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. 2023 Jul 21;14:4422. doi: 10.1038/s41467-023-40144-w

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© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 3 — A Schematic illustration of analysis pipeline. Population responses to 100 images were simulated using the model weights estimated from each session. For each image, correlations were computed between all sessions, yielding a correlation matrix. These matrices were then averaged across all 100 images. B Empirical population response correlation matrix. C Correlation drops as function of number of intervening sessions, reflecting representational drift of population responses (r = −0.18, p < 0.001). D Null distribution of correlation values. Black vertical line, empirical correlation between population response correlations and number of intervening sessions. Gray histogram, null distribution of correlation values computed by randomizing order of sessions 1000 times. Bottom, representational dissimilarity matrices are stable across sessions. E Cross-correlation of voxels’ mean response amplitude across sessions. Each colored line is the distribution of cross-correlations for V1 voxels from a single subject. Some correlations are positive while others are negative, indicating that voxels are not undergoing a uniform change in mean response amplitude across the entire V1. F Schematic illustration of analysis pipeline. Simulated population responses to different images were correlated with each other, yielding a dissimilarity matrix for each individual session. Next, correlations were computed between each possible pair of dissimilarity matrices. G Empirical correlation matrix. H Correlation between dissimilarity matrices does not drop with increasing number of intervening sessions, indicating stability across sessions (r = −0.01, p = 0.339). I Null distribution of correlation values. Black vertical line, empirical correlation between dissimilarity matrix correlations and number of intervening sessions. Gray histogram, null distribution of correlation values computed by randomizing the order of sessions 1000 times. Source data are provided as a Source Data file.