Cerebro-cerebellar networks facilitate learning through feedback decoupling.
Cerebro-cerebellar networks facilitate learning through feedback decoupling.
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DOI：
10.1038/s41467-022-35658-8
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发表时间：
2023-01-04
影响因子：
16.6
通讯作者：
Costa, Rui Ponte
中科院分区：
文献类型：
--
作者：
Boven, Ellen;Pemberton, Joseph;Chadderton, Paul;Apps, Richard;Costa, Rui Ponte
行为反馈对大脑皮层的学习至关重要。然而，这样的反馈往往并不容易获得。尽管反馈的稀疏性，大脑皮层如何有效地学习仍不清楚。受最近的深度学习算法的启发，我们引入了一个系统级的大脑-小脑相互作用计算模型。在这个模型中，大脑递归网络从小脑网络接收反馈预测，从而将大脑网络中的学习与未来的反馈分离。当在一个简单的感觉运动任务中训练时，该模型显示出更快的学习速度和减少了类似运动障碍的行为，这与广泛观察到的小脑功能影响是一致的。接下来，我们证明这些结果适用于更复杂的运动和认知任务。最后，该模型对大脑-小脑任务特异性表征对学习的影响、小脑预测的任务特异性优势以及小脑和下橄榄病变的不同影响做出了几个可实验验证的预测。总体而言，我们的工作提供了大脑-小脑网络作为反馈解耦机器的理论框架。行为反馈对学习至关重要，但它往往是不可用的。在这里，作者介绍了一个深度学习模型，在该模型中，小脑向大脑提供反馈预测，从而促进学习，减少运动障碍，并做出几个实验预测。
Behavioural feedback is critical for learning in the cerebral cortex. However, such feedback is often not readily available. How the cerebral cortex learns efficiently despite the sparse nature of feedback remains unclear. Inspired by recent deep learning algorithms, we introduce a systems-level computational model of cerebro-cerebellar interactions. In this model a cerebral recurrent network receives feedback predictions from a cerebellar network, thereby decoupling learning in cerebral networks from future feedback. When trained in a simple sensorimotor task the model shows faster learning and reduced dysmetria-like behaviours, in line with the widely observed functional impact of the cerebellum. Next, we demonstrate that these results generalise to more complex motor and cognitive tasks. Finally, the model makes several experimentally testable predictions regarding cerebro-cerebellar task-specific representations over learning, task-specific benefits of cerebellar predictions and the differential impact of cerebellar and inferior olive lesions. Overall, our work offers a theoretical framework of cerebro-cerebellar networks as feedback decoupling machines. Behavioral feedback is critical for learning, but it is often not available. Here, the authors introduce a deep learning model in which the cerebellum provides the cerebrum with feedback predictions, thereby facilitating learning, reducing dysmetria, and making several experimental predictions.
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影响因子：
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作者：
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影响因子：
16.2
作者：
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通讯作者：
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