Shigeru Shinomoto

33 papers A* 1Journal 25Unranked 6

Year	Rank	Type	Title / Venue / Authors
2021	J	`jnl`	Estimating the time-varying reproduction number of COVID-19 with a state-space method. PLoS Comput. Biol. Shinsuke Koyama, Taiki Horie, Shigeru Shinomoto
2018	J	`jnl`	Identifying exogenous and endogenous activity in social media. CoRR Kazuki Fujita, Alexey N. Medvedev, Shinsuke Koyama, Renaud Lambiotte, Shigeru Shinomoto
2018	J	`jnl`	Inferring objects from a multitude of oscillations. Neural Comput. Appl. Masahiro Furukawa, Shigeru Shinomoto
2014	—	`conf`	Bursting Activity Spreading through Asymmetric Interactions. SITIS Tomokatsu Onaga, Shigeru Shinomoto
2014	—	`ch.`	Estimation of Neuronal Firing Rate. Encyclopedia of Computational Neuroscience Shigeru Shinomoto
2013	—	`conf`	Estimating inputs and an internal neuronal parameter from a single spike train. EMBC Shigeru Shinomoto, Hideaki Kim
2013	J	`jnl`	Information Transmission Using Non-Poisson Regular Firing. Neural Comput. Shinsuke Koyama, Takahiro Omi, Robert E. Kass, Shigeru Shinomoto
2012	J	`jnl`	Neurons as ideal change-point detectors. J. Comput. Neurosci. Hideaki Kim, Barry J. Richmond, Shigeru Shinomoto
2011	J	`jnl`	Deciphering Elapsed Time and Predicting Action Timing from Neuronal Population Signals. Frontiers Comput. Neurosci. Shigeru Shinomoto, Takahiro Omi, Akihisa Mita, Hajime Mushiake, Keisetsu Shima, Yoshiya Matsuzaka, Jun Tanji
2011	J	`jnl`	Elemental Spiking Neuron Model for Reproducing Diverse Firing Patterns and Predicting Precise Firing Times. Frontiers Comput. Neurosci. Satoshi Yamauchi, Hideaki Kim, Shigeru Shinomoto
2011	—	`conf`	Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron. NIPS Ryota Kobayashi, Yasuhiro Tsubo, Petr Lánský, Shigeru Shinomoto
2011	J	`jnl`	Estimation of Time-Dependent Input from Neuronal Membrane Potential. Neural Comput. Ryota Kobayashi, Shigeru Shinomoto, Petr Lánský
2011	J	`jnl`	Optimizing Time Histograms for Non-Poissonian Spike Trains. Neural Comput. Takahiro Omi, Shigeru Shinomoto
2010	J	`jnl`	A characterization of the time-rescaled gamma process as a model for spike trains. J. Comput. Neurosci. Takeaki Shimokawa, Shinsuke Koyama, Shigeru Shinomoto
2010	J	`jnl`	Fitting a stochastic spiking model to neuronal current injection data. Neural Networks Shigeru Shinomoto
2010	J	`jnl`	Kernel bandwidth optimization in spike rate estimation. J. Comput. Neurosci. Hideaki Shimazaki, Shigeru Shinomoto
2009	J	`jnl`	Estimating Instantaneous Irregularity of Neuronal Firing. Neural Comput. Takeaki Shimokawa, Shigeru Shinomoto
2009	J	`jnl`	Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold. Frontiers Comput. Neurosci. Ryota Kobayashi, Yasuhiro Tsubo, Shigeru Shinomoto
2009	J	`jnl`	Relating Neuronal Firing Patterns to Functional Differentiation of Cerebral Cortex. PLoS Comput. Biol. Shigeru Shinomoto, Hideaki Kim, Takeaki Shimokawa, Nanae Matsuno, Shintaro Funahashi, Keisetsu Shima, Ichiro Fujita, Hiroshi Tamura, Taijiro Doi, Kenji Kawano, Naoko Inaba, Kikuro Fukushima, Sergei Kurkin, Kiyoshi Kurata, Masato Taira, Ken-Ichiro Tsutsui, Hidehiko Komatsu, Tadashi Ogawa, Kowa Koida, Jun Tanji, Keisuke Toyama
2007	J	`jnl`	A Method for Selecting the Bin Size of a Time Histogram. Neural Comput. Hideaki Shimazaki, Shigeru Shinomoto
2007	J	`jnl`	Inference of intrinsic spiking irregularity based on the Kullback-Leibler information. Biosyst. Shinsuke Koyama, Shigeru Shinomoto
2006	—	`conf`	A recipe for optimizing a time-histogram. NIPS Hideaki Shimazaki, Shigeru Shinomoto
2006	—	`conf`	Predicting spike times from subthreshold dynamics of a neuron. NIPS Ryota Kobayashi, Shigeru Shinomoto
2004	J	`jnl`	Predicting spike timings of current-injected neurons. Neural Networks Yasuhiro Tsubo, Takeshi Kaneko, Shigeru Shinomoto
2003	J	`jnl`	Differences in Spiking Patterns Among Cortical Neurons. Neural Comput. Shigeru Shinomoto, Keisetsu Shima, Jun Tanji
2002	J	`jnl`	New classification scheme of cortical sites with the neuronal spiking characteristics. Neural Networks Shigeru Shinomoto, Keisetsu Shima, Jun Tanji
1999	—	`conf`	Inter-spike Interval Statistics of Cortical Neurons. IWANN (1) Shigeru Shinomoto, Yutaka Sakai
1999	J	`jnl`	Temporally correlated inputs to leaky integrate-and-fire models can reproduce spiking statistics of cortical neurons. Neural Networks Yutaka Sakai, Shintaro Funahashi, Shigeru Shinomoto
1999	J	`jnl`	The Ornstein-Uhlenbeck Process Does Not Reproduce Spiking Statistics of Neurons in Prefrontal Cortex. Neural Comput. Shigeru Shinomoto, Yutaka Sakai, Shintaro Funahashi
1995	J	`jnl`	Learning a Decision Boundary from Stochastic Examples: Incremental Algorithms with and without Queries. Neural Comput. Yoshiyuki Kabashima, Shigeru Shinomoto
1993	A*	`conf`	Acceleration of Learning in Binary Choice Problems. COLT Yoshiyuki Kabashima, Shigeru Shinomoto
1992	J	`jnl`	Four Types of Learning Curves. Neural Comput. Shun-ichi Amari, Naotake Fujita, Shigeru Shinomoto
1992	J	`jnl`	Learning Curves for Error Minimum and Maximum Likelihood Algorithms. Neural Comput. Yoshiyuki Kabashima, Shigeru Shinomoto