A COMPUTATIONAL MODEL OF 4 REGIONS OF THE CEREBELLUM BASED ON FEEDBACK-ERROR LEARNING
A COMPUTATIONAL MODEL OF 4 REGIONS OF THE CEREBELLUM BASED ON FEEDBACK-ERROR LEARNING
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DOI：
10.1007/bf00201431
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发表时间：
1992-12-01
影响因子：
1.9
通讯作者：
GOMI, H
中科院分区：
文献类型：
--
作者：
KAWATO, M;GOMI, H
我们提出了一种基于反馈-误差学习方案的计算一致性小脑运动学习模型。我们假设攀爬纤维反应代表了一些运动前网络产生的运动指令错误，例如脊椎、脑干和大脑水平的反馈控制器。因此，在我们的模型中，攀爬纤维的反应被认为是在运动指令坐标中传递运动错误，而不是在感觉坐标中。基于浦肯野细胞的长期抑制，小脑不同区域的每个皮质核微复合体都学会了对不同类型的运动进行预测性和协调性控制。最终，它获得特定被控对象的逆模型，并通过前置电机网络补充原始控制。这一通用模型是作为侧半球的特定神经电路模型而详细开发的。提出了一种新的实验来阐明表示攀升纤维响应的坐标系。
We propose a computationally coherent model of cerebellar motor learning based on the feedback-error-learning scheme. We assume that climbing fiber responses represent motor-command errors generated by some of the premotor networks such as the feedback controllers at the spinal-, brain stem- and cerebral levels. Thus, in our model, climbing fiber responses are considered to convey motor errors in the motor-command coordinates rather than in the sensory coordinates. Based on the long-term depression in Purkinje cells each corticonuclear microcomplex in different regions of the cerebellum learns to execute predictive and coordinative control of different types of movements. Ultimately, it acquires an inverse model of a specific controlled object and complements crude control by the premotor networks. This general model is developed in detail as a specific neural circuit model for the lateral hemisphere. A new experiment is suggested to elucidate the coordinate frame in which climbing fiber responses are represented.