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--- Recent publications of Shigeru Shinomoto ---------------------------
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A convolutional neural network for estimating synaptic connectivity from
spike trains
D. Endo, R. Kobayashi, R. Bartolo, B.B. Averbeck, Y. Sugase-Miyamoto, K. Hayashi, K. Kawano, B.J. Richmond, and S. Shinomoto
Scientific Reports (2021) 11:12087. [open access]
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Estimating the time-varying reproduction number of COVID-19 with a state-space method
S. Koyama, T. Horie, and S. Shinomoto,
PLoS Computational Biology (2021) 17(1):e1008679. [open access]
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Statistical physics of discovering exogenous and endogenous factors in
a chain of events
S. Koyama and S. Shinomoto,
Physical Review Research (2020) 2:043358. [open access]
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Improved hyperacuity estimation of spike timing from calcium imaging
H. Hoang, M. Sato, S. Shinomoto, S. Tsutsumi, M. Hashizume, T. Ishikawa,
M. Kano, Y. Ikegaya, K. Kitamura, M. Kawato and K. Toyama,
Scientific Reports (2020) 10:17844 [open access]
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Reconstructing Neuronal Circuitry from Parallel Spike Trains
R. Kobayashi, S. Kurita, A. Kurth, K. Kitano, K. Mizuseki, M. Diesmann, B.J. Richmond, and S. Shinomoto,
Nature Communications (2019) 10:4468. [open access]
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Identifying exogenous and endogenous activity in social media
K. Fujita, A. Medvedev, S. Koyama, R. Lambiotte, and S. Shinomoto,
Physical Review E (2018) 98:052304, arXiv1808.00810.
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Computational Neuroscience: Mathematical and Statistical Perspectives
R.E. Kass, S. Amari, K. Arai, E.N. Brown, C.O. Diekman, M. Diesmann, B.
Doiron, U.T. Eden, A. Fairhall, G.M. Fiddyment, T. Fukai, S. Gruen, M.T.
Harrison, M. Helias, H. Nakahara, J. Teramae, P.J. Thomas, M. Reimers,
J. Rodu, H.G. Rotstein, E. Shea-Brown, H. Shimazaki, S. Shinomoto, B.M.
Yu, and M.A. Kramer,
Annual Review of Statistics and Its Application (2018) 5:183-214.
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Inferring objects from a multitude of oscillations
M. Furukawa and S. Shinomoto,
Neural Computing and Applications (2018) 30:2471-2478. [open access]
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Correlations and forecast of death tolls in the Syrian conflict
K. Fujita, S. Shinomoto, and L.E.C. Rocha,
Scientific Reports (2017) 7:15737. [open access]
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Emergence of event cascades in inhomogeneous networks
T. Onaga and S. Shinomoto,
Scientific Reports (2016) 6:33321. [open access]
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Similarity in neuronal firing regimes across mammalian species
Y. Mochizuki, T. Onaga, H. Shimazaki, T. Shimokawa, Y. Tsubo, R. Kimura,
A. Saiki, Y. Sakai, Y. Isomura, S. Fujisawa, K. Shibata, D. Hirai, T. Furuta,
T. Kaneko, S. Takahashi, T. Nakazono, S. Ishino, Y. Sakurai, T. Kitsukawa,
J.W. Lee, H. Lee, M.W. Jung, C. Babul, P.E. Maldonado, K. Takahashi, F.I.
Arce-McShane, C.F. Ross, B.J. Sessle, N.G. Hatsopoulos, T. Brochier, A.
Riehle, P. Chorley, S. Gruen, H. Nishijo, S. Ichihara-Takeda, S. Funahashi,
K. Shima, H. Mushiake, Y. Yamane, H. Tamura, I. Fujita, N. Inaba, K. Kawano,
S. Kurkin, K. Fukushima, K. Kurata, M. Taira, K. Tsutsui, T. Ogawa, H.
Komatsu, K. Koida, K. Toyama, B.J. Richmond, and S. Shinomoto,
Journal of Neuroscience (2016) 36:5736-5747. Supplementary materials.
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Efficient information transfer by Poisson neurons
L. Kostal and S. Shinomoto,
Mathematical Biosciences and Engineering (2016) 13:506-520.
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Microscopic instability in recurrent neural networks
Y. Yamanaka, S. Amari, and S. Shinomoto,
Physical Review E (2015) 91:032921. arXiv: 1502.01513
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Estimation of neuronal firing rate
S. Shinomoto,
Encyclopedia of Computational Neuroscience (Springer 2014) DOI 10.1007/978-1-4614-7320-6_392-5.
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Bursting transition in a linear self-exciting point process
T. Onaga and S. Shinomoto,
Physical Review E (2014) 89:042817. arXiv:1401.5186.
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Analog and digital codes in the brain
Y. Mochizuki and S. Shinomoto,
Physical Review E (2014) 89:022705. arXiv:1311.4035. Taken up in MIT Technology Review.
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Estimating nonstationary inputs from a single spike train based on a neuron
model with adaptation
H. Kim and S. Shinomoto,
Mathematical Biosciences and Engineering (2014) 11: 49-62.
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Information transmission using non-Poisson regular firing
S. Koyama, T. Omi, R.E. Kass, and S. Shinomoto,
Neural Computation (2013) 25:854-876.
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Estimating nonstationary input signals from a single neuronal spike train
H. Kim and S. Shinomoto,
Physical Review E (2012) 86:051903.
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Detection limit for rate fluctuations in inhomogeneous Poisson processes
T. Shintani and S. Shinomoto,
Physical Review E (2012) 85:041139.
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Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron
R. Kobayashi, Y. Tsubo, P. Lansky, and S. Shinomoto,
Advances in NIPS (2012) 24: 217-225.
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Neurons as ideal change-point detectors
H. Kim, B.J. Richmond, and S. Shinomoto,
J. Computational Neuroscience (2012) 32:137-146.
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Optimizing time histograms for non-Poissonian spike trains
T. Omi and S. Shinomoto,
Neural Computation (2011) 23:3125-3144.
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Estimation of time-dependent input from neuronal membrane potential
R. Kobayashi, S. Shinomoto, and P. Lansky,
Neural Computation (2011) 23:3070-3093.
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Elemental spiking neuron model for reproducing diverse firing patterns
and predicting precise firing times
S. Yamauchi, H. Kim, and S. Shinomoto
Frontiers in Computational Neuroscience (2011) 5:42. [open access]
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Deciphering elapsed time and predicting action timing from neuronal population
signals
S. Shinomoto, T. Omi, A. Mita, H. Mushiake, K. Shima, Y. Matsuzaka, and J. Tanji,
Frontiers in Computational Neuroscience (2011) 5:29. [open access]
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Optimal observation time window for forecasting the next earthquake
T. Omi, I. Kanter, and S. Shinomoto,
Physical Review E (2011) 83:026101.
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--- Papers of my own selection (before 2010) -------------------- |
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Estimating the firing rate
S. Shinomoto,
in Analysis of Parallel Spike Train Data (eds. S. Gruen and S. Rotter) (Springer, New York, 2010).
[adopted as sample pages]
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A non-universal aspect in the temporal occurrence of earthquakes
X. Zhao, T. Omi, N. Matsuno, and S. Shinomoto,
New Journal of Physics (2010) 12:063010. [open access]
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Kernel bandwidth optimization in spike rate estimation
H. Shimazaki and S. Shinomoto,
J. Computational Neuroscience (2010) 29:171-182. [open access]
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Made-to-order spiking neuron model equipped with a multi-timescale adaptive
threshold
R. Kobayashi, Y. Tsubo, and S. Shinomoto,
Frontiers in Computational Neuroscience (2009) 3:9. [open access]
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Relating neuronal firing patterns to functional differentiation of cerebral
cortex
S. Shinomoto, H. Kim, T. Shimokawa, N. Matsuno, S. Funahashi, K. Shima,
I. Fujita, H. Tamura, T. Doi, K. Kawano, N. Inaba, K. Fukushima, S. Kurkin,
K. Kurata, M. Taira, K. Tsutsui, H. Komatsu, T. Ogawa, K.Koida, J. Tanji,
and K. Toyama,
PLoS Computational Biology (2009) 5:e1000433. [open access]
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Estimating instantaneous irregularity of neuronal firing
T. Shimokawa and S. Shinomoto,
Neural Computation (2009) 21:1931-1951.
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A benchmark test for a quantitative assessment of simple neuron models
R. Jolivet, R. Kobayashi, A. Rauch, R. Naud, S. Shinomoto, and W. Gerstner,
J. Neurosci. Methods (2008) 169:417-424.
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Phase transitions in the estimation of event-rate: a path integral analysis
S. Koyama T. Shimokawa, and S. Shinomoto,
J. Phys. A (2007) 40:F383-F390.
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MST neurons code for visual motion in space independent of pursuit eye movements
N. Inaba, S. Shinomoto, S. Yamane, A. Takemura, and K. Kawano,
J. Neurophysiol (2007) 97:3473-3483.
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A method for selecting the bin size of a time histogram
H. Shimazaki and S. Shinomoto,
Neural Computation (2007) 19:1503-1700.
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Empirical Bayes interpretations of random point events
S. Koyama and S. Shinomoto,
J. Phys. A (2005) 38:L531-L537.
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Regional and laminar differences in in vivo firing patterns of primate
cortical neurons
S. Shinomoto, Y. Miyazaki, H. Tamura, and I. Fujita,
J. Neurophysiol. (2005) 94:567-575.
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Histogram bin-width selection for time-dependent point processes
S. Koyama and S. Shinomoto,
J. Phys. A (2004) 37:7255-7265.
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Differences in spiking patterns among cortical neurons
S. Shinomoto, K. Shima, and J. Tanji,
Neural Computation (2003) 15:2823-2842.
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The Ornstein-Uhlenbeck process does not reproduce spiking statistics of neurons in prefrontal cortex
S. Shinomoto, Y. Sakai, and S. Funahashi,
Neural Computation (1999) 11:935-951.
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Learning curves for error minimum and maximum likelihood algorithms
Y. Kabashima and S. Shinomoto,
Neural Computation (1992) 4:712-719.
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Four types of learning curves
S. Amari, N. Fujita, and S. Shinomoto,
Neural Computation (1992) 4:605-618.
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Information classification scheme of feed-forward networks organized under unsupervised learning
S. Shinomoto,
Network: Computation in Neural Systems (1990) 1:135-147.
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Memory maintenance in neural networks
S. Shinomoto,
J. Phys. A (1987) 20:L1305-L1309.
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A cognitive and associative memory
S. Shinomoto,
Biol. Cybern. (1987) 57:197-206.
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Local and global self-entrainments in oscillator-lattices
H. Sakaguchi, S. Shinomoto, and Y. Kuramoto,
Prog. Theor. Phys. (1987) 77:1005-1010.
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Phase transitions of active rotator systems
S. Shinomoto and Y. Kuramoto,
Prog. Theor. Phys. (1986) 75:1105-1110.
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--- RETURN ------------------------------------------------------------------------
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