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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.
2008 Course Faculty & Lecturers:
Larry Abbott, Columbia University
Stephen Baccus, Stanford University
William Bialek, Princeton University
Carlos Brody, Princeton University
Yang Dan, UC Berkeley
Rob de Ruyter, Indiana University Bloomington
James DiCarlo, MIT
Rava da Silveira, Ecole Normale Supérieure
Bard Ermentrout, University of Pittsburgh
Ila Fiete, California Institute of Technology
Mark Goldman, University of California, Davis
Michael Häusser, University College London
Michael Hines, Yale University
Eugene Izhikevich, Neuroscience Institute, San Diego
Daniel Johnston, University of Texas at Austin
Nancy Kopell, Boston University
Simon Laughlin, University of Cambridge
Eve Marder, Brandeis University
Michael Mauk, University of Texas at Austin
David McCormick, Yale University
Mayank Mehta, Brown University
Bence Olveczky, Harvard University
Jonathan Pillow, UCL
Jennifer Raymond, Stanford University
Elad Schneidman, Weizmann Institute of Science
Terrence Sejnowski, Salk Institute
Sara Solla, Northwestern University
Haim Sompolinsky, Hebrew University
William Spain, University of Washington
David Tank, Princeton University
Roger Traub, SUNY Downstate Medical Center
Xiao-Jing Wang, Yale University School of Medicine
Charles Wilson, Univ. Texas at San Antonio
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