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Synchronization and Phase Locking in Networks of Heterogeneous Model Neurons
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Synchronization and Phase Locking in Networks of Heterogeneous Model Neurons. |
| 개인저자 | Davison, Elizabeth N. |
| 단체저자명 | Princeton University. Mechanical and Aerospace Engineering. |
| 발행사항 | [S.l.]: Princeton University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 207 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781085772167 |
| 학위논문주기 | Thesis (Ph.D.)--Princeton University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Ehrich Leonard, Naomi. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | This dissertation examines the effect of two types of system complexity, nonlinearity and heterogeneity, on oscillatory dynamics in networked systems. In particular, we focus on finding conditions for complete synchronization, where the dynamics of multiple systems are identical, phase locking, where the dynamics of multiple systems share critical features, and mixed mode oscillations (MMOs), where the dynamics of a single system demonstrate periodic oscillations with peaks of markedly different sizes. A fascinating application of these conditions is to networks of model neurons and the crucial role of synchronization in brain function.We establish conditions for synchronization in networks of heterogeneous systems with nonlinear dynamics and diffusive coupling. We leverage a passivity-based Lyapunov approach to find a condition for complete synchronization in networks of identical nonlinear systems in terms of the network structure and the dynamics of individual systems. An application to networked model neurons with biologically relevant parameter values demonstrates improvement over alternative methods. Cluster synchronization is an extension of complete synchronization where the network can be partitioned into distinct subgroups of systems that are synchronized. We find conditions for cluster synchronization in networks of non-identical systems with nonlinear dynamics and diffusive coupling using a passivity-based Lyapunov approach and a contraction based approach.We examine a system of two model neurons where the first neuron receives a constant external input and the second neuron receives input from the first through diffusive coupling. Large networks that are cluster synchronized can be represented by simpler systems |
| 일반주제명 | Mechanical engineering. Applied mathematics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
