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. 2021 Mar 31;12:1985. doi: 10.1038/s41467-021-22195-z

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© The Author(s) 2021

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. 7 — a Population decoding accuracy (distance between model and empirical data) for self (blue) position, prey (green), and predator (red) position improves (i.e., gets lower) as number of neurons analyzed rises. This analysis indicates that these variables are encoded in a distributed manner, and that all three agents can be readily decoded. b Population decoding accuracy for self (dark green) direction, prey (purple) direction, predator (red) direction, prey relative angle (dark blue), and predator relative angle (maroon). All curves decrease modestly, indicative of distributed decoding. c Time-to-time decoding performance when either prey is closer or predator is closer. Then, the decoding performance for either prey position or predator position was estimated. Decoding accuracy was estimated as a function of number of cells, in groups corresponding to 40, 80, 120, and 150 cells. Statistical significance and error bar widths (i.e., SD) are obtained from bootstrapping the neural population for 30 times (total N = 167 neurons).