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Efficient combination of pairwise feature networks

This paper presents a novel method for the reconstruction of a neural network connectivity using calcium fluorescence data. We introduce a super fast unsupervised method to integrate different networks that reconstructs structural connectivity from network activity monitored through calcium imaging. Our method improves the state-of-the-art reconstruction method General Transfer Entropy (GTE). Via a normalization and ensemble process of GTE and three new informative features we are able to eliminate indirect recovered links, improving therefore the quality of the network. The approach is based on a simple combination of networks, which is remarkably fast. The performance of our approach is benchmarked on simulated time series provided at the connectomics challenge and also submitted at the public competition.