This book shows how to develop efficient quantitative methods to characterize neural data and extra information that reveals underlying dynamics and neurophysiological mechanisms. Written by active experts in the field, it contains an exchange of innovative ideas among researchers at both computational and experimental ends, as well as those at the interface. Authors discuss research challenges and new directions in emerging areas with two goals in mind: to collect recent advances in statistics, signal processing, modeling, and control methods in neuroscience; and to welcome and foster innovative or cross-disciplinary ideas along this line of research and discuss important research issues in neural data analysis. Making use of both tutorial and review materials, this book is written for neural, electrical, and biomedical engineers; computational neuroscientists; statisticians; computer scientists; and clinical engineers.
8 Brain-Machine Interfaces
“This is a short monograph on the computational neurosciences of single and populations of neurons. … This serves as a reference for advanced engineers and mathematical neurobiologists primarily. Dayan’s Theoretical Neuroscience, and Neural Engineering by MIT Press are useful for more background material. Brain Machine and Brain-Computer interfacing is also considered here.” (Joseph Grenier, Amazon.com, June, 2018)
Sridevi Sarma is Associate Professor in the Department of Biomedical Engineering at Johns Hopkins University (JHU), having previously worked at Massachusetts Institute of Technology and Harvard Medical School. She is the Associate Director of the Institute for Computational Medicine at JHU. Professor Sarma is a recipient of the GE faculty for the future scholarship, a L’Oreal For Women in Science fellow, the Burroughs Wellcome Fund Careers at the Scientific Interface Award, the Krishna Kumar New Investigator Award from the North American Neuromodulation Society (NANS), and the Presidential Early Career Award for Scientists and Engineers (PECASE).
This book shows how to develop efficient quantitative methods to characterize neural data and extra information that reveals underlying dynamics and neurophysiological mechanisms. Written by active experts in the field, it contains an exchange of innovative ideas among researchers at both computational and experimental ends, as well as those at the interface. Authors discuss research challenges and new directions in emerging areas with two goals in mind: to collect recent advances in statistics, signal processing, modeling, and control methods in neuroscience; and to welcome and foster innovative or cross-disciplinary ideas along this line of research and discuss important research issues in neural data analysis. Making use of both tutorial and review materials, this book is written for neural, electrical, and biomedical engineers; computational neuroscientists; statisticians; computer scientists; and clinical engineers.
- Presents innovative methodological and algorithmic development in statistics, modeling, control, and signal processing for neural data analysis;
- Includes a coherent framework for a broad class of neural signal processing and control problems in neuroscience;
- Covers a wide range of representative case studies in neuroscience applications.
Includes a coherent framework for a broad class of neural signal processing and control problems in neuroscience;
Covers a wide range of representative case studies in neuroscience applications.
Presents innovative methodological and algorithmic development in statistics, modeling, control, and signal processing for neural data analysis
