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Abstract：
The cerebellum is widely implicated in having an important role in adaptive motor control. Many of the computational studies on cerebellar motor control to date have focused on the associated architecture and learning algorithms in an effort to further understand cerebellar function. In this paper we switch focus to the signals driving cerebellar adaptation that arise through different motor behavior. To do this, we investigate computationally the contribution of the cerebellum to the optokinetic reflex (OKR), a visual feedback control scheme for image stabilization. We develop a computational model of the adaptation of the cerebellar response to the world velocity signals that excite the OKR (where world velocity signals are used to emulate head velocity signals when studying the OKR in head-fixed experimental laboratory conditions). The results show that the filter learnt by the cerebellar model is highly dependent on the power spectrum of the colored noise world velocity excitation signal. Thus, the key finding here is that the cerebellar filter is determined by the statistics of the OKR excitation signal.
摘要：
小脑广泛涉及在自适应运动控制中的重要作用。迄今为止，有关小脑运动控制的许多计算研究都集中在相关的体系结构和学习算法上，以进一步了解小脑功能。在本文中，我们将重点转向通过不同运动行为产生的驱动小脑适应的信号。要做到这一点，我们通过计算研究小脑对视动反射（OKR）的贡献，用于图像稳定的视觉反馈控制方案。我们开发了小脑响应对激发OKR的世界速度信号的适应性的计算模型（在头部固定的实验实验室条件下研究OKR时，世界速度信号用于模拟头部速度信号）。结果表明，小脑模型学习的滤波器高度依赖于有色噪声世界速度激励信号的功率谱。因此，这里的关键发现是小脑滤波器由OKR激励信号的统计量决定。
