Signal Processing for Neuroscientists

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.