Extended Data Fig. 7: The correlation between the learned tuning of units and their intrinsic and MAZE tuning properties.

(a) Left, the distribution of locations of peak tuning across POST ripple LTs and maze place field (PFs; best linear fit in black with 95% confidence intervals in shaded gray). Right, the marginal distributions of peak locations relative to the center of the track show similar distributions between POST LTs (top) and PFs (bottom). (b) Relationship between PF features and stability and fidelity of the POST LTs. First row, distribution of each MAZE spatial tuning metric by pooling units across all sessions (n = 660 units). The median and interquartile ranges corresponding to individual sessions are depicted using overlaid lines. To analyze the connection between the POST stability and fidelity with each MAZE spatial tuning metric, the set of units within each session was divided into low or high categories according to the median. Among the spatial tuning metrics, peak place field firing rate (peak PF FR), and PF stability were predictive of the POST LT fidelity and stability. We saw no effect from metrics such as spatial information or PF distance from the track center. Cross-group comparisons used two-sided Mann Whitney U Tests. (c) Similar analysis on unit firing characteristics indicates that firing burstiness is not a factor driving LT stability or fidelity. Additionally, higher firing rates during the POST ripples affected the stability of POST LTs but not their fidelity. Median and interquartile ranges corresponding to individual sessions are superimposed with colored dots and lines. Cross-group comparisons used two-sided Mann Whitney U Tests. (d) The distribution of θ-oscillation amplitude (z-scored), frequency, and velocity of the animal observed during MAZE theta periods for a sample session (top row) and for overall distributions (bottom row) by pooling over all sessions (n = 2250347 20-ms time bins). Median and interquartile ranges corresponding to individual sessions are superimposed with colored dots and lines. (e) From left to right, PF fidelity of MAZE θ-oscillation LTs calculated based on distinct subsets of 20-ms time bins into Low/High relative to session medians showed significant effects for theta amplitude (1^st column) (P = 0.01) or frequency (2^nd column) (P = 7.9 × 10⁻¹⁶). The impact of θ phase (3^rd column) on MAZE θ-oscillation LTs was investigated by calculating the LTs based on distinct set of 20-ms time bins according to θ-oscillation phase: Trough (−π/4 to π/4), Ascend (π/4 to 3π/4), Peak (3π/4 to 5π/4), Descend (5π/4 to 7π/4). LTs associated with the trough and descending phase of theta displayed higher PF fidelity than other theta phases (cross-group comparison using Friedman’s test; P = 2.2 × 10⁻¹³ with post hoc comparisons within each pair; Trough vs. Ascend: P = 2.1 × 10⁻⁵; Trough vs. Peak: P = 2.2 × 10⁻¹²; Trough vs. Descend: P = 0.002; Ascend vs. Peak: P = 3.6 × 10⁻⁵; Ascend vs. Descend: P = 0.12; Peak vs. Descend: P = 9.2 × 10⁻⁸). θ-oscillation periods split according to the animal’s velocity (4^th column) during the θ-oscillation periods (p = 6.7 × 10⁻¹⁸). These panels indicate significant differences compared to chance levels (vs. unit-ID shuffle surrogates) within each group, as well as comparisons across groups (two-sided Wilcoxon Signed-Rank Tests). (f) Multiple regression analysis revealed that learned tunings calculated based on firing during MAZE θ-wave trough, but not θ-wave peak, strongly predict POST learned tunings, along with MAZE ripple LTs (θ-wave peak LTs: P = 0.35; θ-wave trough, PRE, ripple LTs, and MAZE PFs: P < 10⁻⁴). P values were obtained by comparing (one-sided) the R² and each coefficient against surrogate distributions from 10⁴ unit-identity shuffles of POST LTs. Results obtained by leaving out individual sessions are superimposed with dots. *P < 0.05, **P < 0.01, ***P < 0.001.