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020 ▼a 9780438118256
035 ▼a (MiAaPQ)AAI10831343
035 ▼a (MiAaPQ)arizona:16444
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 519
1001 ▼a Greene, Patrick.
24512 ▼a A Bayesian Approach to Spike Sorting of Neural Data via Source Localization.
260 ▼a [S.l.] : ▼b The University of Arizona., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 117 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
500 ▼a Adviser: Kevin K. Lin.
5021 ▼a Thesis (Ph.D.)--The University of Arizona, 2018.
520 ▼a This dissertation describes a novel mathematical algorithm for extracting spike data and positional information from extracellular electrophysiological neural recordings. By capturing the electrical signals emitted by individual neurons using a
520 ▼a This dissertation consists of three main components. The primary component is the spike sorting algorithm itself, whose overall mathematical framework is described in chapters 3 and 4. There are two key ideas. The first is the usage of a dipole-
520 ▼a The second component of this dissertation is a relatively realistic model of the extracellular signal generation and recording process (the "forward model") which we describe in chapter 2. We construct this model in order to better understand th
520 ▼a The final component of this dissertation is an investigation into the effect of probe geometry and the dipole prior distribution on how well we can estimate neuron positions. This makes extensive use of the forward model described above, and is
590 ▼a School code: 0009.
650 4 ▼a Applied mathematics.
650 4 ▼a Neurosciences.
650 4 ▼a Statistics.
690 ▼a 0364
690 ▼a 0317
690 ▼a 0463
71020 ▼a The University of Arizona. ▼b Applied Mathematics.
7730 ▼t Dissertation Abstracts International ▼g 79-11B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0009
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999508 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자