Machine Learning, Statistical Inference, and Neuroscience
May 6-9, 2012

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 will bring 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 Farm/HHMI
  
  Sean Eddy, Janelia Farm/HHMI
  
  Elena Rivas, Janelia Farm/HHMI

• INVITED PARTICIPANTS:

  Peter Bartlett University of California, Berkeley Sophie Deneve Ecole Normale Supérieure David Donoho Stanford University Brendan Frey University of Toronto Stuart Geman Brown University Zoubin Ghahramani University of Cambridge Philip Kegelmeyer Sandia National Laboratories Yann LeCun New York University

  Jun Liu Harvard University Bruno Olshausen University of California, Berkeley Stanley Osher University of California, Los Angeles Liam Paninski Columbia University Fernando Pereira Google Research Maneesh Sahani University College London Eero Simoncelli HHMI/New York University Haim Sompolinsky The Hebrew University

Back to Top