NITRC News Group Forum: methods-in-computational-neuroscience

NITRC News Group Forum: methods-in-computational-neuroscience http://www.nitrc.org/forum/forum.php?forum_id=4148 <h1 class="entry-title">Methods in Computational Neuroscience</h1> <div class="entry-content"> Welcome to Methods in Computational Neuroscience Course at the <a href="http://www.mbl.edu/" target="_blank">Marine Biological Laboratory</a> in Woods Hole, MA. This website contains information for current students and faculty. The official course website for students interested in applying for the course can be found <a href="http://www.mbl.edu/education/courses/special_topics/mcn.html" target="_blank">here</a>. <table align="right"> <tbody> <tr> <td valign="top"><img alt="" height="10" src="http://courses.mbl.edu/mcn/images/clear.gif" width="10" /></td> <td valign="top"><a href="http://www.mbl.edu/mcn/files/2013/07/brain_fractal.jpg"><img alt="brain_fractal" class="alignleft size-full wp-image-24" height="232" src="http://www.mbl.edu/mcn/files/2013/07/brain_fractal.jpg" width="300" /></a></td> </tr> <tr> <td valign="top"></td> <td valign="top"></td> </tr> </tbody> </table> 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. 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 <h1 id="travel"></h1> </div> en-us Copyright 2000-2026 NITRC OSI Thu, 16 Apr 2026 18:55:22 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator