September 1, 2014

Methods in Computational Neuroscience

courses_mcn

Course Date: July 30 – August 27, 2014

Deadline: March 6, 2014 | Online Application Form

Course Schedule

Course Website

Directors: Michale Fee, Massachusetts Institute of Technology; and Mark Goldman, University of California, Davis

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.

Current Faculty

2013 Course Faculty & Lecturers:
Abbott, Larry, Columbia University
Baccus, Stephen, Stanford University
Bean, Bruce, Harvard Medical School
Beck, Jeff, Duke University
Bell, Curtis, Retired from Oregon Science & Health University
Bialek, William, Princeton University
Brody, Carlos, HHMI / Princeton University
Chklovskii, Dmitri, Howard Hughes Medical Institute
Dayan, Peter, University College London
Deisseroth, Karl, Stanford University
Denk, Winfried, Max-Planck Institute for Medical Research
Ermentrout, Bard, University of Pittsburgh
Fairhall, Adrienne, University of Washington
Fiete, Ila, University of Texas at Austin
Froemke, Robert, New York University School of Medicine
Gage, Gregory, Backyard Brains
Gallant, Jack, University of California Berkeley
Ganguli, Surya, Stanford University
Hausser, Michael, University College London
Kleinfeld, David, University of California San Diego
Kopell, Nancy, Boston University
Lisman, John, Brandeis University
Marder, Eve, Brandeis University
Mel, Bartlett, University of Southern California
Miller, Kenneth, Columbia University
Nirenberg, Sheila, Cornell University
Niv, Yael, Princeton University
Paninski, Liam, Columbia University
Pillow, Jonathan, University of Texas at Austin
Schwartz, Andrew, University of Pittsburgh
Sejnowski, Terrence, Salk Institute
Seung, H. Sebastian, Massachusetts Institute of Technology
Shadlen, Michael, Columbia University Medical Center
Shea-brown, Eric, University of Washington
Solla, Sara, Northwestern University
Sompolinsky, Haim, Hebrew University
Tank, David, Princeton University
Wolpert, Daniel, University of Cambridge

Partial Student Support
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