Temporal Dynamics in Learning: Networks and Neural Data
Recent developments in multielectrode recording methods and imaging have unveiled a wealth of data about the dynamics of neural circuits during behavior in animal model systems. In parallel, theoreticians have developed abstract network models that combine rich temporal dynamics with plastic synapses to produce powerful learning and discriminative mechanisms. This meeting brought together experimentalists and theoreticians in an attempt to compare abstract circuit models to experimental evidence from neural circuits in behaving animals. The goal was for participants to bridge the gap between theory and experiment by identifying principles of neural circuit operation that may subserve and promote adaptive behavior.
Organizers
Joshua Dudman, Janelia/HHMI
Timothy Gardner, Boston University
Alla Karpova, Janelia/HHMI
Joseph Paton, Champalimaud Neuroscience Programme
Invited Participants
Misha Ahrens, Janelia/HHMI
Dean Buonomano, University of California, Los Angeles
Stijn Cassenaer, California Institute of Technology
Mark Churchland, Columbia University
Rui Costa, HHMI/Champalimaud Center for the Unknown
Sophie Denève, Ecole Normale Supérieure
Michale Fee, Massachusetts Institute of Technology
Rainer Friedrich, Friedrich Miescher Institute for Biomedical Research
Stefano Fusi, Columbia University
Surya Ganguli, Stanford University
Michael Long, NYU Langone Medical Center
Eugene Lubenov, California Institute of Technology
Wolfgang Maass, Technische Universität Graz
Christian Machens, Champalimaud Centre for the Unknown
Zachary Mainen, Champalimaud Neuroscience Programme
Valerio Mante, Stanford University
Michael Mauk, University of Texas at Austin
Bence Ölveczky, Harvard University
Eva Pastalkova, Janelia/HHMI
David Sussillo, Stanford University
Jochen Triesch, J.W. Goethe University, Frankfurt
Xiao-Jing Wang, Yale University
