First Connectomics Challenge: From Imaging to Connectivity

Javier G. Orlandi, Bisakha Ray, Demian Battaglia, Isabelle Guyon, Vincent Lemaire, Mehreen Saeed, Alexander Statnikov, Olav Stetter, Jordi Soriano
; Proceedings of the Neural Connectomics Workshop at ECML 2014, PMLR 46:1-22, 2015.

Abstract

We organized a Challenge to unravel the connectivity of simulated neuronal networks. The provided data was solely based on fluorescence time series of spontaneous activity in a net- work constituted by 1000 neurons. The task of the participants was to compute the effective connectivity between neurons, with the goal to reconstruct as accurately as possible the ground truth topology of the network. The procured dataset is similar to the one measured in in vivo and in vitro recordings of calcium fluorescence imaging, and therefore the algorithms developed by the participants may largely contribute in the future to unravel major topological features of living neuronal networks from just the analysis of recorded data, and without the need of slow, painstaking experimental connectivity labeling methods. Among 143 entrants, 16 teams participated in the final round of the challenge to compete for prizes. The winners significantly outperformed the baseline method provided by the organizers. To measure influences between neurons the participants used an array of diverse methods, including transfer entropy, regression algorithms, correlation, deep learning, and network deconvolution. The development of connectivity reconstruction techniques is a major step in brain science, with many ramifications in the comprehension of neuronal computation, as well as the understanding of network dysfunctions in neuropathologies.

Cite this Paper


BibTeX
@InProceedings{pmlr-v46-orlandi15, title = {First Connectomics Challenge: From Imaging to Connectivity}, author = {Javier G. Orlandi and Bisakha Ray and Demian Battaglia and Isabelle Guyon and Vincent Lemaire and Mehreen Saeed and Alexander Statnikov and Olav Stetter and Jordi Soriano}, booktitle = {Proceedings of the Neural Connectomics Workshop at ECML 2014}, pages = {1--22}, year = {2015}, editor = {Demian Battaglia and Isabelle Guyon and Vincent Lemaire and Jordi Soriano}, volume = {46}, series = {Proceedings of Machine Learning Research}, month = {15 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v46/orlandi15.pdf}, url = {http://proceedings.mlr.press/v46/orlandi15.html}, abstract = {We organized a Challenge to unravel the connectivity of simulated neuronal networks. The provided data was solely based on fluorescence time series of spontaneous activity in a net- work constituted by 1000 neurons. The task of the participants was to compute the effective connectivity between neurons, with the goal to reconstruct as accurately as possible the ground truth topology of the network. The procured dataset is similar to the one measured in in vivo and in vitro recordings of calcium fluorescence imaging, and therefore the algorithms developed by the participants may largely contribute in the future to unravel major topological features of living neuronal networks from just the analysis of recorded data, and without the need of slow, painstaking experimental connectivity labeling methods. Among 143 entrants, 16 teams participated in the final round of the challenge to compete for prizes. The winners significantly outperformed the baseline method provided by the organizers. To measure influences between neurons the participants used an array of diverse methods, including transfer entropy, regression algorithms, correlation, deep learning, and network deconvolution. The development of connectivity reconstruction techniques is a major step in brain science, with many ramifications in the comprehension of neuronal computation, as well as the understanding of network dysfunctions in neuropathologies.} }
Endnote
%0 Conference Paper %T First Connectomics Challenge: From Imaging to Connectivity %A Javier G. Orlandi %A Bisakha Ray %A Demian Battaglia %A Isabelle Guyon %A Vincent Lemaire %A Mehreen Saeed %A Alexander Statnikov %A Olav Stetter %A Jordi Soriano %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-orlandi15 %I PMLR %J Proceedings of Machine Learning Research %P 1--22 %U http://proceedings.mlr.press %V 46 %W PMLR %X We organized a Challenge to unravel the connectivity of simulated neuronal networks. The provided data was solely based on fluorescence time series of spontaneous activity in a net- work constituted by 1000 neurons. The task of the participants was to compute the effective connectivity between neurons, with the goal to reconstruct as accurately as possible the ground truth topology of the network. The procured dataset is similar to the one measured in in vivo and in vitro recordings of calcium fluorescence imaging, and therefore the algorithms developed by the participants may largely contribute in the future to unravel major topological features of living neuronal networks from just the analysis of recorded data, and without the need of slow, painstaking experimental connectivity labeling methods. Among 143 entrants, 16 teams participated in the final round of the challenge to compete for prizes. The winners significantly outperformed the baseline method provided by the organizers. To measure influences between neurons the participants used an array of diverse methods, including transfer entropy, regression algorithms, correlation, deep learning, and network deconvolution. The development of connectivity reconstruction techniques is a major step in brain science, with many ramifications in the comprehension of neuronal computation, as well as the understanding of network dysfunctions in neuropathologies.
RIS
TY - CPAPER TI - First Connectomics Challenge: From Imaging to Connectivity AU - Javier G. Orlandi AU - Bisakha Ray AU - Demian Battaglia AU - Isabelle Guyon AU - Vincent Lemaire AU - Mehreen Saeed AU - Alexander Statnikov AU - Olav Stetter AU - Jordi Soriano BT - Proceedings of the Neural Connectomics Workshop at ECML 2014 PY - 2015/10/21 DA - 2015/10/21 ED - Demian Battaglia ED - Isabelle Guyon ED - Vincent Lemaire ED - Jordi Soriano ID - pmlr-v46-orlandi15 PB - PMLR SP - 1 DP - PMLR EP - 22 L1 - http://proceedings.mlr.press/v46/orlandi15.pdf UR - http://proceedings.mlr.press/v46/orlandi15.html AB - We organized a Challenge to unravel the connectivity of simulated neuronal networks. The provided data was solely based on fluorescence time series of spontaneous activity in a net- work constituted by 1000 neurons. The task of the participants was to compute the effective connectivity between neurons, with the goal to reconstruct as accurately as possible the ground truth topology of the network. The procured dataset is similar to the one measured in in vivo and in vitro recordings of calcium fluorescence imaging, and therefore the algorithms developed by the participants may largely contribute in the future to unravel major topological features of living neuronal networks from just the analysis of recorded data, and without the need of slow, painstaking experimental connectivity labeling methods. Among 143 entrants, 16 teams participated in the final round of the challenge to compete for prizes. The winners significantly outperformed the baseline method provided by the organizers. To measure influences between neurons the participants used an array of diverse methods, including transfer entropy, regression algorithms, correlation, deep learning, and network deconvolution. The development of connectivity reconstruction techniques is a major step in brain science, with many ramifications in the comprehension of neuronal computation, as well as the understanding of network dysfunctions in neuropathologies. ER -
APA
Orlandi, J.G., Ray, B., Battaglia, D., Guyon, I., Lemaire, V., Saeed, M., Statnikov, A., Stetter, O. & Soriano, J.. (2015). First Connectomics Challenge: From Imaging to Connectivity. Proceedings of the Neural Connectomics Workshop at ECML 2014, in PMLR 46:1-22

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