Simple connectome inference from partial correlation statistics in calcium imaging

Sutera, Antonio; Joly, Arnaud; François-Lavet, Vincent; Qiu, Zixiao; Louppe, Gilles; Ernst, Damien; Geurts, Pierre

Reference : Simple connectome inference from partial correlation statistics in calcium imaging


	Document type :	Parts of books : Contribution to collective works
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/2268/169767

Title :	Simple connectome inference from partial correlation statistics in calcium imaging
Language :	English
Author, co-author :	Sutera, Antonio [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique >]
	Joly, Arnaud [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
	François-Lavet, Vincent [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids >]
	Qiu, Zixiao [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids >]
	Louppe, Gilles [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
	Ernst, Damien [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids >]
	Geurts, Pierre [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique >]
Publication date :	Jun-2014
Main document title :	Neural Connectomics Challenge
Editor :	Soriano, Jordi
	Battaglia, Demian
	Guyon, Isabelle
	Lemaire, Vincent
	Orlandi, Javier
	Ray, Bisakha
Publisher :	Springer
Collection and collection volume :	The Springer Series on Challenges in Machine Learning
Peer reviewed :	Yes
ISBN :	978-3-319-53070-3
Keywords :	[en] Connectomics ; Network inference ; Partial correlation
Abstract :	[en] 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.
Research centres :	Systems and Modeling Research Unit
Funders :	CECI ; Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS ; PASCAL2 ; IUAP DYSCO
Target :	Researchers ; Professionals
Permalink :	http://hdl.handle.net/2268/169767
	also: http://hdl.handle.net/2268/172139 ; http://hdl.handle.net/2268/176594
Other URL :	http://arxiv.org/abs/1406.7865
	http://github.com/asutera/kaggle-connectomics
Commentary :	This is the paper that explains the methodology we developed for winning the Connectomics challenge for which the goal was to infer from observed data the wiring diagram from the brain. 144 teams were participating to this challenge. Previously published in Proceedings of Connectomics 2014 (Conf: 7th European machine learning and data mining conference (ECML-PKDD 2014) 15/09/14 - 19/09/14, Nancy, France)

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Open access	sutera14.pdf		Author postprint	735.88 kB	View/Open

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Open access	slides.pdf	Slides / Transparents	814.94 kB	View/Open
Open access	poster.pdf		313.58 kB	View/Open
Open access	spotlight.pdf		61.96 kB	View/Open

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