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Shigeru Shinomoto, Ph.D. - Publications

Affiliations: 
Kyoto University, Kyōto-shi, Kyōto-fu, Japan 
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64 high-probability publications. We are testing a new system for linking publications to authors. You can help! If you notice any inaccuracies, please sign in and mark papers as correct or incorrect matches. If you identify any major omissions or other inaccuracies in the publication list, please let us know.

Year Citation  Score
2021 Endo D, Kobayashi R, Bartolo R, Averbeck BB, Sugase-Miyamoto Y, Hayashi K, Kawano K, Richmond BJ, Shinomoto S. A convolutional neural network for estimating synaptic connectivity from spike trains. Scientific Reports. 11: 12087. PMID 34103546 DOI: 10.1038/s41598-021-91244-w  0.33
2019 Kobayashi R, Kurita S, Kurth A, Kitano K, Mizuseki K, Diesmann M, Richmond BJ, Shinomoto S. Reconstructing neuronal circuitry from parallel spike trains. Nature Communications. 10: 4468. PMID 31578320 DOI: 10.1038/S41467-019-12225-2  0.464
2018 Kass RE, Amari SI, Arai K, Brown EN, Diekman CO, Diesmann M, Doiron B, Eden UT, Fairhall AL, Fiddyment GM, Fukai T, Grün S, Harrison MT, Helias M, Nakahara H, ... ... Shinomoto S, et al. Computational Neuroscience: Mathematical and Statistical Perspectives. Annual Review of Statistics and Its Application. 5: 183-214. PMID 30976604 DOI: 10.1146/annurev-statistics-041715-033733  0.703
2018 Fujita K, Medvedev A, Koyama S, Lambiotte R, Shinomoto S. Identifying exogenous and endogenous activity in social media Physical Review E. 98. DOI: 10.1103/Physreve.98.052304  0.312
2016 Onaga T, Shinomoto S. Emergence of event cascades in inhomogeneous networks. Scientific Reports. 6: 33321. PMID 27625183 DOI: 10.1038/Srep33321  0.325
2016 Mochizuki Y, Onaga T, Shimazaki H, Shimokawa T, Tsubo Y, Kimura R, Saiki A, Sakai Y, Isomura Y, Fujisawa S, Shibata K, Hirai D, Furuta T, Kaneko T, Takahashi S, ... ... Shinomoto S, et al. Similarity in Neuronal Firing Regimes across Mammalian Species. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 36: 5736-47. PMID 27225764 DOI: 10.1523/Jneurosci.0230-16.2016  0.721
2016 Kostal L, Shinomoto S. Efficient information transfer by Poisson neurons. Mathematical Biosciences and Engineering : Mbe. 13: 509-20. PMID 27106184 DOI: 10.3934/Mbe.2016004  0.412
2015 Yamanaka Y, Amari S, Shinomoto S. Microscopic instability in recurrent neural networks. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 91: 032921. PMID 25871186 DOI: 10.1103/Physreve.91.032921  0.52
2014 Mochizuki Y, Shinomoto S. Analog and digital codes in the brain. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 89: 022705. PMID 25353507 DOI: 10.1103/Physreve.89.022705  0.383
2014 Kim H, Shinomoto S. Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation. Mathematical Biosciences and Engineering : Mbe. 11: 49-62. PMID 24245682 DOI: 10.3934/Mbe.2014.11.49  0.46
2013 Shinomoto S, Kim H. Estimating inputs and an internal neuronal parameter from a single spike train. Conference Proceedings : ... Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual Conference. 2013: 7096-9. PMID 24111380 DOI: 10.1109/EMBC.2013.6611193  0.366
2013 Koyama S, Omi T, Kass RE, Shinomoto S. Information transmission using non-poisson regular firing. Neural Computation. 25: 854-76. PMID 23339613 DOI: 10.1162/Neco_A_00420  0.37
2013 Mochizuki Y, Shinomoto S. Difference in modes of firing rate modulation between cortical areas Bmc Neuroscience. 14. DOI: 10.1186/1471-2202-14-S1-P359  0.492
2012 Kim H, Shinomoto S. Estimating nonstationary input signals from a single neuronal spike train. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 86: 051903. PMID 23214810 DOI: 10.1103/Physreve.86.051903  0.477
2012 Shintani T, Shinomoto S. Detection limit for rate fluctuations in inhomogeneous Poisson processes. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 85: 041139. PMID 22680450 DOI: 10.1103/Physreve.85.041139  0.334
2012 Kim H, Richmond BJ, Shinomoto S. Neurons as ideal change-point detectors. Journal of Computational Neuroscience. 32: 137-46. PMID 21643776 DOI: 10.1007/S10827-011-0344-X  0.446
2011 Yamauchi S, Kim H, Shinomoto S. Elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing times. Frontiers in Computational Neuroscience. 5: 42. PMID 22203798 DOI: 10.3389/Fncom.2011.00042  0.479
2011 Kobayashi R, Shinomoto S, Lansky P. Estimation of time-dependent input from neuronal membrane potential. Neural Computation. 23: 3070-93. PMID 21919789 DOI: 10.1162/Neco_A_00205  0.43
2011 Omi T, Shinomoto S. Optimizing time histograms for non-Poissonian spike trains. Neural Computation. 23: 3125-44. PMID 21919781 DOI: 10.1162/Neco_A_00213  0.468
2011 Shinomoto S, Omi T, Mita A, Mushiake H, Shima K, Matsuzaka Y, Tanji J. Deciphering elapsed time and predicting action timing from neuronal population signals. Frontiers in Computational Neuroscience. 5: 29. PMID 21734877 DOI: 10.3389/Fncom.2011.00029  0.461
2011 Omi T, Kanter I, Shinomoto S. Optimal observation time window for forecasting the next earthquake. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 83: 026101. PMID 21405883 DOI: 10.1103/Physreve.83.026101  0.304
2010 Shinomoto S. Fitting a stochastic spiking model to neuronal current injection data. Neural Networks : the Official Journal of the International Neural Network Society. 23: 764-9. PMID 20478693 DOI: 10.1016/J.Neunet.2010.04.004  0.481
2010 Shimokawa T, Koyama S, Shinomoto S. A characterization of the time-rescaled gamma process as a model for spike trains. Journal of Computational Neuroscience. 29: 183-91. PMID 19844786 DOI: 10.1007/S10827-009-0194-Y  0.363
2010 Shimazaki H, Shinomoto S. Kernel bandwidth optimization in spike rate estimation. Journal of Computational Neuroscience. 29: 171-82. PMID 19655238 DOI: 10.1007/S10827-009-0180-4  0.559
2010 Shinomoto S, Shimazaki H, Shimokawa T. Characterizing neuronal firing with the rate and the irregularity Neuroscience Research. 68: e50-e51. DOI: 10.1016/J.Neures.2010.07.471  0.452
2009 Kobayashi R, Tsubo Y, Shinomoto S. Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold. Frontiers in Computational Neuroscience. 3: 9. PMID 19668702 DOI: 10.3389/Neuro.10.009.2009  0.484
2009 Shinomoto S, Kim H, Shimokawa T, Matsuno N, Funahashi S, Shima K, Fujita I, Tamura H, Doi T, Kawano K, Inaba N, Fukushima K, Kurkin S, Kurata K, Taira M, et al. Relating neuronal firing patterns to functional differentiation of cerebral cortex. Plos Computational Biology. 5: e1000433. PMID 19593378 DOI: 10.1371/Journal.Pcbi.1000433  0.414
2009 Shimokawa T, Shinomoto S. Estimating instantaneous irregularity of neuronal firing. Neural Computation. 21: 1931-51. PMID 19323639 DOI: 10.1162/Neco.2009.08-08-841  0.51
2009 Kobayashi R, Tshubo Y, Shinomoto S. A fast-computational spiking neuron model adaptable to any cortical neuron Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P22  0.469
2009 Shimazaki H, Shinomoto S. Histogram binwidth and kernel bandwidth selection for the spike-rate estimation Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P116  0.526
2009 Shinomoto S, Kim H, Shimokawa T. Neuronal firing patterns and cerebral cortical functions Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P106  0.38
2009 Shimokawa T, Shinomoto S. Bayesian estimation of the time-varing rate and irregularity of neuronal firing Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-O6  0.495
2009 Kim H, Richmond BJ, Shinomoto S. Detecting a change point by a single neuron Neuroscience Research. 65: S133. DOI: 10.1016/J.Neures.2009.09.652  0.382
2009 Kobayashi R, Tsubo Y, Shinomoto S. A simple model for predicting spike times of a variety of neurons Neuroscience Research. 65: S65. DOI: 10.1016/J.Neures.2009.09.197  0.448
2008 Jolivet R, Kobayashi R, Rauch A, Naud R, Shinomoto S, Gerstner W. A benchmark test for a quantitative assessment of simple neuron models. Journal of Neuroscience Methods. 169: 417-24. PMID 18160135 DOI: 10.1016/J.Jneumeth.2007.11.006  0.429
2007 Omi T, Shinomoto S. Reverberating activity in a neural network with distributed signal transmission delays. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 76: 051908. PMID 18233688 DOI: 10.1103/Physreve.76.051908  0.343
2007 Shinomoto S, Koyama S. A solution to the controversy between rate and temporal coding. Statistics in Medicine. 26: 4032-8. PMID 17525932 DOI: 10.1002/Sim.2932  0.345
2007 Shimazaki H, Shinomoto S. A method for selecting the bin size of a time histogram. Neural Computation. 19: 1503-27. PMID 17444758 DOI: 10.1162/Neco.2007.19.6.1503  0.581
2007 Kobayashi R, Shinomoto S. State space method for predicting the spike times of a neuron. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 75: 011925. PMID 17358202 DOI: 10.1103/Physreve.75.011925  0.453
2007 Inaba N, Shinomoto S, Yamane S, Takemura A, Kawano K. MST neurons code for visual motion in space independent of pursuit eye movements. Journal of Neurophysiology. 97: 3473-83. PMID 17329625 DOI: 10.1152/Jn.01054.2006  0.329
2007 Koyama S, Shinomoto S. Inference of intrinsic spiking irregularity based on the Kullback-Leibler information. Bio Systems. 89: 69-73. PMID 17321039 DOI: 10.1016/J.Biosystems.2006.05.012  0.372
2007 Koyama S, Shimokawa T, Shinomoto S. Phase transitions in the estimation of event rate: A path integral analysis Journal of Physics a: Mathematical and Theoretical. 40: F383-F390. DOI: 10.1088/1751-8113/40/20/F01  0.309
2007 Kobayashi R, Shinomoto S. Predicting spike times from subthreshold dynamics of a neuron Advances in Neural Information Processing Systems. 721-728.  0.325
2007 Shimazaki H, Shinomoto S. A recipe for optimizing a time-histogram Advances in Neural Information Processing Systems. 1289-1296.  0.418
2006 Shimokawa T, Shinomoto S. Inhibitory neurons can facilitate rhythmic activity in a neural network. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 73: 066221. PMID 16906960 DOI: 10.1103/Physreve.73.066221  0.445
2005 Shinomoto S, Miyazaki Y, Tamura H, Fujita I. Regional and laminar differences in in vivo firing patterns of primate cortical neurons. Journal of Neurophysiology. 94: 567-75. PMID 15758054 DOI: 10.1152/Jn.00896.2004  0.457
2005 Shinomoto S, Miura K, Koyama S. A measure of local variation of inter-spike intervals. Bio Systems. 79: 67-72. PMID 15649590 DOI: 10.1016/J.Biosystems.2004.09.023  0.342
2005 Kobayashi R, Miyazaki Y, Shinomoto S. Faithful and unfaithful students in time series learning Ima Journal of Applied Mathematics (Institute of Mathematics and Its Applications). 70: 657-665. DOI: 10.1093/Imamat/Hxh090  0.349
2005 Koyama S, Shinomoto S. Empirical Bayes interpretations of random point events Journal of Physics a: Mathematical and General. 38: L531-L537. DOI: 10.1088/0305-4470/38/29/L04  0.387
2004 Tsubo Y, Kaneko T, Shinomoto S. Predicting spike timings of current-injected neurons. Neural Networks : the Official Journal of the International Neural Network Society. 17: 165-73. PMID 15036335 DOI: 10.1016/J.Neunet.2003.11.005  0.422
2004 Koyama S, Shinomoto S. Histogram bin width selection for time-dependent Poisson processes Journal of Physics a: Mathematical and General. 37: 7255-7265. DOI: 10.1088/0305-4470/37/29/006  0.338
2003 Shinomoto S, Shima K, Tanji J. Differences in spiking patterns among cortical neurons. Neural Computation. 15: 2823-42. PMID 14629869 DOI: 10.1162/089976603322518759  0.473
2003 Miyazaki Y, Kinzel W, Shinomoto S. Learning of time series through neuron-to-neuron instruction Journal of Physics a: Mathematical and General. 36: 1315-1322. DOI: 10.1088/0305-4470/36/5/309  0.377
2002 Shinomoto S, Sakai Y, Ohno H. Recording site dependence of the neuronal spiking statistics. Bio Systems. 67: 259-63. PMID 12459306 DOI: 10.1016/S0303-2647(02)00083-7  0.446
2002 Shinomoto S, Shima K, Tanji J. New classification scheme of cortical sites with the neuronal spiking characteristics. Neural Networks : the Official Journal of the International Neural Network Society. 15: 1165-9. PMID 12425435 DOI: 10.1016/S0893-6080(02)00093-X  0.374
2001 Shinomoto S, Tsubo Y. Modeling spiking behavior of neurons with time-dependent Poisson processes. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics. 64: 041910. PMID 11690055 DOI: 10.1103/Physreve.64.041910  0.445
1999 Sakai Y, Funahashi S, Shinomoto S. Temporally correlated inputs to leaky integrate-and-fire models can reproduce spiking statistics of cortical neurons. Neural Networks : the Official Journal of the International Neural Network Society. 12: 1181-1190. PMID 12662653 DOI: 10.1016/S0893-6080(99)00053-2  0.432
1999 Shinomoto S, Sakai Y, Funahashi S. The Ornstein-Uhlenbeck process does not reproduce spiking statistics of neurons in prefrontal cortex. Neural Computation. 11: 935-51. PMID 10226190 DOI: 10.1162/089976699300016511  0.479
1999 Shinomoto S, Sakai Y. Inter-spike interval statistics of cortical neurons Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1606: 171-179. DOI: 10.1007/BFb0098171  0.366
1998 Shinomoto S, Sakai Y. Spiking mechanisms of cortical neurons Philosophical Magazine B: Physics of Condensed Matter; Statistical Mechanics, Electronic, Optical and Magnetic Properties. 77: 1549-1555. DOI: 10.1080/13642819808205047  0.46
1992 Amari S, Fujita N, Shinomoto S. Four Types of Learning Curves Neural Computation. 4: 605-618. DOI: 10.1162/Neco.1992.4.4.605  0.435
1990 Shinomoto S. Information classification scheme of feedforward networks organised under unsupervised learning Network: Computation in Neural Systems. 1: 135-147. DOI: 10.1088/0954-898X_1_2_002  0.41
1987 Shinomoto S. A cognitive and associative memory. Biological Cybernetics. 57: 197-206. PMID 3676357 DOI: 10.1007/Bf00364151  0.361
1970 Kobayashi R, Tsubo Y, Shinomoto S. A fast-computational spiking neuron model for a variety of cortical neuron Frontiers in Neuroinformatics. DOI: 10.3389/Conf.Neuro.11.2009.08.088  0.468
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