2018年6月1日
Connectivity inference from neural recording data: Challenges, mathematical bases and research directions
Neural Networks
- ,
- ,
- 巻
- 102
- 号
- 開始ページ
- 120
- 終了ページ
- 137
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1016/j.neunet.2018.02.016
- 出版者・発行元
- Elsevier Ltd
This article presents a review of computational methods for connectivity inference from neural activity data derived from multi-electrode recordings or fluorescence imaging. We first identify biophysical and technical challenges in connectivity inference along the data processing pipeline. We then review connectivity inference methods based on two major mathematical foundations, namely, descriptive model-free approaches and generative model-based approaches. We investigate representative studies in both categories and clarify which challenges have been addressed by which method. We further identify critical open issues and possible research directions.
- リンク情報
- ID情報
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- DOI : 10.1016/j.neunet.2018.02.016
- ISSN : 1879-2782
- ISSN : 0893-6080
- ORCIDのPut Code : 49365580
- SCOPUS ID : 85044132822