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.
Ohnishi H, Shimada Y, Fujiwara K, Ikeguchi T. Chaotic neurodynamical search with small number of neurons for solving QAP, Nonlinear Theory and Its Applications, IEICE, Vol. 8, No. 3, pp. 255-265, 2017.
Kobayashi T, Shimada Y, Fujiwara K, Ikeguchi T. Reproducing infra-slow oscillations with dopaminergic modulation, Scientific Reports, 7: 2411, 2017.
Fujiwara K, Suzuki H, Ikeguchi T, Aihara K. Method for analyzing time-varying statistics on point process data with multiple trials, Nonlinear Theory and its Applications, IEICE, Vol. 6, No. 1, 2015.
Kuroda K, Hashiguchi H, Fujiwara K, Ikeguchi T. Reconstruction of network structures from marked point processes using multi-dimensional scaling, Physica A, Vol.415, pp.194-204, 2014.
Kurebayashi W, Fujiwara K, Ikeguchi T. Colored noise induces synchronization of limit cycle oscillators, EPL (Europhysics Letters), Vol.97, p.50009, 2012.
Kantaro Fujiwara and Kazuyuki Aihara. Time-varying irregularities in multiple trial spike data, European Physical Journal B, Vol. 68, pp. 283-289, 2009.
Fujiwara K, Aihara K. Trial-to-trial Variability and its influence on higher order statistics, Journal of Artificial Life and Robotics, Vol. 13, pp. 470-473, 2009.
Fujiwara K, Fujiwara H, Tsukada M, Aihara K. Reproducing bursting interspike interval statistics of the gustatory cortex, Biosystems, Vol. 90, pp. 442-448, 2007.
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.