TY - JOUR
T1 - Prediction of arm trajectory from the neural activities of the primary motor cortex with modular connectionist architecture
AU - Choi, Kyuwan
AU - Hirose, Hideaki
AU - Sakurai, Yoshio
AU - Iijima, Toshio
AU - Koike, Yasuharu
PY - 2009/11/1
Y1 - 2009/11/1
N2 - 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.
AB - 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.
KW - Arm movement
KW - BMI
KW - EMG
KW - M1
KW - Neural activity
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U2 - 10.1016/j.neunet.2009.09.003
DO - 10.1016/j.neunet.2009.09.003
M3 - Article
C2 - 19793637
AN - SCOPUS:70350247473
VL - 22
SP - 1214
EP - 1223
JO - Neural Networks
JF - Neural Networks
SN - 0893-6080
IS - 9
ER -