A HIERARCHICAL NEURAL-NETWORK MODEL FOR CONTROL AND LEARNING OF VOLUNTARY MOVEMENT
A HIERARCHICAL NEURAL-NETWORK MODEL FOR CONTROL AND LEARNING OF VOLUNTARY MOVEMENT
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DOI：
10.1007/bf00364149
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发表时间：
1987-01-01
影响因子：
1.9
通讯作者：
SUZUKI, R
中科院分区：
文献类型：
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作者：
KAWATO, M;FURUKAWA, K;SUZUKI, R
为了控制自主运动，中枢神经系统（CNS）必须解决以下三个不同层次的计算问题：在视觉坐标系中确定所需的轨迹，将其坐标转换为身体坐标和运动指令的产生。基于生理知识和以前的模型，我们提出了一个分层的神经网络模型，占电机命令的产生。在我们的模型中，关联皮层为运动皮层提供了身体坐标中所需的轨迹，然后通过长回路感觉反馈计算运动命令。在脊髓小脑-大细胞红核系统中，由于异突触可塑性，肌肉骨骼系统动力学的内部神经模型随着实践而获得，同时监测运动命令和运动结果。利用该动态模型的内部反馈控制通过预测可能的运动误差来更新电机命令。在小脑-小细胞红核系统中，获得肌肉骨骼系统逆动力学的内部神经模型，同时监测期望的轨迹和运动命令。逆动力学模型在复杂的运动指令计算中替代了其他大脑区域。动力学和逆动力学模型由并行分布式神经网络实现，该网络包括计算输入信号的各种非线性变换的多个子系统和具有异突触可塑性的神经元（即，假设突触权重的变化与两种突触输入的乘积成比例）。以机器人为被控对象，通过计算机仿真研究了该模型的控制和学习性能，结果表明：（1）在运动控制过程中获得了动力学和逆动力学模型。(2)随着运动学习的进行，逆动力学模型逐渐取代外部反馈作为主控制器。与此同时，整体控制性能变得更好。（三）、一旦神经网络模型学会控制某种运动，它就可以控制完全不同的、更快的运动。(4)神经网络模型工作良好，即使只有非常有限的信息的基本动态结构的控制系统。 因此，该模型不仅占CNS的学习和控制能力，但也提供了一个有前途的并行分布式控制方案的大规模复杂对象的动态只有部分已知。
In order to control voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: the determination of a desired trajectory in the visual coordinates, the transformation of its coordinates to the body coordinates and the generation of motor command. Based on physiological knowledge and previous models, we propose a hierarchical neural network model which accounts for the generation of motor command. In our model the association cortex provides the motor cortex with the desired trajectory in the body coordinates, where the motor command is then calculated by means of long-loop sensory feedback. Within the spinocerebellum-magnocellular red nucleus system, an internal neural model of the dynamics of the musculoskeletal system is acquired with practice, because of the heterosynaptic plasticity, while monitoring the motor command and the results of movement. Internal feedback control with this dynamical model updates the motor command by predicting a possible error of movement. Within the cerebrocerebellum-parvocellular red nucleus system, an internal neural model of the inverse-dynamics of musculo-skeletal system is acquired while monitoring the desired trajectory and the motor command. The inverse-dynamics model substitutes for other brain regions in the complex computation of the motor command. The dynamics and the inverse-dynamics models are realized by a parallel distributed neural network, which comprises many sub-systems computing various nonlinear transformations of input signals and a neuron with heterosynaptic plasticity (that is, changes of synaptic weights are assumed proportional to a product of two kinds of synaptic inputs). Control and learning performance of the model was investigated by computer simulation, in which a robotic manipulator was used as a controlled system, with the following results: (1) Both the dynamics and the inverse-dynamics models were acquired during control of movements. (2) As motor learning proceeded, the inverse-dynamics model gradually took the place of external feedback as the main controller. Concomitantly, overall control performance became much better. (3). Once the neural network model learned to control some movement, it could control quite different and faster movement. (4) The neural network model worked well even when only very limited information about the fundamental dynamical structure of the controlled system was available. Consequently, the model not only accounts for the learning and control capability of the CNS, but also provides a promising parallel-distributed control scheme for a large-scale complex object whose dynamics are only partially known.