研究成果

論文・書籍

  1. Tadashi Yamazaki, Jun Igarashi, Hiroshi Yamaura. Human-scale brain simulation via supercomputer: a case study on the cerebellum. Neuroscience, In Press.
  2. Hiroshi Yamaura, Jun Igarashi, Tadashi Yamazaki. Simulation of a Human-Scale Cerebellar Network Model on the K Computer. Frontiers in Neuroinformatics, 14, 16. https://doi.org/10.3389/fninf.2020.00016, 2020.
  3. Benoît Girard, Jean Lienard, Carlos Enrique Gutierrez, Bruno Delord, Kenji Doya. A biologically constrained spiking neural network model of the primate basal ganglia with overlapping pathways exhibits action selection. European Journal of Neuroscience, https://doi.org/10.1111/ejn.14869, 2020.
  4. Jan Morén, Jun Igarashi, Osamu Shouno, Junichiro Yoshimoto, Kenji Doya. Dynamics of basal ganglia and thalamus in parkinsonian tremor. Cutsuridis V, Multiscale Models of Brain Disorders, Spriinger pp13-20. https://doi.org/978-3-030-18830-6_2, 2019.
  5. Jun Igarashi, Hiroshi Yamaura, Tadashi Yamazaki. Large-scale simulation of a layered cortical sheet of spiking network model using a tile partitioning method. Frontiers in Neuroinformatics, Accepted (2019/11/12).
  6. Skibbe Henrik, Reisert Marco, Nakae Ken, Watakabe Akiya, Hata Junichi, Mizukami Hiroaki, Okano Hideyuki, Yamamori Tetsuo, Ishii Shin. PAT: Probabilistic axon tracking for densely labeled neu-rons in large 3D micrographs. IEEE Transactions on Medical Imaging, 38(1):69-78, 2019.
  7. Jakob Jordan, Tammo Ippen, Moritz Helias, Itaru Kitayama, Mitsuhisa Sato, Jun Igarashi, Markus Diesmann, Susanne Kunkel. Ex-tremely Scalable Spiking NeuronalNetwork Simulation Code: From Laptops to Exascale, Frontiers in Neuroinformatics, 10.3389/fninf.2018.00002.
  8. 山浦 洋, 山﨑 匡. 小脳モデル. 人工知能AI辞典. (印刷中)
  9. 山浦 洋, 山﨑 匡. 小脳神経回路をコンピュータ上に作る. Clinical Neuroscience 37(8):973-975, 2019.
  10. 高橋 恒一, 渡部 匡己. 「現代科学を超えて―AI駆動型科学へ」 実験医学別冊 「あなたのラボにAI(人工知能)×ロボットがやってくる」, Eds. 夏目 徹 (羊土社), 2017.
  11. 山川 宏(インタビュイー). 強いAI・弱いAI 研究者に聞く人工知能の実像「全脳アーキテクチャ― 汎用人工知能の実現」pp157-189. 丸善出版, 2017.
  12. 高橋 恒一. 将来の機械知性に関するシナリオと分岐点, 人工知能学会誌 33(6):867-872, 2018.
  13. 高橋 恒一, 草刈 ミカ, 中ザワ ヒデキ. 特集「AIと美学・芸術」にあたって, 人工知能学会誌 33(6):698-780, 2018.
  14. 上野 聡, 高橋 恒一, 中田 秀基. 特集「AI 計算資源」にあたって, 人工知能学会誌 33(1):5-45, 2018.
  15. Osamu Shouno, Yoshihisa Tachibana, Atsushi Nambu, Kenji Doya. Computational model of recurrent subthalamo-pallidal circuit for generation of parkinsonian oscillations, Frontiers in Neuroanatomy, 11, 21, https://doi.org/10.3389/fnana.2017.00021, 2017.
  16. 山﨑 匡, 五十嵐 潤. 高性能神経計算による神経回路モデルのリアルタイムシミュレーション. 日本神経回路学会誌 24(4): 172–181, 2017.

受賞

  1. Jun Igarashi, Tadashi Yamazaki, Hiroshi Yamaura. OCNS*2019 Trainee Poster Competition Award, CNS2019, July 13-17, 2019.
  2. Fu, X. Poster Award Engineering Prize, RIKEN Summer School 2019, Octber 7-8, 2019.
  3. Shi L . Poster Award Engineering Prize, RIKEN Summer School 2019, Octber 7-8, 2019.
  4. 銅谷賢治. 日本神経回路学会 学術賞, 2019年.
  5. Kenji Doya. Donald O. Hebb Award, International Neural Network Society, 2018.

シンポジウム開催等

  1. 脳と心のメカニズム第19回冬のワークショップ(後援), 2019年1月9日-11日, 北海道留寿都村.
  2. 日本神経回路学会第28回全国大会JNNS2018(後援), 2018年10月24日-27日, OIST, 沖縄県国頭郡恩納村.
  3. 第41回日本神経科学大会シンポジウム, 「スパコンは神経科学を加速するか:エクサフロップス時代に向けて」, 2018年7月26日-29日, 神戸コンベンションセンター, 兵庫県神戸市.

広報・アウトリーチ

  1. Tadashi Yamazaki, Jun Igarashi. Introduction to high-performance neurocomputing. Tutorial T-7, Computational Neuroscience (CNS*2019) Barcelona, July 13-17, 2019, Barcelona, Spain.
  2. 五十嵐 潤. 「ヒト脳理解に向けてのスタートライン〜ポスト「京」で目指す全脳規模シミュレーション」. 計算科学の世界 第17号, 2018.
  3. 山﨑 匡. 「研究室へようこそ!」. 計算科学の世界 第17号, 2018.
  4. 五十嵐 潤. 全脳シミュレーションについて, N予備校, 脳神経科学と汎用人工知能, ドワンゴ ニコニコAIスクール, ビデオ講義収録 (http://nico2.ai/neuro-ai/), 2018年8月.
  5. 五十嵐 潤, 山﨑 匡 ランチョン大討論会「脳科学は次の10~20年に何をどう目指すべきか?」第41回日本神経科学学会全国大会, 2018年7月29日, 神戸.
  6. 山﨑 匡. スパコンで脳を再現する. 脳科学ライフサポート研究センタースプリングスクール, 2018年3月28-29日, 電通大.
  7. 五十嵐 潤.ヒトの脳全体シミュレーションを可能にするアルゴリズム -脳シミュレーションの大幅な省メモリ化と高速化を実現-. https://www.riken.jp/press/2018/20180326_1/, 2018年3月26日
  8. Tadashi Yamazaki. Tutorial: Introduction of high-performance computing for neuroinformatics. Advances in Neuroinformatics 2017 (AINI2017), November 20-21, 2017, RIKEN, Wako.

主要国際会議発表
Computational Neuroscience (CNS*2019) July 13-17 2019, Barcelona, Spain

  1. Igarashi J, Yamazaki T, Yamaura H. Parallel computing of a cortico-thalamo-cerebellar circuit using tile partitioning parallelization method by MONET simulator.
  2. Gutierrez CE, et al. A whole-brain spiking neural network model linking basal ganglia, cerebellum, cortex and thalamus.
  3. Morteza H, Sun Z, Igarashi J. Hierarchy of inhibitory circuit acts as a switch key for network function in a model of the primary motor cortex.
  4. Sun Z, Morteza H, Igarashi J. Spatially organized connectivity for signal processing in a model of the rodent primary somatosensory cortex.
  5. Yamaura H, Igarashi J, Yamazaki T. Building a spiking network model of the cerebellum on K computer using NEST and MONET simulators

The 48th Annual Meeting of Society for Neuroscience (Neuroscience 2018), November 3-7 2018, San Diego, USA

  1. Igarashi J, Yamazaki T, Yamaura H. Parallel computing of a spiking neural network model of layered cortical sheet consisting of multiple regions with long-range connections.
  2. Lienard J, Girard B, Doya K. Examination of the roles of basal ganglia afferents in action selection and learning by spiking neuron models.
  3. Watakabe A, et al., Prefrontal projection mapping of the common marmoset.
  4. Yamazaki T, Yamaura H, Igarashi J. Implementation and simulationof a cerebellar model on a tile-based general spiking neural networksimulator for K supercomputer.

その他

  1. Carlos Enrique Gutierrez, Zhe Sun, Hiroshi Yamaura, Heidarinejad  Morteza, Jun Igarashi, Tadashi Yamazaki, Kenji Doya. Simulation of resting-state neural activity in a loop circuit of the cerebral cortex, basal ganglia, cerebellum, and thalamus using NEST simulator. JNNS2020.
  2. Gutierrez C. Large-scale simulation of a spiking neural network model consisting of cortex, thalamus, cerebellum and basal ganglia on K computer. NEST conference 2019, Norway.
  3. Jun Igarashi. Parallelization method of cortico-thalamo-cerebellar circuits toward exascale computing. Neuromodulator of neural microcircutis NM2, 2019, Champery, Switzerland.
  4. Jun Igarashi, Hiroshi Yamaura, Tadashi Yamazaki. Introduction of large-scale neural network simulations in the project for a next-generation supercomputer in Japan. 4th EU-Japan workshop on Neurorobotics, 2019, Tokyo.
  5. Kotone Itaya, Hiroshi Yamakawa, Masaru Tomita, Koichi Takahashi.BriCA Kernel: Cognitive Computing Platform for Large-scale Distri-buted Memory Environments. JNNS2018, October 26, 2018, OIST, Okinawa
  6. Yamakawa H, Arakawa N, Takahashi K. Reinterpreting the cortical circuit. Architectures for Generality & Autonomy Workshop, August 15-20 2017, Melbourne, Australia.
  7. Sei Ueno, Masahiko Osawa, Michita Imai, Tsuneo Kato, Hiroshi Yamakawa. Re:ROS”: Prototyping of Reinforcement Learning Environ-ment for Asynchronous Cognitive Architecture. 2017 Annual Intern-ational Conference on Biologically Inspired Cognitive Architectures: Eighth Annual Meeting of the BICA Society (BICA 2017), Moscow, Russia.
  8. Jun Igarashi. Parallelization of a spiking neural network model of layered cortical sheet consisting of multiple cortical regions. NOLTA2017, December 2017, Cancún, Mexico.
  9. Itaya K, Takahashi K, Nakamura M, Koizumi M, Arakawa N, Tomita M, Yamakawa H. BriCA: A modular software platform for whole brain architecture. Neural information processing – 23rd international conference (ICONIP2016), October 16-21 2016, Kyoto.