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ORCIDShigeru Shinomoto
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2020 – today
- 2021
[j24]Shinsuke Koyama, Taiki Horie, Shigeru Shinomoto
:
Estimating the time-varying reproduction number of COVID-19 with a state-space method. PLoS Comput. Biol. 17(1) (2021)
2010 – 2019
- 2018
- [j23]Masahiro Furukawa, Shigeru Shinomoto:
Inferring objects from a multitude of oscillations. Neural Comput. Appl. 30(8): 2471-2478 (2018) - [i1]Kazuki Fujita, Alexey N. Medvedev, Shinsuke Koyama, Renaud Lambiotte, Shigeru Shinomoto:
Identifying exogenous and endogenous activity in social media. CoRR abs/1808.00810 (2018) - 2014
- [c7]Tomokatsu Onaga, Shigeru Shinomoto:
Bursting Activity Spreading through Asymmetric Interactions. SITIS 2014: 388-395 - [r1]Shigeru Shinomoto:
Estimation of Neuronal Firing Rate. Encyclopedia of Computational Neuroscience 2014 - 2013
- [j22]Shinsuke Koyama, Takahiro Omi, Robert E. Kass, Shigeru Shinomoto:
Information Transmission Using Non-Poisson Regular Firing. Neural Comput. 25(4): 854-876 (2013) - [c6]Shigeru Shinomoto, Hideaki Kim:
Estimating inputs and an internal neuronal parameter from a single spike train. EMBC 2013: 7096-7099 - 2012
- [j21]Hideaki Kim, Barry J. Richmond, Shigeru Shinomoto:
Neurons as ideal change-point detectors. J. Comput. Neurosci. 32(1): 137-146 (2012) - 2011
- [j20]Shigeru Shinomoto, Takahiro Omi, Akihisa Mita, Hajime Mushiake, Keisetsu Shima, Yoshiya Matsuzaka, Jun Tanji:
Deciphering Elapsed Time and Predicting Action Timing from Neuronal Population Signals. Frontiers Comput. Neurosci. 5: 29 (2011) - [j19]Satoshi Yamauchi, Hideaki Kim, Shigeru Shinomoto:
Elemental Spiking Neuron Model for Reproducing Diverse Firing Patterns and Predicting Precise Firing Times. Frontiers Comput. Neurosci. 5: 42 (2011) - [j18]Ryota Kobayashi, Shigeru Shinomoto, Petr Lánský:
Estimation of Time-Dependent Input from Neuronal Membrane Potential. Neural Comput. 23(12): 3070-3093 (2011) - [j17]Takahiro Omi, Shigeru Shinomoto:
Optimizing Time Histograms for Non-Poissonian Spike Trains. Neural Comput. 23(12): 3125-3144 (2011) - [c5]Ryota Kobayashi, Yasuhiro Tsubo, Petr Lánský, Shigeru Shinomoto:
Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron. NIPS 2011: 217-225 - 2010
- [j16]Hideaki Shimazaki, Shigeru Shinomoto:
Kernel bandwidth optimization in spike rate estimation. J. Comput. Neurosci. 29(1-2): 171-182 (2010) - [j15]Takeaki Shimokawa, Shinsuke Koyama, Shigeru Shinomoto:
A characterization of the time-rescaled gamma process as a model for spike trains. J. Comput. Neurosci. 29(1-2): 183-191 (2010) - [j14]Shigeru Shinomoto:
Fitting a stochastic spiking model to neuronal current injection data. Neural Networks 23(6): 764-769 (2010)
2000 – 2009
- 2009
- [j13]Ryota Kobayashi, Yasuhiro Tsubo, Shigeru Shinomoto:
Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold. Frontiers Comput. Neurosci. 3: 9 (2009) - [j12]Takeaki Shimokawa, Shigeru Shinomoto:
Estimating Instantaneous Irregularity of Neuronal Firing. Neural Comput. 21(7): 1931-1951 (2009) - [j11]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:
Relating Neuronal Firing Patterns to Functional Differentiation of Cerebral Cortex. PLoS Comput. Biol. 5(7) (2009) - 2007
- [j10]Shinsuke Koyama, Shigeru Shinomoto:
Inference of intrinsic spiking irregularity based on the Kullback-Leibler information. Biosyst. 89(1-3): 69-73 (2007) - [j9]Hideaki Shimazaki, Shigeru Shinomoto:
A Method for Selecting the Bin Size of a Time Histogram. Neural Comput. 19(6): 1503-1527 (2007) - 2006
- [c4]Ryota Kobayashi, Shigeru Shinomoto:
Predicting spike times from subthreshold dynamics of a neuron. NIPS 2006: 721-728 - [c3]Hideaki Shimazaki, Shigeru Shinomoto:
A recipe for optimizing a time-histogram. NIPS 2006: 1289-1296 - 2004
- [j8]Yasuhiro Tsubo, Takeshi Kaneko, Shigeru Shinomoto:
Predicting spike timings of current-injected neurons. Neural Networks 17(2): 165-173 (2004) - 2003
- [j7]Shigeru Shinomoto, Keisetsu Shima, Jun Tanji:
Differences in Spiking Patterns Among Cortical Neurons. Neural Comput. 15(12): 2823-2842 (2003) - 2002
- [j6]Shigeru Shinomoto, Keisetsu Shima, Jun Tanji:
New classification scheme of cortical sites with the neuronal spiking characteristics. Neural Networks 15(10): 1165-1169 (2002)
1990 – 1999
- 1999
- [j5]Shigeru Shinomoto, Yutaka Sakai, Shintaro Funahashi:
The Ornstein-Uhlenbeck Process Does Not Reproduce Spiking Statistics of Neurons in Prefrontal Cortex. Neural Comput. 11(4): 935-951 (1999) - [j4]Yutaka Sakai, Shintaro Funahashi, Shigeru Shinomoto:
Temporally correlated inputs to leaky integrate-and-fire models can reproduce spiking statistics of cortical neurons. Neural Networks 12(7-8): 1181-1190 (1999) - [c2]Shigeru Shinomoto, Yutaka Sakai:
Inter-spike Interval Statistics of Cortical Neurons. IWANN (1) 1999: 171-179 - 1995
- [j3]Yoshiyuki Kabashima, Shigeru Shinomoto:
Learning a Decision Boundary from Stochastic Examples: Incremental Algorithms with and without Queries. Neural Comput. 7(1): 158-172 (1995) - 1993
- [c1]Yoshiyuki Kabashima, Shigeru Shinomoto:
Acceleration of Learning in Binary Choice Problems. COLT 1993: 446-452 - 1992
- [j2]Shun-ichi Amari, Naotake Fujita, Shigeru Shinomoto:
Four Types of Learning Curves. Neural Comput. 4(4): 605-618 (1992) - [j1]Yoshiyuki Kabashima, Shigeru Shinomoto:
Learning Curves for Error Minimum and Maximum Likelihood Algorithms. Neural Comput. 4(5): 712-719 (1992)
Coauthor Index
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