Canonical Neural Computation

The GIAS on Canonical Neural Computation held a major public symposium: Canonical Computation in Brains and Machines, at NYU from March 16-18, 2018. The talks were recorded for the public, and can be found here. Additional information on the symposium can be found here.

The goal of neuroscience is to link the activity of the nervous system to the behavior of organisms. Studies of the nervous system result in measurements from many different scales of both space and time, ranging from the molecular to the whole organism, and from microseconds to lifetimes. Linking measurements at different levels to derive a comprehensive understanding can only be achieved through an understanding of the underlying neural computations. The goal of this GIAS brings focus to identifying key elements of neural computation that exist in multiple species and brain regions. Once identified, such “canonical” neural computations can be applied modularly in the brain to bring the same set of algorithms to bear on different problems.

A series of international meetings to promote identification of canonical neural computations began in 2009 at NYU’s Villa La Pietra. Global leaders working on different aspects of the problem were brought together to crystallize these goals. With the support of this Institute, follow-up workshops were held at La Pietra in 2012 and 2015, which developed these ideas; the report of the final workshop is appended. These workshops engaged 42 scholars from 27 major research institutions in 8 countries.

Project Members

Principal Investigators: Anthony Movshon (NYU); Matteo Carandini (UCL); David Heeger (NYU)

  • Larry Abbott (Columbia)

  • Dora Angelaki (Baylor)

  • Carlos Brody (Princeton),

  • Nathaniel Daw (NYU)

  • James Dicarlo (MIT)

  • Adrienne Fairhall (U. of Washington)

  • Michale Fee (MIT)

  • Benedikt Grothe (LMU Munich)

  • Kenneth Harris (University College, London)

  • Michael Hausser (University College, London)

  • Jennifer Linden (University College, London)

  • Wei Ji Ma (NYU)

  • Zach Mainen (Champalimaud Research)

  • Kevan Martin (Institute of Neuroinformatics)

  • David McCormick (Yale)

  • Ken Miller (Columbia)

  • Concetta Morrone (University of Pisa)

  • Anthony Norcia (Stanford)

  • John Reynolds (The Salk Institute)

  • Dario Ringach (UCLA)

  • Nicole Rust (U. of Pennsylvania)

  • Massimo Scanziani (UC San Diego)

  • Shihab Shamma (U. of Maryland)

  • Eero Simoncelli (NYU)

  • Sam Solomon (University College, London)

  • Duje Tadin (U. of Rochester)

  • Nachum Ulanovsky (Weizmann Institute of Science)

  • Xiao-Jing Wang (NYU)

  • Rachel Wilson (Harvard)