Simple connectome inference from partial correlation statistics in calcium imaging
This work addresses the problem of mapping neural connections for researchers in neuroscience, though it appears incremental as it simplifies a previously successful method.
The authors tackled connectome inference from calcium imaging data by proposing a two-step algorithm that detects neural peak activities and uses partial correlation statistics to infer neuronal associations, winning the Connectomics Challenge with this approach.
In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data. The proposed algorithm consists of two steps. First, processing the raw signals to detect neural peak activities. Second, inferring the degree of association between neurons from partial correlation statistics. This paper summarises the methodology that led us to win the Connectomics Challenge, proposes a simplified version of our method, and finally compares our results with respect to other inference methods.