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Thalamic cooperation between the cerebellum and basal ganglia with a new tropism-based action-dependent heuristic dynamic programming method

机译:小脑和基底神经节之间的丘脑协作与新的基于向性的基于动作的启发式动态规划方法

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In order to explore the possible cooperation mechanism between the cerebellum and basal ganglia in the central nervous system and to establish a more intelligent learning mechanism for robots, a new tropism-based ADHDP (action-dependent heuristic dynamic programming) learning mechanism involving the cortico-basal ganglia and cerebellar circuitry and the thalamic function is proposed. The cerebellum specializes in the actor part, while the basal ganglia are related to critic prediction. The thalamic function is considered as the tropism mechanism. Tropism value denoting the biological propensity is introduced to illustrate the degree of closing to the target. Although several motor control models have been proposed to explain the control and learning mechanism in the cerebellum and basal ganglia separately, it seems that the cooperation mechanism between them has not received much attention. In our proposed learning mechanism, the thalamic function and the cooperation between the cerebellum and basal ganglia are considered, and with a neurophysiological view, a striato-striatal lateral weight in the basal ganglia was added in the critic network. We present the detailed design architecture and explain how effective learning and optimization can be achieved with this novel tropism-based ADHDP architecture. Furthermore, we test its performance on the balance learning task of a two-wheeled self-balancing robot (TWSBR), which simulates the typical motor control and learning of the human body. In order to illustrate the effect of the thalamic function, some comparison researches about the balance learning problem have been done.
机译:为了探索中枢神经系统中小脑和基底神经节之间可能的协作机制,并为机器人建立更智能的学习机制,一种新的基于向性的涉及动作的启发式ADHDP(动作依赖启发式动态编程)学习机制提出了基底神经节和小脑回路以及丘脑功能。小脑专长于演员部分,而基底神经节与评论家的预测有关。丘脑功能被认为是向性机制。引入表示生物学倾向的趋向值来说明接近目标的程度。尽管已经提出了几种运动控制模型来分别解释小脑和基底神经节的控制和学习机制,但是似乎它们之间的协作机制并未受到太多关注。在我们提出的学习机制中,考虑了丘脑功能以及小脑与基底神经节之间的协作,并且从神经生理学的角度来看,在批评家网络中增加了基底神经节的纹状体-纹状体横向重量。我们介绍了详细的设计架构,并说明了如何通过这种新颖的基于向性的ADHDP架构来实现有效的学习和优化。此外,我们在两轮自平衡机器人(TWSBR)的平衡学习任务上测试了其性能,该机器人模拟了典型的人体运动控制和学习。为了说明丘脑功能的作用,已经进行了一些关于平衡学习问题的比较研究。

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