Coordinated cerebellar climbing fiber activity signals learned sensorimotor predictions.
Coordinated cerebellar climbing fiber activity signals learned sensorimotor predictions.
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DOI：
10.1038/s41593-018-0228-8
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发表时间：
2018-10
影响因子：
25
通讯作者：
Hull C
中科院分区：
文献类型：
--
作者：
Heffley W;Song EY;Xu Z;Taylor BN;Hughes MA;McKinney A;Joshua M;Hull C
小脑学习的主流模型认为，攀爬纤维(CFs)既由错误的运动输出驱动，也用于纠正错误的运动输出。然而，这个模型在很大程度上是基于对行为的研究，这些行为利用硬连接的神经路径将感觉输入与运动输出联系起来。为了测试这个模型是否适用于需要任意感觉运动联系的更灵活的学习机制，我们在小鼠身上开发了一种小脑依赖的运动学习范式，兼容中尺度和单一树突分辨率的钙成像。在这里，我们发现，CFS优先受到正确执行的动作和其他任务参数的驱动，这些动作和其他任务参数预测奖励结果，在受这些预测控制的矢状面旁加工区内显示出广泛的相关活动。总而言之，这样的CF活动模式通过提供与不完全依赖于运动错误的无符号强化学习信号一致的预测性教学输入，非常适合于驱动学习。
The prevailing model of cerebellar learning states that climbing fibers (CFs) are both driven by, and serve to correct, erroneous motor output. However, this model is grounded largely in studies of behaviors that utilize hardwired neural pathways to link sensory input to motor output. To test whether this model applies to more flexible learning regimes that require arbitrary sensorimotor associations, we have developed a cerebellar-dependent motor learning paradigm compatible with both mesoscale and single dendrite resolution calcium imaging in mice. Here, we find that CFs are preferentially driven by and more time-locked to correctly executed movements and other task parameters that predict reward outcome, exhibiting widespread correlated activity within parasagittal processing zones that is governed by these predictions. Together, such CF activity patterns are well-suited to drive learning by providing predictive instructional input consistent with an unsigned reinforcement learning signal that does not rely exclusively on motor errors.
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