| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000330990 |
| 005 | | 20241107141021 |
| 008 | | 181129s2018 ||| | | | eng d |
| 020 | |
▼a 9780438159853 |
| 035 | |
▼a (MiAaPQ)AAI10830688 |
| 035 | |
▼a (MiAaPQ)columbia:14791 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Mena, Gonzalo E. |
| 245 | 10 |
▼a Statistical Machine Learning Methods for the Large-Scale Analysis of Neural Data. |
| 260 | |
▼a [S.l.] :
▼b Columbia University.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 197 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B. |
| 500 | |
▼a Adviser: Liam M. Paninski. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Columbia University, 2018. |
| 520 | |
▼a Modern neurotechnologies enable the recording of neural activity at the scale of entire brains and with single-cell resolution. However, the lack of principled approaches to extract structure from these massive data streams prevent us from fully |
| 520 | |
▼a The second part focuses on the simultaneous electrical stimulation and recording of neurons using large electrode arrays. There, identification of neural activity is hindered by stimulation artifacts that are much larger than spikes, and overlap |
| 520 | |
▼a The third part is motivated by the problem of inference of neural dynamics in the worm C.elegans: when taking a data-driven approach to this question, e.g., when using whole-brain calcium imaging data, one is faced with the need to match neural |
| 590 | |
▼a School code: 0054. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a Columbia University.
▼b Statistics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-11B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0054 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999470
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |