Professor Gohei TANAKA

Faculty Staff Information

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


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).