Signal Processing for Neuroscientists

Specificaties
Gebonden, blz. | Engels
Elsevier Science | e druk, 2018
ISBN13: 9780128104828
Rubricering
Elsevier Science e druk, 2018 9780128104828
€ 131,80
Levertijd ongeveer 8 werkdagen

Samenvatting

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.

Specificaties

ISBN13:9780128104828
Taal:Engels
Bindwijze:Gebonden

Inhoudsopgave

1. Introduction<br>2. Data Acquisition <br>3. Noise<br>4. Signal Averaging<br>5. Real and Complex Fourier Series<br>6. Continuous, Discrete, and Fast Fourier Transform<br>7. 1D and 2D Fourier Transform Applications<br>8. Lomb’s Algorithm and Multi-Taper Power Spectrum Estimation<br>9. Differential Equations: Introduction<br>10. Differential Equations: Phase Space and Numerical Solutions<br>11. Modeling<br>12. Laplace and z-Transform <br>13. LTI Systems, Convolution, Correlation, Coherence, and the Hilbert Transform<br>14. Causality<br>15. Introduction to Filters: The RC-Circuit <br>16. Filters: Analysis <br>17. Filters: Specification, Bode Plot, and Nyquist Plot<br>18. Filters: Digital Filters <br>19. Kalman Filter<br>20. Spike Train Analyses <br>21. Wavelet Analysis: Time Domain Properties <br>22. Wavelet Analysis: Frequency Domain Properties <br>23. Low Dimensional Nonlinear Dynamics: Fixed Points, Limit Cycles and Bifurcations<br>24. Volterra Series <br>25. Wiener Series <br>26. Poisson-Wiener Series <br>27. Nonlinear Techniques <br>28. Decomposition of Multi-Channel Data <br>29. Modeling Neural Systems: Cellular Models <br>30. Modeling Neural Systems: Network Models <br>
€ 131,80
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        Signal Processing for Neuroscientists