2026-04-13T06:30:07Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/718462025-11-13T17:07:21Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Magrans de Abril, Ildefons author Yoshimoto, Junichiro author Doya, Kenji author 2025-11-11T07:13:48Z 2025-11-11T07:13:48Z 2018 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. http://hdl.handle.net/10230/71846 Connectivity inference Functional connectivity Effective connectivity Multi-electrode recording Calcium fluorescence imaging Connectivity inference from neural recording data: challenges, mathematical bases and research directions