中嶋研究室 / Nakajima Lab​

 

東京大学大学院 情報理工学系研究科 情報理工学教育研究センター 次世代知能科学研究部門
東京大学 連携研究機構 次世代知能科学研究センター(AIセンター)(兼務)
東京大学大学院 情報理工学系研究科 先端人工知能学教育寄付講座(兼務)
東京大学大学院 情報理工学系研究科 創造情報学専攻(兼担)

Next Generation Artificial Intelligence Research Center (AI Center),
The University of Tokyo

Chair for Frontier AI Education, Graduate School of Information Science and Technology,

The University of Tokyo

Department of Creative Informatics, Graduate School of Information Science and Technology,
The University of Tokyo

東京都文京区本郷7-3-1

7-3-1 Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan

Email: k_nakajima[at]mech.t.u-tokyo.ac.jp

Information,

Dynamics,

Computation,...

 

Soft Robotics,

Physical Reservoir Computing,...

中嶋研究室へようこそ!

 

東京大学大学院情報理工学系研究科創造情報学専攻の研究室として発足しました。
中嶋研究室では、特任研究員、大学院生(博士課程・修士課程)を募集しています。
興味のある方は、メールにてご連絡ください。

 


Welcome to Nakajima Lab!

 

Our lab is diverse and interdisciplinary. 
Prospective students/postdocs are encouraged to email k_nakajima@mech.t.u-tokyo.ac.jp for more details.

Reservoir Computing Seminarを開催しています

 

2021年度秋学期のReservoir Computing Seminarを10月より開催します。
Reservoir Computingを中心に、
ソフトロボティクス、カオス力学系、スピントロニクス、量子機械学習などの
研究発表やジャーナルクラブを行います。
参加を希望される方は、メールにてご連絡ください。
現在は、zoomによる参加となります。

2021/08/06

Springer Book Project "Reservoir Computing---Theory, Physical Implementations, and Applications" has been published!

Reservoir Computing --- Theory, Physical Implementations, and Applications
Editors: Kohei Nakajima, Ingo Fischer (Eds.)

The first comprehensive book on reservoir computing
Provides an introduction and cutting-edge research in a wide range of domains
Contributed by leading researchers in the field

 

info: https://www.springer.com/gp/book/9789811316869

 

bfm:978-981-13-1687-6/1.pdf (springer.com)

Foreword Herbert Jaeger

Preface Kohei Nakajima and Ingo Fischer

 

Part I: Fundamental Aspects and New Developments in Reservoir Computing
• The cerebral cortex: A delay coupled recurrent oscillator network?
Wolf Singer


• Cortico-Striatal Origins of Reservoir Computing, Mixed Selectivity and Higher Cognitive Function
Peter Ford Dominey


• Reservoirs learn to learn
Anand Subramoney, Franz Scherr, Wolfgang Maass


• Deep Reservoir Computing
Claudio Gallicchio, Alessio Micheli


• On the characteristics and structures of dynamical systems suitable for reservoir computing
Masanobu Inubushi, Kazuyuki Yoshimura, Yoshiaki Ikeda, Yuto Nagasawa


• Reservoir Computing for Forecasting Large Spatiotemporal Dynamical Systems
Jaideep Satyajit Pathak, Edward Ott

 

Part II: Physical Implementations of Reservoir Computing
• Reservoir Computing in Material Substrates
Matthew Dale, Julian F. Miller, Susan Stepney, Martin A. Trefzer

 

Part III: Mechanics and Bio-inspired Machines
• Physical Reservoir Computing in Robotics
Helmut Hauser


• Reservoir Computing in MEMS
Guillaume Dion, Anouar Idrissi-El Oudrhiri, Bruno Barazani, Albert Tessier-Poirier and Julien Sylvestre

 

Part IV: Neuromorphic Devices and Nanotechnology
• Neuromorphic Electronic Systems for Reservoir Computing
Fatemeh Hadaeghi


• Reservoir Computing using Autonomous Boolean Networks Realized on Field-Programmable Gate Arrays
Stefan Apostel, Nicholas D. Haynes, Eckehard Schöll, Otti D'Huys, and Daniel J. Gauthier


• Programmable Fading Memory in Atomic Switch Systems for Error Checking Applications
Renato Aguilera, Henry O. Sillin, Adam Z. Stieg, James K. Gimzewski

 

Part V: Spintronics Reservoir Computing
• Reservoir computing leveraging the transient non-linear dynamics of spin-torque nano-oscillators
Mathieu Riou, Jacob Torrejon, Flavio Abreu Araujo, Sumito Tsunegi, Guru Khalsa, Damien Querlioz, Paolo Bortolotti, Nathan Leroux, Danijela Markovic, Vincent Cros, Kay Yakushiji, Akio Fukushima, Hitoshi Kubota, Shinji Yuasa, Mark D. Stiles, Julie Grollier


• Reservoir computing based on spintronics technology
Tomohiro Taniguchi, Sumito Tsunegi, Shinji Miwa, Keisuke Fujii, Hitoshi Kubota, Kohei Nakajima


• Reservoir computing with dipole-coupled nanomagnets
Hikaru Nomura, Hitoshi Kubota, Yoshishige Suzuki

 

Part VI: Photonic Reservoir Computing
• Performance improvement of delay-based photonic reservoir computing
Kazutaka Kanno, Atsushi Uchida


• Computing with integrated photonic reservoirs
Joni Dambre, Andrew Katumba, Chonghuai Ma, Stijn Sackesyn, Floris Laporte, Matthias Freiberger, Peter Bienstman

 

Part VII: Quantum Reservoir Computing
• Quantum reservoir computing: a reservoir approach toward quantum machine learning on near-term quantum devices
Keisuke Fujii, Kohei Nakajima


• Towards NMR Quantum Reservoir Computing
Makoto Negoro, Kosuke Mitarai, Kohei Nakajima, Keisuke Fujii