Prediction of arm trajectory from the neural activities of the primary motor cortex with modular connectionist architecture
Prediction of arm trajectory from the neural activities of the primary motor cortex with modular connectionist architecture
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DOI：
10.1016/j.neunet.2009.09.003
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发表时间：
2009-11-01
期刊：
影响因子：
7.8
通讯作者：
Koike, Yasuharu
中科院分区：
文献类型：
--
作者：
Choi, Kyuwan;Hirose, Hideaki;Koike, Yasuharu
在我们以前的研究中[Korke，Y，Hirose，H，Sakuran，Y.，饭岛T（2006年）。根据初级运动皮层的少量神经元活动预测手臂轨迹。Neuroscience Research，55，146-153]，我们成功地从在猴子的初级运动皮层中以连续方式记录的单个神经元活动的离线组合重建肌肉活动，并且使用人工神经网络在运动条件期间从重建的肌肉活动重建关节角度。然而，在静态条件下的关节角度没有重建。在静态和运动条件下重建的困难主要是由于肌肉的特性，如速度-张力关系和长度-张力关系。在这项研究中，为了克服由于这些肌肉属性的限制，我们将人工神经网络分为两个网络。一个用于运动控制，另一个用于姿势控制我们还训练了选通网络在两个神经网络之间切换。结果，选通网络适当地切换模块，并且与仅使用一个人工神经网络的情况相比，估计角度的精度提高。(C)2009爱思唯尔有限公司保留所有权利。
In our previous study [Korke, Y, Hirose, H, Sakuran, 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. (C) 2009 Elsevier Ltd. All rights reserved.