Reference : Simple connectome inference from partial correlation statistics in calcium imaging
Parts of books : Contribution to collective works
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/169767
Simple connectome inference from partial correlation statistics in calcium imaging
English
Sutera, Antonio mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique >]
Joly, Arnaud mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
François-Lavet, Vincent mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids >]
Qiu, Zixiao mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids >]
Louppe, Gilles mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Ernst, Damien mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids >]
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique >]
Jun-2014
Neural Connectomics Challenge
Soriano, Jordi
Battaglia, Demian
Guyon, Isabelle
Lemaire, Vincent
Orlandi, Javier
Ray, Bisakha
Springer
The Springer Series on Challenges in Machine Learning
Yes
978-3-319-53070-3
[en] Connectomics ; Network inference ; Partial correlation
[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.
Systems and Modeling Research Unit
CECI ; Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS ; PASCAL2 ; IUAP DYSCO
Researchers ; Professionals
http://hdl.handle.net/2268/169767
also: http://hdl.handle.net/2268/172139 ; http://hdl.handle.net/2268/176594
http://arxiv.org/abs/1406.7865
http://github.com/asutera/kaggle-connectomics
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)

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
sutera14.pdfAuthor postprint735.88 kBView/Open

Additional material(s):

File Commentary Size Access
Open access
slides.pdfSlides / Transparents814.94 kBView/Open
Open access
poster.pdf313.58 kBView/Open
Open access
spotlight.pdf61.96 kBView/Open

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.