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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.
2012 Course Directors
Michael Berry, Princeton University
Adrienne Fairhall, University of Washington
2012 Faculty
Larry Abbott, Columbia University
Emre Aksay, Weil Cornell Medical College
Rava Azeredo da Silveira, Ecole Normale Superieure
Steve Baccus, Stanford University
Bruce Bean, Harvard Medical School
Hagai Bergman, Hebrew University
William Bialek, Princeton University
Bard Ermentrout, University of Pittsburgh
Michale Fee, MIT
Ila Fiete, UT Austin
Rob Froemke, NYU School of Medicine
Mark Goldman, UC Davis
Roozbeh Kiani, Stanford University
Christof Koch, CalTech
David Kleinfeld, UCSD
David Lewis, University of Pittsburgh
Daniel Johnston, UT Austin
Nancy Kopell, Boston University
May-Britt Moser, NTNU, Norway
Ken Miller, Columbia University
Jonathan Pillow, UT Austin
Elad Schneidman, Weizmann Institute
Wolfram Schultz, University of Cambridge
Sebastian Seung, MIT
Reza Shadmehr, Johns Hopkins
Eric Shea-Brown, University of Washington
Sara Solla, Northwestern University
Haim Sompolinsky, Hebrew University
John White, Janelia Farm
Miles Whittington, University of Newcastle
Charles Wilson, UT San Antonio
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