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

Magrans de Abril, Ildefons; Yoshimoto, Junichiro; Doya, Kenji

doi:http://dx.doi.org/10.1016/j.neunet.2018.02.016

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

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Citació

Magrans de Abril I, Yoshimoto J, Doya K. Connectivity inference from neural recording data: challenges, mathematical bases and research directions. Neural Netw. 2018 Jun;102:120-37. DOI: 10.1016/j.neunet.2018.02.016

Resum

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.