Signal processing for neuroscientists 2nd edition

Signal processing for neuroscientists 2nd edition – Ebook Instant Download/Delivery ISBN(s): 9780123849151,9780128104828,0123849152,9780128104835, 012810483X

Product details:

• ISBN 10: 012810483X
• ISBN 13:9780128104835
• Author:Wim van Drongelen

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.

Table contents:

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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