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The ability to digitally acquire, store and analyze large volumes of multidimensional data in the neurosciences, ranging from multiple spike trains to static and dynamic brain images, has given rise to a new and growing body of research. This two-week course is structured around the issues related to such data, and will contain lectures on fundamental analytical techniques, lectures on emerging and established applications, and focused laboratory sessions to provide hands-on experience. Limited to 26 participants.
Scope: The scope includes all forms of time series data as well as anatomical data gathered in a neuroscientific context. This includes point processes (single and multiple spike trains), continuous processes (local field potential, EEG/MEG recordings, optical imaging data, fMRI and PET data), and neuroanatomical data (MRI, light microscopy as well as EM). Techniques for exploratory and confirmatory analysis of the data will be treated, along with the underlying scientific questions and potential biomedical applications. The goal is to provide pedagogical material as well as a forum for discussion. The course also includes tutorials on database management, web programming and surveys of neuroinformatics resources.
Computer laboratory: A hands-on approach will be taken in a computer laboratory that forms an integral part of this course. Example data sets will be supplied, and participants are encouraged to bring their own data. We will primarily use the high-level language MATLAB, with additional tools brought forth as needed (e.g., MySQL, domain-specific data analysis packages). The participants will be guided in applying analytical techniques to the example data sets and will further participate in a structured "data analysis challenge", in which teams will analyze data sets in the context of specific questions. This should benefit both experimental researchers that wish to analyze their own data sets and theorists who want to work with data.
Intended audience: The course is targeted broadly, ranging from experimental researchers (starting from the graduate level upwards) who are gathering data, to researchers with a theoretical or analytical orientation who work closely with data. A main aim of the course is to foster close working relations between the theorists and experimentalists.
Structure of the course: The first week will contain lectures dealing with fundamental statistical and analytical techniques appropriate for neural data analysis. A concurrent computer laboratory will run in the evenings to supplement the lectures. The second week contains application-based lectures, focused on emerging research areas and associated analytical and experimental techniques, along with the "data analysis challenge".
This course is supported by grants from the National Institute of Mental Health, the National Institute of Neurological Disorders and Stroke, and the National Institute on Drug Abuse.
2009 Course Faculty and Lecturers:
Peter Andrews, Cold Spring Harbor Laboratory
Helen Barbas, Boston University
Mikhail Belkin, Ohio State University
Amy Bernard, Allen Institute for Brain Science
Jason Bohland, Cold Spring Harbor Laboratory
Hemant Bokil, Cold Spring Harbor Laboratory
Uri Eden, Boston University
Ilan Golani, Tel Aviv University
Matti Hamalainen, Massachusetts General Hospital
Satish Iyengar, University of Pittsburgh
Robert Kass, Carnegie Mellon University
Shawn Mikula, University of California, Davis
Bijan Pesaran, New York University
Barry Richmond, DHHS/NIMH/NIH
John Rolston, Emory University
Sridevi Sarma, Johns Hopkins University
Nicholas Schiff, Weill Medical College
Andrew Sornborger, University of Georgia
Srinivas Turaga, Massachusetts Institute of Technology
David Van Essen, Washington University
Jonathan Victor, Weill Cornell Medical College
Haibin Wang, Cold Spring Harbor Laboratory
Ross Whitaker, University of Utah
Vasileios Zikopoulos, Boston University
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