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Task-Dependent Representations for Cerebellar Learning- [electronic resource]
![Task-Dependent Representations for Cerebellar Learning - [electronic resource]](/Users/Baul/Images/book.png)
Task-Dependent Representations for Cerebellar Learning- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101248
- ISBN
- 9798379786953
- DDC
- 616
- 저자명
- Xie, Marjorie.
- 서명/저자
- Task-Dependent Representations for Cerebellar Learning - [electronic resource]
- 발행사항
- [S.l.]: : Columbia University., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(114 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
- 주기사항
- Advisor: Litwin-Kumar, Ashok.
- 학위논문주기
- Thesis (Ph.D.)--Columbia University, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약The 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.
- 일반주제명
- Neurosciences.
- 일반주제명
- Biomedical engineering.
- 키워드
- Cerebellum
- 키워드
- Motor control
- 키워드
- Neural networks
- 키워드
- Classic theories
- 기타저자
- Columbia University Neurobiology and Behavior
- 기본자료저록
- Dissertations Abstracts International. 85-01B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798379786953
■035 ▼a(MiAaPQ)AAI30529433
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a616
■1001 ▼aXie, Marjorie.
■24510▼aTask-Dependent Representations for Cerebellar Learning▼h[electronic resource]
■260 ▼a[S.l.]:▼bColumbia University. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(114 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-01, Section: B.
■500 ▼aAdvisor: Litwin-Kumar, Ashok.
■5021 ▼aThesis (Ph.D.)--Columbia University, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aThe 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.
■590 ▼aSchool code: 0054.
■650 4▼aNeurosciences.
■650 4▼aBiomedical engineering.
■653 ▼aCerebellum
■653 ▼aMotor control
■653 ▼aNeural networks
■653 ▼aLearned behaviors
■653 ▼aClassic theories
■690 ▼a0317
■690 ▼a0800
■690 ▼a0541
■71020▼aColumbia University▼bNeurobiology and Behavior.
■7730 ▼tDissertations Abstracts International▼g85-01B.
■773 ▼tDissertation Abstract International
■790 ▼a0054
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16933449▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
