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020 ▼a 9780438125223
035 ▼a (MiAaPQ)AAI10902916
035 ▼a (MiAaPQ)umichrackham:001257
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 519
1001 ▼a Rich, Scott.
24510 ▼a Interacting Mechanisms Driving Synchrony in Neural Networks with Inhibitory Interneurons.
260 ▼a [S.l.]: ▼b University of Michigan., ▼c 2018.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2018.
300 ▼a 179 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Advisers: Victoria Booth
5021 ▼a Thesis (Ph.D.)--University of Michigan, 2018.
520 ▼a Computational neuroscience contributes to our understanding of the brain by applying techniques from fields including mathematics, physics, and computer science to neuroscientific problems that are not amenable to purely biologic study. One area
520 ▼a Inhibitory interneurons are thought to drive synchrony in ways described by two computational mechanisms: Interneuron Network Gamma (ING), which describes how an inhibitory network synchronizes itself
520 ▼a My research reveals a variety of ways in which interneuronal diversity alters synchronous oscillations in networks containing inhibitory interneurons and the mechanisms likely driving these dynamics. For example, oscillations in networks of Type
520 ▼a Taken together, this research reveals that network-driven and cellularly-driven mechanisms promoting oscillatory activity in networks containing inhibitory interneurons interact, and oftentimes compete, in order to dictate the overall network dy
590 ▼a School code: 0127.
650 4 ▼a Applied mathematics.
690 ▼a 0364
71020 ▼a University of Michigan. ▼b Applied and Interdisciplinary Mathematics.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0127
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000428 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 201812 ▼f 2019
990 ▼a ***1012033