| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000435334 |
| 005 | | 20200228093120 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781085772167 |
| 035 | |
▼a (MiAaPQ)AAI13884461 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 519 |
| 100 | 1 |
▼a Davison, Elizabeth N. |
| 245 | 10 |
▼a Synchronization and Phase Locking in Networks of Heterogeneous Model Neurons. |
| 260 | |
▼a [S.l.]:
▼b Princeton University.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 207 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: B. |
| 500 | |
▼a Advisor: Ehrich Leonard, Naomi. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Princeton University, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0181. |
| 650 | 4 |
▼a Mechanical engineering. |
| 650 | 4 |
▼a Applied mathematics. |
| 690 | |
▼a 0548 |
| 690 | |
▼a 0364 |
| 710 | 20 |
▼a Princeton University.
▼b Mechanical and Aerospace Engineering. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-04B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0181 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15491367
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1816162 |
| 991 | |
▼a E-BOOK |