研究業績

学術論文 (査読あり)

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* は Corresponding Author, + は Equal Contribution です。
[Open access] と書かれている論文はリンク先でダウンロードできます。

  1. *Matsuki A, Kori H, and *Kobayashi R.
    Network inference applicable to both synchronous and desynchronous systems from oscillatory signals.
    PLoS Complex Systems 2(9):e0000063 (2025)   [Open access]
  2. *Kobayashi R and Shinomoto S.
    Inference of Monosynaptic Connections from Parallel Spike Trains: A Review.
    Neuroscience Research 215: 37-46 (2025)   [Open access]
  3. Yamamoto T, Nakao H, and *Kobayashi R.
    Gaussian Process Phase Interpolation for estimating the asymptotic phase of a limit cycle oscillator from time series data.
    Chaos, Solitons & Fractals 191: 115913 (2025)   [Open access]
  4. 武富 有香, 中山 悠理, 須田 永遠, 宇野 毅明, 橋本 隆子, 豊田 正史, 吉永 直樹, 喜連川 優, *小林 亮太.
    Twitterにおける新型コロナワクチンに関する話題の変化: ツイート本文の読解を通じた仮説構築による分析.
    人工知能学会 論文誌 39: 5 (2024)   [Open access]
  5. 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]
  6. 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]
  7. *+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]
  8. *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)
  9. 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]
  10. Kobayashi R, Gildersleve P, Uno T, and Lambiotte R.
    Modeling Collective Anticipation and Response on Wikipedia.
    AAAI ICWSM-2021: 315-326 (2021),   [Open access]
    Video,   データ, Cコード   (Python にも移植予定)
  11. +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アプリ   CoNNECT (Pythonコード)   GLMCC (Pythonコード)
  12. Murayama T, Wakamiya S, Aramaki E, and *Kobayashi R.
    Modeling the Spread of Fake News on Twitter.
    PLOS ONE 16(4): e0250419 (2021)   Pythonコード   [Open access]
  13. 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]
  14. +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]
  15. Kostal L and Kobayashi R.
    Critical size of neural population for reliable information transmission.
    Physical Review E, 100:050401(R) (2019)
    Preprint (PDF)   Supplementary material (PDF)
  16. 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]
    プレスリリース   Webアプリ   Pythonコード
  17. *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]
  18. Han X, Shinozaki T, and Kobayashi R.
    Effective and Stable Neuron Model Optimization Based on Aggregated CMA-ES.
    ICASSP 2019:1264-1268 (2019)  
  19. *Kobayashi R, Nishimaru H, Nishijo H, and Lansky P.
    A single spike deteriorates synaptic conductance estimation.
    BioSystems, 161:41-45 (2017)   [Open access]
  20. 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]
    採択率: 17% (164/966 submissions)
  21. 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
  22. +*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)
  23. *Kobayashi R and 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]
  24. Kobayashi R and Lambiotte R.
    TiDeH: Time-Dependent Hawkes Process for Predicting Retweet Dynamics.
    AAAI ICWSM-2016: 191-200 (2016)   [Open access]
    採択率: 17% (52/306 submissions)
  25. Kobayashi R and 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]
  26. Koyama S and Kobayashi R.
    Fluctuation scaling in neural spike trains.
    Mathematical Biosciences and Engineering, 13:537-550 (2016)
  27. Kostal L and Kobayashi R.
    Optimal decoding and information transmission in Hodgkin-Huxley neurons under metabolic cost constraints.
    BioSystems, 136:3-10 (2015),   Preprint
  28. *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]
  29. *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)
  30. Kobayashi R and *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)
  31. 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]
    採択率: 22% (305/1400 submissions)
  32. *Kobayashi R, Shinomoto S, and Lansky P.
    Estimation of time-dependent input from neuronal membrane potential.
    Neural Computation, 23:3070-3093 (2011)
  33. +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]
  34. Kobayashi R.
    Influence of firing mechanisms on gain modulation.
    Journal of Statistical Mechanics, P01017 (2009)   [Open access]
  35. 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)
  36. Kobayashi R and Shinomoto S.
    State space method for predicting the spike times of a neuron.
    Physical Review E, 75:011925 (2007)
  37. Kobayashi R and Shinomoto S.
    Predicting spike times from subthreshold dynamics of a neuron.
    NIPS-06:721-728 (2006)   [Open access]
    採択率: 24% (203/833 submissions)
  38. Kobayashi R, Miyazaki Y, and Shinomoto S.
    Faithful and unfaithful students in time series learning.
    IMA Journal of Applied Mathematics, 70:657-665 (2005)

著書

  1. 小林 亮太, 篠本 滋, 監修: 甘利 俊一 (2022)
    AI新世 人工知能と人類の行方, 文春新書.
  2. 小林 亮太, 新津 葵一 (監訳), 大前 奈月 (翻訳) (2022) 原著: Nan Zheng, Pinaki Mazumder
    ニューロモルフィックコンピューティング: 省エネルギーな機械学習のハードウェア実装に向けて, エヌ・ティー・エス (監訳および4章付録の執筆を担当).
  3. 小林 亮太 (2019) AI事典 第3版, 近代科学社 (Universal AI を担当).
  4. 小林 亮太 (2017) 人工知能の見る夢は　AIショートショート集, 文春文庫 (脳のシミュレーション: コンピュータの中に人工脳を作る を担当).

解説論文

  1. 小林 亮太 (2023) 神経スパイクデータからシナプス結合を推定する, 神経回路学会誌 30, 66-72.
  2. 小林 亮太, 北野 勝則 (2021) 積分発火モデル, 脳科学辞典.
  3. 小林 亮太, 北野 勝則 (2021) ネットワーク結合推定, 脳科学辞典.
  4. 小林 亮太, 木村 睦, 三宅 陽一郎, 市瀬 龍太郎 (2019) 特集 「マテリアルズインフォマティクス」にあたって, 人工知能 34, 324.
  5. 小林 亮太, 岡本 洋, 山川 宏 (2018) 特集 「物理学とAI」にあたって, 人工知能 33, 391.
  6. 小林 亮太 (2015) 大規模脳シミュレーションについての研究動向, 人工知能 30, 647-651.
  7. 小林 亮太, 相澤 彰子 (2014) 汎用エージェントの理論的枠組み ─ Marcus Hutter が提唱するAIXI の紹介─, 人工知能 29, 253-257.

受賞

  1. Best Reviewer Award (SocInfo 2020) (2020).
  2. 電子情報通信学会 CCS研究会 CCS奨励賞 (2016).
  3. 船井情報科学振興財団 船井研究奨励賞 (2010).
  4. INCF Prize (2009).
  5. EPFL-Brain Mind Institute Neuron Modeling Award (2008).

査読委員 (論文誌)

Advances in Complex Systems; BioSystems; Brain Research; Chaos: An Interdisciplinary Journal of Nonlinear Science; Cognitive Neurodynamics; Communications Engineering; Computers in human behavior reports; eLife; Entropy; Frontiers in Computational Neuroscience; IEICE Transactions on Information and Systems; International Journal of Neural Systems; Journal of Medical Internet Research (JMIR); 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; Neurons, Behavior, Data analysis, and Theory; Neuroscience Research; Physical Review E; PLoS Computational Biology; Science Advances; Scientific Reports.
人工知能学会誌; 電子情報通信学会誌.

プログラム委員 (国際会議)

AAAI Conference on Web and Social Media (ICWSM 2016-2023),
ACM Web Science Conference (WebSci 2020),
Advances in Neural Information Processing Systems (NIPS 2013, 2014),
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2021, 2022): Multidisciplinary Track,
International Conference on Social Informatics (SocInfo 2019, 2020),
International Workshop on Neural Coding (Neural Coding 2021, 2023),
LREC COLING 2024