Description
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
Table of Contents
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
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