Signal Processing for Neuroscientists

Inbunden, Engelska, 2018

AvWim van Drongelen,Wim Van Drongelen,Wim Van Drongelen

1 519 kr

Beställningsvara. Skickas inom 7-10 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.

Signal Processing for Neuroscientists, Second Edition provides an introduction to signal processing and modeling for those with a modest understanding of algebra, trigonometry and calculus. With a robust modeling component, this book describes modeling from the fundamental level of differential equations all the way up to practical applications in neuronal modeling. It features nine new chapters and an exercise section developed by the author. Since the modeling of systems and signal analysis are closely related, integrated presentation of these topics using identical or similar mathematics presents a didactic advantage and a significant resource for neuroscientists with quantitative interest.

Although each of the topics introduced could fill several volumes, this book provides a fundamental and uncluttered background for the non-specialist scientist or engineer to not only get applications started, but also evaluate more advanced literature on signal processing and modeling.

Includes an introduction to biomedical signals, noise characteristics, recording techniques, and the more advanced topics of linear, nonlinear and multi-channel systems analysis
Features new chapters on the fundamentals of modeling, application to neuronal modeling, Kalman filter, multi-taper power spectrum estimation, and practice exercises
Contains the basics and background for more advanced topics in extensive notes and appendices
Includes practical examples of algorithm development and implementation in MATLAB
Features a companion website with MATLAB scripts, data files, figures and video lectures

Produktinformation

Utgivningsdatum2018-05-15
Mått152 x 229 x 46 mm
Vikt1 300 g
FormatInbunden
SpråkEngelska
Antal sidor740
Upplaga2
FörlagElsevier Science
ISBN9780128104828

Tillhör följande kategorier

Wim van Drongelen studied Biophysics at the University Leiden, The Netherlands. After a period in the Laboratoire d'Electrophysiologie, Université Claude Bernard, Lyon, France, he received the Doctoral degree cum laude. In 1980 he received the Ph.D. degree. He worked for the Netherlands Organization for the Advancement of Pure Research (ZWO) in the Department of Animal Physiology, Wageningen, The Netherlands. He lectured and founded a Medical Technology Department at the HBO Institute Twente, The Netherlands. In 1986 he joined the Benelux office of Nicolet Biomedical as an Application Specialist and in 1993 he relocated to Madison, WI, USA where he was involved in research and development of equipment for clinical neurophysiology and neuromonitoring. In 2001 he joined the Epilepsy Center at The University of Chicago, Chicago, IL, USA. Currently he is Professor of Pediatrics, Neurology, and Computational Neuroscience. In addition to his faculty position he serves as Technical and Research Director of the Pediatric Epilepsy Center and he is Senior Fellow with the Computation Institute. Since 2003 he teaches applied mathematics courses for the Committee on Computational Neuroscience. His ongoing research interests include the application of signal processing and modeling techniques to help resolve problems in neurophysiology and neuropathology. For details of recent work see http://epilepsylab.uchicago.edu/

1. Introduction2. Data Acquisition 3. Noise4. Signal Averaging5. Real and Complex Fourier Series6. Continuous, Discrete, and Fast Fourier Transform7. 1D and 2D Fourier Transform Applications8. Lomb’s Algorithm and Multi-Taper Power Spectrum Estimation9. Differential Equations: Introduction10. Differential Equations: Phase Space and Numerical Solutions11. Modeling12. Laplace and z-Transform 13. LTI Systems, Convolution, Correlation, Coherence, and the Hilbert Transform14. Causality15. Introduction to Filters: The RC-Circuit 16. Filters: Analysis 17. Filters: Specification, Bode Plot, and Nyquist Plot18. Filters: Digital Filters 19. Kalman Filter20. Spike Train Analyses 21. Wavelet Analysis: Time Domain Properties 22. Wavelet Analysis: Frequency Domain Properties 23. Low Dimensional Nonlinear Dynamics: Fixed Points, Limit Cycles and Bifurcations24. Volterra Series 25. Wiener Series 26. Poisson-Wiener Series 27. Nonlinear Techniques 28. Decomposition of Multi-Channel Data 29. Modeling Neural Systems: Cellular Models 30. Modeling Neural Systems: Network Models