The goal of neuroscience is to link the activity of the nervous system to the behavior of organisms. Studies of the nervous system are done at 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 seek a comprehensive understanding can only be achieved through an understanding of the underlying computation. The goal of this GIAS has to identify key elements of neural computation that exist in multiple species and brain regions. Such “canonical” neural computations can be applied modularly in the brain to bring the same set of algorithms to bear on different problems.
The principal organizers of this GIAS are Matteo Carandini (University College London), and David Heeger and Tony Movshon (NYU).
To bridge the study of brain circuits and behavior, we believe that it is essential to identify and characterize an intermediate level: the level of computation. If one understands the computations that are being performed by – and that result in the activity of – neurons and populations of neurons, one can then break the big problem in two: (1) how do circuits perform those computations and (2) how do those computations give rise to behavior.
The aim of the Institute was to run a series of international meetings to promote further study of these canonical neural computations, by bringing together key researchers working on different aspects of the problem. We established the precedent from a highly successful meeting at NYU’s Villa La Pietra in 2009 that crystallized many of these goals. With the support of this Institute, we held follow-up workshops 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.
To complete the work of the GIAS and to bring its conclusions to a wider audience, we are organizing a 3 day capstone international conference to be held in New York in the spring of 2018.
Principal Investigators: Anthony Movshon (NYU); Matteo Carandini (NYU); 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)
- Michale 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)