Connectivity inference from neural recording data: Challenges, mathematical bases and research directions. (June 2018)

• Record Type: Journal Article
• Authors: Magrans de Abril, Ildefons; Yoshimoto, Junichiro; Doya, Kenji
• Is Part Of: Neural networks. Volume 102(2018)
• Page Start: 120
• Page End: 137
• Publication Date: 2018-06
• Subjects: Connectivity inference -- Functional connectivity -- Effective connectivity -- Multi-electrode recording -- Calcium fluorescence imaging; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2018.02.016
• Languages: English
• ISSNs: 0893-6080

Abstract

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.