Michigan State University - Research Groups

Neuroscience, like many other areas of biology, has experienced a data outburst in the last few years. Many remarkable findings were triggered since, and have undeniably improved our understanding of many aspects of the nervous system. Yet, the study of the brain and its inner workings is an extraordinarily complex endeavor, and undoubtedly requires the collective effort of many experts across multiple disciplines to accelerate the science of information management that neuroscience data has invigorated.

Signal processing and statistics have a long withstanding history of mutual interaction with neuroscience, going back to the Hudgkin-Huxley circuit models of membrane potential, to statistical inference about stimulus-response relationships using Bayes' rule. Classical signal processing, however, quickly erode in the face of the dynamic, multivariate nature of neuroscience and behavioral data. Realizing the immediate need for a rigorous theoretical and practical analysis of such data, this text was written by experts in the field to provide a comprehensive reference summarizing state of the art solutions to some fundamental problems in neuroscience and neurotechnology. It is intended for theorists and experimentalists at the graduate student and postdoctoral level in the electrical,computer, and biomedical engineering disciplines, as well as mathematicians, statisticians, computational and systems neuroscientists. Secondary audience may include neurobiologists, neurophysiologists and clinicians involved in basic and translational neuroscience research. It can also be used as a supplement for a quantitative course in neurobiology, or as a textbook for instruction on neural signal processing and its applications. Industry partners who would like to explore research and clinical applications of these concepts in the context of neural interface technology would certainly find useful content in a number of chapters.

This book was written with the following goals in mind: 1) serve as an introductory text to readers interested inlearning how traditional and modern principles, theories, and techniques in statistical signal processing, information theory and machine learning can be applied to neuroscience problems;2) serve as a reference book for researchers and clinicians working at the interface between neuroscience and engineering; and 3) serve as a textbook for graduate level course within an electrical engineering, biomedical engineering, or computational and systems neuroscience curricula. Readers of this book are expected to have an introductory course in probability and statistics at the undergraduate level within an engineering or neuroscience curriculum. It is organized in such a way that progressively addresses specific problems arising in the course of a neurophysiological experiment or in the design of a brain machine interface system. An ultimate goal is to encourage the next generation of scientists to develop advanced statistical signal processing theory and techniques in their pursuit of answers to the numerous intricate questions that arise in the course of these experiments.

I owe a considerable amount of gratitude to my fellow contributors and their willingness to spare a significant amount of time to write, edit and help in the review of every chapter in this book since its inception. I am also very thankful to a number of colleagues who helped me in the review of early versions of the manuscript.

Developing the tools needed to understand brain function and its relation to behavior from the collected data is no less important than developing the devices used to collect these data, or developing the biological techniques to decipher its anatomical structure. I believe this book is a first step towards fulfilling an urgent need to hasten the development of these tools and techniques that will certainly thrive in the years to come.

Karim G. Oweiss

East Lansing, Michigan

USA