Cerebro-cerebellar networks facilitate learning through feedback decoupling

Behavioural feedback is critical for learning in the cerebral cortex, but such feedback is often not
readily available which slows down learning. Inspired by deep learning algorithms, we introduce a
systems-level computational model of cerebro-cerebellar interactions in which a cerebral recurrent network
receives continuous cerebral 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 ataxia-like behaviours, in line with experimental observations.
Next, we demonstrate that these results generalise to a range of more complex motor and cognitive
tasks. Finally, we highlight a number of experimentally testable predictions regarding (1) how cerebral
and cerebellar representations develop over learning, (2) how cerebral and task feedback properties
shape the need for cerebellar predictions and (3) the differential impact of lesions of the cerebellar
output and inferior olive. Overall, our work offers a novel theoretical framework of cerebro-cerebellar
networks as feedback decoupling machines.