Conference

Oral presentation (in English)

    1. Detection of task-relevant and task-irrelevant motion sequences: application to motor adaptation in goal-directed and whole-body movements, Ken Takiyama, Forum at RIKEN CBS, Jul. 17 (2018)
    2. Prospective coding in motor learning and motor decision making, Ken Takiyama, the 18th workshop of brain and mind, Rusutsu, Jan. 13 (2018)
    3. Detecting the relevance of each motion component in whole-body motion to performance, Ken Takiyama, Daisuke Furuki, the XXVI Congress of the International Society of Biomechanics (ISB), Brisbane, Jul. 25 (2017)
    4. Prospective errors determine motor learning - a step towards a unified model of motor learning-, Ken Takiyama, Neurolunch, Harvard University, Nov. 7 (2016)
    5. Prospective errors determine motor learning - a step towards a unified model of motor learning -, Ken Takiyama, Modeling Neural Activity (MONA2), Jun. 21 (2016)
    6. Modulation of preferred direction can unify motor learning in unimanual and bimanual movements, Ken Takiyama, Yutaka Sakai, 日本神経科学学会, パシフィコ横浜, Sep. 13 (2014)
    7. Balanced motor primitive can unify motor learning effects in unimanual and bimanual movements, Ken Takiyama, Yutaka Sakai, 日本神経回路学会, はこだて未来大学, Aug. 29 (2014)
    8. Simultaneous estimation of state transitions and instantaneous firing rates using switching state space model, Ken Takiyama, Modelling neural activity (MONA), Kauai, USA, Jun. 27. (2013)
    9. Recovery in stroke patients through rotations of preferred directions induced by bimanual movement: a computational study, Ken Takiyama, Masato Okada, 日本神経科学学会, 名古屋国際会議場, Sep. 21 (2012)
    10. Simultaneous estimation of neural state transitions, neural state numbers, and nonstationary firing rates using switching state space model., 瀧山健，岡田真人, Neuro 2010, 神戸コンベンションセンター, Sep. 2.(2010)
    11. Maximization of learning speed in the motor cortex due to neuronal redundancy , Ken Takiyama, Japan-France Joint Workshop , Kyoto University, Jan. 11. (2012)


Poster presentation

    1. Detection of task-relevant and task-irrelevant motion sequences: application to motor adaptation in goal-directed and whole-body movements, Ken Takiyama, Daisuke Furuki, Annual meeting of Society for Neuroscience (SfN2018), San Diego, USA, Nov 1-8. (2018)
    2. Not movement duration but movement velocity is altered by implicit adaptation to movement-amplitude perturbation in self-paced reaching task., Takuji Hayashi, Ken Takiyama, Annual meeting of Society for Neuroscience (SfN2018), San Diego, USA, Nov 1-8. (2018)
    3. Influence of switching rule on motor learning, Ken Takiyama, Koutaro Ishii, Takuji Hayashi, Annual meeting of Society for Neuroscience (SfN2017), Washington DC, USA, Nov 11-17. (2017)
    4. Competitive game influences risk-sensitivity in motor decision-making, Keiji Ota, Takuji Hayashi, Ken Takiyama, Annual meeting of Society for Neuroscience (SfN2017), Washington DC, USA, Nov 11-17. (2017)
    5. Motor learning rate is influenced by prior motor learning through reconfiguration of directional preference of motor primitives, Takuji Hayashi, Ken Takiyama, Daichi Nozaki, Annual meeting of Society for Neuroscience (SfN2017), Washington DC, USA, Nov 11-17. (2017)
    6. Application of POrtable Motor learning LABoratory (PoMLab): cross-syndrome comparison of implicit visuomotor adaptation among patients with stroke and Parkinson’ s disease, Masahiro Shinya, Ken Takiyama, Takeshi Sakurada, Shin-ichi Muramatsu, Hirofumi Ogihara, Takaaki Sato, Taiki Komatsu, Annual meeting of Society for Neuroscience (SfN2017), Washington DC, USA, Nov 11-17. (2017)
    7. Portable Motor Learning Laboratory (PoMLab), Mashiro Shinya, Ken Takiyama, Annual meeting of Society for Neuroscience (SfN2016), San Diego, USA, Nov 11-16. (2016)
    8. Rotation of preferred direction of motor primitive explains the dependence of shape of visuomotor map on visuomotor adaptation rate, Takuji Hayashi, Ken Takiyama, Daichi Nozaki, Annual meeting of Society for Neuroscience (SfN2016), San Diego, USA, Nov 11-16. (2016)
    9. Simultaneous estimation of state transitions and instantaneous firing rates using switching state space model, Ken Takiyama, ASPIPA BioSiPS Workshop 2016, Koganei, Japan, Jul 14. (2016)
    10. Prospective error determines motor learning: a step towards a unified model of motor learning, Ken Takiyama, Masaya Hirashima, Daichi Nozaki, Conference on Systems Neuroscience and Rehabilitation (SNR2015), Tokorozawa, Jpn, Mar. 11-12. (2015),
    11. Transfer of learning effects between unimanual and bimanual movements through modulation of preferred directions: a computational study, Ken Takiyama, Yutaka Sakai, Annual meeting of Neural Control of Movement (NCM2014), Amsterdam, Netherland, Apr. 23-24. (2014)
    12. Prospective error determines motor learning: a step towards a unified model of motor learning, Ken Takiyama, Masaya Hirashima, Daichi Nozaki, Annual meeting of Society for Neuroscience (SfN2013), San Diego, USA, Nov. 11. (2013)
    13. Prospective error to determine motor learning: a step towards a unified model of motor learning, Ken Takiyama, Masaya Hirashima, Daichi Nozaki, Annual meeting of Neural Control of Movement (NCM2013), Puerto Rico, USA, Apr. 17. (2013)
    14. Recovery in stroke patients through rotations of preferred direction induced by bimanual movement: a computational study, Ken Takiyama, Masato Okada, Annual meeting of Society for Neuroscience (SfN2012), New Orleans, USA, Oct. 14. (2012)
    15. Maximization of learning speed in the motor cortex due to neuronal redundancy
    16. Ken Takiyama and Masato Okada.
    17. Annual meeting of Society for Neuroscience (SfN2011)
    18. Washington D.C., USA, Nov. 15. (2011
    19. Maximization of learning speed in motor cortex due to neuron redundancy
    20. Ken Takiyama, Masato Okada.
    21. Japan-Germany Joint Workshop on "Computational Neuroscience" 2011,
    22. OIST, Japan, Mar. 2-5 (2011).
    23. Maximization of learning speed in motor cortex due to neuron redundancy
    24. Ken Takiyama, Masato Okada.
    25. Computational and Systems Neuroscience (Cosyne 2011),
    26. Salt Lake City, USA, Feb. 24-27 (2011).
    27. Switching state space model for simultaneously estimating state transitions and nonstationary firing rates.
    28. Ken Takiyama, Masato Okada.
    29. Neural Information Processing Systems (NIPS 2010),
    30. Vancouver, B. C. Canada, Dec. 6 (2010).
    31. Hidden structures detection in nonstationary spike trains.
    32. Ken Takiyama, Masato Okada.
      1. Computational and Systems Neuroscience (Cosyne 2010),
      2. Salt Lake City, USA, Feb. 27 (2010).
    33. Which model can properly describe dynamics and smoothness of firing rate?
    34. Ken Takiyama, Kentaro Katahira, Masato Okada.
    35. Computational and Systems Neuroscience (Cosyne 2009),
    36. Salt Lake City, USA, Feb. 26 (2009).