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. 2022 Jan 27;13:534. doi: 10.1038/s41467-022-28193-z

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© The Author(s) 2022

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 — a Model incorporating the sensory input distribution s_i, the neuronal response function G(s_i) derived from the GEVI signal in S1, and the behavioral readout P(GO│s_i). The function f(.) represents a downstream process, matching the evoked fluorescence to the lick response. Model predictions and measured data at the “switch-naive” training stage, when subjects are challenged with a change in stimulus statistics the first time. b Model predictions and measured data at the “switch-experienced” training stage, when subjects have undergone at least four switches. c Relative explanatory power in S1 of mice undergoing multiple changes. Model results are shown separately for the overlap range (left) and the full range (right). Bars represent means across mice with SD (n = 3). d Pie plots depict the fraction explained by S1 versus downstream quantified for the behavior of mice undergoing a different number of switches and different number of sessions per condition (M04, M05, M06, n = 2 switches, n = 10–11 sessions, M07, M08, M09, n = 4–5 switches, n = 5 sessions). e Timescale of S1 explanatory power. The fraction explained by S1 is plotted as a function of the number of sessions. Solid curve is a logistic fit to all data.