Practical Considerations.- 3. Traditional Approaches to Physiological System Identification.- 3.1. Stimulus-Response Relations in Linear Systems.- 3.1.1. Time Domain.- 3.1.2. Frequency Domain.- 3.2. Transfer Functions and Bode Plots.- 3.2.1. Analysis.- 3.2.2. (Non-) Minimum-Phase Systems.- 3.2.3. Synthesis.- 3.2.4. Delays in Transfer Functions.- 3.3. Transfer Functions from Stimulus-Response Spectra.- 3.3.1. The Effect of Noise.- 3.3.2. Application to a Physiological System: Light ? ERG.- 3.4. Coherence Function.- 3.5. Mufti-Input Linear Systems.- 3.5.1. Two-Input Systems.- 3.5.2. Application to a Two-Input Neural System.- 3.5.3. n-Input Systems.- 3.6. Nonlinear Systems: Identification Using "Describing Functions"..- 3.6.1. Describing Functions.- 3.6.2. Use of Describing Functions for Identification of Systems..- 3.6.3. A Linearization Technique.- 3.7. Effects of Feedback in Physiological Systems.- 3.7.1. On the System Gain.- 3.7.2. On Reliability of Processing Signals.- 3.7.3. On Signal-to-Noise Ratio ..- 3.7.4. On System Bandwidth.- 3.7.5. On System Response and Stability.- 3.7.6. On Sustained Physiological Oscillations.- 3.8. Feedback Analysis in a Neurosensory System.- 4. The White-Noise Method in System Identification.- 4.1. Linear and Nonlinear Systems-The Volterra Series.- 4.1.1. Linear Systems.- 4.1.2. Nonlinear Systems.- 4.1.3. Analogy between Volterra and Taylor Series.- 4.1.4. Functional Meaning of the Volterra Kernels.- 4.2. The Wiener Theory.- 4.2.1. System Representation by Functionals.- 4.2.2. The Wiener Series.- 4.2.3. Comparison of Wiener and Volterra Representations.- 4.2.4. Meaning of Wiener Kernels.- 4.2.5. Kernels of System Cascades.- 4.3. Schemes for the Estimation of the System Kernels.- 4.3.1. The Wiener-Bose Approach.- 4.3.2. The Lee-Schetzen Approach (Crosscorrelation Technique)..- 4.3.3. A Paradigm: White-Noise Analysis of a Physiological System.- 4.4. Multi-Input, Multi-Output Systems.- 4.5. Other Formulations of the White-Noise Approach.- 5. Applicability of the White-Noise Method and the Use of Quasiwhite Test Signals.- 5.1. The Band-Limited Gaussian White Noise.- 5.1.1. General Description and Generation of GWN.- 5.1.2. Autocorrelation Properties of GWN and Application in Nonlinear System Identification.- 5.2. The Pseudorandom Signals Based on m Sequences.- 5.2.1. General Description and Generation of PRS.- 5.2.2. Autocorrelation Properties of PRS and Application in Nonlinear System Identification.- 5.3. The Constant-Switching-Pace Symmetric Random Signals.- 5.3.1. General Description and Generation of CSRS.- 5.3.2. Autocorrelation Properties of CSRS and Application in Nonlinear System Identification.- 5.3.3. An Analytical Example.- 5.4. Comparative Study of the Use of GWN, PRS, and CSRS in System Identification.- 5.4.1. Discussion on Relative Advantages and Disadvantages of GWN, PRS, and CSRS.- 5.4.2. Computer-Simulated Applications of GWN, PRS, and CSRS.- 5.5. Validation of Generated Quasiwhite Test Signals.- 5.5.1. Check on Autocorrelation Functions.- 5.5.2. Check on Stationarity.- 5.5.3. Check on Amplitude Distribution.- 5.5.4. Check on Power Spectrum.- 5.5.5. Check on Independence of Multiple Stimuli.- 6. Methods of Computation of System Kernels.- 6.1. Computational Considerations for Kernel Measurement.- 6.2. Time-Domain Approaches to Kernel Computation.- 6.2.1. Utilization of Intermediate Products.- 6.2.2. Treatment of Long Stimulus-Response Records.- 6.2.3. Quantization of the Input Signal.- 6.2.4. Monte Carlo Methods for Kernel Computation.- 6.3. Frequency-Domain Approach: Use of the Fast Fourier Transform Algorithm.- 6.3.1. Frequency-Domain Formulation and Procedure.- 6.3.2. Analysis of Kernel Computation via the Frequency Domain.- 6.4. Special Cases of Kernel Computation.- 6.4.1. The Use of Binary and Ternary Inputs.- 6.4.2. Spike Train Output.- 6.5. Analog (Hybrid) Methods for the Computation of Kernels.- 6.6. Evaluation of the System Kernels.- 6.7. Evaluation of Results of Experiment.- 6.7.1. One-Input System.- 6.7.2. Two-Input System.- 6.7.3. Physical Units of Kernels.- 7. Errors in the Estimation of System Kernels.- 7.1. Estimation Errors Using GWN Stimulus.- 7.1.1. Errors Due to the Finite Record Length.- 7.1.2. Errors Due to the Finite Stimulus Bandwidth.- 7.1.3. Errors Due to Experimental Limitations.- 7.1.4. Dependence of Kernel Estimate Accuracy on the Degree of System Nonlinearity.- 7.1.5. Effect of Kernel Memory Truncation on Frequency Response Estimate.- 7.1.6. Errors Due to the Presence of Other Inputs in the Mufti-Input Case.- 7.2. Estimation Errors Using PRS Stimuli.- 7.3. Estimation Errors Using CSRS Stimuli.- 7.3.1. The Deconvolution Error.- 7.3.2. The Statistical Fluctuation Error.- 7.3.3. The Approximate Orthogonality Errors.- 7.3.4. The Erroneous Power Level Error.- 7.3.5. The Finite Transition Time Error.- 7.3.6. Computational Errors.- 7.3.7. General Error Management.- 7.3.8. Minimization of the Deconvolution and Statistical Fluctuation Errors-The Fundamental Error Equation.- 7.4. Errors Due to the Presence of Contaminating Noise.- 7.4.1. Noise at the Output.- 7.4.2. Internal Noise.- 7.4.3. Noise at the Input.- 8. Tests and Analyses Preliminary to Identification Experiment.- 8.1. Determination of the System Input and Output and Region of Operation.- 8.2. Examination of System Stationarity and Noise Conditions.- 8.2.1. System Stationarity.- 8.2.2. Noise Conditions.- 8.3. Removal of Drifts in the Response Data.- 8.3.1. Trend Removal by Fitting Least-Squares Polynomials.- 8.3.2. High-Pass Filtering of the Response.- 8.4. The Measurement of System Memory and Bandwidth.- 8.5. Measurement of Extent of System Nonlinearity.- 8.6. Recording and Digitalization of Stimulus-Response Data.- 8.6.1. Effect of Aliasing on Kernel Estimation.- 8.6.2. Effect of Digitalization on Kernel Estimation.- 8.7. Choice of GWN Bandwidth and Record Length.- 8.8. Optimal Choice of CSRS Step and Record Length.- 9. Peeking into the Black Box.- 9.1. Analysis of Cascades in Physiological Systems.- 9.1.1. Linear System Followed by Zero-Memory Nonlinearity.- 9.1.2. Linear System Preceded by Zero-Memory Nonlinearity.- 9.1.3. Illustrative Applications to Physiological Systems.- 9.2. Zero-Memory Systems.- 9.3. Combinations of Systems.- 9.3.1. Identity System.- 9.3.2. Sum System.- 9.3.3. Cascade System.- 9.3.4. Feedback System.- 9.3.5. Illustrative Applications to Physiological Systems.- 10. Applications of the White-Noise Method to Neural Systems.- 10.1. Practical Considerations in Application of the White-Noise Method to Neural Systems.- 10.1.1. Dynamic Range of Stimulus.- 10.1.2. Stationarity of System Response.- 10.1.3. Lower-Frequency Limitations.- 10.1.4. Intracellular Recording.- 10.1.5. Modeling of Neural Systems.- 10.2. Identification of One-Input Neural Systems Using GWN Stimulus.- 10.2.1. System with Continuous Input and Continuous Output: Light? Horizontal Cell.- 10.2.2. System with Continuous Input and Discrete Output: Horizontal Cell? Ganglion Cell.- 10.3. Identification of Two-Input Neural Systems Using GWN Stimulus.- 10.3.1. System with Continuous Inputs and Continuous Output: Spot and Annulus Light? Horizontal Cell.- 10.3.2. System with Continuous Inputs and Discrete Output: Two Spot Light? Horizontal Motion Detection Fiber.- 10.4. Identification of One-Input Neural System Using Pseudorandom Binary Stimulus.- 10.5. Identification of One-Input Neural System Using CSRS Stimulus.- 10.6. Applications of Alternate Identification Techniques to Neural Systems with Discrete Input or Output.- 10.6.1. System with Continuous Input and Discrete Output.- 10.6.2. System with Discrete Input and Continuous Output.- 11. Physiological Systems Requiring Special Treatment.- 11.1. Physiological Systems with Point Process Inputs and Outputs.- 11.1.1. Continuous-to-Discrete System.- 11.1.2. Discrete-to-Continuous System.- 11.1.3. Discrete-to-Discrete System.- 11.2. Systems with Spatiotemporal Inputs.- 11.3. Nonstationary Systems.- 11.4. Systems with Nonwhite Random Inputs.- 12. Dialogue for Epilogue.- References.- Related Literature.
Series Title: Computers in biology and medicine (New York, 1977- )
Responsibility: Panos Z. Marmarelis and Vasilis Z. Marmarelis.

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