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.
Editors: Demian Battaglia, Isabelle Guyon, Vincent Lemaire, Javier Orlandi, Bisakha Ray, Jordi Soriano
The original form of this article appears in JMLR W&CP Volume 46.
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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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Bellot, P., Meyer, P.E. (2017). Efficient Combination of Pairwise Feature Networks. In: Battaglia, D., Guyon, I., Lemaire, V., Orlandi, J., Ray, B., Soriano, J. (eds) Neural Connectomics Challenge. The Springer Series on Challenges in Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-53070-3_7
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DOI: https://doi.org/10.1007/978-3-319-53070-3_7
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