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. 2018 Jan 26;8:1710. doi: 10.1038/s41598-018-20051-7

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Demixing shared population signals. (a) Cumulative signal variance explained by PCA (black) and dPCA (red) in AIP. dPCA explains almost the same amount of variance as standard PCA. Pie chart shows how the total signal variance is split between dimensions. (b) Individual dPCs, separated into boxes by factor. Thick black lines show time intervals during which the respective task parameters can be reliably decoded from single-trial activity. Note that the vertical scale differs across subplots. (c,d) same as (a,b) for F5 data.