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 gathered global leaders in both neuroscience and artificial intelligence. Speakers not only described their work, but also shared their thoughts on the best path going forward.

(Click on any title below to link to video of the lecture)

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


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