상세정보
Export to Refworks부가기능
Statistical Machine Learning Methods for the Large-Scale Analysis of Neural Data
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Statistical Machine Learning Methods for the Large-Scale Analysis of Neural Data. |
| 개인저자 | Mena, Gonzalo E. |
| 단체저자명 | Columbia University. Statistics. |
| 발행사항 | [S.l.]: Columbia University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 197 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-11B(E). Dissertation Abstract International |
| ISBN | 9780438159853 |
| 학위논문주기 | Thesis (Ph.D.)--Columbia University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Adviser: Liam M. Paninski. |
| 요약 | 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 |
| 요약 | 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 |
| 요약 | 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 |
| 일반주제명 | Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
