a. The clustering algorithm used in Fig. 2d and Fig. 5 (see Methods) clusters cells into groups that share high within-group, cue-driven, trial-by-trial noise correlations. This algorithm automatically chooses the optimal number of clusters for each session. The number of clusters per session did not differ between stimulus types (two-tailed Wilcoxon rank-sum test). b. Cluster sizes (number of cells per cluster) also did not differ between stimulus types (two-tailed Wilcoxon rank-sum test). c. Within-cluster noise correlations were substantially higher than between-cluster noise correlations (see Methods; two-tailed Wilcoxon rank-sum test; *** p < 0.0001). d. Same violin plots as Fig. 5a, but using the Naïve Bayes classifier. These data confirms that the finding in Fig. 5a is not due to the fact that the AODE classifier used in Fig. 5a includes pairwise activity as part of the information used for identification of reactivation events. e. Similar plots as in d, but separately examining subsets of pairs of cells considered in d that belong to reward-related clusters (pink) or to non-reward-related clusters (orange; two-tailed Wilcoxon rank-sum test; *** p < 0.0001). f. Bottom: CDFs and KDEs of the distributions of cross-day changes in total functional connectivity. Reactivated food-cue-driven cells from reward clusters (purple, 194 cells) increased their next-day total connectivity compared to all non-reactivated food-cue-driven cells (gray, 238 cells). Conversely, reactivated food-cue-driven cells from non-reward clusters (orange, 245 cells) decreased their next-day total functional connectivity. P-values were computed using a linear GLMM involving a categorical comparison involving 3 categories: non-reactivated cells, cells from reward clusters, and cells from non-reward clusters. Permutation tests within days demonstrated that cells from non-reward clusters had significantly decreased next-day connectivity when reactivated (* p < 0.05; see Methods). Top: excess frequency of reactivated cells with a given change in next-day connectivity above that observed for non-reactivated cells (i.e. rectified difference of colored vs. gray distributions). g. Data from Fig. 5c, presented as a correlation matrix between all pairs of cells and a hierarchical clustering dendrogram. The clusters identified on the left are labeled on the right. Note the distinct reward-related clusters and non-reward-related clusters. h. Cluster size (i.e. the number of cells per cluster), with 1 dot per cluster for all clusters from all recordings, separated by reward-related clusters (purple) or non-reward-related clusters (orange). i. Number of clusters of a given type per session. Approximately 44% of sessions contained at least one cluster of each type. j. Within both Day 1 and Day 2 of each pair of days (more specifically, on Day i relative to a pair of days [i, i+1]), the total functional connectivity was not different between cells belonging to reward-related clusters vs. those belonging to non-reward-related clusters. The overall responsivity of each cell group was also unchanged across days and did not differ between groups (two-tailed Wilcoxon rank-sum test). k. Total connectivity on Day 1 was indistinguishable between (i) all food-cue-driven cells in reward-related clusters, (ii) Food-cue cells in reward-related clusters, defined as cells that are responsive to food cues but not to rewards, and (iii) Food-cue cells in non-reward-related clusters (p = 0.81, Kruskal-Wallis test). This was true both when comparing the subsets of cells that did not participate in any food-cue reactivations on Day 1 (left bars), and when comparing the subsets of cells that participated in at least one food-cue reactivation on Day 1 (right bars, p = 0.96, Kruskal-Wallis test). All error bars are mean ± SEM across sessions, including in violin plots (some bars are too small to show).