Extended Data Fig. 8: Context-sensitive signals aggregate in complex transitions and preferentially encode past transitions.
a, Distribution of signal integrals (y axis; whiskers show full range, boxes show first and third quartiles, and lines show medians) for ROIs in Fig. 4a. Text label is colour coded by phrase type in i–iv. F numbers, P values, and η2 (95% CI) for one-way ANOVA relating history (x axis) and signal (y axis) in n = 15 song sequences. b, ROIs in a retain their song-context bias for songs that terminate at end of the third phrase rather than continuing. Box plots repeat the ANOVA tests in a for n = 16 songs in which the last phrase is replaced by the end of the song. c–f, Dark grey slices indicate the fraction of correlations that occur in complex behavioural transitions. c, d, Data from Fig. 4c separated into the two birds. e, f, The fraction in c, d expected by the null hypothesis of correlations distributing by the frequency of each phrase type among Nphrases phrases in the dataset. g, In sequence-correlated ROIs, multi-way ANOVA is used to separate the effects of the preceding and following phrase types on the signal (see Methods). Pie chart shows the percentage of sequence-correlated ROIs that were significantly influenced by the past, future, or both phrase identities among n = 336 significant ANOVA tests. h, Restricting analysis to complex transitions, more ROIs correlated with the preceding phrase type (blue) than with the following one (red). This is true in both naive signal values (left, n = 185 tests) and after we removed dependencies on phrase durations and time-in-song (right, n = 185). One-sided binomial z-test: *proportion difference 0.33 ± 0.09, Z = 6.45, P = 5.5 × 10−11; ‡proportion difference 0.19 ± 0.09, Z = 4.05, P = 2 × 10−5. i, Restricting the analysis to phrase types that are not in complex transitions (n = 136 ANOVA tests) reveals more ROIs correlated with the future phrase type, but the difference is not significant (left, right, n.a.: one-sided binomial z-test, P = 0.14, 0.11). j, Fig. 4a showed maximum projection images, calculated with denoised videos (see Methods). The algorithm CNMF-E49 involves estimating the source ROI shapes, de-convolving spike times and estimating the background noise. Here, recreating the maximum projection images with the original fluorescence videos shows the background as well, but the preceding-context-sensitive neurons remain the same. Namely, the same ROI footprints annotated in i–iv show the colour bias (cyan or red) that indicates coding of the past phrase with the same colour.
