A Computational Study of Robotic Therapy for Stroke Rehabilitation Based on Population Coding

  • Yuki Ueyama
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8609)


We evaluated the efficiency of robotic therapy for stroke survivors by using a computational approach in motor theory with a stroke rehabilitation model. In computational neuroscience, hand movement can be represented by population coding of neuronal preferred directions (PDs) in the motor cortex. We modeled the recovery processes of arm movement in conventional and robotic therapies as reoptimization of PDs in different learning rules, and compared the efficiencies after stroke. Conventional therapy did not induce complete recovery of stroke lesions, and the neuronal state depended on the training direction. However, robotic therapy reoptimized the PDs uniformly regardless of the training direction. These observations suggest that robotic therapy may be effective for recovery and not have a negative effect on motor performance depending the training direction. Furthermore, this study provides computational evidence to promote robotic therapy for stroke rehabilitation.


Motor learning Motor cortex Preferred direction Reinforcement learning Reaching 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Feigin, V.L., Forouzanfar, M.H., Krishnamurthi, R., Mensah, G.A., Connor, M., Bennett, D.A., Moran, A.E., Sacco, R.L., Anderson, L., Truelsen, T.: Global and regional burden of stroke during 1990-2010: findings from the Global Burden of Disease Study 2010. The Lancet 383, 245–255 (2014)CrossRefGoogle Scholar
  2. 2.
    Langhorne, P., Coupar, F., Pollock, A.: Motor recovery after stroke: a systematic review. The Lancet Neurology 8, 741–754 (2009)CrossRefGoogle Scholar
  3. 3.
    Li, C.-S.R., Padoa-Schioppa, C., Bizzi, E.: Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field. Neuron 30, 593–607 (2001)CrossRefGoogle Scholar
  4. 4.
    Padoa-Schioppa, C., Li, C.-S.R., Bizzi, E.: Neuronal activity in the supplementary motor area of monkeys adapting to a new dynamic environment. J. Neurophysiol. 91, 449–473 (2004)CrossRefGoogle Scholar
  5. 5.
    Molina-Luna, K., Hertler, B., Buitrago, M.M., Luft, A.R.: Motor learning transiently changes cortical somatotopy. Neuroimage 40, 1748–1754 (2008)CrossRefGoogle Scholar
  6. 6.
    Nudo, R.J., Wise, B.M., SiFuentes, F., Milliken, G.W.: Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Science 272, 1791–1794 (1996)CrossRefGoogle Scholar
  7. 7.
    Han, C.E., Arbib, M.A., Schweighofer, N.: Stroke rehabilitation reaches a threshold. PLoS Comput. Biol. 4, e1000133 (2008)Google Scholar
  8. 8.
    Takiyama, K., Okada, M.: Recovery in stroke rehabilitation through the rotation of preferred directions induced by bimanual movements: a computational study. PLoS One 7, e37594 (2012)Google Scholar
  9. 9.
    Georgopoulos, A.P., Schwartz, A.B., Kettner, R.E.: Neuronal population coding of movement direction. Science 233, 1416–1419 (1986)CrossRefGoogle Scholar
  10. 10.
    Reinkensmeyer, D.J., Iobbi, M.G., Kahn, L.E., Kamper, D.G., Takahashi, C.D.: Modeling reaching impairment after stroke using a population vector model of movement control that incorporates neural firing-rate variability. Neural Computation 15, 2619–2642 (2003)CrossRefzbMATHGoogle Scholar
  11. 11.
    Georgopoulos, A.P., Kalaska, J.F., Caminiti, R., Massey, J.T.: On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. J. Neuroscience 2, 1527–1537 (1982)Google Scholar
  12. 12.
    Kalaska, J.F., Cohen, D., Hyde, M.L., Prud’Homme, M.: A comparison of movement direction-related versus load direction-related activity in primate motor cortex, using a two-dimensional reaching task. J. Neuroscience 9, 2080–2102 (1989)Google Scholar
  13. 13.
    Todorov, E.: Cosine tuning minimizes motor errors. Neural Computation 14, 1233–1260 (2002)CrossRefzbMATHGoogle Scholar
  14. 14.
    Matthews, P.: Relationship of firing intervals of human motor units to the trajectory of post-spike after-hyperpolarization and synaptic noise. J. Physiol-London 492, 597–628 (1996)Google Scholar
  15. 15.
    Harris, C.M., Wolpert, D.M.: Signal-dependent noise determines motor planning. Nature 394, 780–784 (1998)CrossRefGoogle Scholar
  16. 16.
    Izawa, J., Shadmehr, R.: Learning from Sensory and Reward Prediction Errors during Motor Adaptation. PLoS Comput. Biol. 7, e1002012 (2011)Google Scholar
  17. 17.
    Hogan, N., Krebs, H.I., Rohrer, B., Palazzolo, J.J., Dipietro, L., Fasoli, S.E., Stein, J., Hughes, R., Frontera, W.R., Lynch, D.: Motions or muscles? some behavioral factors underlying robotic assistance of motor recovery. Journal of Rehabilitation Research & Development 43, 605–618 (2006)CrossRefGoogle Scholar
  18. 18.
    Schultz, W., Dayan, P., Montague, P.R.: A neural substrate of prediction and reward. Science 275, 1593–1599 (1997)CrossRefGoogle Scholar
  19. 19.
    Casadio, M., Morasso, P., Sanguineti, V., Giannoni, P.: Minimally assistive robot training for proprioception enhancement. Exp. Brain Res. 194, 219–231 (2009)CrossRefGoogle Scholar
  20. 20.
    Emken, J.L., Benitez, R., Sideris, A., Bobrow, J.E., Reinkensmeyer, D.J.: Motor adaptation as a greedy optimization of error and effort. J. Neurophysiol. 97, 3997–4006 (2007)CrossRefGoogle Scholar
  21. 21.
    Volpe, B.T., Huerta, P.T., Zipse, J.L., Rykman, A., Edwards, D., Dipietro, L., Hogan, N., Krebs, H.I.: Robotic devices as therapeutic and diagnostic tools for stroke recovery. Arch. Neurol. 66, 1086 (2009)CrossRefGoogle Scholar
  22. 22.
    Marchal-Crespo, L., Reinkensmeyer, D.J.: Review of control strategies for robotic movement training after neurologic injury. Journal of Neuroengineering and Rehabilitation 6, 20 (2009)CrossRefGoogle Scholar
  23. 23.
    Casadio, M., Sanguineti, V.: Learning, Retention, and Slacking: A Model of the Dynamics of Recovery in Robot Therapy. IEEE Transactions on Neural Systems and Rehabilitation Engineering 20, 286–296 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yuki Ueyama
    • 1
  1. 1.Department of Rehabilitation EngineeringResearch Institute of National Rehabilitation Center for Persons with DisabilitiesTokorozawaJapan

Personalised recommendations