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. 2020 Nov 12;11:5725. doi: 10.1038/s41467-020-19632-w

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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. 5 — Representational consistency decreases with increasing layer depth for both tested DNN architectures, and across multiple ways to measure distances in multivariate population responses (cosine (a), Euclidean distance and unit length pattern-based Euclidean distance (b), and differences in vector norm (c)). Average representational consistency shown for each layer, computed across all pairwise comparisons of network instances (45 comparisons for 10 instances), together with a 95% bootstrapped confidence interval.