國立虎尾科技大學 |

Task-Dependent Representations for Cerebellar Learning.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Task-Dependent Representations for Cerebellar Learning./
作者:	Xie, Marjorie.
面頁冊數:	1 online resource (114 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:	Dissertations Abstracts International85-01B.
標題:	Neurosciences. -
電子資源:	click for full text (PQDT)
ISBN:	9798379786953

Task-Dependent Representations for Cerebellar Learning.
Xie, Marjorie.

Task-Dependent Representations for Cerebellar Learning. - 1 online resource (114 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Thesis (Ph.D.)--Columbia University, 2023.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379786953Subjects--Topical Terms:

593561
Neurosciences.
Subjects--Index Terms:

CerebellumIndex Terms--Genre/Form:

554714
Electronic books.

Task-Dependent Representations for Cerebellar Learning.
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520 $a 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.
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