The biomedical sciences have recently undergone revolutionary change, due to the ability to digitize and store large data sets. In neuroscience, the data sources include measurements of neural activity measured using electrode arrays, EEG and MEG, brain imaging data from PET, fMRI and optical imaging methods. Analysis, visualization and management of these time series data sets is a growing field of research that has become increasingly important both for experimentalists andtheorists interested in brain function. Written by investigators who have played an important role in developing the subject and in its pedagogical exposition, the current volume addresses the need for a textbook in this interdisciplinary area. The book is written for a broad spectrum of readers ranging from physical scientists, mathematicians and statisticians wishing to educate themselves about neuroscience, as well as biologists who would like to learn time series analysis methods in particular, and refresh their mathematical and statistical knowledge in general, through self-pedagogy. It could also be used as a supplement for a quantitative course in neurobiology or as a textbook for instruction on neural signal processing. The first part of the book contains a set of essays meant to provide conceptual background which are not technical and should be generally accessible. Salient features include the adoption of an active perspective of the nervous system, an emphasis on function, and a brief survey of different theoretical accounts in neuroscience. The second part is the longest in the book, and contains a refresher course in mathematics and statistics leading up to time series analysis techniques. The thirdpart contains applications of data analysis techniques to the range of data sources indicated above (also available as part of the Chronux data analysis platform from http://chronux.org), and the fourth part contains special topics.
I Conceptual Background; Why Study Brain Dynamics?; Theoretical Accounts of the Nervous System; Engineering Theories and Nervous System Function; Methodological Considerations; II Tutorials; Mathematical Preliminaries; Statistical Protocols; Time Series Analysis; III Applications; Electrophysiology: Microelectrode Recordings; Spike Sorting; Electro- and Magnetoencephalography; PET and fMRI; Optical Imaging; IV Special Topics; Local Regression and Likelihood; Entropy and Mutual Information; Appendix A: The Bandwagon; Appendix B: Two Famous Papers;
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