Collaborative Research Center for Innovative Mathematical Modeling

 

Collaborative Research Center for Innovative Mathematical Modeling (Institute of Industrial Science at the University of Tokyo)
– Challenge for Complex Dynamics in the Real World –

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合原 一幸
Kazuyuki AIHARA

Professor
田中 剛平
Gouhei TANAKA

Project Associate Professor
近江 崇宏
Takahiro OMI

Project Associate Professor
Fundamental and Application Studies on Mathematical Modelling of Complex Systems
We perform theoretical studies for mathematical modelling and analysis of complex systems and application studies for real-world phenomena including artificial intelligence and power grids.
Complex Dynamics Analysis
Complex dynamical behavior is ubiquitous in a variety of phenomena, ranging from microscopic activities in cells and genes to macroscopic activities in earth and cosmos.  Towards understanding complex dynamical behavior and solving practical issues through mathematical modeling and analyses, we aim at developing a method for predicting, controlling, and optimizing complex dynamical phenomena. The subjects of this research include biology, medicine, public health, engineering, economics, and social problems. 

Professor Gouhei TANAKA

Faculty Staff Information

Gouhei TANAKA
田中 剛平

Project Associate Professor
International Research Center for Neurointelligence
Department of Mathematical Informatics, Graduate School of Information Science and Technology
Department of Electrical Engineering and Information Systems, Graduate School of Engineering

Room N308, IRCN, Faculty of Medicine Bldg. 1, The University of Tokyo, 3-7-1 Bunkyo-ku, Hongo, Tokyo 113-0033, Japan.
Phone: not available

E-mail:gtanaka@g.ecc.u-tokyo.ac.jp

[Website]

Research theme

  1. Brain-like energy-efficient information processing

    For realizing next-generation information processing systems, it is indispensable to miniaturize devices and make structures compact for enhancement of energy efficiency. We aim to develop mathematical methodologies for making brain-like computing systems energy efficient such that efficient computing is realized with low power and high speed.

  2. Applications of machine learning and advanced mathematical methods

    Machine learning technologies have enabled to efficiently perform tasks that have been manually handled by people. We aim to mathematically formulate problems in fields that are not approached by machine learning and mathematical modeling, and solve the problems by combining appropriate machine learning methods and advanced mathematical techniques.

  3. Mathematical studies on medical and social systems

    It is becoming possible to obtain real data on medical and social systems due to the developments of sensor devices and measurement techniques. We aim to propose effective control strategies for solving medically and socially important problems and improving quality of life.                                               
  4. Network robustness

    Networked systems are ubiquitous in the world, such as the Internet, power networks, and biological networks. Networking often accompanies a risk that a partial failure causes a breakdown of the whole system. We are investigating how network robustness depends on network structure, dynamics, and element interactions. Our aim is to develop a design method of robust networks and a recovery method of damaged networks.

    Recent Publications

    G. Tanaka, R. Nakane, T. Takeuchi, T. Yamane, D. Nakano, Y. Katayama, and A. Hirose
    Spatially Arranged Sparse Recurrent Neural Networks for Energy Efficient Associative Memory
    IEEE Transactions on Neural Networks and Learning Systems, vol. 31, issue 1, pp. 24-38 (2020). DOI: 10.1109/TNNLS.2019.2899344

    A. Matsuki and G. Tanaka
    Intervention threshold for epidemic control in susceptible-infected-recovered metapopulation models
    Physical Review E, vol. 100, 022302 (2019).

    G. Tanaka et al.
    Recent Advances in Physical Reservoir Computing: A Review
    Neural Networks, vol. 115, pp. 100-123 (2019).

    Z. Tong and G. Tanaka
    Hybrid pooling for enhancement of generalization ability in deep convolutional neural networks
    Neurocomputing, vol. 333. pp. 76-85 (2019).

    G. Tanaka, E. Dominguez-Huttinger, P. Christodoulides, K. Aihara, and R. J Tanaka
    Bifurcation analysis of a mathematical model of atopic dermatitis to determine patient-specific effects of treatments on dynamic phenotypes
    Journal of Theoretical Biology, vol. 448, pp. 66-79 (2018).

    Professor Tetsuya J. KOBAYASHI

    Faculty Staff Information

    Tetsuya J. KOBAYASHI
    小林 徹也

    Associate Professor
    Institute of Industrial Science, The University of Tokyo
    Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo
    Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo

    Room Ce-501, Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8505, Japan.
    Phone: +(81)-3-5452-6798 (Extension: 56798)
    Fax: +(81)-3-5452-6798

    E-mail:tetsuya@sat.t.u-tokyo.ac.jp

    [Website]

    Research topics

    Systems biology, Quantitative biology, Bioinformatics

    Relevant Publications

    ・Hideki Ukai, Tetsuya J. Kobayashi, Mamoru Nagano, Koh-hei Masumoto, Mitsugu Sujino, Takao Kondo, Kazuhiro Yagita, Yasufumi Shigeyoshi and Hiroki R. Ueda:Melanopsin-dependent photo-perturbation reveals desynchronizationunderlying the singularity of mammalian circadian clocks, Nature CellBiology, Vol. 9, No.11, pp. 1327-1334, October 2007.
    ・Tetsuya J. Kobayashi: Implementation of Dynamic Bayesian Decision Making by Intracellular Kinetics, Physical Review Letters, Vol.104, p.0228104, June 2010.
    ・Tetsuya J. Kobayashi & Yuki Sughiyama: Fluctuation Relations of Fitness and Information in Population Dynamics, Physical Review Letters, Vol. 115, pp. 238102, December 2015.

    Professor Takashi KOHNO

    Faculty Staff Information

    Takashi KOHNO
    河野 崇

    Professor
    Institute of Industrial Science, The University of Tokyo

    Department of Electrical Engineering and Information Systems, Graduate School of Engineering, The University of Tokyo
    Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo

    Room Ee-512, Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan.
    Phone: +(81)-3-5452-6900 (Extension: 56900)
    Fax: +(81)-3-5452-6901

    E-mail:kohno@sat.t.u-tokyo.ac.jp

    [Website]

    Research topics

    Neuromorphic systems, Neuronal system modeling

    Relevant Publications

    Takashi Kohno and Kazuyuki Aihara:
    Mathematical-model-based design method of silicon burst neurons, Neurocomputing, in press.
    Takashi Takemoto, Takashi Kohno, and Kazuyuki Aihara:
    MOSFET Implementation of Class I* Neurons Coupled by Gap Junctions, Journal of Artificial Life and Robotics, Vol. 10, No. 1, pp. 1-5, July 2006.
    Takashi Kohno and Kazuyuki Aihara:
    A MOSFET-based model of a Class 2 Nerve membrane, IEEE Transactions on Neural Networks, Vol. 16, No. 3, pp. 754-773, May 2005.

    Professor Kazuyuki AIHARA

    Faculty Staff Information

    Kazuyuki AIHARA
    合原 一幸

    Professor
    Institute of Industrial Science, The University of Tokyo
    Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo

    Room Ce-601, Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan.
    Phone: +(81)-3-5452-6691 (Extension: 56691)
    Fax: +(81)-3-5452-6692 (Extension: 56692)

    Room 353, Faculty of Engineering Bldg.6, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan.
    Phone: +(81)-3-5841-6910 (Extension: 26910)
    Fax: +(81)-3-5841-8594

    E-mail:aihara@sat.t.u-tokyo.ac.jp

    [Website]

    Research Topics

    Mathematical Modeling for Complex Systems, Chaotic and Optoelectronic Neural Networks, Mathematics of Artificial Intelligence

     

    Laboratories for Mathematics, Lifesciences, and Informatics

    Laboratories for Mathematics, Lifesciences, and Informatics (Institute of Industrial Science, IRCN) Group website→
    河野 崇 Takeshi  KOHNO Professor 小林 徹也 Tetsuya J. KOBAYASHI Associate Professor
    田中 剛平
    Gouhei TANAKA

    Project Associate Professor
    Kantaro Fujiwara Kantaro FUJIWARA Project Associate Professor
    Research Topics
    Mathematical Analysis of Complex Systems
    In order to comprehend diverse complex systems ranging over biology, brain science, and socio-economics, we are developing new mathematical techniques based on, e.g., bifurcation theory, time-series analysis, and statistics, and trying to construct universal theoretical frameworks for complex systems.

    Understanding Information processing of biological systems
    In order to unveil the design principle of biological systems and their information processing, we are working on theoretical biology and bioinformatics for different types of biological phenomena and data, which cover neuroscience, cell biology, developmental biology, immunology, physiology, epidemics, bioimaging, and next-generation sequencing. Additionally, we are employing our mathematical models of brain and neural networks for engineering new neuromorphic hardware.

    Chaos engineering
    We are developing new information processing systems taking full advantage of the diverse dynamical behaviors that chaos and complex systems generate. Those systems are applied to the implementation of nonlinear analog circuits, constructing an artificial brain, and other engineering problems.