c:o/re short-term Fellow (21/05 − 31/07/2025)
Shigeru Shinomoto is a theoretical physicist who has worked in the fields of nonlinear dynamics and computational neuroscience. Born in Himeji, Japan, he studied physics at Tohoku University. After receiving his Ph.D. in 1984, he moved to the Yukawa Institute for Theoretical Physics at Kyoto University. There, he began working with Yoshiki
Kuramoto on nonlinear dynamics, particularly on a theoretical model of
cooperative phenomena of nonlinear oscillators, which is now known worldwide as the Kuramoto model. Shinomto also began working on computational learning theory with Shun-ichi Amari, a leading figure in theoretical neuroscience. For more than 35 years, he has been working in these fields at Kyoto University, focusing particularly on analyzing massive data recorded with state-of-the-art technologies using machine learning theories. After retiring from Kyoto University in 2021, he joined the Brain Information Communication Research Laboratory at the ATR Institute International. He keeps working as a visiting researcher at several universities, including Kyoto University, Ritsumeikan University, University of Hyogo, and Hiroshima University.
Research on non-differentiable activity in the brain and the mechanical optimization of skateboard pumping
I have recently developed an analysis method of estimating inter-neuronal connectivity from spike trains, and discovered that neurons in vivo exhibit non-differentiable synchronous activity. I have also found an optimal control strategy for skateboarding and found that skilled skateboarders follow the optimal control strategy more closely than unskilled skateboarders. I wish to further explore these findings in collaboration with the Institute for Advanced Simulation (IAS-6) Computational and Systems Neuroscience & JARA-Institute Brain Structure-Function Relationships (INM-10) at Jülich Research Centre and JARA Jülich.
Publications (Selection)
Kobayashi, R., Kurita, S., Kurth, A., Kitano, K., Mizuseki, K., Diesmann, M., Richmond, B. J., & Shinomoto, S. (2019). Reconstructing neuronal circuitry from parallel spike trains. Nature Communications, 10(1), 4468. https://doi.org/10.1038/s41467-019-12225-2
Shimazaki, H., & Shinomoto, S. (2007). A Method for Selecting the Bin Size of a Time Histogram. Neural Computation, 19(6), 1503–1527. https://doi.org/10.1162/neco.2007.19.6.1503
Shinomoto, S., Shima, K., & Tanji, J. (2003). Differences in Spiking Patterns Among Cortical Neurons. Neural Computation, 15(12), 2823–2842. https://doi.org/10.1162/089976603322518759
Amari, S., Fujita, N., & Shinomoto, S. (1992). Four Types of Learning Curves. Neural Computation, 4(4), 605–618. https://doi.org/10.1162/neco.1992.4.4.605
Shinomoto, S., & Kuramoto, Y. (1986). Phase Transitions in Active Rotator Systems. Progress of Theoretical Physics, 75(5), 1105–1110. https://doi.org/10.1143/PTP.75.1105
