論文

査読有り
2018年6月1日

Connectivity inference from neural recording data: Challenges, mathematical bases and research directions

Neural Networks
  • Ildefons Magrans de Abril
  • ,
  • Junichiro Yoshimoto
  • ,
  • Kenji Doya

102
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
DOI
https://doi.org/10.1016/j.neunet.2018.02.016
URL
http://www.scopus.com/inward/record.url?eid=2-s2.0-85044132822&partnerID=MN8TOARS
URL
http://orcid.org/0000-0001-7995-0321
ID情報
  • DOI : 10.1016/j.neunet.2018.02.016
  • ISSN : 1879-2782
  • ISSN : 0893-6080
  • ORCIDのPut Code : 49365580
  • SCOPUS ID : 85044132822

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