The cerebellar granule cell layer has inspired numerous theoretical models of neural representations that support learned behaviors, beginning with the work of David Marr and James 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.
In this thesis, I 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. I show that for such tasks, the optimal granule cell representation is substantially denser than predicted by classic theories. The results provide a general theory of learning in cerebellum-like systems and suggest that optimal cerebellar representations are task-dependent.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/p4mh-w355 |
| Date | January 2023 |
| Creators | Xie, Marjorie |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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