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

Ildefons Magrans de Abril, Junichiro Yoshimoto, Kenji Doya

Research output: Contribution to journalReview articlepeer-review

25 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)120-137
Number of pages18
JournalNeural Networks
Volume102
DOIs
Publication statusPublished - 06-2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Artificial Intelligence

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