October 23, 2014

Methods in Computational Neuroscience

Welcome to Methods in Computational Neuroscience Course at the Marine Biological Laboratory in Woods Hole, MA.

About the Course

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, the National Institute for Drug Abuse, NIH, and the Organization for Computational Neurosciences.

Faculty

2014 Course Directors
Michale Fee, MIT
Mark Goldman, UC Davis

2014 Faculty
Larry Abbott, Columbia University
Steve Baccus, Stanford University
William Bialek, Princeton University
Carlos Brody, Princeton University
Dmitri Chklovskii, Simons Center for Data Analysis
Nathaniel Daw, NYU
Bard Ermentrout, University of Pittsburgh
Adrienne Fairhall, University of Washington
Ila Fiete, UT Austin
Loren Frank, UC San Francisco
Gregory Gage, Backyard Brains
Jack Gallant, UC Berkeley
Surya Ganguli, Stanford University
Michael Hines, Yale University
David Kleinfeld, UC San Diego
Peter Latham, Gatsby Institute, UCL
Tim Lewis, UC Davis
John Lisman, Brandeis University
Eve Marder, Brandeis University
Bartlett Mel, USC
Jonathan Pillow, UT Austin
A. David Redish, University of Minnesota
John Rinzel, NYU
Jonathan Rubin, University of Pittsburgh
Nate Sawtell, Columbia University
Terry Sejnowski, Salk Institute
Maurice Smith, Harvard University
Sara Solla, Northwestern University
Haim Sompolinsky, Hebrew University
David Tank, Princeton University
Misha Tsodyks, Weizman Institute of Science

 

Contact

Please feel free to direct questions/comments to one of the course TAs or our course coordinator.

Directors:

Michale Fee, fee@mit.edu
Mark Goldman, msgoldman@ucdavis.edu

Course Coordinator:

Kelsey Allen, allen.kelsey.r@gmail.com

Teaching Assistants:

James Fitzgerald, james.eliot.fitzgerald@gmail.com
Ann Kennedy, ak3024@columbia.edu
Emily Mackevicius, elm@mit.edu
Srini Turaga, sturaga@gatsby.ucl.ac.uk

Computer Technology Assistant:

Tom Chartrand, tmchartrand@gmail.com

Travel Information

Information on getting to Woods Hole and the MBL can be found here. If you’re coming from Logan airport, and are not renting a car, you may want to skip directly to the Peter Pan bus co’s website.

The Woods Hole Oceanographic Institution (WHOI) also has a transportation website you may find helpful.

If you’re driving your own car, MBL’s address is:

7 MBL Street
Woods Hole, MA 02543

Google Maps