Ryota Kobayashi, Ph. D

• News

• Our paper has been accepted in Journal of Computational neuroscience.


• Our paper was accepted in NIPS 2011.


• Received INCF Prize in 2nd INCF Congress of Neuroinformatics (See Science Perspectives).


• MAT* model has been implemented in NEST simulator, with which one can simulate
  large scale networks of spiking neurons.



• Research: Quantitative Modeling of a cortical neuron

      We have developed MAT model that can accurately predict a rich variety of spike responses to not only
    the fluctuating current inputs but also the conductance inputs, for which the previous models cannot to predict.
    (Web Apllication for the MAT model)


• Awards and Prizes

    [1] INCF Prize (Sep. 2009)
          Reference: Gerstner and Naud, Science Vol.326 pp. 379-380 (2009): Link
    [2] EPFL-Brain Mind Institute Neuron Modeling Award (April. 2008)
          Reference: Jolivet et. al., J. Neurosci. Methods. Vol 169, pp.417-424 (2008): Link


• Selected Publications

    [1] "Impact of network topology on inference of synaptic connectivity from multi-neuronal spike data simulated by a large-scale cortical network model"
          Kobayashi R., Kitano K.
          Journal of Computational Neuroscience (In press) LINK

    [2] "Estimation of time-dependent input from neuronal membrane potential."
          Kobayashi R., Shinomoto S., Lansky P.
          Neural Computation 23, 12, pp.3070-3093 (2011) PDF

    [3] "Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold."
          Kobayashi R., Tsubo Y., Shinomoto S.
          Frontiers in Computational Neuroscience 3, 9 (2009) Full Text (Open Access)



• All Publications

    [1] "Population coding is essential for rapid information processing in the moth antennal lobe"
          Kobayashi R., Namiki S., Kanzaki R., Kitano K., Nishikawa I., Lansky P.
          Brain Research (Accepted)

    [2] "Impact of network topology on inference of synaptic connectivity from multi-neuronal spike data simulated by a large-scale cortical network model"
          Kobayashi R., Kitano K.
          Journal of Computational Neuroscience (In press) LINK

    [3] "Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron."
          Kobayashi R., Tsubo Y., Lansky P., Shinomoto S.
          Advances in Neural Information Processing Systems 24 (NIPS 2011) PDF
          Acceptance rate 22% (305 papers/ 1400 submissions)

    [4] "Estimation of time-dependent input from neuronal membrane potential."
          Kobayashi R., Shinomoto S., Lansky P.
          Neural Computation 23, 12, pp.3070-3093 (2011) PDF

    [5] "Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold."
          Kobayashi R., Tsubo Y., Shinomoto S.
          Frontiers in Computational Neuroscience 3, 9 (2009) Full Text (Open Access)

    [6] "Influence of firing mechanisms on gain modulation"
          Kobayashi R.
          Journal of Statistical Mechanics: Theory and Experiment (2009) P01017. Full Text (Open Access)

    [7] "A benchmark test for a quantitative assessment of simple neuron models"
          Jolivet R., Kobayashi R., Rauch A., Naud R., Shinomoto S. and Gerstner W.
          Journal of Neuroscience Methods, 169, pp.417-424 (2008). PubMeD

    [8] "State space method for predicting the spike times of a neuron"
          Kobayashi R., Shinomoto S.,
          Phyical Review E 75, 011925 (2007). PDF

    [9] "Predicting spike times from subthreshold dynamics of a neuron"
          Kobayashi R., Shinomoto S.,
          Advances in Neural Information Processing Systems 19 (NIPS 2006) pp.721-728. PDF
          Acceptance rate 24% (203 papers/ 833 submissions)

    [10] "Faithful and unfaithful students in time series learning"
          Kobayashi R., Miyazaki Y., Shinomoto S.,
          IMA Journal of Applied Mathematics 70, pp.657-665 (2005) Journal


• Presentations

    See List of Presentations.