PT  - JOURNAL ARTICLE
AU  - Marjorie Xie
AU  - Samuel Muscinelli
AU  - Kameron Decker Harris
AU  - Ashok Litwin-Kumar
TI  - Task-dependent optimal representations for cerebellar learning
AID  - 10.1101/2022.08.15.504040
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.08.15.504040
4099  - http://biorxiv.org/content/early/2022/08/15/2022.08.15.504040.short
4100  - http://biorxiv.org/content/early/2022/08/15/2022.08.15.504040.full
AB  - The cerebellar granule cell layer has inspired numerous theoretical models of neural representations that support learned behaviors, beginning with the work of Marr and Albus. In these models, granule cells form a sparse, combinatorial encoding of diverse sensorimotor inputs. Such sparse representations are optimal for learning to discriminate random stimuli. However, recent observations of dense, low-dimensional activity across granule cells have called into question the role of sparse coding in these neurons. Here, we generalize theories of cerebellar learning to determine the optimal granule cell representation for tasks beyond random stimulus discrimination, including continuous input-output transformations as required for smooth motor control. We show that for such tasks, the optimal granule cell representation is substantially denser than predicted by classic theories. Our results provide a general theory of learning in cerebellum-like systems and suggest that optimal cerebellar representations are task-dependent.Competing Interest StatementThe authors have declared no competing interest.