Effcient combination of pairwise feature networks

Pau Bellot, Patrick E. Meyer
Proceedings of the Neural Connectomics Workshop at ECML 2014, PMLR 46:77-84, 2015.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v46-bellot15, title = {Effcient combination of pairwise feature networks}, author = {Bellot, Pau and Meyer, Patrick E.}, booktitle = {Proceedings of the Neural Connectomics Workshop at ECML 2014}, pages = {77--84}, year = {2015}, editor = {Battaglia, Demian and Guyon, Isabelle and Lemaire, Vincent and Soriano, Jordi}, volume = {46}, series = {Proceedings of Machine Learning Research}, month = {15 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v46/bellot15.pdf}, url = {https://proceedings.mlr.press/v46/bellot15.html}, 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.} }
Endnote
%0 Conference Paper %T Effcient combination of pairwise feature networks %A Pau Bellot %A Patrick E. Meyer %B Proceedings of the Neural Connectomics Workshop at ECML 2014 %C Proceedings of Machine Learning Research %D 2015 %E Demian Battaglia %E Isabelle Guyon %E Vincent Lemaire %E Jordi Soriano %F pmlr-v46-bellot15 %I PMLR %P 77--84 %U https://proceedings.mlr.press/v46/bellot15.html %V 46 %X 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.
RIS
TY - CPAPER TI - Effcient combination of pairwise feature networks AU - Pau Bellot AU - Patrick E. Meyer BT - Proceedings of the Neural Connectomics Workshop at ECML 2014 DA - 2015/10/21 ED - Demian Battaglia ED - Isabelle Guyon ED - Vincent Lemaire ED - Jordi Soriano ID - pmlr-v46-bellot15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 46 SP - 77 EP - 84 L1 - http://proceedings.mlr.press/v46/bellot15.pdf UR - https://proceedings.mlr.press/v46/bellot15.html AB - 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. ER -
APA
Bellot, P. & Meyer, P.E.. (2015). Effcient combination of pairwise feature networks. Proceedings of the Neural Connectomics Workshop at ECML 2014, in Proceedings of Machine Learning Research 46:77-84 Available from https://proceedings.mlr.press/v46/bellot15.html.

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