Prediction of arm trajectory from the neural activities of the primary motor cortex with modular connectionist architecture

Kyuwan Choi, Hideaki Hirose, Yoshio Sakurai, Toshio Iijima, Yasuharu Koike

    Research output: Contribution to journalArticlepeer-review

    15 Citations (Scopus)


    In our previous study [Koike, Y., Hirose, H., Sakurai, Y., Iijima T., (2006). Prediction of arm trajectory from a small number of neuron activities in the primary motor cortex. Neuroscience Research, 55, 146-153], we succeeded in reconstructing muscle activities from the offline combination of single neuron activities recorded in a serial manner in the primary motor cortex of a monkey and in reconstructing the joint angles from the reconstructed muscle activities during a movement condition using an artificial neural network. However, the joint angles during a static condition were not reconstructed. The difficulties of reconstruction under both static and movement conditions mainly arise due to muscle properties such as the velocity-tension relationship and the length-tension relationship. In this study, in order to overcome the limitations due to these muscle properties, we divided an artificial neural network into two networks: one for movement control and the other for posture control. We also trained the gating network to switch between the two neural networks. As a result, the gating network switched the modules properly, and the accuracy of the estimated angles improved compared to the case of using only one artificial neural network.

    Original languageEnglish
    Pages (from-to)1214-1223
    Number of pages10
    JournalNeural Networks
    Issue number9
    Publication statusPublished - 2009 Nov


    • Arm movement
    • BMI
    • EMG
    • M1
    • Neural activity

    ASJC Scopus subject areas

    • Cognitive Neuroscience
    • Artificial Intelligence


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