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Animals interact with a
complex world, encountering a variety of challenges: They must gather
data about the environment, discover useful structures in these data,
store and recall information about past events, plan and guide actions,
learn the consequences of these actions, etc. These are, in part,
computational problems that are solved by networks of neurons, from
roughly 100 cells in a small worm to 100 billion in humans. Methods in
Computational Neuroscience introduces students to the computational and
mathematical techniques that are used to address how the brain solves
these problems at levels of neural organization ranging from single
membrane channels to operations of the entire brain.
In each of the first three weeks, the course focuses on material at
increasing levels of complexity (molecular/cellular, network,
cognitive/behavioral), but always with an eye on these questions: Can
we derive biologically plausible mechanisms that explain how nervous
systems solve specific computational problems that arise in the
laboratory or natural environment? Can these problems be decomposed
into manageable pieces, and can we relate such mathematical
decompositions to the observable properties of individual neurons and
circuits? Can we identify the molecular mechanisms that provide the
building blocks for these computations, as well as understand how the
building blocks are organized into cells and circuits that perform
useful functions?
Core presentations in weeks one to three will be given jointly by
theorists and experimentalists who have worked, often together, on the
same problems. In the first week, to supplement the lectures, there
will be numerous optional tutorials covering topics including dynamical
systems, information theory, UNIX basics, and simulation using NEURON,
MATLAB, and XPP. As each week progresses, the issues brought up in
these presentations will be explored in laboratory demonstrations and
exercises that invite the students to follow and generalize from the
paths outlined in the lectures. Exercises involve both quantitative
analysis of experimental data and exploration of models through
analytic and numerical techniques. To reinforce the theme of
collaboration between theory and experiment, exercises are often
performed in teams that combine students with theoretical and
experimental backgrounds.
The fourth week of the course is reserved for student projects. These
projects provide the opportunity for students to work closely with the
resident faculty, to develop ideas that grew out of the lectures and
seminars, and to connect these ideas with problems from the students’
own research topics.
This course is appropriate for graduate students, postdocs and faculty
in a variety of fields, from zoology, ethology, and neurobiology, to
physics, engineering, and mathematics. Students are expected to have a
strong background in one discipline, and to have made some effort to
introduce themselves to a complementary discipline. The course is
limited to 24 students, who will be chosen to balance the
representation of theoretical and experimental backgrounds.
This course is partially supported by the National Institute of Mental
Health, National Institute for Neurological Disorders and Stroke, and
the National Institute for Drug Abuse, NIH.
2011 Course Faculty and Lecturers:
Larry
Abbott, Columbia University
Rava Azeredo da Silveira, Ecole Normale Superieure
William Bialek, Princeton University
Gyuri Buszaki, Rutgers University
Mitya Chklovskii, Howard Hughes Medical
Institute, Janelia Farm Research
Campus
Winfried Denk, Max-Planck Institute
for Medical Research
James DiCarlo, Massachusetts Institute of Technology
Bard Ermentrout, University of Pittsburgh
Michale Fee, Massachusetts Institute of Technology
Ila Fiete, University of Texas, Austin
Mark Goldman, University of California, Davis
Michael Hausser, University College London
Daniel Johnston, University of Texas, Austin
Roozbeh Kiani, University of Washington
David Kleinfeld, University of California, San Diego
Nancy Kopell, Boston University
Marcelo Magnasco, The Rockefeller University
Eve Marder, Brandeis University
Kevan Martin, Institute of
Neuroinformatics
Mayank Mehta, Brown University
Bartlett Mel, University of Southern California
Jonathan Pillow, University of Texas, Austin
Elad Schneidman, Weizmann Institute
Terry Sejnowski, Salk Institute
Sebastian Seung, Massachusetts Institute of Technology
Reza Shadmehr, The Johns Hopkins University
Sara Solla, Northwestern University
Haim Sompolinsky, Hebrew University
Bill Spain, University of Washington
Karel Svoboda, Howard Hughes Medical
Institute, Janelia Farm Research Campus
Miles Whittington, University of
Newcastle
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