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. 2020 Jan 7;3:3. doi: 10.1038/s42003-019-0729-3

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© The Author(s) 2020

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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Fig. 4 — a–c Representative example of Deep-Channel classification performance with low activity ion channels (data from model M1, Fig. 3a, b): a The raw semi-simulated ion channel event data (black). b The ground truth idealisation/annotation labels (blue) from the raw data above in (a). c The Deep-Channel predictions (red) for the raw data above (a). d–f Representative example of Deep-Channel classification performance with 5 channels opening simultaneously (data from model M2, Fig. 3c, d). d The semi-simulated raw ion channel event data (black). e The ground truth idealisation/annotation labels (blue) from the raw data above in (d). f The Deep-Channel label predictions (red) for the raw data above (d).