Methods in Computational Neuroscience

Course Information

Due to the Covid-19 pandemic, the 2020 class of students are automatically accepted into the class of 2021 and we are not taking applications at this time.

Watch MCN WebinarCourse Date: August 1 – August 27, 2021

Deadline: TBD

2019 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.

Financial Information: Tuition: $3,450.00; Room & Board: $2,106.00* (*Actual costs may be greater based on number of nights of occupancy). The admissions process at the Marine Biological Laboratory is need-blind, meaning that we evaluate students on their merits alone, without weighing their financial situations. Financial assistance will be considered for those admitted students who are in need. Upon acceptance, students will be asked to complete a financial aid request form if they need assistance.

In 2019, 100% of those students in the Methods in Computational Neuroscience course who requested financial aid received some support. The amount of financial aid available from the MBL varies by course based on funding from grants and scholarships, but typically covers 70-100% of student need.

2020 Course Faculty & Lecturers

Larry Abbott, Columbia University
Emery Brown, MIT
Randy Buckner, Harvard University
Dmitri Chklovskii, Simons Institute
Claudia Clopath, Imperial College London
Shaul Druckmann, Stanford University
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
Greg Gage, Backyard Brains
Surya Ganguli, Stanford University
Juliana Gjorgjieva, Max Planck Institute
Mark Goldman, UC Davis
Nancy Kopell, Boston University
Eve Marder, Brandeis University
Bartlett Mel, Univ. of Southern California
David Redish, U. Minnesota
Robb Rutledge, Yale University
Terry Sejnowski, Salk Institute
Mike Shadlen, Columbia U.
Reza Shadmehr, Johns Hopkins Univ.
Sara Solla, Northwestern University
Haim Sompolinsky, Hebrew University
Nelson Spruston, Janelia Research Campus
Josh Tenenbaum, MIT
Nao Uchida, Harvard University
David Van Essen, Washington University
Greg Wayne, DeepMind

Course Support