Efficient Combination of Pairwise Feature Networks

Chapter
First Online:
Part of the The Springer Series on Challenges in Machine Learning book series (SSCML)

Abstract

This paper presents a novel method for the reconstruction of a neural network connectivity using calcium fluorescence data. We introduce a fast unsupervised method to integrate different networks that reconstructs structural connectivity from neuron activity. Our method improves the state-of-the-art reconstruction method General Transfer Entropy (GTE). We are able to better eliminate indirect links, improving therefore the quality of the network via a normalization and ensemble process of GTE and three new informative features. 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.

Keywords

Network reconstruction algorithms Elimination of indirect links Connectomes 
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Notes

Acknowledgements

This work has been partially supported by the Spanish “MECD” FPU Research Fellowship, the Spanish “MICINN” project TEC2013-43935-R and the Cellex foundation.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Signal Theory and CommunicationsTechnical University of CataloniaBarcelonaSpain
  2. 2.Bioinformatics and Systems Biology (BioSys), Faculty of SciencesUniversité de Liège (ULg)LiègeBelgium

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