Signal Processing for Neuroscientists, 2e

Signal Processing for Neuroscientists 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.

The book includes many examples of algorithm development and implementation in MATLAB. Signal Processing Toolbox is also used to solve some examples in the text, and there is a small section on Wavelet Toolbox in the Wavelet Analysis chapter.

Features

• 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
• Features a companion website with MATLAB scripts, data files, figures and video lectures