2026-04-13T15:09:22Zhttps://recercat.cat/oai/requestoai:recercat.cat:10230/718462025-11-13T17:07:21Zcom_2072_6col_2072_452952
2025-11-13T17:07:21Z
urn:hdl:10230/71846
Connectivity inference from neural recording data: challenges, mathematical bases and research directions
Magrans de Abril, Ildefons
Yoshimoto, Junichiro
Doya, Kenji
Connectivity inference
Functional connectivity
Effective connectivity
Multi-electrode recording
Calcium fluorescence imaging
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.
2025-11-13T17:07:21Z
2025-11-13T17:07:21Z
2025-11-11T07:13:48Z
2025-11-11T07:13:48Z
2018
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://hdl.handle.net/10230/71846
Neural Networks. 2018 Jun;102:120-37
© 2018 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Elsevier