  The brain solves the credit assignment problem remarkably well. For credit to
be assigned across neural networks they must, in principle, wait for specific
neural computations to finish. How the brain deals with this inherent locking
problem has remained unclear. Deep learning methods suffer from similar locking
constraints both on the forward and feedback phase. Recently, decoupled neural
interfaces (DNIs) were introduced as a solution to the forward and feedback
locking problems in deep networks. Here we propose that a specialised brain
region, the cerebellum, helps the cerebral cortex solve similar locking
problems akin to DNIs. To demonstrate the potential of this framework we
introduce a systems-level model in which a recurrent cortical network receives
online temporal feedback predictions from a cerebellar module. We test this
cortico-cerebellar recurrent neural network (ccRNN) model on a number of
sensorimotor (line and digit drawing) and cognitive tasks (pattern recognition
and caption generation) that have been shown to be cerebellar-dependent. In all
tasks, we observe that ccRNNs facilitates learning while reducing ataxia-like
behaviours, consistent with classical experimental observations. Moreover, our
model also explains recent behavioural and neuronal observations while making
several testable predictions across multiple levels. Overall, our work offers a
novel perspective on the cerebellum as a brain-wide decoupling machine for
efficient credit assignment and opens a new avenue between deep learning and
neuroscience.
