Vanderbilt Hall
NYU School of Law
40 Washington Square South
New York, NY 10012

Google Map

Badge pick-up hours: Friday 3pm – 7pm; Saturday/Sunday 7:15am – 5pm.

The meeting is supported by the NYU Global Institutes for Advanced Study, which provides free registration for anyone who works at a university or other non-profit organization. To be admitted free of charge, you must be registered and present a current photo ID from a qualifying organization. Other attendees are also welcome; the registration fee is $100.

Registration has now closed – the meeting has reached capacity. If you are on the wait list, or just wish to attend, you may attempt to register for a single session at a time, in the 30 minutes immediately preceding the start of the session. If space permits, we will be happy to accommodate you. See Will at the registration desk for more information.

Conference Program

Friday evening (Chaired by Anne Churchland)

7 pm — Session 1 — Brains and machines 

David Heeger, NYU – ORGaNICs: A theory of working memory in brains and machines

Eve Marder, Brandeis – Surprising robustness and reliability in neuronal circuits

Yoshua Bengio, Université de Montréal – Bridging the gap between deep learning and neuroscience


Saturday morning (Chaired by David Heeger)

8 am — Session 2 — Biological and artificial mechanisms

Blaise Agüera y Arcas, Google – Learning locally and globally

Mu-ming Poo, Chinese Academy of Sciences – Synaptic plasticity and brain-inspired machine learning

Terry Sejnowski, Salk Institute – The global brain

Coffee break

10.30 am — Session 3 — Action

Leslie Pack Kaelbling, MIT – Making robots behave

Daniel Wolpert, Cambridge – Probabilistic models of sensorimotor control

Matteo Carandini, UCL – Testing the textbook model of brain function

Lunch break

Saturday afternoon (Chaired by Matteo Carandini)

2 pm — Session 4 — Cognition

Stanislas Dehaene, Collège de France – What is consciousness, and could machines have it?

Anne Churchland, Cold Spring Harbor Laboratory – Assessing large scale cortical networks during decision-making

Matthew Botvinick, DeepMind – Meta-learning in brains and machines

Coffee break

4.30 pm — Session 5 — Navigating and Remembering

David Tank, Princeton – Characterizing neural dynamics during navigation and decision-making

Ila Fiete, University of Texas, Austin – Understanding and decoding the brain’s spatial navigation circuits

May-Britt Moser, Norwegian University of Science and Technology – Where, when and what—episodic memory in the hippocampus


Sunday morning (Chaired by Tony Movshon)

8 am — Session 6 — Vision 1

Yann LeCun, Facebook – What are the principles of learning in newborns?

Jim DiCarlo, MIT – Reverse engineering visual intelligence

Eero Simoncelli, NYU – Perceptual implications of hierarchical visual models

Coffee break

10.30 am — Session 7 — Vision 2

Adrienne Fairhall, University of Washington – Rules of adaptation across cortex

Nicole Rust, University of Pennsylvania – Adaptation as a canonical mechanism for memory

Shimon Ullman, Weizmann Institute of Science – Image understanding beyond object recognition

Lunch break

Sunday afternoon (Chaired by Anne Churchland)

2 pm — Session 8 — Learning 1

Kenji Doya, Okinawa Institute of Science and Technology – Neural circuits for reinforcement learning and mental simulation

Zach Mainen, Fundação Champalimaud – Serotonin and the regulation of neural inference and learning

Upi Bhalla, National Centre for Biological Sciences, Tata Institute of Fundamental Research – Molecular computation: the other deep network in the brain

 Coffee break

4.30 pm — Session 9 — Learning 2

Greg Corrado, Google – Practical intelligence and skeptical minds

Josh Tenenbaum, MIT – Building machines that learn and think like people

Larry Abbott, Columbia – Fly AI


Meeting theme

The fields of neuroscience and AI have undergone significant transformation over the last ten years, thanks in large part to a rapid expansion in technological capacities that are permitting ever more complex experiments and computations to be carried out. Brain-like computing is becoming common in artificial intelligence and machine learning. But can brains and machines think alike?

How will neuroscience and AI shape each other’s futures?

This is a crucial question as our understanding of the brain increases, and the AI in our devices and machines increasingly permeate society, business and the economy. To map the road ahead, this symposium will gather global leaders in both neuroscience and artificial intelligence. Speakers will not only describe their work, but also share their thoughts on the best path going forward. A preliminary program is above.