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. 2022 Feb 8;13:787. doi: 10.1038/s41467-022-28057-6

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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. 2 — a Stimulus-specificity in sequential firing fields. Peri-stimulus time histograms (PSTH; 150 ms gaussian) show the normalized firing rate of all active neurons for each odor type (correct InSeq trials only; active neurons for each odor: A = 267, B = 192, C = 209, D = 223; neurons collapsed across five subjects, one session per subject). To visualize how sequential firing fields varied across odor types, PSTHs are shown for each odor presented (in columns), with neurons sorted by their time of peak firing relative to the port entry for each odor (in rows). b Temporal coding accuracy for each odor type was above-chance levels. Plots show reconstructed time estimates obtained from each animal’s PSTH (correctly sorted for each odor; i.e., diagonal of panel a but separated by animal) and averaged across subjects. c Accuracy of reconstructed time varied by the lag between the odor type used to train the model and the odor type in which time was reconstructed (e.g., when the model was trained on odor B trials, decoding time during odor B, C, D or E trials represented lags of 0, 1, 2, or 3, respectively). Black bars depict mean ± SEM (Lag 0: n = 220; Lag ± 1: n = 165; Lag ± 2: n = 110; Lag ± 3: n = 55; trial data pooled across subjects), shaded regions the Q1–Q3 range (median denoted by white line), and red lines the permuted chance levels. Lag data show a significant one-way ANOVA (F_(6,873) = 166.9, p < 0.0001; A***), difference between lag 0 and all other lags (two-tailed Dunnett’s posthoc tests, adjusted for multiple comparisons; ***p < 0.0001), and linear trends (positive lag: F_(1,327) = 12.93, p = 0.0004; negative lag: F_(1,327) = 12.20, p = 0.0005; L**). d The sequential organization of firing fields extended across the full sequence of odors. Since the task is self-paced, the PSTH (250 ms gaussian) shows data from a single subject with median times for odors B, C, and D across sequences. e Accuracy of reconstructed time across the sequence of odors (PSTH from panel d) was above-chance levels. Acc*, reconstructed time accuracy significantly above-chance levels (determined by random permutations). Color coding of odor types: odor A = sky blue, B = brown, C = green, D = purple, E = orange.