2019　2020　2021　2022　2023

2023

Articles in Journals

• 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.
• 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).

Peer-reviewed Conferences

Non-reviewed Conferences

• 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 Winter Workshop on Mechanism of Brain and Mind 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 Winter Workshop on Mechanism of Brain and Mind 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.

2022

Articles in Journals

• 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.

Peer-reviewed Conferences

Non-reviewed Conferences

• 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/.
• Y. Kawai, J. Park, I. Tsuda and M. Asada. “Emergence of Dynamical Orthogonal Basis Acquiring Large Memory Capacity in Modular Reservoir Computing .” In Neurocomputing, 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, 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.

Invited Talks

• Osamu Nomura. 2022. “Analog Neuromorphic Hardware for Energy-Efficient AI Computing.” Invited talk presented at the The 18th Embedded Vision Workshop In Conjunction With CVPR 2022, New Orleans, June 20. https://embeddedvisionworkshop.wordpress.com/2023/01/01/2022/.

2021

Articles in Journals

• 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.
• Minoru Asada. “Robotics and Reinforcement Learning.” Journal of the Robotics Society of Japan 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.
• Tomoyo Isoguchi Shiramatsu, Taiki Abe, Kotaro Ishizu, and Hirokazu Takahashi. “Experimental System to Evaluate Auditory Perception Induced by Microstimulation of Auditory Thalamus of Rats.” IEEJ Transactions on Electronics, Information and Systems 141, no. 5 (2021): 627–33. https://doi.org/10.1541/ieejeiss.141.627.
• Narumitsu Ikeda and Hirokazu Takahashi. “Learning in Dissociated Neuronal Cultures by Low-frequency Stimulation.” IEEJ Transactions on Electronics, Information and Systems 141, no. 5 (2021): 654–60. https://doi.org/10.1541/ieejeiss.141.654.
• Tomoyuki Kubota, Kazuhiro Sakurayama, Tomoyo Isoguchi Shiramatsu, and Hirokazu Takahashi. “Deviance Detection Property in Dissociated Cultures of Neurons.” IEEJ Transactions on Electronics, Information and Systems 141, no. 5 (2021): 661–67. https://doi.org/10.1541/ieejeiss.141.661.
• Yuji Kawai. “Functional Significance of Complex Networks in the Brain.” SYSTEMS, CONTROL AND INFORMATION 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.

Peer-reviewed Conferences

• 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.

Non-reviewed Conferences

• 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

Articles in Journals

• 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.
• 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.

Peer-reviewed Conferences

• 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.

Non-reviewed Conferences

• 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”, 第81回応用物理学会秋季学術講演会, 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.

2019

Articles in Journals

• 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.
• Kohei Nakajima and Taichi Haruna, “Spatiotemporal dynamics driven by the maximization of local information transfer,” New Journal of Physic, 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. 
• 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.

Peer-reviewed Conferences

• 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.

Non-reviewed Conferences

• Tsunegi, T. Taniguchi, K. Nakajima, S. Miwa, K. Yakushiji, A. Fukushima, S. Yuasa, and H. Kubota, “Physical reservoir computing based on spin torque oscillator,” The 43th Annual Conference on Magnetics in Japan, 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,” The 43th Annual Conference on Magnetics in Japan, 2019.