Shigeru Shinomoto, Ph.D.
|Kyoto University, Kyōto-shi, Kyōto-fu, Japan|
Mean distance: 15.97 (cluster 17)
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|Koyama S, Horie T, Shinomoto S. (2021) Estimating the time-varying reproduction number of COVID-19 with a state-space method. Plos Computational Biology. 17: e1008679|
|Kass RE, Amari SI, Arai K, et al. (2018) Computational Neuroscience: Mathematical and Statistical Perspectives. Annual Review of Statistics and Its Application. 5: 183-214|
|Mochizuki Y, Onaga T, Shimazaki H, et al. (2016) Similarity in Neuronal Firing Regimes across Mammalian Species. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 36: 5736-47|
|Kostal L, Shinomoto S. (2016) Efficient information transfer by poisson neurons Mathematical Biosciences and Engineering. 13: 509-520|
|Yamanaka Y, Amari S, Shinomoto S. (2015) Microscopic instability in recurrent neural networks. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 91: 032921|
|Onaga T, Shinomoto S. (2015) Bursting activity spreading through asymmetric interactions Proceedings - 10th International Conference On Signal-Image Technology and Internet-Based Systems, Sitis 2014. 388-395|
|Mochizuki Y, Shinomoto S. (2014) Analog and digital codes in the brain. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 89: 022705|
|Onaga T, Shinomoto S. (2014) Bursting transition in a linear self-exciting point process. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 89: 042817|
|Kim H, Shinomoto S. (2014) Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation. Mathematical Biosciences and Engineering : Mbe. 11: 49-62|
|Shinomoto S, Kim H. (2013) Estimating inputs and an internal neuronal parameter from a single spike train. Conference Proceedings : ... Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual Conference. 2013: 7096-9|