Applications are open for the Methods in Computational Neurosciencecourse at the Marine Biology Laboratory in Woods Hole, MA. The course will run from July 26 to August 23, 2023. The online application form and additional information can be found at: https://www.mbl.edu/education/advanced-research-training-courses/course-offe... The course application deadline is *March 31*. The course covers a range of topics in computational neuroscience including neuronal biophysics, neural coding & information processing, circuit dynamics, learning & memory, motor control, and cognitive processing & disease. In addition, numerous tutorials and problem sets will cover a broad range of computational and mathematical modeling methods. The course strongly emphasizes the collaboration between theory and experiment in solving neuroscience problems, and lectures will be given by a mixture of theorists and experimentalists. The final weeks of the course are primarily reserved for work on projects that students design in collaboration with the resident faculty. 2023 Course Directors: Stephen Baccus, Stanford University Xiao Jing Wang, New York University 2023 Faculty: Larry Abbott, Columbia University Emery Brown, MIT Nicolas Brunel, Duke University Randy Buckner, Harvard University Anne Churchland, UCLA Claudia Clopath, Imp. College London Marlene Cohen, U. of Pittsburgh Shaul Druckmann, Stanford University Uri Eden, Boston University Bard Ermentrout, U. of Pittsburgh Adrienne Fairhall, U. of Washington Michale Fee, MIT Loren Frank, UCSF Stefano Fusi, Columbia University Surya Ganguli, Stanford University Paul Glimcher, New York University Mark Goldman, UC Davis Nancy Kopell, Boston University Eve Marder, Brandeis University John Murray, Yale University Srdjan Ostojic, ENS - PSL Yiota Poirazi, IMBB-FORTH David Redish, U. Minnesota Terry Sejnowski, Salk Institute Sebastian Seung, Princeton University Reza Shadmehr, Johns Hopkins Univ. Haim Sompolinsky, Hebrew University Nelson Spruston, Janelia Res., HHMI Nao Uchida, Harvard University Byron Yu, Carnegie Mellon University Greg Wayne, DeepMind