Signal Processing for Neuroscientists - 2nd Edition - ISBN: 9780128104828, 9780128104835

Signal Processing for Neuroscientists

2nd Edition

eBook ISBN: 9780128104835
Hardcover ISBN: 9780128104828
Imprint: Academic Press
Published Date: 1st May 2018
Page Count: 700
Sales tax will be calculated at check-out Price includes VAT/GST

Institutional Access

Secure Checkout

Personal information is secured with SSL technology.

Free Shipping

Free global shipping
No minimum order.


Signal Processing for Neuroscientists provides an introduction to signal processing and modeling for those with a modest understanding of algebra, trigonometry, and calculus as a sufficient starting point. This second edition combines the first edition (2006) and its more advanced companion volume (2010), additionally incorporating a set of new related topics. With an added robust modeling component, this book describes modeling from the fundamental level of differential equations up to practical applications in neuronal modeling, featuring nine new chapters and a new exercise section developed by the author over the past decade. 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 get applications started and to evaluate more advanced literature on signal processing and modeling.

Key Features

  • Includes an introduction to biomedical signals, noise characteristics, recording techniques, and the more advanced topics of linear and nonlinear systems analysis and multi-channel analysis that were previously split into two separate volumes
  • Features new chapters on the fundamentals of modeling, application to neuronal modeling, Kalman filter, multi-taper power spectrum estimation, and practice exercises
  • Contains basics and background for more advanced topics in extensive notes and appendices
  • Includes practical examples of algorithm development and implementation in MATLAB
  • Provides multiple references to the basics to help the student
  • Features a companion website with MATLAB scripts, data files, figures, and video lectures


Graduate and advanced undergraduate students in biological and biomedical sciences, neuroscientists, neurologists, biomedical engineers, post-doctoral fellows, researchers. Potential users include neuroscientists, clinicians, engineers, mathematicians

Table of 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


No. of pages:
© Academic Press 2018
Academic Press
eBook ISBN:
Hardcover ISBN:

Ratings and Reviews