Data Science Core Core Manager
Associate Professor
Computational Neuroscience, Neural Data Analysis and its Applications
Research
Neuroscience is an interdisciplinary research field that is linked by a wide variety of research fields. Theoretical approaches to neuroscience have helped to introduce new ideas and shape directions of neuroscience research. In modern neuroscience, theoretical neuroscience is assuming an increasingly important role due to big data obtained by experimental measurement and the recent development of artificial intelligence. In my current work, I focus on theories of neuronal data analysis and neural network modeling with the aim of bridging the gap between experimental and theoretical neuroscience studies. I also work on algorithmic study on brain-inspired machine learning.
Publications
Ziqiang Li, Kantaro Fujiwara, Gouhei Tanaka, “Dynamical Graph Echo State Networks with Snapshot Merging for Dissemination Process Classification”, Communications in Computer and Information Science, Vo. 1964, pp. 523-534, 2023.
Ziqiang Li, Kantaro Fujiwara, Gouhei Tanaka, “An Echo State Network-Based Method for Identity Recognition with Continuous Blood Pressure Data”, Lecture Notes in Computer Science, Vol. 14257, pp. 13-25, 2023.
Keita Koyama, Hiroyasu Ando, Kantaro Fujiwara, "Functional improvement in β cell models of type 2 diabetes using on-demand feedback control", AIP Advances, 13, 045317, 2023.
Ryota Nomura, Kantaro Fujiwara, Tohru Ikeguchi, "Superposed recurrence plots for reconstructing a common input applied to neurons", Physical Review E, 106, 034205, 2022.
Kotaro Kasahara, Yutaka Shimada, Kantaro Fujiwara, Tohru Ikeguchi, "Analysis on the mechanism of enhancing insulin secretion by TRPM2 channel in a pancreatic β-cell", Nonlinear Theory and Its Applications, IEICE, Vol. 12, No.3, pp. 500-511, 2021.
Ryota Nomura, Ying-Zong Liang, Kenji Morita, Kantaro Fujiwara, Tohru Ikeguchi, "Threshold-varying integrate-and-fire model reproduces distributions of spontaneous blink intervals", PLOS ONE, 13(10) e0206528, 2018.
Toshihiro Kobayashi, Yutaka Shimada, Kantaro Fujiwara, Tohru Ikeguchi, “Reproducing Infra-Slow Oscillations with Dopaminergic Modulation”, Scientific Reports, 7: 2411, 2017.
Biography
I received a Ph. D. in Information Science and Technology from the University of Tokyo. I studied computational neuroscience, especially the mathematical modeling of single neurons, and neural network modeling of learning and adaptation. As a postdoctoral researcher for JSPS, I studied data analysis of neural systems, including a theory for neural spike train analysis. As an Assistant Professor at Saitama University and Tokyo University of Science, I studied nonlinear mathematics and its applications. Current, I am aiming to bridge the gap between experimental and theoretical neuroscience. I also manage the data science core servers and software for the IRCN community.
