Methods in Computational Neuroscience

Course Information

Course Date: July 29 – August 24, 2018

Deadline: March 20, 2018 | Apply here

2017 Lecture Schedule

Course Website

Directors: Stephen Baccus, Stanford University; and Xiao Jing Wang, New York University

Course Description

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, Simons Collaboration on the Global Brain, the National Institute for Drug Abuse, NIH and the Organization for Computational Neurosciences.

2018 Course Faculty & Lecturers

Larry Abbott, Columbia University
Carlos Brody, Princeton University
Emery Brown, MIT
Dmitri Chklovskii, Simons Institute
Claudia Clopath, Imperial Col. London
Kenji Doya, Okinawa Inst. Sci. Tech.
Uri Eden, Boston University
Bard Ermentrout, U. of Pittsburgh
Adrienne Fairhall, U. of Washington
Ila Fiete, University of Texas Austin
James Fitzgerald, Janelia Res., HHMI
Loren Frank, UCSF
Michale Fee, MIT
Stefano Fusi, Columbia University
Surya Ganguli, Stanford University
Mark Goldman, UC Davis
Kenneth Harris, U. College London
James Haxby, Dartmouth University
Nancy Kopell, Boston University
Eve Marder, Brandeis University
Bartlett Mel, Univ. of Southern California
Jonathan Pillow, Princeton University
David Redish, U. Minnesota
Terry Sejnowski, Salk Institute
Reza Shadmehr, Johns Hopkins Univ.
Sara Solla, Northwestern University
Haim Sompolinsky, Hebrew University
Josh Tenenbaum, MIT
Nao Uchida, Harvard University
Greg Wayne, DeepMind
Hongkui Zeng, Allen Institute

Course Support

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