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

• Published in: Neural networks Vol. 22; no. 9; pp. 1214 - 1223
• Main Authors: Choi, Kyuwan, Hirose, Hideaki, Sakurai, Yoshio, Iijima, Toshio, Koike, Yasuharu
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.11.2009
• Subjects: Action Potentials; Algorithms; Animals; Arm - physiology; Arm movement; Biomechanical Phenomena; BMI; Electromyography; EMG; Joints - physiology; Macaca; Male; Microelectrodes; Motor Activity - physiology; Motor Cortex - physiology; Muscle, Skeletal - physiology; Neural activity; Neural Networks (Computer); Neurons - physiology; M1; Arm movement; Neural activity; EMG; BMI
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2009.09.003

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.