Author

Stephen J Eglen

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Neuroscience· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Organism:mouse· 2 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Research Focus:cell signaling & trafficking· 1 Assay:electronic health records· 1 Assay:electrophysiology· 1 Research Focus:software / tool· 1 Approach:statistical model· 1

Frequent Co-Authors

Alexander Konovalov· 1 shared Catherine S Cutts· 1 shared Catherine Sarah Cutts· 1 shared Christian Fufezan· 1 shared Daniel S. S Katz· 1 shared Felipe Leprevost· 1 shared Juan Antonio Vizcaino· 1 shared Julian Uszkoreit· 1 shared

Activity by Year

2014-2016 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 28 mutual co-author links

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1 shared paperBioinformatics

Alexander Konovalov

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Author profile →

3 papers

Recent·Most cited

Ten Simple Rules for Taking Advantage of git and GitHub

Yasset Perez-Riverol, Laurent Gatto, Rui Wang, Timo Sachsenberg, Julian Uszkoreit, Felipe Leprevost, Christian Fufezan, Tobias Ternent, Stephen J Eglen, Daniel S. S Katz, Tom J Pollard, Alexander Konovalov, Robert M Flight, Kai Blin, Juan Antonio Vizcaino

The paper provides guidelines on using git and GitHub for project tracking, collaboration, and code dissemination.

PLOS Computational Biology · Jul 14, 2016Peer-reviewedBioinformatics

A Bayesian framework for comparing the structure of spontaneous correlated activity recorded under different conditions

Catherine S Cutts, Stephen J Eglen

This paper introduces a Bayesian framework to compare distance-dependent correlations in retinal activity across conditions and applies it to two studies.

bioRxiv · Jan 20, 2016 · PreprintNeuroscience

Detecting pairwise correlations in spike trains: an objective comparison of methods and application to the study of retinal waves.

Catherine Sarah Cutts, Stephen J Eglen

The paper introduces a new measure of spike train correlation, the Spike Time Tiling Coefficient, and demonstrates its advantages over the correlation index through synthetic and experimental data analysis.

bioRxiv · Sep 9, 2014Neuroscience