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Shigeru 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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