Signal Processing for Neuroscientists

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.

Autorentext
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/

Klappentext
Signal Processing for Neuroscientists provides an introduction to signal processing and modeling for those with a modest understanding of algebra, trigonometry, and calculus as a sufficient starting point. This second edition combines the first edition (2006) and its more advanced companion volume (2010), additionally incorporating a set of new related topics. With an added robust modeling component, this book describes modeling from the fundamental level of differential equations up to practical applications in neuronal modeling, featuring nine new chapters and a new exercise section developed by the author over the past decade. 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 get applications started and to evaluate more advanced literature on signal processing and modeling.

Inhalt

1. Introduction
   
   2. Data Acquisition
   3. Noise
   4. Signal Averaging
   5. Real and Complex Fourier Series
   6. Continuous, Discrete, and Fast Fourier Transform
   7. 1D and 2D Fourier Transform Applications
   8. Lomb's Algorithm and Multi-Taper Power Spectrum Estimation
   9. Differential Equations: Introduction
   10. Differential Equations: Phase Space and Numerical Solutions
   11. Modeling
   12. Laplace and z-Transform
   13. LTI Systems, Convolution, Correlation, Coherence, and the Hilbert Transform
   14. Causality
   15. Introduction to Filters: The RC-Circuit
   16. Filters: Analysis
   17. Filters: Specification, Bode Plot, and Nyquist Plot
   18. Filters: Digital Filters
   19. Kalman Filter
   20. 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 Bifurcations
   24. 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