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The ability to digitally
acquire, store and analyze large volumes of multichannel data in
the neurosciences, ranging from multiple spike trains to brain images,
has given rise to a new and growing body of research. This two-week
course is structured around the related issues, and will contain
both pedagogical lectures on the basic statistical techniques as
well as focussed mini-workshops on specific neuroscience topics
where applications of these techniques are critical. The course
is an outgrowth of the Workshop on the Analysis of Neural Data,
which was previously held at the MBL from 1996 to 2001. Limited
to 25 participants.
Scope: The scope is all forms of time series data gathered in a
neuroscientific context. This includes point processes (single and
multiple spike trains) and continuous processes (local field potential,
EEG/MEG recordings, optical imaging data, fMRI and PET data). 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. Questions of data formats, databases
etc., will also be included in the workshop to the extent that they
relate to the data types described above.
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 use the high level languages MATLAB and
S. The participants will be guided through applications of the analytical
techniques to example data sets, and are also expected to perform
research on their own data. This should benefit both experimental
researchers and theorists who want to work with data.
Intended audience: The course is targeted fairly broadly, ranging
from experimental researchers (starting from the graduate level
upwards) who are gathering the data to researchers with a theoretical
or analytical orientation who work closely with data. A main aim
of the course is to foster close interactions between the theorists
and experimentalists. Course participants are expected to include
researchers with specific interest in one of the data types that
are in the scope, as well as researchers who have a broader scope.
We have kept the scope broad since an important current direction
in neuroscience is to integrate across different experimental modalities
ranging from spike trains to fMRI.
Structure of the course: The first week will contain a set of pedagogical
lectures dealing mostly with the statistical techniques. A concurrent
computer laboratory will run in the evenings to supplement the lectures.
The second week will contain one-day mini-workshops, whose topics
will rotate from year to year (examples being temporal codes, neural
prosthetics, data format, and database issues), as well as extensive
laboratory and interaction time.
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.
2008 Course Faculty and Lecturers:
Peter Andrews, Cold Spring Harbor Laboratory
Helen Barbas, Boston University
Jason Bohland, Cold Spring Harbor Laboratory
Hemant Bokil, Cold Spring Harbor Laboratory
Uri Eden, Boston University
Chris Fall, University of Illinois at Chicago
Ilan Golani, Tel Aviv University
Kenneth Harris, Rutgers University
Michael Hawrylycz, Allen Institute for Brain Science
Satish Iyengar, University of Pittsburgh
Robert Kass, Carnegie Mellon University
John Lin, Cold Spring Harbor Laboratory
Partha Niyogi, University of Chicago
Bijan Pesaran, New York University
Keith Purpura, Weill Medical College/Cornell University
Barry Richmond, NIMH/NIH
Sridevi Sarma, Massachusetts Institute of Technology
Nicholas Schiff, Weill Medical College
Andrew Sornborger, University of Georgia
David Van Essen, Washington University
Jonathan Victor, Weill Medical College
Ross Whitaker, University of Utah
Vasileios Zikopoulos, Boston University
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