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2023年

論文
  • Yuji Kawai, Jihoon Park, Ichiro Tsuda, and Minoru Asada. 2023. “Learning Long-Term Motor Timing/Patterns on an Orthogonal Basis in Random Neural Networks.” Neural Networks 163 (June): 298–311. https://doi.org/10.1016/j.neunet.2023.04.006.
  • 許鶴馨, Amit Yaron, 白松（磯口）知世, 高橋宏知. 2023. “Common Mechanism Underlying Multimodal Integration.” 医用・生体工学研究会 MBE23 (029): 77–82.
  • 高野雄基, 秋田大, 諏訪瑛介, 高橋宏知. 2023. “自由エネルギー原理に基づく培養神経細胞による無意識的推論.” 医用・生体工学研究会 MBE23 (020): 23–27.
  • 高木永遠, 大島果林, 白松知世, 高橋宏知. 2023. “音環境に依存したげっ歯類の超音波コミュニケーション.” 医用・生体工学研究会 MBE23 (019): 17–22.
  • 金井智美, 可部泰生, 森本隆司, 高橋宏知. 2023. “日常使用に適した他覚的聴力検査手法の提案.” 医用・生体工学研究会 MBE23 (039): 123–28.
  • 諏訪瑛介, 秋田大, 池田成満, 高野雄基, 高橋宏知. 2023. “神経細胞の分散培養系による臨界的物理リザバー計算.” 医用・生体工学研究会 MBE23 (034): 97–102.
  • 石田直輝, 白松(磯口)知世, 高橋宏知. 2023. “ラット聴覚野の情報処理容量に対する音楽曝露の効果.” 医用・生体工学研究会 MBE23 (024): 47–52.
  • Seiichi Bun, Tomoyo I. Shiramatsu, Hirokazu Takahashi, and Tetsuya Asai. 2023. “Active Charge Balancer towards CMOS Integration of an Array of Neural Stimulators.” Nonlinear Theory and Its Applications, IEICE 14 (2).
  • 浅田稔. 2023. “ロボット學の創成と社会工学としてのロボット工学.” 科学 93 (1): 18–25.
査読付き学会発表
査読なし学会発表
  • 河合祐司, 熱田洋史, 浅田稔. 2023. “モジュール型レザバーコンピュータを用いたロボットアームの適応的な軌道制御.” In 電子情報通信学会 ニューロコンピューティング研究会, NC1-17. 公立はこだて未来大学. IEEE Computational Intelligence Society Japan Chapter Young Researcher Award
  • 湊宏太郎, 香取勇一. 2023. “レザバーアクタークリティックモデルによるロボットの連続値制御.” 電子情報通信学会技術研究報告; 信学技報, NC研究会, 122 (374): 118–22.
  • Hiroshi Atsuta, Yuji Kawai, and Minoru Asada. 2023. “Small Sample Learning of Reaching Movements of a Redundant Robotic Arm with an Echo State Network.” In 脳と心のメカニズム 冬のワークショップ2023.
  • Yuji Kawai, Jihoon Park, Ichiro Tsuda, and Minoru Asada. 2023. “Small Random Neural Networks Orthogonally Oscillate to Learn Long-Term Motor Timing/Patterns.” In 脳と心のメカニズム 冬のワークショップ2023.
  • Soichiro Yamakawa, Kota Ando, Megumi Akai-Kasaya, and Tetsuya Asai. 2023. “Design and Evaluation of Brain-Computer Communication Devices Using Divergence Properties of Non-Linear Dynamical Systems.” In The 9th Japan-Korea Joint Workshop on Complex Communication Sciences 2022. Gyeong Ju, Korea.
招待講演
プレス発表

2022年

論文
  • Mitsumasa Nakajima, Katsuma Inoue, Kenji Tanaka, Yasuo Kuniyoshi, Toshikazu Hashimoto, and Kohei Nakajima. 2022. “Physical Deep Learning with Biologically Inspired Training Method: Gradient-Free Approach for Physical Hardware.” Nature Communications 13 (1): 7847. https://doi.org/10.1038/s41467-022-35216-2.
  • Yusuke Imai, Kohei Nakajima, Sumito Tsunegi, and Tomohiro Taniguchi. 2022. “Input-Driven Chaotic Dynamics in Vortex Spin-Torque Oscillator.” Scientific Reports 12 (1): 21651. https://doi.org/10.1038/s41598-022-26018-z.
  • Yoshiki Ito, Tomoyo Isoguchi Shiramatsu, Naoki Ishida, Karin Oshima, Kaho Magami, and Hirokazu Takahashi. 2022. “Spontaneous Beat Synchronization in Rats: Neural Dynamics and Motor Entrainment.” Science Advances 8 (45): eabo7019. https://doi.org/10.1126/sciadv.abo7019.
  • Kazutoshi Tanaka, Yuna Minami, Yuji Tokudome, Katsuma Inoue, Yasuo Kuniyoshi, and Kohei Nakajima. 2022. “Continuum-Body-Pose Estimation From Partial Sensor Information Using Recurrent Neural Networks.” IEEE Robotics and Automation Letters 7 (4): 11244–51. https://doi.org/10.1109/LRA.2022.3199034.
  • Ninnart Fuengfusin, and Hakaru Tamukoh. 2022. “INT8 Activation Ternary or Binary Weights Networks: Unifying Between INT8 and Lower-Bit Width Quantization.” Journal of Robotics, Networking and Artificial Life 9 (2): 171–76. https://doi.org/10.57417/jrnal.9.2_171.
  • Nozomi Akashi, Yasuo Kuniyoshi, Sumito Tsunegi, Tomohiro Taniguchi, Mitsuhiro Nishida, Ryo Sakurai, Yasumichi Wakao, Kenji Kawashima, and Kohei Nakajima. 2022. “A Coupled Spintronics Neuromorphic Approach for High-Performance Reservoir Computing.” Advanced Intelligent Systems 4 (10): 2200123. https://doi.org/10.1002/aisy.202200123.
  • Wentao Sun, Nozomi Akashi, Yasuo Kuniyoshi, and Kohei Nakajima. 2022. “Physics-Informed Recurrent Neural Networks for Soft Pneumatic Actuators.” IEEE Robotics and Automation Letters 7 (3): 6862–69. https://doi.org/10.1109/LRA.2022.3178496.
  • Osamu Nomura, Yusuke Sakemi, Takeo Hosomi, and Takashi Morie. 2022. “Robustness of Spiking Neural Networks Based on Time-to-First-Spike Encoding Against Adversarial Attacks.” IEEE Transactions on Circuits and Systems II: Express Briefs 69 (9): 3640–44. https://doi.org/10.1109/TCSII.2022.3184313.
  • Yu Yoshino and Yuichi Katori. “Short-Term Memory Ability of Reservoir-Based Temporal Difference Learning Model.” Nonlinear Theory and Its Applications, IEICE 13, no. 2 (2022): 203–208. https://doi.org/10.1587/nolta.13.203.
  • Hiroshi Kubota, Tsuyoshi Hasegawa, Megumi Akai-Kasaya, and Tetsuya Asai. “Noise Sensitivity of Physical Reservoir Computing in a Ring Array of Atomic Switches.” Nonlinear Theory and Its Applications, IEICE 13, no. 2 (2022): 373–78. https://doi.org/10.1587/nolta.13.373.
  • Yuichiro Tanaka and Hakaru Tamukoh. “Reservoir-Based Convolution.” Nonlinear Theory and Its Applications, IEICE 13, no. 2 (2022): 397–402. https://doi.org/10.1587/nolta.13.397.
  • Yoshihiro Yonemura and Yuichi Katori. “Functional Connectivity Analysis on Hierarchical Reservoir Computing Model.” Nonlinear Theory and Its Applications, IEICE 13, no. 2 (2022): 446–451. https://doi.org/10.1587/nolta.13.446.
  • Ryo Sakurai, Mitsuhiro Nishida, Taketomo Jo, Yasumichi Wakao, and Kohei Nakajima. 2022. “Durable Pneumatic Artificial Muscles with Electric Conductivity for Reliable Physical Reservoir Computing.” Journal of Robotics and Mechatronics 34 (2): 240–48. https://doi.org/10.20965/jrm.2022.p0240.
  • Ryo Terajima, Katsuma Inoue, Shogo Yonekura, Kohei Nakajima, and Yasuo Kuniyoshi. “Behavioral Diversity Generated From Body–Environment Interactions in a Simulated Tensegrity Robot.” IEEE Robotics and Automation Letters 7, no. 2 (April 2022): 1597–1604. https://doi.org/10.1109/LRA.2021.3139083.
  • Katsuma Inoue, Soh Ohara, Yasuo Kuniyoshi, and Kohei Nakajima. “Transient Chaos in Bidirectional Encoder Representations from Transformers.” Physical Review Research 4, no. 1 (March 16, 2022): 013204. https://doi.org/10.1103/PhysRevResearch.4.013204.
  • Kazutoshi Tanaka, Yuji Tokudome, Yuna Minami, Satoko Honda, Toshiki Nakajima, Kuniharu Takei, and Kohei Nakajima. “Self-Organization of Remote Reservoirs: Transferring Computation to Spatially Distant Locations.” Advanced Intelligent Systems 4, no. 3 (March 2022): 2100166. https://doi.org/10.1002/aisy.202100166.
  • Hiroshi Kubota, Tsuyoshi Hasegawa, Megumi Akai-Kasaya, and Tetsuya Asai. “Behavioral Model of Molecular Gap-Type Atomic Switches and Its SPICE Integration.” Circuits and Systems 13, no. 1 (January 30, 2022): 1–12. https://doi.org/10.4236/cs.2022.131001.
  • Nozomi Akashi, Kohei Nakajima, Mitsuru Shibayama, and Yasuo Kuniyoshi. “A Mechanical True Random Number Generator.” New Journal of Physics 24, no. 1 (January 2022): 013019. https://doi.org/10.1088/1367-2630/ac45ca.
査読付き学会発表
  • Nagai Tatsuro, and Katori Yuichi. 2022. “Online Reinforcement Learning on Reservoir Based Actor-Critic Model with Gibbs’s Policy.” In 2022 International Symposium on Nonlinear Theory and Its Applications, 87–90. The Institute of Electronics, Information and Communication Engineers. https://doi.org/10.34385/proc.71.A3L-D-03.
  • Izumi Tomohito, and Katori Yuichi. 2022. “Visual Predictive Coding Model with Reservoir Computing for Reinforcement Learning Tasks in 3D Environment.” In 2022 International Symposium on Nonlinear Theory and Its Applications, 91–94. The Institute of Electronics, Information and Communication Engineers. https://doi.org/10.34385/proc.71.A3L-D-04.
  • Yonemura Yoshihiro, and Katori Yuichi. 2022. “Mental Simulation on Reservoir Computing as an Efficient Planning Method for Mobile Robot Navigation.” In 2022 International Symposium on Nonlinear Theory and Its Applications, 83–86. The Institute of Electronics, Information and Communication Engineers. https://doi.org/10.34385/proc.71.A3L-D-02.
  • Seiichi Bun, Kota Ando, Megumi Akai-Kasaya, and Tetsuya Asai. 2022. “An Active Charge Balancer Towards CMOS Integration of an Array of Neural Stimulators.” In 2022 International Symposium on Nonlinear Theory and Its Applications. https://confit.atlas.jp/guide/event/nolta2022/subject/B4L-D-05/detail.
  • Kazuo Nakahara, Yuichi Katori, Osamu Nomura, Hakaru Tamukoh, and Takashi Morie. 2022. “Evaluation of Modular Reservoirs Using Chaotic Boltzmann Machines.” In The 4th International Symposium on Neuromorphic AI Hardware. Fukuoka, Japan.
  • Katsunori Tamai, Yuichi Katori, Hakaru Tamukoh, Osamu Nomura, and Takashi Morie. 2022. “Performance Evaluation of a Reservoir Reinforcement Learning Model Considering Nonlinear Write Characteristics of Analog Memory.” In The 4th International Symposium on Neuromorphic AI Hardware. Fukuoka, Japan.
  • Yuichiro Tanaka, and Hakaru Tamukoh. 2022. “Reservoir-Based 1D Convolution.” In The 4th International Symposium on Neuromorphic AI Hardware. Fukuoka, Japan.
  • Sumito Tsunegi, Tomohiro Taniguchi, Akira Kamimaki, Kay Yakushiji, Akio Fukushima, Shinji Yuasa, Hitoshi Kubota “Physical Reservoir Computing using spin torque oscillator with loop circuit”, 2022 Joint MMM-Intermag, 2022.
  • Yuji Kawai, Jihoon Park, Ichiro Tsuda, and Minoru Asada. 2022. “Self-Organization of a Dynamical Orthogonal Basis Acquiring Large Memory Capacity in Modular Reservoir Computing.” In The 31st International Conference on Artificial Neural Networks.
  • Osamu Nomura, Yusuke Sakemi, Takeo Hosomi, and Takashi Morie. 2022. “Robustness of Spiking Neural Networks Based on Time-to-First-Spike Encoding Against Adversarial Attacks (MWSCAS2022).” In The 65th IEEE International Midwest Symposium on Circuits and Systems, 9018. Fukuoka, Japan.
  • Wentao Sun, Nozomi Akashi, Yasuo Kuniyoshi, and Kohei Nakajima. 2022. “Self-Organization of Physics-Informed Mechanisms in Recurrent Neural Networks: A Case Study in Pneumatic Artificial Muscles.” In 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft), 409–15. https://doi.org/10.1109/RoboSoft54090.2022.9762181.
  • Katsushi Kagaya, Bowei Yu, Yuna Minami, and Kohei Nakajima. 2022. “Echo State Property and Memory in Octopus-Inspired Soft Robotic Arm.” In 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft), 224–30. https://doi.org/10.1109/RoboSoft54090.2022.9762119.
  • Kazuo Nakahara, Yuichi Katori, Hakaru Tamukoh, Osamu Nomura, and Takashi Morie. “Memory Capacity of Reservoir Computing Using Chaotic Boltzmann Machines.” In The 3rd International Symposium on Neuromorphic AI Hardware. Fukuoka, Japan, 2022.
  • Katsunori Tamai, Kohei Kawazoe, Yuka Shishido, Yuichi Katori, Hakaru Tamukoh, Osamu Nomura, and Takashi Morie. “Numerical Simulation for Analog VLSI Implementation of Reinforcement Learning Using Reservoir Computing.” In The 3rd International Symposium on Neuromorphic AI Hardware. Fukuoka, Japan, 2022.
  • Yuka Shishido, Kohei Kawazoe, Katsunori Tamai, Hakaru Tamukoh, Osamu Nomura, and Takashi Morie. “A Co-Design Environment for AI Hardware Simulation Using PyLTSpice.” In The 3rd International Symposium on Neuromorphic AI Hardware. Fukuoka, Japan, 2022. Student Presentation Award
  • Daiju Kanaoka, Yuichiro Tanaka, and Hakaru Tamukoh. “Applying Center Loss to Multidimensional Feature Space in Deep Neural Networks for Open-Set Recognition.” In Proceedings of the 17th International Conference on Computer Vision Theory and Applications, 5:359–65, 2022. https://doi.org/10.5220/0010816600003124.
査読なし学会発表
  • 崎野也真人, 田向権, 森江隆, 香取勇一. 2022. “超立方体上の擬似ビリヤードダイナミクスに基づくレザバー計算の性能評価とその改善.” In NEURO2022, 1P – 314. 沖縄コンベンションセンター. https://confit.atlas.jp/guide/event/neuro2022/subject/1P-314/detail.
  • 吉野遊, 香取勇一. 2022. “部分観測マルコフ決定過程におけるリザバーコンピューティングを基にしたTD学習モデルの短期記憶性能.” In NEURO2022, 1P – 315. 沖縄コンベンションセンター. https://confit.atlas.jp/guide/event/neuro2022/subject/1P-315/detail.
  • 中村仁, 香取勇一. “動的シナプスを用いた報酬修飾型レザバー計算に基づく行動計画の数理モデルの構築と解析.” In NEURO2022, 1P – 068. 沖縄コンベンションセンター, 2022. https://confit.atlas.jp/guide/event/neuro2022/subject/1P-068/detail.
  • 徳野将士, 田中悠一朗, 川節拓実, 細田耕, 田向権. 2022. “ロボットの触覚の記憶獲得と記憶に基づいた違和感の検知.” In 第40回日本ロボット学会学術講演会, 4I3-08.
  • 野村修, 森江隆, 田向権, 川島一郎, 中原和勇, 香取勇一. 2022. “カオスボルツマンマシンとそのレザバー応用，および超低消費電力型ＬＳＩの開発.” In 2022年 電気学会 電子・情報・システム部門大会, TC16-1.
  • 玉井克典, 田向権, 香取勇一, 野村修, 森江隆. 2022. “アナログメモリの非線形書き込み特性を考慮したリザバー強化学習モデルの性能評価.” In 第83回応用物理学会秋季学術講演会, 21a-C201-7. 東北大学.
  • 山川綜一郎, 安藤洸太, 赤井恵, 浅井哲也. 2022. “非線形動的システムの発散特性を利用した微小信号検出回路の設計と評価.” In 第35回 回路とシステムワークショップ, 239–44. 北九州国際会議場.
  • Y. Kawai, J. Park, I. Tsuda and M. Asada. “Learning Long-Term Motor Timing and Patterns Using Modular Reservoir Computing.” In International Symposium on Artificial Intelligence and Brain Science, 2022. http://www.brain-ai.jp/jp/symposium2022/.
  • 河合祐司, 朴志勲, 津田一郎, 浅田稔. “モジュール型レザバーコンピューティングにおける大記憶容量を有する動的直交基底の創発.” In 電子情報通信学会 ニューロコンピューティング研究会, 2022. https://www.ieice.org/ken/paper/20220629JCkT/.
  • K. Tamai, K. Kawazoe, Y. Shishido, Y. Katori, H. Tamukoh, O. Nomura, and T. Morie. “Numerical Simulation for VLSI Implementation of Reinforcement Learning Using Reservoir Computing.” In The 10th RIEC International Symposium on Brain Functions and Brain Computer. Sendai, Japan, 2022.
  • Y. Shishido, K. Kawazoe, K. Tamai, Y. Katori, H. Tamukoh, O. Nomura, and T. Morie. “A Co-Design Environment for Computational Models and Circuits Using PyLTSpice and Its Application to Circuit Design for Reinforcement Learning Using Reservoir Computing.” In The 10th RIEC International Symposium on Brain Functions and Brain Computer. Sendai, Japan, 2022.
  • A. Mizutani, Y. Tanaka, H. Tamukoh, Y. Katori, K. Tateno, and T. Morie. “A Situation-Dependent Navigation System by Brain-Inspired Neural Networks with Hippocampus, Prefrontal Cortex, and Amygdala Functions.” In The 10th RIEC International Symposium on Brain Functions and Brain Computer. Sendai, Japan, 2022.
招待講演
プレス発表

2021年

論文
  • Akira Kamimaki, Tomoyuki Kubota, Sumito Tsunegi, Kohei Nakajima, Tomohiro Taniguchi, Julie Grollier, Vincent Cros, et al. “Chaos in Spin-Torque Oscillator with Feedback Circuit.” Physical Review Research 3, no. 4 (December 27, 2021): 043216. https://doi.org/10.1103/PhysRevResearch.3.043216.
  • Tomoyuki Kubota, Hirokazu Takahashi, and Kohei Nakajima. “Unifying Framework for Information Processing in Stochastically Driven Dynamical Systems.” Physical Review Research 3, no. 4 (November 23, 2021): 043135. https://doi.org/10.1103/PhysRevResearch.3.043135.
  • Yuichiro Yada, Shusaku Yasuda, and Hirokazu Takahashi. “Physical Reservoir Computing with FORCE Learning in a Living Neuronal Culture.” Applied Physics Letters 119, no. 17 (October 25, 2021): 173701. https://doi.org/10.1063/5.0064771.
  • Kanato Mori, Tomoyo Isoguchi Shiramatsu, Kotaro Ishizu, and Hirokazu Takahashi. “Simultaneous Mapping of Neural Activities in Auditory and Visual Cortex of Rat.” Electronics and Communications in Japan 104, no. 3 (2021): e12322. https://doi.org/10.1002/ecj.12322.
  • Naoki Wake, Kotaro Ishizu, Taiki Abe, and Hirokazu Takahashi. “Prepulse Inhibition Predicts Subjective Hearing in Rats.” Scientific Reports 11, no. 1 (September 23, 2021): 18902. https://doi.org/10.1038/s41598-021-98167-6.
  • Kotaro Ishizu, Tomoyo I. Shiramatsu, Rie Hitsuyu, Masafumi Oizumi, Naotsugu Tsuchiya, and Hirokazu Takahashi. “Information Flow in the Rat Thalamo-Cortical System: Spontaneous vs. Stimulus-Evoked Activities.” Scientific Reports 11, no. 1 (September 28, 2021): 19252. https://doi.org/10.1038/s41598-021-98660-y.
  • Tomoyo Isoguchi Shiramatsu, Kanato Mori, Kotaro Ishizu, and Hirokazu Takahashi. “Auditory, Visual, and Cross-Modal Mismatch Negativities in the Rat Auditory and Visual Cortices.” Frontiers in Human Neuroscience 15 (September 17, 2021). https://doi.org/10.3389/fnhum.2021.721476.
  • 浅田稔. “ロボティクスと強化学習.” 日本ロボット学会誌 39, no. 7 (2021): 575–80. https://doi.org/10.7210/jrsj.39.575.
  • Hiroshi Kubota, Tsuyoshi Hasegawa, Megumi Akai-Kasaya, and Tetsuya Asai. “Reservoir Computing on Atomic Switch Arrays with High Precision and Excellent Memory Characteristics.” Journal of Signal Processing 25, no. 4 (2021): 123–26. https://doi.org/10.2299/jsp.25.123.
  • Ken Goto, Kohei Nakajima, and Hirofumi Notsu. “Twin Vortex Computer in Fluid Flow.” New Journal of Physics 23, no. 6 (June 2021): 063051. https://doi.org/10.1088/1367-2630/ac024d.
  • 白松(磯口)知世, 阿部泰己, 石津光太郎, 高橋宏知. “視床刺激が聴知覚に及ぼす影響を評価する実験系の構築.” 電気学会論文誌ｃ（電子・情報・システム部門誌） 141, no. 5 (2021): 627–33. https://doi.org/10.1541/ieejeiss.141.627.
  • 成満池田, 高橋宏知. “低頻度刺激による神経細胞の分散培養系の学習.” 電気学会論文誌ｃ（電子・情報・システム部門誌） 141, no. 5 (2021): 654–60. https://doi.org/10.1541/ieejeiss.141.654.
  • 窪田智之, 櫻山和浩, 白松(磯口)知世, 高橋宏知. “神経細胞の分散培養系の逸脱検出特性.” 電気学会論文誌ｃ（電子・情報・システム部門誌） 141, no. 5 (2021): 661–67. https://doi.org/10.1541/ieejeiss.141.661.
  • 河合祐司. “脳神経の複雑ネットワークの機能的意義.” システム／制御／情報 65, no. 5 (2021): 188–93. https://doi.org/10.11509/isciesci.65.5_188.
  • Yoshihiro Yonemura and Yuichi Katori. “Network Model of Predictive Coding Based on Reservoir Computing for Multi-Modal Processing of Visual and Auditory Signals.” Nonlinear Theory and Its Applications, IEICE 12, no. 2 (2021): 143–56. https://doi.org/10.1587/nolta.12.143.
  • Katsuma Inoue, Yasuo Kuniyoshi, Katsushi Kagaya, and Kohei Nakajima. “Skeletonizing the Dynamics of Soft Continuum Body from Video.” Soft Robotics, February 18, 2021. https://doi.org/10.1089/soro.2020.0110.
  • Shaohua Kan, Kohei Nakajima, Yuki Takeshima, Tetsuya Asai, Yuji Kuwahara, and Megumi Akai-Kasaya. “Simple Reservoir Computing Capitalizing on the Nonlinear Response of Materials: Theory and Physical Implementations.” Physical Review Applied 15, no. 2 (February 12, 2021): 024030. https://doi.org/10.1103/PhysRevApplied.15.024030.
査読付き学会発表
  • Ichiro Kawashima, Yuichi Katori, Takashi Morie, and Hakaru Tamukoh. 2021. “An Area-Efficient Multiply-Accumulation Architecture and Implementations for Time-Domain Neural Processing.” In 2021 International Conference on Field-Programmable Technology (ICFPT), 1–4. https://doi.org/10.1109/ICFPT52863.2021.9609809.
  • Koutaro Minato and Yuichi Katori. “Robot Arm Control Using Reward-Modulated Hebbian Learning.” In ICONIP 2021: Neural Information Processing, 55–63. Cham: Springer International Publishing, 2021. https://doi.org/10.1007/978-3-030-92310-5_7.
  • Ruihong Wu, Kohei Nakajima, and Yongping Pan. “Performance Improvement of FORCE Learning for Chaotic Echo State Networks.” In ICONIP 2021: Neural Information Processing, 262–72. Cham: Springer International Publishing, 2021. https://doi.org/10.1007/978-3-030-92270-2_23.
  • Kohei Kawazoe, Yuichi Katori, Hakaru Tamukoh, Osamu Nomura, and Takashi Morie. “Design of a VLSI Circuit with Non-Volatile Analog Memory for Reinforcement Learning Using Reservoir Computing.” In 9th International Symposium on Applied Engineering and Sciences (SAES2021), 2021.
  • Daiju Kanaoka, Yuichiro Tanaka, and Takashi Morie. “Open Set Recognition Using Hotelling’s T2 Focusing on Multidimensional Feature Space in Deep Neural Networks.” In 9th International Symposium on Applied Engineering and Sciences (SAES2021), 2021.
  • Yu Yoshino and Yuichi Katori. “Short-Term Memory Ability of Reservoir-Based Temporal Difference Learning Model.” In The 2021 NonLinear Science Workshop. Online, 2021.
  • Yoshihiro Yonemura and Yuichi Katori. “Analysis of Functional Connectivity on Reservoir Computing Model.” In The 2021 NonLinear Science Workshop. Online, 2021.
  • Yuichiro Tanaka and Hakaru Tamukoh. “Reservoir-Based Convolutional Neural Network.” In The 2021 NonLinear Science Workshop. Online, 2021.
  • Yamato Sakino, Hakaru Tamukoh, Takashi Morie, and Yuichi Katori. “Benchmarks for Reservoir Computing Based on Pseudo-Billiard Dynamics in Hypercube.” In The 2021 NonLinear Science Workshop. Online, 2021.
  • Hiroshi Kubota, Tsuyoshi Hasegawa, Megumi Akai, and Tetsuya Asai. “On the Noise Sensitivity of Physical Reservoir Computing in a Ring Array of Atomic Switches.” In The 2021 NonLinear Science Workshop. Online, 2021.
  • Daiju Kanaoka, Yuichiro Tanaka, and Hakaru Tamukoh. “Open Set Recognition Using the Feature Space of Deep Neural Networks.” In 2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 2021. https://doi.org/10.1109/ISPACS51563.2021.9650985.
  • Akinobu Mizutani, Yuichiro Tanaka, Hakaru Tamukoh, Yuichi Katori, Katsumi Tateno, and Takashi Morie. “Brain-Inspired Neural Network Navigation System with Hippocampus, Prefrontal Cortex, and Amygdala Functions.” In 2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 2021. https://doi.org/10.1109/ISPACS51563.2021.9651058.
  • Hidenobu Sumioka, Kohei Nakajima, Kurima Sakai, Takashi Minato, and Masahiro Shiomi. “Wearable Tactile Sensor Suit for Natural Body Dynamics Extraction: Case Study on Posture Prediction Based on Physical Reservoir Computing*.” In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 9504–9511, 2021. https://doi.org/10.1109/IROS51168.2021.9636194.
  • Yuta Horii, Katsuma Inoue, Satoshi Nishikawa, Kohei Nakajima, Ryuma Niiyama, and Yasuo Kuniyoshi. “Physical Reservoir Computing in a Soft Swimming Robot.” MIT Press, 2021. https://doi.org/10.1162/isal_a_00426.
  • Dinda Pramanta and Hakaru Tamukoh. “FPGA Implementation of Pulse-Coupled Phase Oscillators Working as a Reservoir at the Edge of Chaos.” In 2021 IEEE International Symposium on Circuits and Systems (ISCAS), 1–5, 2021. https://doi.org/10.1109/ISCAS51556.2021.9401215o.
  • Akira Kamimaki, Sumito Tsunegi, Kohei Nakajima, Kay Yakushiji, Akio Fukushima, Shinji Yuasa, and Hitoshi Kubota. “Enhanced Computational Ability of Spin Torque Oscillator With Delayed-Feedback Circuit for Physical Reservoir Computing.” In Intermag 2021, GB-07, 2022.
査読なし学会発表
  • 川添皓平・玉井克典・宍戸優樺・香取勇一，田向権，野村修，森江隆, “不揮発性アナログメモリの適用を目指したリザバー計算に基づく強化学習回路の設計 ”, 電気学会・電子回路研究会, 2021.

  • 金岡大樹・田中悠一朗・田向権, “多次元特徴空間に着目したオープンセット認識手法の開発”,電子情報通信学会スマートインフォメディアシステム研究会, 2021.

  • 徳野将士・田中悠一朗・川節拓実・細田耕・田向権, “柔軟接触センサを搭載したロボットハンドを用いたアクティブセンシングによる物体認識”, 第39回日本ロボット学会学術講演会, 2021.

  • 田中悠一朗・田向権・立野勝巳・田中啓文・森江隆, “海馬・扁桃体・前頭前野の機能を統合した脳型AIハードウェア”, 第82回応用物理学会秋季学術講演会, 2021.

  • 久保田宙・長谷川剛・赤井恵・浅井哲也, “原子スイッチを用いたリザバーコンピューティングのノイズ評価”, 第82回応用物理学会秋季学術講演会, 2021.
  • 和泉友人・香取勇一, “レザバー計算と予測符号化に基づく二経路視覚情報処理モデル”, 第31回日本神経回路学会全国大会, 2021
  • 中村仁・香取勇一, “動的シナプスを用いた報酬修飾型レザバー計算に基づく行動計画の数理モデル”, 第31回日本神経回路学会全国大会, 2021
  • 文清一・赤井恵・浅井哲也, “神経刺激電極アレイのCMOS集積化に向けた能動的チャージバランス回路”, 第31回日本神経回路学会全国大会, 2021
  • 香取勇一, “超立方体上の疑似ビリアードダイナミクスに基づく計算機構とそのニューロモルフィック応用”, 第44回日本神経科学大会, 2021
  • 小菅佑太・川節拓実・田向権・細田耕, “光学式触覚センサとエコーステートネットワークによる物体認識”, ロボティクス・メカトロニクス講演会, 2021.
  • 伊藤 圭基・白松(磯口)知世・石田直輝・眞神花帆・高橋宏知, “齧歯類におけるビート知覚の神経基盤”, 電気学会研究会資料医用・生体工学研究会, 2021.
  • 諏訪瑛介・窪田智之・高橋宏知, “神経細胞の分散培養系の情報処理容量”, 電気学会研究会資料医用・生体工学研究会, 2021.
  • 森叶人・白松（磯口）知世・高橋宏知, “多感覚情報処理が脳の逸脱検出に及ぼす影響”, 電気学会研究会資料医用・生体工学研究会, 2021.
  • 久保田 宙・長谷川 剛・赤井恵等, “原子スイッチアレイを用いた物理リザバーコンピューティング”, 第68回応用物理学会春季学術講演会, 2021.
  • 木村武龍・窪田智之・高橋宏知, “リザバー計算の状態空間別情報処理能力”, 電気学会研究会資料医用・生体工学研究会, 2021.
  • 岡田青空・寺前順之介, “ランダム時系列を利用する運動の教師あり学習”, 日本物理学会第76回年次大会, 2021.
  • 石田直輝・伊藤圭基等, “ラット聴覚野における情報処理容量の計測”, 電気学会研究会資料医用・生体工学研究会, 2021.
  • Hiroshi Kubota, Tsuyoshi Hasegawa, Megumi Akai-Kasaya, and Tetsuya Asai, “Reservoir Computing on Atomic Switch Arrays with High Precision and Excellent Memory Characteristics”, RISP international Workshop on Nonlinear Circuits, Communications and Signal Processing 2021 (NCSP ’21), 2021

2020年

論文
  • Masatoshi Yamaguchi, Goki Iwamoto, Yuta Nishimura, Hakaru Tamukoh, Takashi Morie, “An Energy-efficient Time-domain Analog CMOS BinaryConnect Neural Network Processor Based on a Pulse-width Modulation Approach,” IEEE Access, Vol.9, 2020.
  • Nozomi Akashi, Terufumi Yamaguchi, Sumito Tsunegi, Tomohiro Taniguchi, Mitsuhiro Nishida, Ryo Sakurai, Yasumichi Wakao, Kohei Nakajima, “Input-driven bifurcations and information processing capacity in spintronics reservoirs,” Physical Review Research, Vol.2, 043303, 2020.
  • Shogo Yonekura, Yasuo Kuniyoshi, “Spike-induced ordering: Stochastic neural spikes provide immediate adaptability to the sensorimotor system,” National Academy of Sciences, Vol.117, No.22, 12486-12496, 2020.
  • Yutaro Ishida, Takashi Morie, Hakaru Tamukoh, “A hardware intelligent processing accelerator for domestic service robots,” Journal of Robotics and Mechatronics, Vol.34, No.14, 947-957, 2020.
  • Masataka Harada, Mitsue Takahashi, Shigeki Sakai, Takashi Morie, “A Time-domain Analog Weighted-sum Calculation Circuit Using Ferroelectric-gate Field-effect Transistors for Artificial Intelligence Processors,” Japanese Journal of Applied Physics, Vol. 59, No.4, 40604, 2020.
  • Terufumi Yamaguchi, Nozomi Akashi, Kohei Nakajima, Hitoshi Kubota, Sumito Tsunegi, Tomohiro Taniguchi “Step-like dependence of memory function on pulse width in spintronics reservoir computing,” Scientific Reports, Vol.10, 19536, 2020.
  • Kazutoshi Tanaka, Shih-Hsin Yang, Yuji Tokudome, Yuna Minami, Yuyao Lu, Takayuki Arie, Seiji Akita, Kuniharu Takei, Kohei Nakajima, “Flapping-Wing Dynamics as a Natural Detector of Wind Direction,” Advanced Intelligent Systems, Vol. 3, No. 2, 2000174, 2020.
  • Ichiro Kawashima, Takashi Morie, Hakaru Tamukoh, “FPGA implementation of hardware-oriented chaotic Boltzmann machines,” IEEE Access, Vol.8, 204360-204377, 2020.
  • Katsuma Inoue, Kohei Nakajima, Yasuo Kuniyoshi, “Designing spontaneous behavioral switching via chaotic itinerancy,” Science Advances, Vol. 6, No. 46, eabb3989, 2020.
  • Yuichiro Tanaka, Takashi Morie , Hakaru Tamukoh, “An amygdala-inspired classical conditioning model on FPGA for home service robots,” IEEE Access, Vol. 8, 212066-212078, 2020.
  • Mizuka Komatsu, Takaharu Yaguchi, Kohei Nakajima, “Algebraic approach towards the exploitation of “softness”: the input-output equation for morphological computation,” International Journal of Robotics Research, 0278364920912298, 2020.
  • 窪田智之・中嶋浩平・高橋宏知, “1次視覚野の過渡ダイナミクスの推定, 電気学会論文誌C電子情報システム部門誌”, Vol. 140, No. 7, pp.723-729, 2020.
  • Hirokazu Takahashi, Tomoyo Isoguchi Shiramatsu, Rie Hitsuyu, Kenji Ibayashi, and Kensuke Kawai, “Vagus nerve stimulation (VNS)-induced layer-specific modulation of evoked responses in the sensory cortex of rats,” Scientific Reports, No.10, 8932, 2020.
  • Kohei Nakajima, “Physical reservoir computing −an introductory perspective,” Japanese Journal of Applied Physics, Vol.59, No.6, 060501, 2020.
  • Shota Hamaguchi, Takumi Kawasetsu, Takato Horii, Hisashi Ishihara, Ryuma Niiyama, Koh Hosoda, and Minoru Asada, “Soft inductive tactile sensor using flow channel enclosing liquid metal,” IEEE Robotics and Automation Letters, No. 5, Vol. 3, 4028-4034, 2020.
  • Tomoyo Isoguchi Shiramatsu and Hirokazu Takahashi, “Mismatch-negativity (MMN) in animal models: Homology of human MMN?”, Hearing Research, In press.
  • Takuma Tanaka, Kohei Nakajima, and Toshio Aoyagi, “Effect of recurrent infomax on the information processing capability of input-driven recurrent neural networks,” Neuroscience Research, Vol. 156, pp. 225-233, 2020.
  • Terufumi Yamaguchi, Sumito Tsunegi, and Tomohiro Taniguchi, “Phase estimation of spin-torque oscillator by nonlinear spin-torque diode effect,” Japanese Journal of Applied Physics, Vol. 59, No.2, 020903, 2020.
  • Tomohiro Taniguchi, “Synchronization and chaos in spin torque oscillator with two free layers,” AIP Advances, No. 10, Vol. 1, 015112, 2020.
  • Hirokazu Takahashi, “Darwinian computation with functional map in auditory cortex,” Acoustical Science and Technology, Vol. 41, No. 1, pp. 39-47, 2020.
査読付き学会発表
  • Akira Kamimaki, Sumito Tsunegi, Tomohiro Taniguchi, Nozomi Akashi, Kohei Nakajima, Akio Fukushima, Shinji Yuasa, Vincent Cros, Julie Grollier, Hitoshi Kubota “Physical Reservoir Computing by Spin-Torque Oscillator at the Edge of Chaos”, The 2020 Magnetism and Magnetic Materials Conference, 2020.
  • Shoshi Tokuno, Yuichiro Tanaka, Takumi Kawasetsu, Koh Hosoda, Hakaru Tamukoh, “Object Recognition Using Flexible Tactile Sensor”, Asia Pacific Conference on Robot IoT System Development and Platform, 2020.
  • Daichi Kamimura, Yuichi Katori, Hakaru Tamukoh, Takashi Morie, “Performance Evaluation of Reservoir Computing Using Pseudo-billiard Dynamics in Hypercube”, 2020 International Symposium on Nonlinear Theory and Its Applications (NOLTA2020), 2020.
  • Yoshihiro Yonemura, Yuichi Katori, “Multi-Modal Processing of Visual and Auditory Signals on Network Model Based on Predictive CodingAnd Reservoir Computing”, 2020 International Symposium on Nonlinear Theory and Its Applications (NOLTA2020), 2020.
  • Daichi Yamamoto, Ichiro Kawashima, Hakaru Tamukoh, Takashi Morie, Yuichi Katori, “FPGA Implementation and Verification of Reservoir Computing Based on Pseudo-Billiard Dynamics in Hypercube,” 2020 International Symposium on Nonlinear Theory and Its Applications (NOLTA2020), pp. 264, 2020.
  • Yuta Nishimura, Masatoshi Yamaguchi, Daichi Kamimura, Hakaru Tamukoh, Takashi Morie, “A Reservoir Computing System Using a CMOS Chaotic Boltzmann Machine Chip Controlled by SoC FPGA,” 2020 International Symposium on Nonlinear Theory and Its Applications (NOLTA2020), pp. 137, 2020.
  • Masafumi Inada, Yuichiro Tanaka, Hakaru Tamukoh, Katsumi Tateno, Takashi Morie, Yuichi Katori, “A Reservoir Based Q-learning Model for Autonomous Mobile Robots,” 2020 International Symposium on Nonlinear Theory and Its Applications (NOLTA2020), pp. 213-216, 2020.
  • Yuichiro Tanaka, Hakaru Tamukoh, Katsumi Tateno, Yuichi Katori, Takashi Morie, “A Brain-inspired Artificial Intelligence Model of Hippocampus, Amygdala, and Prefrontal Cortex on Home Service Robots,” 2020 International Symposium on Nonlinear Theory and Its Applications (NOLTA2020), pp. 138-141, 2020.
  • Gabor Soter, Helmut Hauser, Andrew Conn, Jonathan Rossiter, Kohei Nakajima, “Shape reconstruction of CCD camera-based soft tactile sensors”, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8957-8962, 2020.
  • Jihoon Park, Yuji Kawai and Minoru Asada, “Self-organization of connectivity in spiking neural networks with balanced excitation and inhibition”, In Proceedings of the 29th Annual Computational Neuroscience Meeting, 2020.
  • Shota Hamaguchi , Takumi Kawasetsu, Takato Horii, Hisashi Ishihara, Ryuma Niiyama, Koh Hosoda, and Minoru Asada, “Soft Inductive Tactile Sensor using Flow Channel Enclosing Liquid Metal,” In Proceedings of the 3rd IEEE International Conference on Soft Robotics, 2020.
  • Ryo Sakurai, Mitsuhiro Nishida, Hideyuki Sakurai, Yasumichi Wakao, Nozomi Akashi, Yasuo Kuniyoshi, Yuna Minami, and Kohei Nakajima, “Emulating a sensor using soft material dynamics: A reservoir computing approach to pneumatic artificial muscle,” In Proceedings of the 3rd IEEE International Conference on Soft Robotics, 2020.
  • Kota Wakamatsu, Katsuma Inoue, Daiki Hagiwara, Haruka Adachi, Daisuke Matsui, Shunichi Kurumaya, Rie Nishihama, Manabu Okui, Kohei Nakajima, Yasuo Kuniyoshi, and Taro Nakamura, “Mixing state estimation of peristaltic continuous mixing conveyor with distributed sensing system based on soft intestine motion,” In Proceedings of the 3rd IEEE International Conference on Soft Robotics, 2020.
査読なし学会発表
  • Akira Kamimaki, Sumito Tsunegi, Tomohiro Taniguchi, Nozomi Akashi, Kohei Nakajima, Akio Fukushima, Shinji Yuasa, Vincent Cros, Julie Grollier, Hitoshi Kubota, “Physical Reservoir Computing by Spin-Torque Oscillator at the Edge of Chaos”, The 2020 Magnetism and Magnetic Materials Conference, 2020
  • 谷口知大・明石望洋・野津裕史・木村正人・塚原宙・中嶋浩平, “遅延回路を含むスピントロニクス素子におけるカオス”, 第81回応用物理学会秋季学術講演会, 2020.
  • 山口皓史・明石望洋・常木澄人・久保田均・中嶋浩平・谷口知大, “遅延回路を含むスピントロニクス・リザバーの短時間記憶容量”, 第81回応用物理学会秋季学術講演会, 2020.

  • Akira Kamimaki, Sumito Tsunegi, Tomohiro Taniguchi, Nozomi Akashi, Kohei Nakajima, Akio Fukushima, Shinji Yuasa, Hitoshi Kubota, “Chaotic Behavior of Spin-Torque Oscillator with Feedback Circuit, 第81回応用物理学会秋季学術講演会, 2020

  • 上牧暎・常木澄人・谷口知大・明石望洋・中嶋浩平・福島章雄・湯浅新司・久保田均, “Chaotic Behavior of Spin-Torque Oscillator with Feedback Circuit”, 第81回応用物理学会秋季学術講演会, 2020.
  • 水野 海渡・川節 拓実・堀井 隆斗・矢野 順彦・石原 尚, “触覚を備える子供アンドロイド用小型ハンドの骨格の改良と表面張力層の実装”, ロボティクス・メカトロニクス　講演会2020, 2020.
  • 森江隆・立野勝巳・中川拓紀・高田健介, “海馬の機能にヒントを得た脳型ハードウェアアーキテクチャ”, 第30回日本神経回路学会 全国大会, 2020.
  • 大河原昂也・香取勇一, “レザバー計算と深層強化学習を組み合せた推論機構の研究”, 第30回日本神経回路学会 全国大会, 2020.
  • 徳野将士・田中悠一朗・田中悠一朗・川節拓実・細田耕・田向権, “柔軟触覚センサを搭載したロボットハンドによる触覚情報からの物体認識”, 日本ロボット学会 学術講演会, 2020.
  • 水野海渡・川節拓実・堀井隆斗・矢野順彦・石原尚, “触覚を備える子供アンドロイド用小型ハンドの骨格の改良と表面張力層の実装”, ロボティクス・メカトロニクス講演会, 2020.
  • 明石望洋・山口皓史・常木澄人・谷口知大・中嶋浩平, “スピントルク発振器における情報処理能力と分岐構造の関係”, 日本物理学会第75回年次大会, 2020.
  • 山口皓史・明石望洋・中嶋浩平・常木澄人・久保田均・谷口知大, “スピントルク発振器におけるカオスの理論”, 日本物理学会第75回年次大会, 2020.
  • Sumito Tsunegi, “Physical Reservoir Computing based on Spin Torque Oscillator with forced synchronization,” The 3rd Symposium for the Core Research Clusters for Materials Science and Spintronics, 2020.
  • Jihoon Park, Yuji Kawai, and Minoru Asada, “Formation of Connectivity between Spiking Neural Networks with Balanced Excitation and Inhibition”, 脳と心のメカニズム 第19回冬のワークショップ, 2020.
招待講演
  • 森江隆, “不揮発性メモリを用いたAIプロセッサ／ニューロモルフィック回路技術の進展と今後の展望”, 第48回薄膜・表面物理セミナー(2020), 2020

2019年

論文
  • Taichi Haruna and Kohei Nakajima, “Optimal short-term memory before the edge of chaos in driven random recurrent networks,” Physical Review E, No, 100, Vol. 6, 062312, 2019.
  • Tomohiro Taniguchi, Nozomi Akashi, Hirofumi Notsu, Masato Kimura, Hiroshi Tsukahara, and Kohei Nakajima, “Chaos in nanomagnet via feedback current,” Physical Review B, 100: 174425, 2019.
  • 中嶋浩平, “物理リザバー計算の射程—ソフトロボットを例に”, システム/制御/情報, No. 63, Vol. 12, pp. 505–511, 2019.
  • 常木澄人・谷口知大・三輪真嗣・中嶋浩平・久保田均, “スピントルク発振器を用いた物理リザバー計算”, 日本磁気学会学会誌, No. 14, Vol. 6, 2019.
  • 高橋宏知, “エンジニアのための脳科学のすすめ”, 電子情報通信学会誌, Vol. 102, Vol.9, pp. 881-888, 2019.
  • Kohei Nakajima and Taichi Haruna, “Spatiotemporal dynamics driven by the maximization of local information transfer,” New Journal of Physics, Vol. 21, 013034, 2019.
  • Jihoon Park, Koki Ichinose, Yuji Kawai, Junichi Suzuki, Minoru Asada, and Hiroki Mori, “Macroscopic cluster organizations change the complexity of neural activity,” Entropy, Vol. 21, No. 2, 214, 2019.
  • Kohei Nakajima, Keisuke Fujii, Makoto Negoro, Kosuke Mitarai, and Masahiro Kitagawa, “Boosting computational power through spatial multiplexing in quantum reservoir computing,” Physical Review Applied, Vol. 11, No. 3, 034021, 2019.
  • Sumito Tsunegi, Tomohiro Taniguchi, Kohei Nakajima, Shinji Miwa, Kay Yakushiji, Akio Fukushima, Shinji Yuasa, and Hitoshi Kubota, “Physical reservoir computing based on spin torque oscillator with forced synchronization,”Applied Physics Letters, Vol. 114, No. 16, 164101, 2019. 
  • 鹿山敦至・矢田祐一郎・高橋宏知，“神経細胞の分散培養系における集団同期発火パターンとネットワーク構造の発達”，電気学会論文誌Ｃ（電子・情報・システム部門誌），Vol. 139, No. 5, pp. 570-578, 2019．
  • 三田毅・Douglas Bakkum・Urs Frey・Andreas Hierlemann・神崎亮平・高橋宏知，“高密度CMOSアレイ上の分散培養系における活動電位波形に基づく興奮性及び抑制性神経細胞の分類”，電気学会論文誌Ｃ（電子・情報・システム部門誌），Vol. 139, No. 5, pp. 615-624, 2019．
  • 石津光太郎・白松（磯口）知世・小河原康一・高橋宏知，“ラットの聴覚タスク中のマイクロ皮質脳波計測”，電気学会論文誌Ｃ（電子・情報・システム部門誌），Vol. 139, No. 5, pp. 625-631, 2019．
  • Terufumi Yamaguchi, Nozomi Akashi, Kohei Nakajima, Sumito Tsunegi, Hitoshi Kubota, and Tomohiro Taniguchi, “Synchronization and chaos in spin torque oscillator with perpendicularly magnetized free layer,” Physical Review B, No. 100, Vol. 22, 224422, 2019.
査読付き学会発表
  • Masatoshi Yamaguchi, Yuichi Katori, Daichi Kamimura, Hakaru Tamukoh, and Takashi Morie. 2019. “A Chaotic Boltzmann Machine Working as a Reservoir and Its Analog VLSI Implementation.” In 2019 International Joint Conference on Neural Networks (IJCNN), 1–7. https://doi.org/10.1109/IJCNN.2019.8852325.
  • Yuichi Katori, Hakaru Tamukoh, and Takashi Morie. 2019. “Reservoir Computing Based on Dynamics of Pseudo-Billiard System in Hypercube.” In 2019 International Joint Conference on Neural Networks (IJCNN), 1–8. https://doi.org/10.1109/IJCNN.2019.8852329.
  • Nozomi Akashi, Kohei Nakajima and Yasuo Kuniyoshi, “Unpredictable as a dice: analyzing riddled basin structures in passive dynamic walker,” In Proceedings of the 30th International Symposium on Micro-NanoMechatronics and Human Science, pp. 119-123, 2019.
  • Katsuma Inoue, Kohei Nakajima, and Yasuo Kuniyoshi, “Soft bodies as input reservoir: role of softness from the viewpoint of reservoir computing,” In Proceedings of the 30th International Symposium on Micro-NanoMechatronics and Human Science, pp. 60-65, 2019.
  • Terufumi Yamaguchi, Nozomi Akashi, Kohei Nakajima, Sumito Tsunegi, Hitoshi Kubota, and Tomohiro Taniguchi, “Injection locking of spin-torque oscillator with perpendicularly magnetized free layer,” In Proceedings of the 64th Annual Conference on Magnetism and Magnetic Materials, 2019.
  • Sumito Tsunegi, Tomohiro Taniguchi, Kohei Nakajima, Shinji Miwa, Kay Yakushiji, Akio Fukushima, and Hitoshi Kubota, “Physical reservoir computing based on spin torque oscillator with synchronization,” In Proceedings of the 64th Annual Conference on Magnetism and Magnetic Materials, 2019.
  • Tomohiro Taniguchi, “Synchronization and chaos in spin torque oscillator with two free layers,” In Proceedings of the 64th Annual Conference on Magnetism and Magnetic Materials, 2019.
  • Yutaro Ishida and Hakaru Tamukoh, “High-level synthesis system to integrate SoC and ROS,” In Proceedings of Asia Pacific Conference on Robot IoT System Development and Platform 2019.
  • Tomoyuki Kubota, Kohei Nakajima, and Hirokazu Takahashi, “Echo state property of neuronal cell culture,” Lecture Notes in Computer Science 11731 (Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions), Vol. 11731, pp. 137-148, 2019.
  • Kaoruko Higuchi, Hoshinori Kanazawa, Yuma Suzuki, Keiko Fujii, and Yasuo Kuniyoshi, “Musculoskeletal bias on infant sensorimotor development driven by predictive learning,” In Proceedings of the 9th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, 2019.
  • Kentaro Suzuki, Jihoon Park, Yuji Kawai, and Minoru Asada, “Small-world networks enhance the inter-brain synchronization”, In Proceedings of the 28th Annual Computational Neuroscience Meeting, 2019.
査読なし学会発表
  • 田中悠一朗, 田向権, 立野勝巳, 香取勇一, and 森江隆. 2019. “海馬・扁桃体・前頭前野の機能を統合した脳型人工知能モデル.” In 第29回日本神経回路学会全国大会（JNNS2019）, O3-41. 東京工業大学.
  • Tsunegi, T. Taniguchi, K. Nakajima, S. Miwa, K. Yakushiji, A. Fukushima, S. Yuasa, and H. Kubota, “Physical reservoir computing based on spin torque oscillator,” 第43回日本磁気学会学術講演会, 2019.
  • 山口皓史・明石望洋・中嶋浩平・常木澄人・久保田均・谷口知大, “スピントルク発振器における強制同期の理論”, 日本物理学会2019年秋季大会, 2019.
  • 鈴木健太郎・朴志勲・河合祐司・浅田稔, “スモールワールド性がもたらす二つの脳神経モデルの同期”, 電子情報通信学会ニューロコンピューティング研究会, Vol. 119, pp. 9-14, 2019.
  • 朴志勲・小椋基弘・河合祐司・浅田稔, “スパイキングニューロンモデルの興奮性／抑制性バランスがネットワーク形成に及ぼす影響”, 電子情報通信学会ニューロコンピューティング研究会, Vol. 119, pp. 15-20, 2019.
  • 原田將敬・森江隆・高橋光恵・酒井滋樹，“FeFETを用いた時間領域アナログ積和演算回路の特性評価”，第66回応用物理学会春季学術講演会，2019．

  • Sumito Tsunegi, Tomohiro Taniguchi, Shinji Miwa, Kohei Nakajima, Kay Yakushiji, Akio Fukushima, Shinji Yuasa and Hitoshi Kubota, “The effect of voltage on reservoir computing performance of a spin torque oscillator,” 第66回応用物理学会春季学術講演会, 2019．

  • 常木澄人・谷口知大・三輪真嗣・中嶋浩平・薬師寺啓・福島章雄・湯浅新治・久保田均, “スピントルク発振素子の短時間記憶容量”, 第66回応用物理学会春季学術講演会, 2019.
  • 常木澄人・谷口知大・三輪真嗣・中嶋浩平・薬師寺啓・福島章雄・湯浅新治・久保田均，“スピントルク発振素子の短時間記憶容量”，第66回応用物理学会春季学術講演会，2019．
  • 久米弘祐・川節拓実・堀井隆斗・石原尚・浅田稔，“三角格子状に配置したコイルと磁性マーカを用いた柔軟触覚センサの基礎特性評価”，ロボティクス・メカトロニクス講演会 2019，2019．
  • 水野海渡・川節拓実・石原尚・堀井隆斗・浅田稔，“子供アンドロイドの接触反応実験に向けた骨格と触覚を備える小型手部の開発”，ロボティクス・メカトロニクス講演会 2019，2019．
招待講演
  • 森江 隆, “次世代人工知能のための脳型集積回路技術とデバイス技術”, 第83回半導体・集積回路技術シンポジウム（電気化学会　電子材料委員会）,  2019年8月29日, 東京理科大（東京）．
  • 森江隆・原田將敬・高橋光恵・酒井滋樹, “3 端子アナログメモリ素子としてのFeFET の適用を目指した人工知能ハードウェアモデルと回路アーキテクチャ”, 応用物理学会秋季学術講演会, 2019.
プレス発表

  • 常木澄人・谷口知大・薬師寺啓・湯浅新治・久保田均，“ナノ磁石を用いたリザバー計算の性能を向上”，2019年4月21日．【産総研ホームページ】