This workshop is about general computational principles for networks of neurons that help us understand experimental data, about principles that enable us to reproduce aspects of the brain's astounding computational capability in models and neuromorphic hardware, and about the connections between computational neuroscience and machine learning.
Support is gratefully acknowledged from:
Dana Ballard (University of Texas at Austin), Matthew Botvinick (DeepMind Technologies Limited, London and University College London), Rishi Chaudhuri (University of Texas at Austin), Hannah Choi (University of Washington), Anne Collins (UC Berkeley), David Cox (Harvard University), Sophie Denève (École Normale Supérieure), Reza Eghbali (University of Washington), Asja Fischer (University of Bonn), David Foster (UC Berkeley), Edward Paxon Frady (UC Berkeley), Veronica Galvin (Yale University), Surya Ganguli (Stanford University), Jeff Hawkins (Numenta), Brian Hayes (American Scientist), Pentti Kanerva (UC Berkeley), Anatoly Khina (California Institute of Technology), Antonina Kolokolova (Memorial University of Newfoundland), Peter Latham (University College London), Robert Legenstein (Graz University of Technology), Timothy Lillicrap (DeepMind Technologies Limited, London), Nancy Lynch (Massachusetts Institute of Technology), Wolfgang Maass (Graz University of Technology), Jason MacLean (University of Chicago), Bartlett Mel (University of Southern California), Ilya Nemenman (Emory University), J. Kevin O'Regan (CNRS), Bruno Olshausen (UC Berkeley), Colin Sandon (Princeton University), Saeed Saremi (UC Berkeley), Peggy Seriès (University of Edinburgh), Fritz Sommer (UC Berkeley), Narayan Srinivasa (Eta Compute), Lena Ting (Emory University and Georgia Institute of Technology), Les Valiant (Harvard University), Rufin VanRullen (CNRS - Toulouse), Xiao-Jing Wang (New York University), Greg Wayne (Columbia University), John Widloski (UC Berkeley), Laurenz Wiskott (Ruhr-Universität Bochum), Noga Zaslavsky (Hebrew University of Jerusalem)