Cerebro-cerebellar networks facilitate learning through feedback decoupling

Ellen Boven, Joseph Pemberton, Paul Chadderton, Richard Apps and Rui Ponte Costa ()
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Ellen Boven: University of Bristol
Joseph Pemberton: University of Bristol
Paul Chadderton: University of Bristol
Richard Apps: University of Bristol
Rui Ponte Costa: University of Bristol

Nature Communications, 2023, vol. 14, issue 1, 1-18

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-022-35658-8

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