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CCBM2018 @UCgUhu1Ocn0tdPdaYpXhQXiw@youtube.com

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The fields of neuroscience and AI have undergone significant


37:42
Kenji Doya, OIST: Neural circuits for reinforcement learning and mental stimulation
43:42
Matteo Carandini, UCL: Testing the textbook model of brain function
33:44
Yoshua Bengio, Université de Montréal: Bridging the gap between deep learning and neuroscience
41:31
Leslie Pack Kaelbling, MIT: Making robots behave
39:12
Eve Marder, Brandeis: Surprising robustness and reliability in neuronal circuits
37:17
Terry Sejnowski, Salk Institute: The global brain
42:51
Matthew Botvinick, DeepMind: Meta-learning in brains and machines
35:28
Adrienne Fairhall, University of Washington: Rules of adaptation across cortex
42:21
Eero Simoncelli, NYU: Perceptual implications of hierarchical models
36:01
Anne Churchland, CSHL: Assessing large scale cortical networks during decision-making
36:01
Zachary Mainen, Fundação Champalimaud: Serotonin and the regulation of neural inference and learning
41:13
Upi Bhalla, NCBS, TIFR: Molecular computation: the other deep network in the brain
41:02
Josh Tenenbaum, MIT: Building machines that learn and think like people
37:41
Blaise Agüera y Arcas, Google: Learning locally and globally
36:12
Daniel Wolpert, Cambridge: Probabilistic models of sensorimotor control
42:37
Yann LeCun, Facebook: What are the principles of learning in newborns
39:50
Mu-ming Poo, Chinese Academy of Sciences: Synaptic plasticity and brain-inspired machine learning
35:34
Larry Abbott, Columbia: Fly AI
40:55
Jim DiCarlo, MIT: Reverse engineering visual intelligence
46:28
Shimon Ullman, Weizmann Institute of Science: Image understanding beyond object recognition
39:35
Ila Fiete, UT Austin: Understanding and decoding the brain's spatial navigation circuits
41:12
David Heeger, NYU: ORGaNICs: A theory of working memory in brains and machines
41:22
Greg Corrado, Google: Practical intelligence and skeptical minds
37:35
Stanislas Dehaene, Collège de France: What is consciousness and could machines have it?