Efficient and Predictive Coding From Compression and Control by Human Brain Networks- [electronic resource]
Efficient and Predictive Coding From Compression and Control by Human Brain Networks- [electronic resource]
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101213
- ISBN
- 9798380387972
- DDC
- 616
- 저자명
- Zhou, Dale.
- 서명/저자
- Efficient and Predictive Coding From Compression and Control by Human Brain Networks - [electronic resource]
- 발행사항
- [S.l.]: : University of Pennsylvania., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(202 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
- 주기사항
- Advisor: Bassett, Dani S.;Satterthwaite, Theodore D.
- 학위논문주기
- Thesis (Ph.D.)--University of Pennsylvania, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Most theories of brain function depend on information processing and the manipulation of neural or cognitive representations. This information processing is thought to be efficient and manipulations are thought to update representations that are predictive of future needs. These ideas are formulated by theories of efficient coding and predictive coding. Efficient coding is transmitting maximal information while minimizing the use of limited resources. Predictive coding is transmitting maximal information about the future while minimizing the use of limited resources. Although these parsimonious theories have accumulated evidence at the cellular level and in sensory regions, different models and data are needed to test the theories at the macroscale and across the brain network. This dissertation investigates how we can generalize efficient and predictive coding to the brain network by drawing from network science, information theory, and control theory. Using these frameworks, we operationalize compression and control as two key processes underlying efficient and predictive coding. Data compression distills predictive from unpredictive information using limited metabolic resources. Optimal control governs how the brain network should distribute the control signals needed to transition to diverse future states according to feedback from structured representations of the world. We test the compression and control models with hypothesized features of an efficient and predictive code. We find relationships between our models and the dimensionality and timescales of brain activity, metabolic resource expenditure, myelin content, areal expansion, functional specialization, and behavioral speed and accuracy. These findings support the efficient and predictive coding hypotheses across the brain and open new avenues to investigate brain function and mental health.
- 일반주제명
- Neurosciences.
- 일반주제명
- Bioengineering.
- 키워드
- Brain network
- 키워드
- Brain function
- 키워드
- Efficient coding
- 기타저자
- University of Pennsylvania Neuroscience
- 기본자료저록
- Dissertations Abstracts International. 85-03B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
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