Professor Gohei TANAKA
Faculty Staff Information
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, 371 Bunkyoku, Hongo, Tokyo 1130033, Japan.
Phone: not available
Email：gtanaka＠g.ecc.utokyo.ac.jp
[Website]
Research theme

Brainlike energyefficient information processing
For realizing nextgeneration 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 brainlike computing systems energy efficient such that efficient computing is realized with low power and high speed. 
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. 
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. 
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. 2438 (2020). DOI: 10.1109/TNNLS.2019.2899344A. Matsuki and G. Tanaka
Intervention threshold for epidemic control in susceptibleinfectedrecovered 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. 100123 (2019).Z. Tong and G. Tanaka
Hybrid pooling for enhancement of generalization ability in deep convolutional neural networks
Neurocomputing, vol. 333. pp. 7685 (2019).G. Tanaka, E. DominguezHuttinger, P. Christodoulides, K. Aihara, and R. J Tanaka
Bifurcation analysis of a mathematical model of atopic dermatitis to determine patientspecific effects of treatments on dynamic phenotypes
Journal of Theoretical Biology, vol. 448, pp. 6679 (2018). 