Workshop: Cold Spring Harbor Laboratory Computational Neuroscience: Vision Course

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This award supports a course on Computational Neuroscience - Vision given at the Cold Spring Harbor Laboratories. Computational approaches to neuroscience have produced important advances in the understanding of neural processing. The CSHL course emphasizes that an understanding of computational problems in conjunction with perceptual and biological data can lead to breakthroughs in neuroscience research. Through a combination of lectures and hands-on computer laboratory experience, this course covers: neural representation and coding; the neural basis of adaptation, color vision, pattern vision, form vision, and visual motion perception; statistics and neural encoding of natural scenes; visual attention; and decision making. There will be an emphasis on encoding and decoding of visual information, including processing of natural scenes, a burgeoning area of sensory and computational neuroscience. The course will be organized by J. Demb (Univ Michigan), E. Simoncelli (NYU) and S. Treue (Univ Gottingen). Lectures in the course will be given by the three organizers and by invited speakers who are working on current research frontiers and are leaders in their field. Invited speakers include: L. Abbott (Columbia) D. Brainard (Univ Penn), M. Carandini (Smith-Kettlewell Eye Research Institute), M. Carrasco (NYU), E.J. Chichilnisky (Salk Institute), Y. Dan (UC Berkeley), J. Gallant (UC Berkeley), W. Geisler (UT-Austin), P. Glimcher (NYU), D. Heeger (NYU), N. Kanwisher (MIT), M. Lewicki (Carnegie Mellon), J.A. Movshon (NYU), P. Reinagel (UCSD), D. Ringach (UCLA).The short, intensive nature of the course enables scientists to become rapidly immersed in the various concepts and techniques being taught and makes it possible for senior scientists and junior faculty as well as graduate students and postdoctoral fellows to attend. The students in the courses can then rapidly disseminate the techniques to others in their own laboratories. The courses are taught by a faculty who are fully active in and have made significant contributions to the areas under study. The value of these courses is reflected in the numbers and quality of the students who apply. Each year, far more applicants apply than can possibly be accommodated, and former students continue to recommend their own students or postdoctoral fellows for enrollment.