Shigeru Shinomoto, Ph.D. - Publications

Kyoto University, Kyōto-shi, Kyōto-fu, Japan 

28 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
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  0.393
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  0.692
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  0.702
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  0.49
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  0.33
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  0.305
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  0.379
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  0.365
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  0.386
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  0.323
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  0.534
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  0.368
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  0.514
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  0.34
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  0.366
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  0.546
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  0.312
2007 Shimazaki H, Shinomoto S. A recipe for optimizing a time-histogram Advances in Neural Information Processing Systems. 1289-1296.  0.425
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  0.392
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  0.313
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  0.359
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  0.397
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  0.33
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  0.386
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  0.346
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  0.366
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  0.397
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  0.44
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