FY2017 Annual Report

Neural Computation Unit
Professor Kenji Doya

Group Photo

Abstract

The Neural Computation Unit pursues the dual goals of developing robust and flexible learning algorithms and elucidating the brain’s mechanisms for robust and flexible learning. Our specific focus is on how the brain realizes reinforcement learning, in which an agent, biological or artificial, learns novel behaviors in uncertain environments by exploration and reward feedback. We combine top-down, computational approaches and bottom-up, neurobiological approaches to achieve these goals.

In FY2017, we continued research along three major externally funded projects: Kakenhi project on Artificial Intelligence and Brain Science, Brain/MINDS project, and Post-K Supercomputing project on Brain and Artificial Intelligence.

In the Kakenhi project on Artificial Intelligence and Brain Science, in oder to explore what we can learn from the brain for next-generation AI, we organized a joint workshop in London with researchers from Gatsby Computational Unit and DeepMind and also symposia at JNSS conference in Yokohama and JNNS conferenve in Fukuoka.

In Japan's major brain science project, Brain/MINDS, we developed neural data analysis pipelines and modeling methodologies in close collaboration with researchers at RIKEN and Kyoto University.

In the Post-K supercomputing project on Brain and Artificial Intellignece, we built biologically constrained spiking neural network model of the basal ganglia circuit and started to integrate that with the thalamo-cortical network model constructed by our collaboratos at RIKEN for large-scale simulations.

1. Staff

Systems Neurobiology Group

  • Katsuhiko Miyazaki, Staff Scientist
  • Kayoko Miyazaki, JSPS Research Fellow
  • Kazumi Kasahara, JSPS Research Fellow
  • Yukako Yamane, JSPS Research Fellow
  • Yuzhe Li, Postdoctoral Scholar
  • Tomohiko Yoshizawa, Technician
  • Zeng Jiafu, Technician
  • Sergey Zobnin, OIST student
  • Masakazu Taira, OIST student

Dynamical Systems Group

  • Carlos Enrique Gutierrez, Postdoctoral Scholar
  • Hiromichi Tsukada, Postdoctoral Scholar
  • Hiroaki Hamada, OIST Student
  • Jessica Verena Schulze, OIST Student
  • Junichiro Yoshimoto, Visiting Researcher
  • Ildefons Magrans, Visiting Researcher

Adaptive Systems Group

  • ​​Marie-Lou Barnaud, Postdoctoral Scholar
  • Shoko Igarashi, OIST Student
  • Qiong Huang, OIST Student
  • Chris Reinke, OIST Student
  • Farzana Rahmen, OIST Student
  • Tadashi Kozuno, OIST Student
  • Paavo Parmas, OIST Student
  • Jiexin Wang, Visiting Researcher

Research Administrators

  • Emiko Asato
  • Kikuko Matsuo
  • Misuzu Saito

 

2. Collaborations

  • Dr. Charles Gerfen, NIH: Cell-type selective calcium imaging of striatal neurons
  • Prof. Kenji Tanaka and Prof. Norio Takata, Keio University: Optogenetic stimulation of serotonin neurons and awake functional MRI mesurement in mice
  • Prof. Shigeo Yamawaki and Prof. Yasumasa Okamoto, Hiroshima University: Diagnosis and sub-type idenfication of depression patients from fMRI and other multi-dimensional data.

3. Activities and Findings

3.1 Neurobiology Experiments [Systems Neurobiology Group]

3.1.1 State value coding by striosome neurons [Kakenhi Project on AI and Brain Science]

The striatum consists of the striosome (patch) compartment, which projects to the dopaminergic neurons of the substantia nigra pars compacta (SNc), and the matrix compartment projecting to the pallidum. We previously hypothesized that the striosome compartment represents the state value, whereas the matrix represents the action value in reinforcement learning (Doya, 2000). To test this hypothesis, we conducted endoscopic in vivo calcium imaging of transgenic mice with selective GCaMP6s expression in the striosome neurons. As mice learned odor-based classical conditioning task, we found reward predictive neural activities, in consistence with the hypothesis (Yoshizawa et al. 2018). 

3.1.2 Neural substrate of dynamic Bayesian inference [Kakenhi Project on AI and Brain Science]

We designed a new experimental paradigm to address how predictive information from actions and sensory information through environmental interaction are integrated across different layers of the  somatosensory and motor cortical circuits in mice. We developed a novel active level manipulation tasks for head-fixed mice and prepared for simultaneous cross-layer calcium imaging by a prism lens.

3.1.3 The role of serotonin in the regulation of patience [Kakenhi Project on AI and Brain Science] 

In order to clarify the role of serotonin in behaviors, we performed neural recording, microdialysis measurement, and optogenetic manipulation of serotonin neural activity from the dorsal raphe nucleus (DRN), the major source of serotonergic projection to the cortex and the basal ganglia. We had found that optogenetic stimulation of DRN serotonin neurons prolonged the time animals spent for waiting for reward (Miyazaki et al., 2016). We further found that serotonin’s effect on waiting was seen only when reward probability was high, and more effective when reward timing was uncertain. We are constructing a Bayesian decision model to reproduce the experimental results.

3.2. Neural Data Analysis and Modeling [Dynamical Systems Group]

3.2.1 Subtype identification of depression from multidimensional data [Strategic Program in Brain Science Research]

For data-driven identification of subtypes of depression, we dveloped a novel multiple co-clustering algorith (Tokuda et al., 2017). We are applying this method to high-dimensional data including resting-state functional MRI data, blood markers, genetic polymorphism, and clinical questionnaires, obtained by our collaborators at the department of psychiatry, Hiroshima University School of Medicine.

3.2.2 Analysis and modeling of marmoset brain data [Brain/MINDS Project]

We develop data-analysis pipelines and modeling methods to utilize the variety of marmoset brain data obtained in the Brain/MIDNS program. We have developed Bayesian synaptic connectivity inference algorithms, a framework for optimizing diffusion MRI-based fiber-tracking algorithm in reference to neural tracer data, and a framework to integrate the structural connectivity based on diffusion MRI data and the resting-state functional MRI data.

3.2.3 Large-scale spiking network models of the basal ganglia [Post-K Supercomputing Project on Brain and Artificial Intelligence]

We constructed an integrate-and-fire neural network model of the basal ganglia circuit based on the anatomical and physiological data and explored the mechanisms for action selection. We further connected the basal ganglia model with the thalamo-cortical network model constructed by our collaborators at RIKEN.

3.3 Robotics and Reinforcement Learning [Adaptive Systems Group]

3.3.1 Data-efficient reinforcement learning [Kakenhi Project on AI and Brain Science]

While the combination of reinforcement learning with deep neural networks has shown remarkable performance, the requirement of huge data for training is a major issue in robotic applications. We developed the Expected Energy-based Restricted Boltzmann Machine (EE-RBM) to approximate the action value function and showed its excellent performance in benchmarks of tetris and Atari games. The study also lead us to propose sigmoid-weighted linear units (SiLU) as a general tool in deep neural networks (Elfwing et al., 2018). We also formulated a class of approximate value iteration algorithms and mathematically showed their data efficiency and robustness to approximation errors. We are also investigating the issues in gradient computation in model-based reinforcement learning.

3.3.2 Deep inverse reinforcement learning by logistic regression [Kakenhi Project on AI and Brain Science]

Inverse reinforcement learning (IRL) is a method of estimating a reward function by observing an agent's behavior. We formulated IRL as a binary logistic regression problem and derived an efficient algorithm that is applicable to deep learning networks (Uchibe, 2017).

 

4. Publications

4.1 Journals

  1. Elfwing, S., Uchibe, E., & Doya, K. (2018). Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Networks, 107, 3-11. doi:10.1016/j.neunet.2017.12.012
  2. Kasahar, K., et al. (2018). Initial experience with a sensorimotor rhythm-based brain-computer interface in a Parkinson’s disease patient. Brain-Computer Interfaces. doi:10.1080/2326263X.2018.1440781
  3. Magrans de Abril, I., Yoshimoto, J., & Doya, K. (2018). Connectivity inference from neural recording data: Challenges, mathematical bases and research directions. Neural Networks, 102, 120-137. doi:10.1016/j.neunet.2018.02.016
  4. Tokuda, T., Yoshimoto, J., Shimizu, Y., Okada, G., Takamura, M., Okamoto, Y., Yamawaki., & Doya, K. (2017). Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions. PLoS ONE, 12(10), e0186566. doi:10.1371/journal.pone.0186566
  5. Uchibe, E. (2017). Model-free deep inverse reinforcement learning by logistic regression. Neural Processing Letters. doi:10.1007/s11063-017-9702-7
  6. Yoshida, K., Shimizu, Y., Yoshimoto, J., Takumura, M., Okada, G., Okamoto, Y., Yamawaki, S., & Doya, K. (2017). Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression. PLoS ONE. doi:10.1371/journal.pone.0179638
  7. Yoshizawa, T., Ito, M., & Doya, K. (2018). Reward-predictive neural activities in striatal striosome compartments. eNeuro, 5(1). doi:10.1523/ENEURO.0367-17.2018

4.2 Books and other one-time publications

Nothing to report

4.3 Oral and Poster Presentations

  1. Doya, K. (2017). Artificial Intelligence and Brain Science. Kyungpook National University, Daegu, Korea.
  2. Doya, K. (2017). BRAINｘBayes. Wako-shi, Saitama: AI&Brain Summer School.
  3. Doya, K. (2017, 2017.07.21). Exploring the deep brain network for reinforcement learning, Makuhari Messe, Chiba.
  4. Doya, K. (2017). Imaging the neural circuit for mental simulation. Osaka.
  5. Doya, K. (2017). Modeling the functions and dysfunctions of the basal ganglia and testing them experimentally. 32nd Japan Basal Ganglia Society：JBAGS2017. Nishio, Aichi.
  6. Doya, K. (2017, 2017.07.20). Multi-scale neural data fusion and network simulation, Makuhari, Chiba.
  7. Doya, K. (2017, 2017.12.14). Neural circuits for reinforcement learning and mental simulation, Queensland Brain Institute, The University of Queensland, Brisbane, Australia.
  8. Doya, K. (2017, 2017.07.24). Neural coding, brain imaging and information extraction by circuit modeling, University of Tokyo, Tokyo.
  9. Doya, K. (2017). Neural mechanisms of mental simulation. Okinawa Convention Center, Ginowan, Okinawa: 32nd Annual Research Meeting of the Japanese Orthopaedic Association.
  10. Doya, K. (2017, 2017.08.16-18). <Talk1>Reinforcement learning: basic concepts and recent advances, Beijing Institute of Technology, China.
  11. Doya, K. (2017, 2017.08.16-18). <Talk2>Neural mechanisms of reinforcement learning and mental simulation, Beijing Institute of Technology.
  12. Doya, K. (2017). What can we further learn from the brain? , Guangzhou, China.
  13. Doya, K. (2017). What should we further learn from the brain? Kyungpook National University, Daegu, Korea.
  14. Doya, K. (2017, 2017.07.07). What should We further Learn from the Brain? , NYU Shanghai, Shanghai, China.
  15. Doya, K. (2018). How does the brain wire up itself on the fly? Institute for Advanced Study, Princeton, USA.
  16. Doya, K. (2018, 2018.03.05). Imaging the neural circuit for mental simulation, Denver City, USA.
  17. Doya, K. (2018). Neural Circuit for Mental Simulation. Princeton Neuroscience Institute, USA.
  18. Doya, K. (2018). Neural circuits for reinforcement learning and mental simulation. Cold Spring Harbor Laboratory, USA.
  19. Doya, K. (2018, 2017.03.18). Neural circuits for reinforcement learning and mental simulation, New York University, USA.
  20. Doya, K. (2018). Neural circuits for reinforcement learning and mental simulation. Gold Hall and Ruby Hall, WellyHilly park, South Korea: Brain and AI Symposium.
  21. Doya, K. (2018, 2018.01.29). What should we further learn from the brain?, WellyHilly park, South Korea.
  22. Girard, B., Heraiz-Bekkis, D., & Doya, K. (2017). A spiking model of the monkey basal ganglia without segregated pathways, but with emerging selection capabilities and Parkinson-like oscillations. Bordeaux, France: 7th International Symposium on Biology of Decision Making (SBDM).
  23. Gutierrez, C., Skibbe, H., Nakae, K., Woodward, A., Watakabe, A., Hata, J., Okano, H., Yamamori, T., Yamaguchi, Y., Ishii, S., Doya, K. (2018). Ga-based parameter optimization of DWI-based global fiber tracking with neuronal tracer signal as a reference (pp. 174). Denver City, USA: COSYNE2018 Computational and Systems Neuroscience (Cosyne)
  24. Gutierrez, C. E. (2017). Parameters Exploration of DWI-based Global Fiber Tracking with Neuronal Tracer Signal as References. RIKEN Institute, Wako, Saitama, Japan: Advances in Neuroinformatics AINI 2017.
  25. Hamada, H., Shimizu, Y., Zeng, J., Hikishima, K., Takata, N., Abe, Y., Tanaka, F., Doya, K. (2017). Short-term and long-term serotonergic medication of intrinsic functional connectivity. the Walter E. Washington Convention Center, Washington, USA: Society for Neuroscience 2017.
  26. Hamada, H., Zeng, J., Shimizu, Y., Hikishima, K., Takata, N., Tanaka, F. K., & Doya, K. (2017). Serotonin control of brain-wide functional network through blockade of serotonin transporter. CHÂTERAISÉ Gateaux Kingdom, Sapporo, Hokkaido: The 44th Naito Conference.
  27. Ito, M., & Doya, K. (2017, 2017.08.01-05). Information Coded in the Striatum During Decision-Making, Spain, Carmona (Seville).
  28. Katsuhiko, M. (2018, 2018.03.28). The role of serotonin in regulating reward waiting behavior-verification by optogenetics-, OIST Seaside House, Onna-son Okinawa.
  29. Kozuno, T. (2017). Robust and Efficient Off-Policy Policy Evaluation via Enhanced Action-Gap. The University of Tokyo, Bunkyo Disctirct, Tokyo: The 20th Information-Based Induction Sciences Workshop.
  30. Miyahara, K., Niikawa, T., Hamada, H., & Nishida, S. (2017). Phenomenological training in the wood of illusions: A methodological proposal for neurophenomenology. Beichen Dong Road, Chaoyang District, Beijing: The 21st Annual Meeting of the Association for the Scientific Study of Consciousness (ASSC).
  31. Miyazaki, K. (2018). The role of serotonin in regulating reward waiting behavior-verification by optogenetics-.  Serotonin Research Meeting. TKP garden city Shinagawa, Japan.
  32. Parmas, P., Peters, J., & Doya, K. (2017). The optimal-baseline estimator is not the optimal baseline-estimator. Tokyo, Japan: Information-based induction sciences workshop (IBIS)　
  33. Reinke, C. (2018, 2018.03.28). The gamma-ensemble - adaptive reinforcement learning via modular discounting, OIST Seaside House, Onna-son Okinawa.
  34. Reinke, C., & Doya, K. (2017). Adaptation of Optimization Algorithms to Problem Domains by Transfer Learning. OIST: 2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS).
  35. Reinke, C., Uchibe, E., & Doya, K. (2017, 2017.10.24). Average Reward Optimization with Multiple Discounting Reinforcement Learners. Paper presented at the ICONIP (Lecture Notes in Computer Science).
  36. Reinke, C., Uchibe, E., & Doya, K. (2017). Fast Adaptation of Behavior to Changing Goals with a Gamma Ensemble. University of Michigan, Ann Arbor, Michigan, USA: 3rd Multidisciplinary conference on reinforcement learning and decision making.
  37. Tsukada, H. (2018, 2018.03.28). whole-brain model and dynamical analysis based on common marmoset MRI data in Brain/MINDS project, OIST Seaside House, Onna-son Okinawa.
  38. Tsukada, H., Hamada, H., Gutierrez, E. C., Nakae, K., Henrik, S., Ishii, S., Alexander, W., Hata, J., Okano, H., Doya, K. (2017). Effect of Local Excitatory-Inhibitory Connection Balance in Reproducing Whole-Brain Functional Connectivity. Okochi Hall, RIKEN 2-1 Hirosawa, Wako, Saitama, JAPAN: Advances in Neuroinformatics (AINI) 2017.
  39. Tsukada, H., Hamada, H., Nakae, K., Ishii, S., Hata, J., Okano, H., & Doya, K. (2017, 2017.08.01-05). Analysis of structure-function relationship using a whole-brain dynamic model based on MRI images of the common marmoset, Spain, Carmona (Seville).
  40. Tsukada, H., Tsukada, M., & Isomura, Y. (2017, 2017.08.01-05). A structure and function of hippocampal memory networks in consolidating spatiotemporal contexts, Spain, Carmona (Seville).
  41. Uchibe, E. (2017). Deep forward and inverse reinforcement learning. Mini Workshop. 4th Mini Workshop on Brain-Inspired Artificial Intelligence and its Applications. Advanced Telecommunications Research Institute International, Seika-cho, Soraku-gun, Kyoto.
  42. Uchibe, E. (2017). Forward and inverse reinforcement learning using deep neural networks. 27th Japanese Neural Network Society Meeting. West Japan Industry and Trade Convention Association, Kita Kyushu, Fukuoka, Japan.
  43. Uchibe, E. (2017, 2017.6.11-14). Model-free deep inverse reinforcement learning by logistic regression, University of Michigan, Ann Arbor, Michigan, USA.
  44. Yoshizawa, T. (2018, 2018.03.28). Reward-predictive neural activities in striatal striosome compartments, OIST Seaside House, Onna-son Okinawa.
  45. Yoshizawa, T., Ito, M., & Doya, K. (2017). Coding of value information in the striatal striosome compartment. CHÂTERAISÉ Gateaux Kingdom SAPPORO, Japan: 44th Naito Conference.
  46. Yoshizawa, T., Ito, M., & Doya, K. (2017). Neural representation of sensory-state value in the striatal striosome compartment. Walter E. Washington Convention Center, Washington DC, USA: Society for Neuroscience 47th Annual Meeting.
  47. Yoshizawa, T., Ito, M., & Doya, K. (2018). Cell-type specific calcium imaging of striatal neurons in the striosome compartments during an odor-conditioning task. Four Points Sheraton/Holiday Inn Express, Ventura, CA, US: Gordon Research Conference.
  48. Zobnin, S., Li, Y., & Doya, K. (2017). Experimental investigation of hierarchical Bayesian inference in sensory and motor cortices. National Center of Science, Tokyo, Japan: KAKENHI 3rd Research Area Meeting “Artificial Intelligence and Brain Science”.
  49. Zobnin, S., Li, Y., & Doya, K. (2018). Experimental investigation of hierarchical Bayesian inference in sensory and motor cortices. Rusutsu, Hokkaido: 18th Winter Workshop on Mechanism of Brain and Mind 2018.

5. Intellectual Property Rights and Other Specific Achievements

  • Uchibe E, Doya K: Direct inverse reinforcement learning with density ratio estimation. PCTJP2017004463, US15425924, EP17766134.

6. Meetings and Events

6.1 Seminars

2D reaching motor adaptation mediated by basal ganglia

  • Date: June 6, 2017
  • Venue: OIST Campus Lab1
  • Speaker: Dr. Dmitrii Todorov, Georgia State University

Dopaminergic memory modulation by two distinct novelty systems

  • Date: August 28, 2017
  • Venue: OIST Campus Lab1
  • Speaker: Dr. Tomonori Takeuchi,  Centre for Cognitive and Neural Systems, The University of Edinburgh

Optically-driven Nano Robotics and Chemical IC Chips by 3D Micro/Nano Fabrication

  • Date: September 8, 2017
  • Venue: OIST Campus Lab1
  • Speaker: Dr. Koji Ikuta,  Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo

Digitization of reproducible data analysis workflows

  • Date: November 1, 2017
  • Venue: OIST Campus Lab1
  • Speaker: Marie-Lou Barnaud, GIPSA-lab, Grenoble, France · Speech and Cognition.

A motor readout of visual perception: Deciphering cuttlefish camouflage at single-chromatophore resolution

  • Date: December 21, 2017
  • Venue: OIST Campus Lab1
  • Speaker: Dr.Samuel Reiter,  Max Planck Institute for Brain Research

Systems Engineering Techniques for Visual Neuroscience

  • Date: February 8, 2018
  • Venue: OIST Campus Center Building
  • Speaker: Dr.Alireza Ghahari, National Eye Institute, NIH

6.2 Events

Gatsby-Kakenhi Joint Workshop on AI and Neuroscience

  • Date: May 11-12, 2017
  • Venue: Gatsby Computational Neuroscience Unit, UCL, London
  • Co-Sponsors:
    • Gatsby Computational Neuroscience Unit, UCL
    • KAKENHI Project on Artificial Intelligence and Brain Science
    • RIKEN Center for Advanced Intelligence Project
    • Whole Brain Architecture Initiative
    • MEXT Supercomputing Project on Brain and Artificial Intelligence
  • Speakers:
    • Masashi Sugiyama (RIKEN AIP/U Tokyo)
    • Shakir Mohammed (Deepmind)  
    • Daisuke Okanohara (Preferred Networks)
    • Arthur Gretton (Gatsby Unit)
    • Jun Morimoto (ATR)  
    • Peter Dayan (Gatsby Unit)  
    • Hidehiko Takahashi(Kyoto University Graduate School of Medicine)
    • Maneesh Sahani (Gatsby Unit)          
    • Shinji Nishimoto (CiNet)    
    • Aapo Hyvarinen (Gatsby Unit)
    • Kenji Doya (OIST)                
    • Matthew Botvinick (Deep Mind)      
    • Masamichi Sakagami (Tamagawa U)
    • Zhaoping Li (Gatsby Unit)      
    • Hiroyuki Nakahara (RIKEN BSI)  
    • Shane Legg (DeepMind)              
    • Tadahiro Taniguchi (Ritsumeikan U)  
    • Karl Friston (UCL)                
    • Hiroshi Yamakawa (Whole Brain Architecture Initiative)

The 2nd Meeting of KAKENHI Project on Artificial Intelligence and Brain Science

  • Date: May 20,  2017
  • Venue: Center for Information and Neural Networks (CiNet)
  • Sponsor: KAKENHI Project on Artificial Intelligence and Brain Science

ISSA Summer School 2017

  • Date: May 22- June 2,  2017
  • Venue: Center for Information and Neural Networks (CiNet)
  • Co-Sponsors:
    • Center for Information and Neural Networks(CiNet) (director: Toshio Yanagida)
    • CREST Artificial Consciousness Project (research director: Ryota Kanai)
    • CREST Social Neuroscience Project (research director: Masahiko Haruno)
    • CREST Cognitive Mirroring Project (research director: Yukie Nagai)
    • Earth-Life Science Institute (ELSI), Tokyo Institute of Technology
    • KAKENHI Artificial Intelligence and Brain Science Project (head investigator: Kenji Doya)
    • KAKENHI Mental Time Project (head investigator: Shigeru Kitazawa)
    • Okinawa Intitute of Science and Technology (OIST)
    • YHouse (executive director: Piet Hut)
  • Speakers:
    • Naotsugu Tsuchiya (Monash U)
    • Ryota Kanai (ARAYA Brain Imaging, YHouse)
    • Larissa Albantakis (U Wisconsin-Madison)
    • Dan Zahavi (U Copenhagen)
    • Jun Tani (KAIST)
    • Shinji Nishimoto (CiNet)
    • Kaoru Amano (CiNet)
    • Noriko Yamagishi (CiNet)
    • Kenji Doya (OIST)
    • Yukie Nagai (CiNet)
    • Hiroshi Ishiguro (Osaka U, ATR)
    • Mariko Osaka (CiNet)
    • Piet Hut (Institute for Advanced Study, ELSI, YHouse)

Joint Workshop: Neuro-Computing, Bioinformatics, Mathematical modeling and Machine Learning

  • Date: June 23-25, 2017
  • Venue: OIST Conference Center
  • Co-organizers:
    • The Institute of ElectronicsInformation and Communication Engineers(IEICE)
    • Information Processing Society of Japan
    • IEEE Computational Intelligence Society Japan Chapter
    • Japan Neural Network Society
  • Keynote Speaker:
    • Kenji Doya (OIST)

AI and Brain Science Symposium in JNSS2017

  • Date: July 23,  2017
  • Venue: Makuhari Messe
  • Co-sponsors:
    • KAKENHI Project on Artificial Intelligence and Brain Science
    • MEXT Supercomputing Project on Brain and Artificial Intelligence
  • Speakers:
    • Karlheinz Meier(Heidelberg University)
    • Shin Ishii(ATR Cognitive Mechanisms Labs., Grad Informatics, Kyoto Univ)
    • James Kozloski(IBM Research)
    • Yutaka Matsuo(University of Tokyo)
    • Matt Botvinick(DeepMind, London, UK)

AI and Brain Science Symposium in JNNS2017

  • Date: September 20,  2017
  • Venue: Kitakyushu International Conference Center
  • Co-sponsors: 
    • KAKENHI Project on Artificial Intelligence and Brain Science
    • MEXT Supercomputing Project on Brain and Artificial Intelligence
  • Speakers:
    • Tatsuya Harada (University of Tokyo, Post-K)
    • Haruo Hosoya (ATR，NEDO）
    • Eiji Uchibe (ATR, NEDO, KAKENHI Project on AI and Brain Science)
    • Masamichi Sakagami (Tamagawa University, KAKENHI Project on AI and Brain Science)

The 3rd Meeting of KAKENHI Project on Artificial Intelligence and Brain Science

  • Date: December 19,  2017
  • Venue: Hitotsubashi Hall, National Center of Science
  • Sponsor: KAKENHI Project on Artificial Intelligence and Brain Science

Joint Workshop at The Next Generation Brain Project 2017 Winter Symposium

  • Date: December 20,  2017
  • Venue: National Center of Science
  • Co-sponsors: Grant-in Aid for Scientific Research on Innovative Areas: 
  • Memory Dynamism
  • Oscillology
  • Adaptive Circuit Shift
  • Artificial Intelligence and Brain Science

Neural Computation Workshop

  • Date: March 28,  2018
  • Venue: OIST Seaside House
  • Speakers:
    • Eiji Uchibe (ATR)
    • Tadashi Kozuno (OIST)
    • Takumi Kamioka (Honda Research Institute Japan)
    • Chris Reinke (OIST)
    • Viktor Zhumatiy (Rakuten Inc.)
    • Katsuhiko Miyazaki (OIST)
    • Tomohiko Yoshizawa (OIST)
    • Junichiro Yoshimoto (NAIST)
    • Hiromichi Tsukada (OIST)
    • Carlos Gutierrez (OIST)
    • Jun Igarashi (RIKEN)
    • Jean Lienard (OIST)
    • Kenji Doya (OIST)

 

7. Other