Shigeru Shinomoto, Ph.D.
|Kyoto University, Kyōto-shi, Kyōto-fu, Japan|
Mean distance: 15.97 (cluster 17)
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|Endo D, Kobayashi R, Bartolo R, et al. (2021) A convolutional neural network for estimating synaptic connectivity from spike trains. Scientific Reports. 11: 12087|
|Kobayashi R, Kurita S, Kurth A, et al. (2019) Reconstructing neuronal circuitry from parallel spike trains. Nature Communications. 10: 4468|
|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|
|Fujita K, Medvedev A, Koyama S, et al. (2018) Identifying exogenous and endogenous activity in social media Physical Review E. 98|
|Onaga T, Shinomoto S. (2016) Emergence of event cascades in inhomogeneous networks. Scientific Reports. 6: 33321|
|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 : Mbe. 13: 509-20|
|Yamanaka Y, Amari S, Shinomoto S. (2015) Microscopic instability in recurrent neural networks. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 91: 032921|
|Mochizuki Y, Shinomoto S. (2014) Analog and digital codes in the brain. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 89: 022705|
|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|