If you are interested in any of these, please do not hesitate to ask a copy.
You might find my recent paper (s) in Google scholar.
*: Corresponding Author, +: Equal Contribution.

  1. Matsuki A, Kori H, and *Kobayashi R.
    An extended Hilbert transform method for reconstructing the phase from an oscillatory signal.
    Scientific Reports, 13: 3535 (2023)   [Open access]
  2. Lee H, Kostal L, Kanzaki R, and *Kobayashi R.
    Spike frequency adaptation facilitates the encoding of input gradient in insect olfactory projection neurons.
    BioSystems 223: 104802 (2023)   [Open access]
  3. *+Kobayashi R, +Takedomi Y, +Nakayama Y, +Suda T, Uno T, Hashimoto T, Toyoda M, Yoshinaga N, Kitsuregawa M, and Rocha LEC.
    Evolution of the public opinion on COVID-19 vaccination in Japan: Large-Scale Twitter Data Analysis.
    Journal of Medical Internet Research, 24, 12, e41928 (2022)   [Open access]
  4. Namura N, Tanaka S, Yamaguchi K, Kobayashi R, and Nakao H.
    Estimating asymptotic phase and amplitude functions of limit-cycle oscillators from time series data.
    Physical Review E 106: 014204 (2022)   Preprint (arXiv)
  5. Hashimoto T, Uno T, Takedomi Y, Shepard D, Toyoda M, Yoshinaga N, Kitsuregawa M, and Kobayashi R.
    Two-stage Clustering Method for Discovering People’s Perceptions: A Case Study of the COVID-19 Vaccine from Twitter.
    BigData 2021 (Short paper): 614-621 (2021)   [Open access]
  6. Kobayashi R, Gildersleve P, Uno T, Lambiotte R.
    Modeling Collective Anticipation and Response on Wikipedia.
    AAAI ICWSM-2021: 315-326 (2021),   [Open access]
    Video,   Data & Code (C)
  7. +Endo D, +Kobayashi R, Bartolo R, Averbeck BB, Sugase-Miyamoto Y, Hayashi K, Kawano K, Richmond BJ, and *Shinomoto S.
    A convolutional neural network for estimating synaptic connectivity from spike trains.
    Scientific Reports 11: 12087 (2021)   [Open access]
    Web App   CoNNECT (Python)   GLMCC (Python)
  8. Murayama T, Wakamiya S, Aramaki E, and Kobayashi R*.
    Modeling the Spread of Fake News on Twitter.
    PLOS ONE 16(4): e0250419 (2021)   Code (Python)   [Open access]
  9. Hashimoto T, Shepard D, Kuboyama T, Shin K, Kobayashi R*, and Uno T.
    Analyzing Temporal Patterns of Topic Diversity using Graph Clustering.
    The Journal of Supercomputing, 77:4375-4388 (2021)   [Open access]
  10. +Adelani DI, +Kobayashi R, Weber I, and Grabowicz PA*.
    Estimating Community Feedback Effect on Topic Choice in Social Media with Predictive Modeling.
    EPJ Data Science 9: 25 (2020)   [Open access]
  11. Kostal L and Kobayashi R.
    Critical size of neural population for reliable information transmission.
    Physical Review E, 100:050401(R) (2019)   Preprint
  12. Kobayashi, R, Kurita S, Kurth A, Kitano K, Mizuseki K, Diesmann M, Richmond BJ, and Shinomoto S*.
    Reconstructing neuronal circuitry from parallel spike trains.
    Nature Communications, 10:4468 (2019)   [Open access]
    BrainPost (Summary)   Web App   Code (Python)
  13. Levakova M*, Kostal L, Monsempès C, Lucas P, and Kobayashi R.
    Adaptive integrate-and-fire model reproduces the dynamics of olfactory receptor neuron responses in moth.
    Journal of the Royal Society Interface, 16:20190246 (2019)   [Open access]
  14. Han X, Shinozaki T, and Kobayashi R.
    Effective and Stable Neuron Model Optimization Based on Aggregated CMA-ES.
    ICASSP 2019:1264-1268 (2019)  
  15. Kobayashi R*, Nishimaru H, Nishijo H, and Lansky P.
    A single spike deteriorates synaptic conductance estimation.
    BioSystems, 161:41-45 (2017)   [Open access]
  16. Proskurnia J, Grabowicz PA, Kobayashi R, Castillo C, Cudre-Mauroux P, and Aberer K.
    Predicting the success of online petitions leveraging multidimensional time-series.
    WWW'17:755-764 (2017)   [Open access]
    Acceptance rate: 17% (164/966 submissions)
  17. Aoki T, Takaguchi T, Kobayashi R, and Lambiotte R.
    Input-output relationship in social communications characterized by spike train analysis.
    Physical Review E, 94:042313 (2016),   Preprint
  18. +Kobayashi R*, +Nishimaru H*, and Nishijo H.
    Estimation of excitatory and inhibitory synaptic conductance variations in motoneurons during locomotor-like rhythmic activity.
    Neuroscience, 335:72-81 (2016)
  19. Kobayashi R*, Kitano K.
    Impact of slow K+ currents on spike generation can be described by an adaptive threshold model.
    Journal of Computational Neuroscience, 40:347-362. (2016)   [Open access]
  20. Kobayashi R, Lambiotte R.
    TiDeH: Time-Dependent Hawkes Process for Predicting Retweet Dynamics.
    AAAI ICWSM-2016:191-200 (2016)   [Open access]
    Acceptance tate: 17% (52/306 submissions)
  21. Kobayashi R, Kitano K.
    A method for estimating of synaptic connectivity from spike data of multiple neurons.
    Nonlinear Theory and Its Applications, IEICE, 7:156-163 (2016)   [Open access]
  22. Koyama S, Kobayashi R.
    Fluctuation scaling in neural spike trains.
    Mathematical Biosciences and Engineering, 13:537-550 (2016)
  23. Kostal L, Kobayashi R.
    Optimal decoding and information transmission in Hodgkin-Huxley neurons under metabolic cost constraints.
    BioSystems, 136:3-10 (2015),   Preprint
  24. Kobayashi R*, He J, and Lansky P.
    Estimation of the synaptic input firing rates and characterization of the stimulation effects in an auditory neuron.
    Frontiers in Computational Neuroscience, 9:59. (2015)   [Open access]
  25. Kobayashi R*, Namiki S, Kanzaki R, Kitano K, Nishikawa I, and Lansky P.
    Population coding is essential for rapid information processing in the moth antennal lobe.
    Brain Research, 1536:88-96 (2013)
  26. Kobayashi R, Kitano K*.
    Impact of network topology on inference of synaptic connectivity from multi-neuronal spike data simulated by a large-scale cortical network model.
    Journal of Computational Neuroscience, 35:109-124 (2013)
  27. Kobayashi R, Tsubo Y, Lansky P, and Shinomoto S.
    Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron.
    NIPS'11:217-225 (2011)   [Open access]
    Acceptance tate: 22% (305/1400 submissions)
  28. Kobayashi R*, Shinomoto S, and Lansky P.
    Estimation of time-dependent input from neuronal membrane potential.
    Neural Computation, 23:3070-3093 (2011)
  29. +Kobayashi R, +Tsubo Y, and Shinomoto S*.
    Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold.
    Frontiers in Computational Neuroscience, 3:9 (2009)   [Open access]
  30. Kobayashi R.
    Influence of firing mechanisms on gain modulation.
    Journal of Statistical Mechanics, P01017 (2009)   [Open access]
  31. Jolivet R, Kobayashi R, Rauch A, Naud R, Shinomoto S, and Gerstner W.
    A benchmark test for a quantitative assessment of simple neuron models.
    Journal of Neuroscience Methods, 169:417-424 (2008)
  32. Kobayashi R, Shinomoto S.
    State space method for predicting the spike times of a neuron.
    Physical Review E, 75:011925 (2007)
  33. Kobayashi R, Shinomoto S.
    Predicting spike times from subthreshold dynamics of a neuron.
    NIPS'06:721-728 (2006)   [Open access]
    Acceptance tate: 24% (203/833 submissions)
  34. Kobayashi R, Miyazaki Y, and Shinomoto S.
    Faithful and unfaithful students in time series learning.
    IMA Journal of Applied Mathematics, 70:657-665 (2005)


  1. Best Reviewer Award (SocInfo 2020) (2020).
  2. FFIT(Funai Foundation for Information Technology) Research Awards (2010).
  3. INCF Prize (2009).
  4. EPFL-Brain Mind Institute Neuron Modeling Award (2008).

Reviewer Service: Journals

Advances in Complex Systems, BioSystems, Brain Research, Chaos: An Interdisciplinary Journal of Nonlinear Science, Chinese Journal of Physiology, Entropy, Frontiers in Computational Neuroscience, International Journal of Neural Systems, Journal of Medical Internet Research, JMIR Infodemiology, Journal of Computational Neuroscience, Journal of the Physical Society of Japan, Mathematical Biosciences and Engineering, Neural Computation, Neural Networks, Neural Processing Letters, Neurocomputing, Physical Review E, PLoS Computational Biology, Science Advances, Scientific Reports.

Reviewer Service: Conferences

12th ACM Web Science Conference (WebSci 2020),
International Conference on Social Informatics (SocInfo 2019, 2020),
AAAI Conference on Web and Social Media (ICWSM 2016, 2017, 2018),
Advances in Neural Information Processing Systems (NIPS 2013, 2014).