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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.
2011 Course Faculty and Lecturers:
Barbas, Helen, Boston University
Belkin, Mikhail, The Ohio State University
Bernard, Amy, Allen Institute for Brain Science
Bokil, Hemant, Intelect Medical Inc.
Eden, Uri, Boston University
Golani, Ilan, Tel Aviv University
Grange, Pascal, Cold Spring Harbor Laboratory
Hamalainen, Matti, Massachusetts General Hospital
Havkin, Gregor, Cold Spring Harbor Laboratory
Helmstaedter, Moritz, Max Planck Institute for Medical Research
Hill, Daniel, Technical University of Munich
Iyengar, Satish, University of Pittsburgh
Pesaran, Bijan, New York University
Purpura, Keith, Weill Cornell Medical College
Richmond, Barry, DHHS/NIH/NIMH
Sarma, Sridevi, The Johns Hopkins University
Schiff, Nicholas, Weill Cornell Medical College
Sornborger, Andrew, University of Georgia
Victor, Jonathan, Weill Cornell Medical College
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