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Uncovering Higher-Order Structures in Complex Systems with Multivariate Information Theory- [electronic resource]
Uncovering Higher-Order Structures in Complex Systems with Multivariate Information Theory- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214100452
- ISBN
- 9798379718541
- DDC
- 616
- 서명/저자
- Uncovering Higher-Order Structures in Complex Systems with Multivariate Information Theory - [electronic resource]
- 발행사항
- [S.l.]: : Indiana University., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(294 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
- 주기사항
- Advisor: Sporns, Olaf;Beggs, John.
- 학위논문주기
- Thesis (Ph.D.)--Indiana University, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Complex systems are defined by the presence of intricate, emergent structures that integrate many disparate elements into a single "whole." A central challenge of modern science is inferring this structure from limited and often noisy data. This thesis explores how information theory can reveal structured dependencies in data, with a particular focus on synergistic interactions: when there is information in the joint state of multiple variables (the "whole") that is inaccessible when considering the "parts" individually. Here, we explore three different mathematical approaches to assessing synergy in complex systems and what they reveal about the structure and dynamics of on-going brain activity at multiple scales. We find that higher-order synergies are widespread in the nervous system, existing at the level of local circuits of spiking neurons, as well at the level of whole regions of cortex. Synergistic information can also exist in the instantaneous functional coupling of elements, as well in higher-order flows of information through time. We find that existing methodologies from complex systems science are often insensitive to these synergies. We end by proposing the existence of a "shadow structure": a combinatorially vast space of dependencies that have gone unexplored due to the limitations of standard statistics.
- 일반주제명
- Neurosciences.
- 일반주제명
- Mathematics.
- 일반주제명
- Statistics.
- 키워드
- Complex systems
- 키워드
- Emergence
- 키워드
- Network science
- 키워드
- Synergy
- 기타저자
- Indiana University Informatics
- 기본자료저록
- Dissertations Abstracts International. 84-12B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214100452
■006m o d
■007cr#unu||||||||
■020 ▼a9798379718541
■035 ▼a(MiAaPQ)AAI30492103
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a616
■1001 ▼aVarley, Thomas F.▼0(orcid)0000-0002-3317-9882
■24510▼aUncovering Higher-Order Structures in Complex Systems with Multivariate Information Theory▼h[electronic resource]
■260 ▼a[S.l.]:▼bIndiana University. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(294 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 84-12, Section: B.
■500 ▼aAdvisor: Sporns, Olaf;Beggs, John.
■5021 ▼aThesis (Ph.D.)--Indiana University, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aComplex systems are defined by the presence of intricate, emergent structures that integrate many disparate elements into a single "whole." A central challenge of modern science is inferring this structure from limited and often noisy data. This thesis explores how information theory can reveal structured dependencies in data, with a particular focus on synergistic interactions: when there is information in the joint state of multiple variables (the "whole") that is inaccessible when considering the "parts" individually. Here, we explore three different mathematical approaches to assessing synergy in complex systems and what they reveal about the structure and dynamics of on-going brain activity at multiple scales. We find that higher-order synergies are widespread in the nervous system, existing at the level of local circuits of spiking neurons, as well at the level of whole regions of cortex. Synergistic information can also exist in the instantaneous functional coupling of elements, as well in higher-order flows of information through time. We find that existing methodologies from complex systems science are often insensitive to these synergies. We end by proposing the existence of a "shadow structure": a combinatorially vast space of dependencies that have gone unexplored due to the limitations of standard statistics.
■590 ▼aSchool code: 0093.
■650 4▼aNeurosciences.
■650 4▼aMathematics.
■650 4▼aStatistics.
■653 ▼aComplex systems
■653 ▼aEmergence
■653 ▼aHigher order interactions
■653 ▼aInformation theory
■653 ▼aNetwork science
■653 ▼aSynergy
■690 ▼a0317
■690 ▼a0405
■690 ▼a0463
■71020▼aIndiana University▼bInformatics.
■7730 ▼tDissertations Abstracts International▼g84-12B.
■773 ▼tDissertation Abstract International
■790 ▼a0093
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16932390▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
