Shigeru Shinomoto

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Hideaki ShimazakiKyoto UniversityVerified email at i.kyoto-u.ac.jp
Ryota KobayashiThe University of TokyoVerified email at k.u-tokyo.ac.jp
Hideaki KimNTT CorporationVerified email at ntt.com
Shintaro FunahashiKyoto University Kokoro Research CenterVerified email at kyoto-u.jp
Yutaka SakaiTamagawa UniversitiyVerified email at tamagawa.ac.jp
Amari S*Verified email at brain.riken.jp
Takahiro OmiStockmark Inc.Verified email at alum.riken.jp
Yoshiyuki KabashimaInstitute for Physics of Intelligence & Department of Physics, The University of TokyoVerified email at phys.s.u-tokyo.ac.jp
Naoko InabaKyoto UniversityVerified email at umin.org
Ichiro FujitaOsaka University Graduate School of Frontier Biosciences & CiNetVerified email at fbs.osaka-u.ac.jp
Hiroshi TamuraAssociate Professor, Osaka UniveristyVerified email at fbs.osaka-u.ac.jp
Markus DiesmannDirector, INM-6 and IAS-6, Jülich Research CentreVerified email at fz-juelich.de
Kowa KoidaToyohashi University of TechnologyVerified email at tut.jp
Renaud B. JolivetMaastricht UniversityVerified email at maastrichtuniversity.nl
Richard NaudDepartment of Cellular and Molecular Medicine, Department of Physics, University of OttawaVerified email at uottawa.ca
Robert E. KassMaurice Falk Professor of Statistics and Computational Neuroscience, Carnegie Mellon UniversityVerified email at stat.cmu.edu
Tomokatsu OnagaAssistant professor, Faculty of Engineering Sciences, Kyushu UniversityVerified email at m.kyushu-u.ac.jp
Moritz HeliasInst. for Neuroscience and medicine (INM-6), Jülich; Faculty of Physics, RWTH AachenVerified email at fz-juelich.de
Eric Shea-BrownApplied Mathematics, University of WashingtonVerified email at uw.edu
Brent DoironProfessor of Neurobiology and Statistics, University of ChicagoVerified email at uchicago.edu

Shigeru Shinomoto

ATR Institute International

Verified email at scphys.kyoto-u.ac.jp - Homepage

Computational Neuroscience Nonlinear Dynamics


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A method for selecting the bin size of a time histogram H Shimazaki, S Shinomoto Neural computation 19 (6), 1503-1527, 2007	643	2007
Kernel bandwidth optimization in spike rate estimation H Shimazaki, S Shinomoto Journal of computational neuroscience 29, 171-182, 2010	408	2010
Phase transitions in active rotator systems S Shinomoto, Y Kuramoto Progress of Theoretical Physics 75 (5), 1105-1110, 1986	299	1986
Local and grobal self-entrainments in oscillator lattices H Sakaguchi, S Shinomoto, Y Kuramoto Progress of Theoretical Physics 77 (5), 1005-1010, 1987	278	1987
Differences in spiking patterns among cortical neurons S Shinomoto, K Shima, J Tanji Neural computation 15 (12), 2823-2842, 2003	252	2003
Relating neuronal firing patterns to functional differentiation of cerebral cortex S Shinomoto, H Kim, T Shimokawa, N Matsuno, S Funahashi, K Shima, ... PLoS computational biology 5 (7), e1000433, 2009	249	2009
Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold R Kobayashi, Y Tsubo, S Shinomoto Frontiers in computational neuroscience 3, 762, 2009	220	2009
Four types of learning curves S Amari, N Fujita, S Shinomoto Neural Computation 4 (4), 605-618, 1992	202	1992
A benchmark test for a quantitative assessment of simple neuron models R Jolivet, R Kobayashi, A Rauch, R Naud, S Shinomoto, W Gerstner Journal of neuroscience methods 169 (2), 417-424, 2008	182	2008
The Ornstein-Uhlenbeck process does not reproduce spiking statistics of neurons in prefrontal cortex S Shinomoto, Y Sakai, S Funahashi Neural Computation 11 (4), 935-951, 1999	147	1999
Mutual entrainment in oscillator lattices with nonvariational type interaction H Sakaguchi, S Shinomoto, Y Kuramoto Progress of theoretical physics 79 (5), 1069-1079, 1988	139	1988
Phase transitions and their bifurcation analysis in a large population of active rotators with mean-field coupling H Sakaguchi, S Shinomoto, Y Kuramoto Progress of Theoretical Physics 79 (3), 600-607, 1988	105	1988
MST neurons code for visual motion in space independent of pursuit eye movements N Inaba, S Shinomoto, S Yamane, A Takemura, K Kawano Journal of neurophysiology 97 (5), 3473-3483, 2007	104	2007
Temporally correlated inputs to leaky integrate-and-fire models can reproduce spiking statistics of cortical neurons Y Sakai, S Funahashi, S Shinomoto Neural Networks 12 (7-8), 1181-1190, 1999	89	1999
A measure of local variation of inter-spike intervals S Shinomoto, K Miura, S Koyama Biosystems 79 (1-3), 67-72, 2005	87	2005
Estimating instantaneous irregularity of neuronal firing T Shimokawa, S Shinomoto Neural computation 21 (7), 1931-1951, 2009	84	2009
A cognitive and associative memory S Shinomoto Biological Cybernetics 57 (3), 197-206, 1987	81	1987
Similarity in neuronal firing regimes across mammalian species Y Mochizuki, T Onaga, H Shimazaki, T Shimokawa, Y Tsubo, R Kimura, ... Journal of Neuroscience 36 (21), 5736-5747, 2016	78	2016
Reconstructing neuronal circuitry from parallel spike trains R Kobayashi, S Kurita, A Kurth, K Kitano, K Mizuseki, M Diesmann, ... Nature communications 10 (1), 4468, 2019	72	2019
Computational neuroscience: Mathematical and statistical perspectives RE Kass, SI Amari, K Arai, EN Brown, CO Diekman, M Diesmann, ... Annual review of statistics and its application 5, 183-214, 2018	72	2018

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