Prediction of arm trajectory from the neural activities of the primary motor cortex with modular connectionist architecture.
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.
Choi
Kyuwan
K
ATR Computational Neuroscience Laboratories, Keihanna Science City, Kyoto, Japan. kwchoi@atr.jp
Hirose
Hideaki
H
Sakurai
Yoshio
Y
Iijima
Toshio
T
Koike
Yasuharu
Y
D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D001132:Arm / Q000502:physiology*; D001696:Biomechanical Phenomena; D004576:Electromyography; D007596:Joints / Q000502:physiology; D008251:Macaca; D008297:Male; D008839:Microelectrodes; D009043:Motor Activity / Q000502:physiology*; D009044:Motor Cortex / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*