<p style="text-align: justify;" class="MsoNormal"><font face="Times New Roman" size="4"><span style="font-size: 14pt;">Neuroinformatics course at
Marine Biological Laboratory, Woods Hole, MA</span></font></p>
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<p style="text-align: justify;" class="MsoNormal"><font face="Times New Roman" size="4"><span style="font-size: 14pt;">Dates: August
14<sup>th</sup> to 29<sup>th</sup>, 2010</span></font></p>
<p style="text-align: justify;" class="MsoNormal"><font face="Times New Roman" size="4"><span style="font-size: 14pt;">Web: <a title="blocked::http://www.mbl.edu/education/courses/special_topics/neufo.html" href="http://www.mbl.edu/education/courses/special_topics/neufo.html">http://www.mbl.edu/education/courses/special_topics/neufo.html</a></span></font></p>
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<p style="text-align: justify;" class="MsoNormal"><font face="Times New Roman" size="4"><span style="font-size: 14pt;">The objective of this two
week course is to develop an understanding of the methods of managing and
analyzing data sets from neurophysiological and behavioral measurements,
particularly large data volumes that require systematic statistical and
computational approaches. The course includes lectures on fundamental analytical
methods, established and emerging applications and focused hands-on
computer-based sessions. Topics include point processes (<i><span style="font-style: italic;">e.g., </span></i>spike trains), continuous processes
(<i><span style="font-style: italic;">e.g.</span></i>, LFP/ECoG/EEG/MEG
recordings, fMRI, and behavioral recordings), and methods for analyzing
neuroanatomical (<i><span style="font-style: italic;">e.g., </span></i>light and
electron microscopy) data. Various statistical techniques for exploratory and
confirmatory analysis of the data will be treated along with underlying
scientific questions and potential applications. The course also includes
tutorials on computer methods and discussions of major open issues in the
field.</span></font></p>
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<p style="text-align: justify;" class="MsoNormal"><font face="Times New Roman" size="4"><span style="font-size: 14pt;">The course is targeted
broadly, from experimental researchers 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.
Researchers at all levels, from advanced graduate student to working
professional, may benefit from the course.</span></font></p>
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<p style="text-align: justify;" class="MsoNormal"><font face="Times New Roman" size="4"><span style="font-size: 14pt;">Application deadline is
April 16, 2010. Limited to 26 participants.</span></font></p>
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<p style="text-align: justify;" class="MsoNormal"><font face="Times New Roman" size="4"><span style="font-size: 14pt;">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 MATLAB, with
additional tools used as needed (<i><span style="font-style: italic;">e.g.</span></i>, MySQL). 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 published 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.</span></font></p>
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<p style="text-align: justify;" class="MsoNormal"><font face="Times New Roman" size="4"><span style="font-size: 14pt;">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".</span></font></p>
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