  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.
