Optimal routing to cerebellum-like structures

The vast expansion from mossy fibers to cerebellar granule cells (GrC) produces a neural representation that supports functions including associative and internal model learning. This motif is shared by other cerebellum-like structures and has inspired numerous theoretical models. Less attention has been paid to structures immediately presynaptic to GrC layers, whose architecture can be described as a 'bottleneck' and whose function is not understood. We therefore develop a theory of cerebellum-like structures in conjunction with their afferent pathways that predicts the role of the pontine relay to cerebellum and the glomerular organization of the insect antennal lobe. We highlight a new computational distinction between clustered and distributed neuronal representations that is reflected in the anatomy of these two brain structures. Our theory also reconciles recent observations of correlated GrC activity with theories of nonlinear mixing. More generally, it shows that structured compression followed by random expansion is an efficient architecture for flexible computation. Sensorimotor inputs are first compressed before being routed to the cerebellum and similar brain structures. The authors develop a theory to understand the computational role of this compression, leading to anatomical and functional predictions.

Automated summary

The study develops a theory to understand the computational role of compressed sensorimotor inputs in cerebellum-like structures and predicts the function of the pontine relay and glomerular organization. It highlights the difference between clustered and distributed neuronal representations and reconciles recent observations with theories of nonlinear mixing. Overall, the study demonstrates the efficiency of structured compression followed by random expansion for flexible computation.