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Isao T. Tokuda
Researcher at Ritsumeikan University
Publications - 173
Citations - 2419
Isao T. Tokuda is an academic researcher from Ritsumeikan University. The author has contributed to research in topics: Circadian clock & Chaotic. The author has an hindex of 26, co-authored 158 publications receiving 1993 citations. Previous affiliations of Isao T. Tokuda include Humboldt University of Berlin & University of Potsdam.
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Nonlinear analysis of irregular animal vocalizations.
TL;DR: It is concluded that nonlinear analysis is primarily useful in animal vocalizations with strong harmonic components (including subharmonics and biphonation) or low-dimensional chaos.
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Age-Related Changes in the Circadian System Unmasked by Constant Conditions
Takahiro Nakamura,Wataru Nakamura,Isao T. Tokuda,Takahiro Ishikawa,Takashi Kudo,Christopher S. Colwell,Gene D. Block +6 more
TL;DR: Ex vivo bioluminescent imaging of cultured SCN slices of young and aged PER2::luciferase knock-in mice revealed that aged SCN cells showed longer circadian periods and that the rhythms of individual cells rapidly became desynchronized, suggesting that aging degrades the SCN circadian ensemble but that recurrent LD cycles mask these effects.
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Coordination of robust single cell rhythms in the Arabidopsis circadian clock via spatial waves of gene expression.
Peter D. Gould,Mirela Domijan,Mirela Domijan,Mark Greenwood,Mark Greenwood,Isao T. Tokuda,Hannah Rees,László Kozma-Bognár,László Kozma-Bognár,Anthony Hall,Anthony Hall,James C. W. Locke,James C. W. Locke +12 more
TL;DR: The results reveal the spatial structure of the plant clock and suggest that unlike the centralised mammalian clock, the Arabidopsis clock has multiple coordination points.
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Global bifurcation structure of chaotic neural networks and its application to traveling salesman problems
TL;DR: This paper studies global bifurcation structure of the chaotic neural networks applied to solve the traveling salesman problem and finds that the crisis-induced intermittent switches among the ruins of the previous localized chaotic attractors underly the ‘chaotic search’ for TSP solutions.
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Inferring phase equations from multivariate time series
TL;DR: An approach is presented for extracting phase equations from multivariate time series data recorded from a network of weakly coupled limit cycle oscillators to estimate important properties of the phase equations including natural frequencies and interaction functions between the oscillators.