
- 740 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Signal Processing for Neuroscientists
About this book
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
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Information
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Preface to the Second Edition
- Preface to the Companion Volume
- Preface to the First Edition
- Chapter 1. Introduction
- Chapter 2. Data Acquisition
- Chapter 3. Noise
- Chapter 4. Signal Averaging
- Chapter 5. Real and Complex Fourier Series
- Chapter 6. Continuous, Discrete, and Fast Fourier Transform
- Chapter 7. 1-D and 2-D Fourier Transform Applications
- Chapter 8. Lomb's Algorithm and Multitaper Power Spectrum Estimation
- Chapter 9. Differential Equations: Introduction
- Chapter 10. Differential Equations: Phase Space and Numerical Solutions
- Chapter 11. Modeling
- Chapter 12. Laplace and z-Transform
- Chapter 13. LTI Systems: Convolution, Correlation, Coherence, and the Hilbert Transform
- Chapter 14. Causality
- Chapter 15. Introduction to Filters: The RC-Circuit
- Chapter 16. Filters: Analysis
- Chapter 17. Filters: Specification, Bode Plot, Nyquist Plot
- Chapter 18. Filters: Digital Filters
- Chapter 19. Kalman Filter
- Chapter 20. Spike Train Analysis
- Chapter 21. Wavelet Analysis: Time Domain Properties
- Chapter 22. Wavelet Analysis: Frequency Domain Properties
- Chapter 23. Low-Dimensional Nonlinear Dynamics: Fixed Points, Limit Cycles, and Bifurcations
- Chapter 24. Volterra Series
- Chapter 25. Wiener Series
- Chapter 26. Poisson–Wiener Series
- Chapter 27. Nonlinear Techniques
- Chapter 28. Decomposition of Multichannel Data
- Chapter 29. Modeling Neural Systems: Cellular Models
- Chapter 30. Modeling Neural Systems: Network Models
- Index