Advanced Methods of Physiological System Modeling, Volume 2
Springer US, Oct 31, 1989 - Medical - 310 pages
This volume is the second in a series of publications sponsored by the Biomedical Simulations Resource (BMSR) at the University of Southern California that report on recent research developments in the area of physiological systems modeling and anal ysis of physiological signals. As in the first volume of this series, the work reported herein is concerned with the development of advanced 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 used by most investigators in this area, which is confined primarily in the linear domain. As the im portance of nonlinearities in understanding the complex mechanisms of physiological function is increasingly recognized, the need for effective and practical methodolo gies that address the issue of nonlinear dynamics in life sciences becomes more and more pressing. The publication of these volumes and the workshops, organized by the BMSR on the same subject, are two key activities in our efforts to promote and intensify research in this area, foster interaction and collaboration among interested investigators, and disseminate recent results throughout the biomedical community.
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VolterraWiener Analysis of a Class of Nonlinear Feedback Systems
Parameter Estimation for Nongaussian Processes via Second and Third Order
Analysis of the WholeNerve Responses from the Exposed Auditory
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action potentials algorithm alpha rhythm amplitude analysis approximation behavior Berger Brain cascade models chaotic complex computed contralateral hippocampus cortex cortical cross-correlograms dentate gyrus dynamics element Emerson ep/s equations experimental fast orthogonal FFT magnitudes first-order kernel first-order Wiener kernel fractal dimension frequency Gaussian quadrature granule cell hippocampal formation identification impulse response increases input mean level input power level interactions kernel measurements kernel waveform Korenberg Marmarelis method Møller negative neocortex neocortical neural network neurons Neurophysiol nonlinear feedback nonlinear system Nunez observed obtained output parameter perforant path photoreceptors polynomial population spike properties pseudorandom noise random recorded region represents sample scale Schetzen Sclabassi second order kernel second-order kernel second-order Wiener kernel self-similar shown in Fig sigmoid feedback simple cell simulations sinusoidal spatial static nonlinearity stimulus test signal threshold time-series transformations values visual vitro vivo Volterra kernels white noise WV representation zero