Machine Learning, Statistical Inference, and Neuroscience
The size and complexity of neural circuits, and of the experimental datasets collected for their study, motivate neuroscientists to study and foster advances in machine learning and statistical inference. Neuroscientists need both innovative new tools for large-scale data analysis, and a better formal understanding of learning and inference that could illuminate how neural circuits function. This meeting brought together creative researchers from a broad range of the machine learning and statistical inference community, including many from outside neuroscience, to discuss current research frontiers and perhaps to spark new ideas for applications to problems in neural circuit analysis.
Organizers
Mitya Chklovskii, Janelia/HHMI
Sean Eddy, Janelia/HHMI
Elena Rivas, Janelia/HHMI
Invited Participants
Kristin Branson, Janelia/HHMI
Aaron Clauset, University of Colorado, Boulder
Sophie Deneve, Ecole Normale Supérieure
Michael Elad, The Technion - Israel Institute of Technology
Brendan Frey, University of Toronto
Stuart Geman, Brown University
Elad Hazan, Technion - Israel Institute of Technology
Viren Jain, Janelia/HHMI
Philip Kegelmeyer, Sandia National Laboratories
Yann LeCun, New York University
Jun Liu, Harvard University
Partha Mitra, Cold Spring Harbor Laboratory
Bruno Olshausen, University of California, Berkeley
Stanley Osher, University of California, Los Angeles
Liam Paninski, Columbia University
Hanchuan Peng, Janelia/HHMI
Fernando Pereira, Google Research
Maneesh Sahani, University College London
Eero Simoncelli, HHMI/New York University
Haim Sompolinsky, The Hebrew University
