Simple connectome inference from partial correlation statistics in   calcium imaging
  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. 
Antonio Sutera, Arnaud Joly, Vincent Fran\c{c}ois-Lavet, Zixiao Aaron   Qiu, Gilles Louppe, Damien Ernst and Pierre Geurts
stat.ML cs.CE cs.LG