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Frontiers in Computational Neuroscience|August 12, 2009
Made-to-order spiking neuron model equipped with a multi-timescale adaptive thresholdRyota Kobayashi, Yasuhiro Tsubo, Shigeru ShinomotoScientific Reports|March 5, 2026
Comparing effective and functional connectivityShigeru Shinomoto, Yasuhiro TsuboScientific Reports|October 11, 2022
Detection and categorization of severe cardiac disorders based solely on heart period measurementsShigeru Shinomoto, Yasuhiro Tsubo, Yoshinori MarunakaNeural Networks : the Official Journal of the International Neural Network Society|March 24, 2004
Predicting spike timings of current-injected neuronsYasuhiro Tsubo, Takeshi Kaneko, Shigeru ShinomotoNeuroscience Research|August 4, 2024
Inference of monosynaptic connections from parallel spike trains: A reviewRyota Kobayashi, Shigeru ShinomotoPhysical Review. E, Statistical, Nonlinear, and Soft Matter Physics|March 16, 2007
State space method for predicting the spike times of a neuronRyota Kobayashi, Shigeru ShinomotoNeural Computation|September 17, 2011
Estimation of time-dependent input from neuronal membrane potentialRyota Kobayashi, Shigeru Shinomoto, Petr LanskyJournal of Neuroscience Methods|December 28, 2007
A benchmark test for a quantitative assessment of simple neuron modelsRenaud Jolivet, Ryota Kobayashi, Alexander Rauch, et al.Neural Networks : the Official Journal of the International Neural Network Society|May 19, 2010
Fitting a stochastic spiking model to neuronal current injection dataShigeru ShinomotoPageof 24