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Contributed algorithms outperform state-of-the-art.

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posted on 2018-05-21, 17:43 authored by Philipp Berens, Jeremy Freeman, Thomas Deneux, Nikolay Chenkov, Thomas McColgan, Artur Speiser, Jakob H. Macke, Srinivas C. Turaga, Patrick Mineault, Peter Rupprecht, Stephan Gerhard, Rainer W. Friedrich, Johannes Friedrich, Liam Paninski, Marius Pachitariu, Kenneth D. Harris, Ben Bolte, Timothy A. Machado, Dario Ringach, Jasmine Stone, Luke E. Rogerson, Nicolas J. Sofroniew, Jacob Reimer, Emmanouil Froudarakis, Thomas Euler, Miroslav Román Rosón, Lucas Theis, Andreas S. Tolias, Matthias Bethge

A. Correlation coefficient of the spike rate predicted by the submitted algorithms (evaluated at 25 Hz, 40 ms bins) on the test set. Colors indicate different data sets (for details, see Table 1). Data sets I, II, and IV were recorded with OGB-1 as indicator, III and V with GCaMP6s. Black dots are mean correlation coefficients across all N = 32 cells in the test set. Colored dots are jittered for better visibility. STM: Spike-triggered mixture model [15]; f-oopsi: fast non-negative deconvolution [9] B. Difference in correlation coefficient on the test set to the STM, split by the calcium indicator used in the data set. C. P-values for difference in mean correlation coefficient on the test set for all pairs of algorithms (Repeated measured ANOVA, N = 32 cells, main effect of algorithm: P < 0.001, shown are p-values for post-hoc pairwise comparisons, corrected using Holm-Bonferroni correction) D. Difference in correlation coefficient split by algorithm type on the training and test set, respectively, to the f-oopsi-result correcting for systematic differences between the training and the test set.

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