研究業績

学術論文 (査読あり)

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  1. Han X, Shinozaki T, Kobayashi R.
    Effective and Stable Neuron Model Optimization Based on Aggregated CMA-ES.
    ICASSP 2019 (Accepted)  
  2. Kobayashi R, Nishimaru H, Nishijo H, Lansky P.
    A single spike deteriorates synaptic conductance estimation.
    BioSystems, 161:41-45 (2017)   [Open access]
  3. Proskurnia J, Grabowicz PA, Kobayashi R, Castillo C, Cudre-Mauroux P, 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)
  4. Aoki T, Takaguchi T, Kobayashi R, Lambiotte R.
    Input-output relationship in social communications characterized by spike train analysis.
    Physical Review E, 94:042313 (2016),   Preprint
  5. *Kobayashi R, *Nishimaru H, Nishijo H. (*: Equal Contribution)
    Estimation of excitatory and inhibitory synaptic conductance variations in motoneurons during locomotor-like rhythmic activity.
    Neuroscience, 335:72-81 (2016)
  6. 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]
  7. Kobayashi R, Lambiotte R.
    TiDeH: Time-Dependent Hawkes Process for Predicting Retweet Dynamics.
    AAAI ICWSM'16:191-200 (2016)   [Open access]
    採択率: 17% (52/306 submissions)
  8. 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]
  9. Koyama S, Kobayashi R.
    Fluctuation scaling in neural spike trains.
    Mathematical Biosciences and Engineering, 13:537-550 (2016)
  10. Kostal L, Kobayashi R.
    Optimal decoding and information transmission in Hodgkin-Huxley neurons under metabolic cost constraints.
    BioSystems, 136:3-10 (2015),   Preprint
  11. Kobayashi R, He J, 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]
  12. Kobayashi R, Namiki S, Kanzaki R, Kitano K, Nishikawa I, Lansky P.
    Population coding is essential for rapid information processing in the moth antennal lobe.
    Brain Research, 1536:88-96 (2013)
  13. 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)
  14. Kobayashi R, Tsubo Y, Lansky P, 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)
  15. Kobayashi R, Shinomoto S, Lansky P.
    Estimation of time-dependent input from neuronal membrane potential.
    Neural Computation, 23:3070-3093 (2011)
  16. *Kobayashi R, *Tsubo Y, Shinomoto S (*: Equal Contribution).
    Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold.
    Frontiers in Computational Neuroscience, 3:9 (2009)   [Open access]
  17. Kobayashi R.
    Influence of firing mechanisms on gain modulation.
    Journal of Statistical Mechanics, P01017 (2009)   [Open access]
  18. 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, 169:417-424 (2008)
  19. Kobayashi R, Shinomoto S.
    State space method for predicting the spike times of a neuron.
    Physical Review E, 75:011925 (2007)
  20. Kobayashi R, 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)
  21. Kobayashi R, Miyazaki Y, Shinomoto S.
    Faithful and unfaithful students in time series learning.
    IMA Journal of Applied Mathematics, 70:657-665 (2005)

解説論文

  1. 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.
  2. 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. 電子情報通信学会 CCS研究会 CCS奨励賞 (2016).
  2. 船井情報科学振興財団 船井研究奨励賞 (2010).
  3. INCF Prize (2009).
  4. 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, Chinese Journal of Physiology, Entropy, Frontiers in Computational Neuroscience, International Journal of Neural Systems, Journal of Computational Neuroscience, Journal of the Physical Society of Japan, Mathematical Biosciences and Engineering, Neural Networks, Neural Processing Letters, Neurocomputing, Physical Review E, PLoS Computational Biology.
人工知能学会誌, 電子情報通信学会誌.

査読委員 (国際会議)

AAAI Conference on Web and Social Media (ICWSM 2016, 2017, 2018, 2019),
Advances in Neural Information Processing Systems (NIPS 2013, 2014).