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. 2023 Nov 20;27(1):187–195. doi: 10.1038/s41593-023-01490-6

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© The Author(s) 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 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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Extended Data Fig. 8 — a–c, Test log-likelihood for HMM models trained on deep features (a), keypoints (b) and neural activity clusters (c), as a function of the number of hidden states in the HMM. Curves represent different mice. b, Top: visualization of keypoint data (after z scoring). Bottom: inferred states by the HMM model. c, Reconstructed keypoint data from the inferred HMM states. d, Top: simulated keypoint data from the HMM. Bottom: simulated state dynamics. e–g, Same as b–d but for modeling neural activity clusters. Each cluster represents the average activity of 100–300 neurons.