About this book
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.
Neural signal processing Neuronal coding theories Neural engineering Neural activity State-space paradigm Hippocampal replay Oscillatory and multivariate data Gaussian approximation Applications to neuronal data Dynamical system model of behavior Neuronal population theories Brain-machine interface systems Enteric nervous system High-resolution electrogastrogram Theoretical neuroscience Statistical neuroscience
Editors and affiliations
- DOI https://doi.org/10.1007/978-3-319-71976-4
- Copyright Information Springer International Publishing AG 2018
- Publisher Name Springer, Cham
- eBook Packages Engineering
- Print ISBN 978-3-319-71975-7
- Online ISBN 978-3-319-71976-4
- Buy this book on publisher's site