Shigeru Shinomoto

According to our database, Shigeru Shinomoto authored at least 33 papers between 1992 and 2021.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2021

Estimating the time-varying reproduction number of COVID-19 with a state-space method.

Shinsuke Koyama, Taiki Horie, Shigeru Shinomoto

PLoS Comput. Biol., 2021

2018

Inferring objects from a multitude of oscillations.

Masahiro Furukawa, Shigeru Shinomoto

Neural Comput. Appl., 2018

Identifying exogenous and endogenous activity in social media.

Kazuki Fujita, Alexey N. Medvedev, Shinsuke Koyama, Renaud Lambiotte, Shigeru Shinomoto

CoRR, 2018

2014

Estimation of Neuronal Firing Rate.

Shigeru Shinomoto

Proceedings of the Encyclopedia of Computational Neuroscience, 2014

Bursting Activity Spreading through Asymmetric Interactions.

Tomokatsu Onaga, Shigeru Shinomoto

Proceedings of the Tenth International Conference on Signal-Image Technology and Internet-Based Systems, 2014

2013

Information Transmission Using Non-Poisson Regular Firing.

Shinsuke Koyama, Takahiro Omi, Robert E. Kass, Shigeru Shinomoto

Neural Comput., 2013

Estimating inputs and an internal neuronal parameter from a single spike train.

Shigeru Shinomoto, Hideaki Kim

Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013

2012

Neurons as ideal change-point detectors.

Hideaki Kim, Barry J. Richmond, Shigeru Shinomoto

J. Comput. Neurosci., 2012

2011

Optimizing Time Histograms for Non-Poissonian Spike Trains.

Takahiro Omi, Shigeru Shinomoto

Neural Comput., 2011

Estimation of Time-Dependent Input from Neuronal Membrane Potential.

Ryota Kobayashi, Shigeru Shinomoto, Petr Lánský

Neural Comput., 2011

Elemental Spiking Neuron Model for Reproducing Diverse Firing Patterns and Predicting Precise Firing Times.

Satoshi Yamauchi, Hideaki Kim, Shigeru Shinomoto

Frontiers Comput. Neurosci., 2011

Deciphering Elapsed Time and Predicting Action Timing from Neuronal Population Signals.

Shigeru Shinomoto, Takahiro Omi, Akihisa Mita, Hajime Mushiake, Keisetsu Shima, Yoshiya Matsuzaka, Jun Tanji

Frontiers Comput. Neurosci., 2011

Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron.

Ryota Kobayashi, Yasuhiro Tsubo, Petr Lánský, Shigeru Shinomoto

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

Fitting a stochastic spiking model to neuronal current injection data.

Shigeru Shinomoto

Neural Networks, 2010

A characterization of the time-rescaled gamma process as a model for spike trains.

Takeaki Shimokawa, Shinsuke Koyama, Shigeru Shinomoto

J. Comput. Neurosci., 2010

Kernel bandwidth optimization in spike rate estimation.

Hideaki Shimazaki, Shigeru Shinomoto

J. Comput. Neurosci., 2010

2009

Relating Neuronal Firing Patterns to Functional Differentiation of Cerebral Cortex.

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

PLoS Comput. Biol., 2009

Estimating Instantaneous Irregularity of Neuronal Firing.

Takeaki Shimokawa, Shigeru Shinomoto

Neural Comput., 2009

Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold.

Ryota Kobayashi, Yasuhiro Tsubo, Shigeru Shinomoto

Frontiers Comput. Neurosci., 2009

2007

A Method for Selecting the Bin Size of a Time Histogram.

Hideaki Shimazaki, Shigeru Shinomoto

Neural Comput., 2007

Inference of intrinsic spiking irregularity based on the Kullback-Leibler information.

Shinsuke Koyama, Shigeru Shinomoto

Biosyst., 2007

2006

A recipe for optimizing a time-histogram.

Hideaki Shimazaki, Shigeru Shinomoto

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Predicting spike times from subthreshold dynamics of a neuron.

Ryota Kobayashi, Shigeru Shinomoto

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

2004

Predicting spike timings of current-injected neurons.

Yasuhiro Tsubo, Takeshi Kaneko, Shigeru Shinomoto

Neural Networks, 2004

2003

Differences in Spiking Patterns Among Cortical Neurons.

Shigeru Shinomoto, Keisetsu Shima, Jun Tanji

Neural Comput., 2003

2002

New classification scheme of cortical sites with the neuronal spiking characteristics.

Shigeru Shinomoto, Keisetsu Shima, Jun Tanji

Neural Networks, 2002

1999

Temporally correlated inputs to leaky integrate-and-fire models can reproduce spiking statistics of cortical neurons.

Yutaka Sakai, Shintaro Funahashi, Shigeru Shinomoto

Neural Networks, 1999

The Ornstein-Uhlenbeck Process Does Not Reproduce Spiking Statistics of Neurons in Prefrontal Cortex.

Shigeru Shinomoto, Yutaka Sakai, Shintaro Funahashi

Neural Comput., 1999

Inter-spike Interval Statistics of Cortical Neurons.

Shigeru Shinomoto, Yutaka Sakai

Proceedings of the Foundations and Tools for Neural Modeling, 1999

1995

Learning a Decision Boundary from Stochastic Examples: Incremental Algorithms with and without Queries.

Yoshiyuki Kabashima, Shigeru Shinomoto

Neural Comput., 1995

1993

Acceleration of Learning in Binary Choice Problems.

Yoshiyuki Kabashima, Shigeru Shinomoto

Proceedings of the Sixth Annual ACM Conference on Computational Learning Theory, 1993

1992

Learning Curves for Error Minimum and Maximum Likelihood Algorithms.

Yoshiyuki Kabashima, Shigeru Shinomoto

Neural Comput., 1992

Four Types of Learning Curves.

Shun-ichi Amari, Naotake Fujita, Shigeru Shinomoto

Neural Comput., 1992