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Written by experts in the field, the book is an ideal reference for researchers working in the field of neural engineering, neural interface, computational neuroscience, neuroinformatics, neuropsychology and neural physiology. By giving a broad overview of the basic principles, theories and methods, it is also an ideal introduction to statistical signal processing in neuroscience.
- A comprehensive overview of the specific problems in neuroscience that require application of existing and development of new theory, techniques, and technology by the signal processing community
- Contains state-of-the-art signal processing, information theory, and machine learning algorithms and techniques for neuroscience research
- Presents quantitative and information-driven science that has been, or can be, applied to basic and translational neuroscience problems
Editorial ReviewsReview"Large-scale recording of multiple single neurons has become an indispensable tool in system neuroscience. The chapters of this edited volume will take the reader from spike detection and processing through analyses to modeling and interpretation. Both experimentalists and theorists will benefit from the well-condensed and organized content."
György Buzsáki, M.D., Ph.D. Center for Molecular and Behavioral Neuroscience Rutgers University
From the Back Cover
This is a uniquely comprehensive reference that summarizes the state of the art of signal processing and machine learning theory and techniques applied to emerging problems in neuroscience, with special emphasis on basic and clinical applications of neurotechnology. Written by experts in the field, the book is an ideal reference for engineering researchers and graduate students working in the field of neural engineering, neuroprosthesis, brain machine and brain computer interfaces, computational and systems neuroscience, neuroinformatics, and neurophysiology. It provides a broad overview of the basic principles, theories and methods of statistical signal processing, information theory and machine learning and their applications in neuroscience.
Features:
● Provides a comprehensive overview of classical and modern signal processing theory and techniques for analyzing neural data
● Presents quantitative and information-driven science that has been, or can be, applied to basic and translational neuroscience problems
● Discusses practical implementation issues and design considerations for neurotechnology, particularly related to neuroprosthetic and brain machine interface system design.
Karim G. Oweiss received his Ph.D. in Electrical Engineering and Computer Science from the University of Michigan, Ann Arbor in 2002 and has been with the Department of Electrical and Computer Engineering and the Neuroscience program at Michigan State University since 2003. He is a member of the IEEE and Society for Neuroscience and was awarded the excellence in Neural Engineering award from the National Science Foundation in 2001.
"Large-scale recording of multiple single neurons has become an indispensable tool in system neuroscience. The chapters of this edited volume will take the reader from spike detection and processing through analyses to modeling and interpretation. Both experimentalists and theorists will benefit from the well-condensed and organized content."
György Buzsáki, M.D., Ph.D., Center for Molecular and Behavioral Neuroscience, Rutgers University
Product Details
- Hardcover: 433 pages
- Publisher: Academic Press; 1 edition (August 18, 2010)
- Language: English
- ISBN-10: 012375027X
- ISBN-13: 978-0123750273
2010 Statistical_Signal_Processing_for_Neuroscience_and_Neurotechnology.pdf
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