图书简介
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.
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
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