AI and Brain Science
To make intelligent machines by electronics,
we should not bother biological constraints.
Thereʼs a superb implementation of intelligencein the brain, so why donʼt we learn from that.
AI in 20th century: program human expertise
AI in 21st century: learn from big data
Brain-like implementation like Deep Learning
gives the best performance.
2

calculated the timefrom the number ofndow of 50 ms, andh the x2 test13 (seeation analysis for theon coded ‘significant’e earliest information
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peaks before levelling off. F1 (monkey identity) and F4 (humanexpression) were encoded at a much lower level than the others. The
response latency of this neuron was 53 ms, and the latencies for theinformation about each category were as follows: G, 45; F1, 157; F2,93; F3, 125; and F4, 125 ms. Thus, the earliest transient discharge
conveyed global information, and the later sustained discharge
579
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encoded one category of the fine information
found by changing the size, shape and orientation of the stimulus until a clear
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Of the 86 face-responsive neurons,responses
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encode significant information in any
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either global or fine category information, and 32 (37%) coded both+7 both global!andcategories. To clarify how single neurons encoded

Coevolution in Pattern Recognition

Brain Science

Feature detectors(Hubel & Wiesel 1959)
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Fig. 3. Same unit as in Fig. 2. A, responses to shinling a rectangular light spot, 1° x 8° ; centre ofslit superimposed on centre of receptive field; successive stimuli rotated clockwise, as shownto left of figure. B, responses to a 1° x 5° slit oriented in various directions, with one end
always covering the centre ofthe receptive field: note that this central region evoked responses
when stimulated alone (Fig. 2 a). Stimulus and background intensities as in Fig. 1; stimulus(Blakemore & Cooper 1970)

duration 1 sec.

centre of the field. The findings thus agree qualitatively with those obtainedwith a small spot (Fig. 2 a).
Receptive fields having a central area and opposing flanks represented aD

common pattern, but several variations were seen. Some fields had long narrow
central regions with extensive flanking areas (Figs. 1-3): others had a large
central area and concentrated slit-shaped flanks (Figs. 6, 9, 10). In many
fields the two flanking regions were asymmetrical, differing in size and shape;in these a given spot gave unequal responses in symmetrically corresponding

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www.brain-ai.jp

4

Concept• Recent advances in “deep learning” realized artificial intelligence (AI)that surpasses humans in certain tasks like visual object recognition
and game playing.
• Today’s AI, however, still lacks the versatility and flexibility of human
intelligence, which motivates AI researchers to learn brain’s working
principles.
• Neuroscientists also need helps of AI in making sense of massive datafrom sequencing, imaging, and so forth.
• The aim of this symposium is to bring together researchers advancing
the forefront of AI and neuroscience to identify next targets increating brain-like intelligence and further advancing neuroscience.

Questions
1. What can we further learn from the brain for next-generation AI?
2. How AI can help bring neuroscience to the next stage?
3. How can fusion of AI and neuroscience affect our society?

Saturday October 10th
13:00 (UTC) Get Connected
13:15 (UTC) Greeting from Kenji Doya
13:30 (UTC) Keynote Lecture
Josh Tenenbaum : Building Machines That See, Think and Learn Like People
14:00 (UTC) Discussion on Session 1 Talks: Deep Learning and Reinforcement Learning
Chair: Jun Morimoto, Discussant: Shun-ichi Amari
Yann LeCun: Self-Supervised Learning
Yutaka Matsuo: World Model for Perception, Control and Language
Doina Precup: Fast Reinforcement Learning with Generalized Policy Updates
David Silver: Deep Reinforcement Learning from AlphaGo to AlphaStar
Masashi Sugiyama: Recent Advances in Robust Machine LearningSaturday October 11th
13:00 (UTC) Discussion on Session 2 Talks: World Model Learning and Inference
Chair: Hiroaki Gomi, Discussant: Mitsuo Kawato
Ila Fiete: Mixed Modular Codes and Remapping for Highly Generalizable Learning and Inference
Karl Friston: Active Inference and Artificial Curiosity
Yukie Nagai: Cognitive Development Based on Predictive Coding
Maneesh Sahani: How Do Neural Systems Learn to Infer?
Tadahiro Taniguchi: Symbol Emergence in Robotics: Pursuing Integrative Cognitive Architectureusing Probabilistic Generative Models for Real-world Language Acquisition14:00 (UTC) Discussion on Session 3 Talks: Metacognition and Metalearning
Chair: Masayuki Matsumoto, Discussant: Keiji Tanaka
Matthew Botvinick: Object-oriented deep learning
Ryota Kanai: Consciousness and Intelligence
Angela Langdon: Model-based Reward Prediction: Algorithms for Learning to Represent a Task
Hiroyuki Nakahara: Neural Computations for Making Decisions with Others’ Rewards andDecisions
Xiao-Jing Wang: Learning to Learn and the Brain

Monday October 12th
13:00 (UTC) Discussion on Session 4 Talks: AI for Neuroscience and Neuromorphic Technologies
Chair: Takatoshi Hikida, Discussant: Kunihiko Fukushima
James J. DiCarlo : Reverse Engineering Visual Intelligence
Yukiyasu Kamitani : The Free Energy Principle and Active Inference in Silico and in Vivo, Visual
Sampling and ‘World Model’ Building
Rosalyn Moran : Cognitive Development Based on Predictive Coding
Terrence Sejnowski : Artificial Intelligence Meets Human Intelligence
Hidehiko Takahashi : Interface between AI and Psychiatry Research
14:00 (UTC) Discussion on Session 5 Talks: Social Impact and Neuro-AI Ethics
Chair: Masamichi Sakagami
Anne Churchland : Single-trial Neural Dynamics are Dominated by Richly Varied MovementsKenji Doya : Toward the Society of AI Agents: What Should We Learn from the Brain and HumanSocietyArisa Ema : Interpretative Flexibility’ in AI and Neuroscience ResearchHiroaki Kitano : Nobel Turing Challenge : A Grand Challenge on AI for Scientific Discovery
Stuart Russell : Brains, Circuits, and Things

Day 1Present• First-principle RL with DNN
can beat human wisdom• Infants start with intuitive
physics/psychology andhack awesome programs• Animal OS + language App• Successor coding allows
zero-shot learning• Weakly supervised learningFuture• Unified theory of DL?
• Self-supervised learning forreconfigurable single model?
• AI to discover algorithms

Day 2Present• Free energy for curiosity• Hypo/hyper prior for autism• Grid-like code, distributed
distributional code
• Joining multi-modal models• Global workspace• Rats/RNNs learn latent
structures• Otherʼs value in rAIFuture• Internal models in cerebellum,hippocampus and cortex• Hierarchy by minimizing
complexity?
• Metalearning without metaagent?
• Metacognition agent orcommittee of agents?

Day 3Present• V1-based coding makes
DNN more robust• DNNs help decode images
and thought disorders• Free-energy principle
explains neuromodulation• Trained NNs to spike• Cortical activity is full ofuninstructed behaviors• Systems biology is science
for AI without cognitive bias
• Risks of AI and neurotech

Future• Single, collaborative grandchallenge for visual intelligence• AI scientists need not ask theright question?
• Provably beneficial AI byinferring human preference• Should AI agents learn fromhuman society/brain?

Special Issue on
Artificial Intelligence and Brain Science• Timeline• www.journals.elsevier.com/neural-networks/• Manuscript submission due: January 10, 2021
• First review completed: March 31, 2021
• Revised manuscript due: May 31, 2021
• Final decisions to authors: June 30, 2021

• Guest Editors:• Kenji Doya, Okinawa Institute of Science and Technology (ncus@oist.jp)• Karl Friston, University College London• Masashi Sugiyama, RIKEN AIP and The University of Tokyo IRCN
• Josh Tenenbaum, Massachusetts Institute of Technology
13

Neuro 2022
Japanese Neuroscience/Neurochemistry/Neural Network Societies
June 30–July 3, 2022 in Okinawa Convention Center14

Program
Saturday, October 10th(Time in UTC)
13:00 Get connected
13:15 Greetings: Kenji Doya (OIST, AIBS)
13:30 Keynote• Josh Tenenbaum (MIT)14:00 Session 1: Deep Learning andReinforcement Learning
Chair: Jun Morimoto (ATR, AIBS)
Discussant: Shunichi Amari (RIKEN CBS)
Speakers:• Yann LeCun (NYU, Facebook)• Yutaka Matsuo (U Tokyo, AIBS)• Doina Precup (McGill U)• David Silver (DeepMind)• Masashi Sugiyama (RIKEN AIP,U Tokyo IRCN)

Sunday, October 11th
13:00 Session 2: World Model Learning
and Inference
Chair: Hiroaki Gomi (NTT, AIBS)
Discussant: Mitsuo Kawato (ATR)
Speakers:• Ila Fiete (MIT)• Karl Friston (UCL)• Yukie Nagai (U Tokyo IRCN)• Maneesh Sahani (Gatsby Unit)• Tadahiro Taniguchi (Ritsumeikan U, AIBS)

Monday, October 12th
13:00 Session 4: AI for Neuroscienceand Neuromorphic Technologies
Chair: Takatoshi Hikida (Osaka U, AIBS)
Discussant: Kunihiko Fukushima (FLSI)
Speakers:• Jim DiCarlo (MIT)• Yukiyaku Kamitani (Kyoto U)• Rosalyn Moran (King’s College London)• Terry Sejnowski (Salk Institute)• Hidehiko Takahashi (Tokyo MDU, AIBS)

14:00 Session 3: Metacognition andMetalearning
Chair: Masayuki Matsumoto (Tsukuba U, AIBS)
Discussant: Keiji Tanaka (RIKEN CBS)
Speakers:• Matthew Botvinick (DeepMind)• Ryota Kanai (ARAYA)• Angela Langdon (Princeton U)• Hiroyuki Nakahara (RIKEN CBS, AIBS)• Xiao-Jing Wang (NYU)

14:00 Session 5: Social Impact andNeuro-AI Ethics
Chair: Masamichi Sakagami (Tamagawa U, AIBS)
Speakers:• Anne Churchland (UCLA)• Kenji Doya (OIST, AIBS)• Arisa Ema (U Tokyo IFI)
• Hiroaki Kitano (SONY CSL)
• Stuart Russell (UC Berkeley)Co-organizers and Sponsors• Kenji Doya (OIST, AIBS)• Arisa Ema (IFI)• Karl Friston (UCL)• Takao Hensch (Harvard U, IRCN)• KAKENHI Project on Artificial
Intelligence and Brain Science (AIBS)• RIKEN Center for AdvancedIntelligence Project (AIP)• The University of Tokyo Institute for• Yutaka Matsuo (U Tokyo, AIBS)
Future Initiatives (IFI)• Masamichi Sakagami (Tamagawa U, AIBS) • The University of Tokyo
International Research Center for• Masashi Sugiyama (RIKEN AIP, IRCN)
Neurointelligence (IRCN)• Tadahiro Taniguchi (Ritsumeikan U, AIBS)• Josh Tenenbaum (MIT)Corporate Sponsors

StaffU Tokyo• Toyoko Nagamoto• Yu SasakiOIST• Hitomi Miyazato• Tadashi Kozuno

Ritsumeikan U• Machiko Maeda• Misuzu Saito• Emiko Asato• Kikuko Matsuo