Sleep microstructure organizes memory replay

Abstract

Recently acquired memories are reactivated in the hippocampus during sleep, an initial step for their consolidation^1-3. This process is concomitant with the hippocampal reactivation of previous memories^4-6, posing the problem of how to prevent interference between older and recent, initially labile, memory traces. Theoretical work has suggested that consolidating multiple memories while minimizing interference can be achieved by randomly interleaving their reactivation^7-10. An alternative is that a temporal microstructure of sleep can promote the reactivation of different types of memories during specific substates. Here, to test these two hypotheses, we developed a method to simultaneously record large hippocampal ensembles and monitor sleep dynamics through pupillometry in naturally sleeping mice. Oscillatory pupil fluctuations revealed a previously unknown microstructure of non-REM sleep-associated memory processes. We found that memory replay of recent experiences dominated in sharp-wave ripples during contracted pupil substates of non-REM sleep, whereas replay of previous memories preferentially occurred during dilated pupil substates. Selective closed-loop disruption of sharp-wave ripples during contracted pupil non-REM sleep impaired the recall of recent memories, whereas the same manipulation during dilated pupil substates had no behavioural effect. Stronger extrinsic excitatory inputs characterized the contracted pupil substate, whereas higher recruitment of local inhibition was prominent during dilated pupil substates. Thus, the microstructure of non-REM sleep organizes memory replay, with previous versus new memories being temporally segregated in different substates and supported by local and input-driven mechanisms, respectively. Our results suggest that the brain can multiplex distinct cognitive processes during sleep to facilitate continuous learning without interference.

Extended Data Fig. 1|. Online pupil tracking in combination with closed-loop optogenetics and high-density electrophysiology.

a, Experimental scheme for high-density electrophysiology recordings in combination with closed-loop optogenetics and pupillometry. Pupil images are captured by a pupil-tracking camera connected to a Raspberry Pi microcontroller, which then sends digital pulses to the Intan USB Interface Board for synchronization. Meanwhile, electrophysiological signals from high-density silicon probes are recorded by the Intan Board, and then transmitted to the Spike2 system for closed-loop optogenetic SWR disruption. b-c, Components for pupil-electrophysiology dual recording headstage. b, Top view of the design. c, Customized headstage for accommodating both silicon probes and pupil camera. d, Hot mirror (left) has a dichroic filter with transmission rates greater than 85% from 450 to 645 nm and reflection rates above 90% from 750 to 1200 nm, enabling reflection of pupil illuminated by the NIR LED (940 nm; right). e-h, Online pupil tracking performance. e, High consistency of manual pupil scoring among the 3 researchers (r = 0.996 for Researcher 1 vs. 2, r = 0.979 for Researcher 1 vs. 3, and r = 0.978 for Researcher 2 vs 3, respectively; see Methods). Each dot is for one image frame (25 random frames per session, 1 session per animal, n = 4 mice). g, Performance of offline DLC and online algorithms using manual tracking as the “ground truth”. Performance was quantified as r-squared score (i.e., coefficient of determination; see Methods; r-squared = 0.953 and 0.950 for manual vs. online and DLC tracking, respectively; n.s., p = 0.15, rank-sum test; each paired line for one animal). h, Direct comparison between DLC and online tracking performance (**p = 0.002, rank-sum test compared to 0). Each circle for one session. i, Percentage of eye-closed periods during 2-h rest sessions (left column for sessions with electrophysiological recordings only, right column for sessions with electrophysiological recording and optogenetic SWR disruption). horizontal bars: median values. Note that only a few sessions (n = 3 out of 33) have a percentage larger than 5%.

Extended Data Fig. 2|. Pupil dynamics in different brain states and their coherence with replay dynamics.

a, Distributions of pupil sizes in different sleep states (vertical line, median; ****p < 1e-16, Kolmogorov-Smirnov tests). b, Power spectral profiles for pupil dilation during undisturbed NREM episodes shown in Fig. 1b. c, 3D scatter plot showing the relationship of pupil sizes with theta (5-10 Hz) power, slope of power spectral density (PSD), and estimated EMG amplitude from LFP (EMG proxy; see Methods). d,e, Auto-correlogram (d) and power spectral profiles (e) of replay probability during NREM sleep. f, Coherence between the spectrum profiles of replay probability and pupil size. Note the peak at ~0.016 Hz (yellow arrowheads) shown in both pupil and replay signals. Same session as in Fig. 1b-d.

Extended Data Fig. 3|. Pupil-ripple dynamics over different phases of sleep periods.

a, Representative recordings showing the relationship between EMG proxy and head motion signal from the head-mounted accelerometer (i.e., acceleration) during a 2-hr sleep session. b, Representative recordings showing REM sleep transitions. LFP spectrogram (top), pupil size (middle), and EMG proxy (bottom) traces are shown. c, Pupil size around REM sleep transitions (vertical dash lines for onset and offset of REM sleep; n = 23 sessions from 5 animals). NREM to REM transitions were associated with pupil contraction, with prolonged reduction of pupil size preceding the transitions from NREM to REM and dilation from REM to NREM, in line with previous results^,,. However, the significant correlation between ripple amplitude and pupil size was observed during NREM both before (60s before REM onset; Pearson’s r = −0.065, ****p = 2.83e-14) and after (60s after REM offset; Pearson’s r = −0.096, ****p = 4.61e-22) REM episodes. d, Pupil size (left) and its correlation (right) with ripple delta and spindle amplitude across a sleep session (whole NREM time during a sleep session was equally divided into 3 parts; n = 21 sessions from 5 animals). Average pupil size (n.s., p = 0.37, Friedman test) and its correlation with ripple/ delta/ spindle amplitudes (all p’s > 0.05, z-test for correlation) did not change significantly. e, Within individual NREM episodes (a NREM episode was equally divided into 3 parts; n = 21 sessions from 5 animals), pupil size was significantly larger at the early phase, presumably owing, at least in part, to the transition from WAKE to sleep (left; ****p = 4.28e-8, **p = 0.0061, Friedman test with Dunn’s post hoc). Delta and spindle correlation with pupil size showed a small, but not significant, increase (z = −0.79 and p = 0.43 for delta waves, z = −1.13 and p = 0.26 for spindles, z-test for correlation). Error bars: sem.

Extended Data Fig. 4|. Relationship between pupil-ripple dynamics and different sleep patterns.

a, Averaged pupil size around SWRs during WAKE (yellow) and NREM sleep (pink; t = 0 for SWR onset; ****p’s < 1e-16 compared to the baseline, rank-sum tests). Black line, time-bin shuffled distribution. b, Cross-correlation between pupil sizes and SWR rates during WAKE (yellow) and NREM sleep (pink; ****p’s < 1e-16 compared to the baseline). c, Example recording showing pupil and ripple dynamics in relation to different sleep patterns. Orange shadings: microarousals (MAs); Blue shadings: SIA periods. Note that only some pupil size transitions coincided with a MA, in line with previous reports^,,. Right panels show SWR rate during 2-hr sleep sessions (top; median = 0.50 Hz) and enlarged view of two 30-s segments denoted on the left (green bars). d, Distribution of amplitude of EMG proxy in different sleep states (vertical line, median; ****p’s < 1e-16, Kolmogorov-Smirnov tests). WAKE was associated with the highest EMG power, whereas REM has the lowest EMG power (n = 9 sessions from 4 animals), in agreement with previous sleep studies^,,. e, Pupil was often dilated when EMG power was high (n = 9 sessions from 4 animals). f, Averaged pupil size around MAs during NREM sleep (****p < 1e-16 compared to the baseline; n = 9 sessions from 4 animals). g, Preserved pupil-ripple relationship during NREM after excluding MA periods (± 20 s around MAs; ****p < 1e-16, rank-sum test; n = 9 sessions from 4 animals). h, Averaged pupil size around SIA periods during NREM sleep (****p < 1e-16 compared to the baseline; n = 23 sessions from 5 animals). i, Cross-correlation between SIA states and SWR rates, in agreement with previous findings that SWRs rarely occurred during SIA^, (n = 23 sessions from 5 animals). j, Preserved pupil-ripple relationship during NREM after excluding SIA periods (****p < 1e-16 compared to the baseline; n = 23 sessions from 5 animals). k, Amplitude of ripples over pupil size sextiles (yellow and pink for WAKE and NREM sleep, respectively). Note the significant decreasing trend (****p < 1e-16 and = 1.23e-9 for NREM and WAKE, respectively, one-way ANOVA with post hoc test for linear trend; n = 23 sessions from 5 animals). l, Correlation of pupil size with ripple amplitude, ripple frequency and SWR-associated HSE duration (p = 9.06e-6****, 0.33e-5****, and 0.0084**, respectively, rank-sum test compared to 0; horizontal bars, median; each circle for one session; n = 23 sessions from 5 animals). m, Averaged pupil size around spindles during NREM sleep (****p < 1e-16 compared to the baseline, rank-sum test). n, Amplitude of spindles and delta waves over pupil size sextiles during NREM sleep (n.s., p = 0.94 and 0.15 for delta waves and spindles, respectively, one-way ANOVA with post hoc test for linear trend; n = 21 sessions from 5 animals). o, Correlation of delta-wave and spindle amplitude with pupil size (n.s., p = 0.76, 0.45, 0.47 and 0.053, from left to right, rank-sum test compared to 0; n = 21 sessions from 5 animals). Error bars: sem.

Extended Data Fig. 5|. Place-field properties and reactivation strength for the T-maze spatial memory task.

a, Left, schematic of the T-maze alternation task (see Methods). Right, two example place fields on the T-maze. b, Linearized place fields on the left (red) and right (blue) trajectory of the two example cells shown in a. c, Normalized rate maps on the linearized trajectories of all spatially-tuned cells used for replay decoding in Fig. 2a, sorted by peak location on the left (top) and right (bottom) trajectory. d, Replay probability over pupil size sextiles after matching MUA firing rates of replay events across sextiles (mean ± sem; 15 sessions from 3 animals; ****p = 1.92e-8, one-way ANOVA with post hoc test for linear trend). e, Reactivation strength over pupil size sextiles during NREM sleep (pink; ****p = 3.09e-13) and WAKE (yellow; ***p = 2.85e-4, one-way ANOVA with post hoc test for linear trend; 17 sessions from 4 animals; mean ± sem). f, Pyramidal cell (PYR) participation ratio during ripples showed a decrease within NREM (*p = 0.036, Kruskal-Wallis test with Dunn’s post hoc), consistent with previous findings. g, Preserved correlation between pupil size and replay probability within NREM (p < 1e-3****, = 0.007**, = 0.001***, from left to right, permutation tests compared to 0), despite the change in PYR participation (f). h, Pupil size did not predict spindle (black) or delta amplitude (brown). i, Ripple amplitude (green) and replay probability (teal) could be significantly predicted by pupil size at the time of SWR onset (green and teal horizontal bars indicate significant time periods with p < 0.05, compared to label shuffled distributions). j, Pupil size remained predictive for replay probability after excluding the periods around SIA states (−10 s before SIA onset to 10 s after SIA offset). Data are shown as mean ± sem, and gray shading for the 95th percentile of shuffles in h-j.

Extended Data Fig. 6|. Closed-loop optogenetic disruption of SWRs contingent upon pupil substates.

a, Left, pupil video frame captured by the camera was streamed in real-time from a Raspberry Pi microcontroller (bottom, schematic illustrations of a mouse carrying head-mounted eye camera, high-density silicon probes and optical fibers). Middle, pupil image was then analyzed by a customized online tracking algorithm with binarization /thresholding and contour detection. A Python API was developed with online monitoring interfaces to enable real-time curation for optimization of tracking accuracy. The pupil size estimated by the algorithm was used to update the thresholds for NREM and WAKE states respectively. If the pupil size crossed the threshold (above or below for large versus small pupil conditions respectively), closed-loop optogenetic disruption of SWRs was triggered^,. Right, physiological SWRs are identified by the co-occurrence of both sharp wave and ripple. SWRs that occurred while pupil size was below threshold were left intact. b, Percentages of true positive (TP), false negative (FN), false positive (FP) events during SWR_pupils (blue bars) versus SWR_pupilL (black bars) disruption from 3 mice (one example session per mice; see Methods). Numbers (N denoted on top) correspond to total event counts. c, False discovery rate and false negative rate are comparable for SWR_pupilS (blue bars) versus SWR_pupilL (black bars) disruption (n.s., p = 0.75 and 0.50, respectively, paired signed-rank test).

Extended Data Fig. 7|. Additional quantifications and control experiments of optogenetic SWR disruption for consolidation of a recent memory.

a, Example of a SWR disruption event. Top, a spontaneous SWR event (triangle, ripple onset). Bottom, disruption of a detected SWR event (blue shading, light stimulation pulse). b, A prolonged suppression of neuronal firing (mean ± sem) induced by the short stimulation pulse (blue shading)^, for SWR_pupilS (left) and SWR_pupilL (right) disruption (****both p’s < 1e-16, rank-sum tests, compared normal vs. disrupted MUA firing rate within the 0-100 ms window). Note that different cells recorded in different sessions contributed to mean firing rate difference for these two plots. c,d, Learning performance quantified as path length (i.e., distance traveled to the goal location) for SWR_pupilS (left) and SWR_pupilL (right) disruption. Data are shown as in Fig. 3d,e (*p = 0.031 and 0.047, n.s., p = 0.30 and 0.47, from left to right in d, paired signed-rank test). e, Animals’ running speed was not significantly different for SWR_pupilS and SWR_pupilL disruption (n.s., p = 0.93, one-way ANOVA compared SWR_pupilS and SWR_pupil disruption; n.s., p > 0.99, = 0.22, 0.71 and 0.20, from left to right, paired signed-rank test). f, Latency of probe and the 1^st testing trial versus the last training trial for all SWR disruption during 2h rest versus no optogenetic disruption sessions (*p = 0.036 and 0.013, n.s., p = 0.47 and 0.063, one-tailed paired t-test). g, Memory impairment for SWR_pupilS disruption cannot be explained by the percentage of SWRs disrupted (n.s., p(SWR_pupilS) = 0.53, p(SWR_pupilL) = 0.79, linear regression; ***p = 0.0002, compared performance index of SWR_pupilS vs. SWR_pupilL, rank-sum test). Each circle for one session (n = 8 for each condition; blue, SWR_pupilS; black, SWR_pupilL). h-m, Additional experiment using closed-loop CA1 PV interneuron stimulation (in PV::ChR2 mice) to suppress SWRs (n = 6 sessions for SWR_pupilS (left) and SWR_pupilL (right) disruption, respectively, 12 sessions from 2 animals in total). h, Fractions of SWRs disrupted were similar in the small and large pupil condition (p = 0.065, rank-sum test). i, Example of a SWR disruption event for PV activation experiments. j, Suppression of neuronal firing (mean ± sem) induced by PV activation using blue light pulses during SWR_pupilS (top) and SWR_pupil disruption (bottom; ****both p’s < 1e-16, rank-sum tests, compared normal vs. disrupted MUA firing rate within the 0-100 ms window). Two different pulse lengths (50 and 100 ms) were used, but results were consistent for the two pulse lengths. k,l, Learning performance quantified as latency (j) and path length (k) across trials. m,n, Memory performance was impaired for SWR_pupilS, but not SWR_pupilL, disruption (p = 0.031*, 0.031*, 0.31 and 0.56, from left to right, in l; p = 0.063, 0.031*, 0.22 and > 0.99, from left to right, in m). Error bars: sem.

Extended Data Fig. 8|. Quantification of goal-direction angle on the one-goal cheeseboard maze task with SWR disruption.

a, Examples of goal-direction distributions during SWR_pupilS (top) and SWR_pupilL (bottom) disruption sessions (same sessions shown in Fig. 3b). Goal-direction angle was defined as the azimuthal angle between the heading direction and the mouse-to-goal direction, as previously described (see Methods). During the first training trial of each day, when the animal did not know where the goal was, the goal-direction angle was widely distributed (i.e., low circular concentration; left column). In the last trial, after the animal had learned, the goal-direction angle showed a sharp distribution around 0 (second left column). Such wide distributions were shown after SWR_pupilS, but not SWR_pupilL, disruption (two right columns). b, For SWR_pupilS disruption (top), goal-direction concentration of the probe and the first testing trial was significantly lower than that during the last training trial (*p = 0.016 and 0.023 for probe and 1^st testing trial, respectively), and was similar to the first training trial (n.s., p = 0.20, paired signed-rank test). However, the distributions of goal-direction angles remained concentrated after SWR_pupilL disruption (n.s., p = 0.81 and 0.81 for probe and 1^st testing trial compared to the last training trial, respectively, paired signed-rank test).

Extended Data Fig. 9|. Additional controls for novel and familiar memory reactivation.

a,b, Additional controls for optogenetic SWR disruption in the familiar-novel goal cheeseboard task. a, Latency of the 1^st testing trial versus the last training trial for the cohort without optogenetic disruption (n.s., p = 0.13 and 0.14, one-tailed paired t-test; probe trial was not presented to this cohort). b, Learning performance quantified as path length for the optogenetic disruption experiments shown in Fig. 3h (*p = 0.016 and 0.047, n.s., p = 0.69 and 0.47, from left to right, paired signed-rank test). c-e, Behavioral parameters during novel and familiar explorations shown in Fig. 4. Meaning running speed (c), proportion of running periods (d), and number of trials (T-maze only; e) are shown. Note that the proportions of running periods over the whole session were lower for T-maze sessions than Cheeseboard maze sessions, which could be due to animals taking more time at the reward wells and the inter-trial delay periods. In addition, although there was a small tendency for exploring more in the novel contexts, the differences across animals were not significant (p = 0.19, 0.19, and 0.13 for c-e, respectively, paired signed-rank test). f, Place-field similarity, measured as Pearson correlation coefficient of rate maps (r), for the pairs of neurons with high weights within the same assembly (assembly members; see Methods)^, versus other neuronal pairs (**p = 0.002, paired signed-rank test). g, Assembly member pairs have higher theta covariance in the same environment, in which they were detected, than that in the different environment (****p = 1.53e-5, rank-sum test compared to 0) and were significantly different from other neuronal pairs (****p = 1.53e-5, paired signed-rank test). h, Fraction of plastic assemblies was significantly larger in the novel environments than in the familiar ones (**p = 0.0039, paired signed-rank test; n = 50 rigid and 108 plastic, 38 rigid and 127 plastic cell assemblies in the familiar and novel environments, respectively).

Extended Data Fig. 10|. Additional quantifications of input and local circuit properties in small and large pupil substates.

a, Larger sharp-wave in the small pupil substates (****p < 1e-16, one-way ANOVA with post hoc test for linear trend). b, CCGs for an additional example pre-/post-synaptic PYR-INT pair during small, middle and large pupil substates. Data are shown as in Fig. 5b. c, Autocorrelations of the INT and PYR pairs shown in b (top two rows) and Fig. 5b (bottom two rows). d, Representative LFP traces from CA1 pyramidal layer (red) and stratum radiatum (rad; black), showing identified ripple bursts (yellow shading) and singlets (gray shadings). e, Ripple bursts had longer duration (left; ****p = 3.76e-7, one-way ANOVA with post hoc test for linear trend) and occurred more frequent (right; ****p < 1e-16, one-way ANOVA with Dunnett’s post hoc test, bootstrapped distributions are shown, n = 100 times) in the small pupil substates. f, INT firing rate during NREM episodes was higher in the small pupil substates (****p < 1e-16, one-way ANOVA with post hoc test for linear trend). g, From left to right, correlation of pupil size with firing rate difference between INTs and PYRs during NREM SWRs, ripple amplitude, INT firing rate, and PYR firing rate during NREM episodes (****p < 1e-16, rank-sum test compared to 0; bootstrapped distributions are shown, n = 100 times). Error bars: sem.

Fig. 1|. Oscillatory pupil dilation revealed micro-architecture in natural sleep.

a, Left, experimental scheme for pupillometry and electrophysiology in free moving mice. Right, pupil tracking with DLC, and our real-time online algorithm across behavioral states. Two pupil snapshots at its peak and valley during NREM are shown at the bottom. b, Representative pupil trace with LFP-scored sleep stages. Gray area highlights the infra-slow oscillatory pattern of pupil dilation during a NREM episode. c, Left, representative auto-correlogram of pupil size during NREM sleep shown in b. Right, peak frequency in pupil power spectral profiles during NREM bouts (mean ± sem = 0.016 ± 0.004 Hz; n = 9 sessions with NREM bouts longer than 10 mins from 5 mice). d, Visualization of network states constructed from CA1 LFPs using unsupervised UMAP. Data from the session shown in b. Left, states occupied during WAKE, NREM and REM sleep. Middle, distribution of pupil size across WAKE, NREM and REM. Right, distribution of pupil size during NREM only (z-scored within NREM; unvisited states in gray). e, Structure index (SI; see Methods), characterizing the pupil distribution across the original high-dimensional state space, tested against pupil-label shuffled distributions (n = 14 days from 5 mice; 95^th percentiles of shuffle distributions are shown; ***both p’s = 1.22e-4, paired signed-rank test). Left dot pairs reflect SI across all states and right pairs within NREM. f, Method schematic. Ripple waveform from time samples in a 50 ms window defined the high-dimensional space and reduced to a low-dimensional space for visualization. g, Individual SWRs formed a continuous distribution of their peak amplitudes (left), and pupil size (right). h, SI of pupil size distribution across the high-dimensional ripple waveform space is significantly higher than shuffles (**p = 0.0098, paired signed-rank test).

Fig. 2|. Small pupil substates promote replay of recent memories.

a, Top: two representative SWR events during small (left) and large (right) pupil substates, respectively. For each event, multi-unit (MUA) spike density (top), CA1 LFP (middle), and decoded position (cyan line, best linear fit) for the event are shown. Bottom: curves showing replay probability and inverted pupil size during a NREM bout. Note the significant sequential representation of a spatial trajectory for the contracted pupil event, but not for the dilated pupil event. b, Replay probability over pupil size sextiles (****p = 6.99e-12, one-way ANOVA with post hoc test for linear trend; 15 sessions from 3 animals; mean ± sem). c, Percent of significant replay events out of all SWRs over pupil sextiles (****p< 1e-16, one-way ANOVA with post hoc test). Bootstrapped distributions are shown (n = 100 times). Note significantly higher replay percentage in the lowest sextile, and lower percentage in the highest sextile (****both p’s < 1e-16, rank-sum tests compared to random shuffles of replay probabilities across SWR events). d, Illustration of the GLM for quantifying the contribution of replay probability versus other SWR properties. GLMs were fitted with all variables (full models), and the coefficients for a target variable (e.g., reactivation strength, RS) were set to zero to make cross-validated predictions on held-out data of pupil sizes (ablated models). The contribution was quantified as the decrease of GLM prediction gain compared to the full model^,. e, GLM gain for the full, pupil-size label shuffled, and ablated models. Note the strongest reduction of GLM prediction gain for the replay probability (****p < 0.0001, one-way ANOVA with Sidak’s post hoc test).

Fig. 3|. Optogenetic disruption of SWRs in small but not large pupil substates selectively disrupts recent memory.

a, Example of optogenetic disruption of SWRs in small (top) and large (bottom) pupil substates during sleep. Yellow line: ripple-band filtered LFP. Green line: pupil size. Triangles: time of optogenetic stimulation. Red circle: time of remaining SWRs detected offline after optogenetic disruption. b, Examples of mouse paths during SWR_pupilS (top) and SWR_pupilL (bottom) disruption sessions. Arrowhead: home box location. Yellow circle: reward location. c, Fractions of SWRs disrupted were similar in the small and large pupil condition. (p = 0.96, rank-sum test; n = 8 sessions from 5 mice for each condition). d, Latency to reward location during training, probe, and test trials for SWR disruption in small (left) and large (right) pupil substates. Thin line for a single session, thick line as mean ± sem across sessions. e, Latency of probe and the 1^st testing trial versus the last training trial. Each dot pair for one session (**p = 0.0078, *p = 0.016, n.s., p = 0.69, paired signed-rank test). f, Experimental paradigm for learning multiple goals (see Methods). g, Example mouse path during the last training trial (left) and the 1st testing trial (right). Black rhombus and black line: familiar goal and path to the familiar goal. Blue circle and blue line: novel goal and path to the novel goal. h, Latency of probe and the 1^st testing trial versus the last training trial to the novel (blue) and the familiar (black) goal. Each dot pair for one session (*p = 0.016 and 0.031, n.s., p = 0.69 and 0.30, paired signed-rank test; n = 7 sessions from 4 mice for each condition).

Fig. 4|. Reactivation of novel and prior memories are temporally segregated by small and large pupil substates.

a, Overview of experimental design. b, Example cell assemblies detected in familiar (left) and novel (right) environments. Weight vectors of all 91 simultaneously recorded putative pyramidal neurons are shown. Neurons with the highest weights (cells 1-4) are highlighted. c, Firing rate maps for the 4 neurons highlighted in b. Note the place-field remapping across environments and similar place fields for the neuronal pairs from the same assembly (Extended Data Fig. 9). d, Reactivation strength (RS) of cell assemblies detected in familiar (black) and novel (blue) environments shown in b and c, along with pupil size (green), during NREM sleep. Note the segregation of reactivation of novel and familiar assemblies with pupil sizes. e, Preferential reactivation of familiar environment during large pupil substates (**p = 0.0076, one-way ANOVA with post hoc test for linear trend; n = 10 novel-familiar session pairs from 5 animals). f, peri-SWR reactivation strength for a representative rigid (top) and plastic (bottom) cell assembly during PRE (gray) versus POST (red) SWRs, and during POST SWR_pupilL (yellow) versus SWR_pupilS (blue) are shown (mean ± sem). Note the increase of RS from PRE to POST for the plastic assembly, and the higher RS during SWR_pupilL compared to SWR_pupilS for the rigid assembly. g,h, Large pupil substates biased to reactivation of rigid cell assemblies. g, RS difference for plastic versus rigid assemblies (*p = 0.014 for small, n.s., p = 0.90 for middle, *p = 0.023 for large, rank-sum test compared to 0; **p = 0.0011, one-way ANOVA with post hoc test for linear trend). h, Rank-order correlation of POST reactivation at different pupil sizes with PRE reactivation. Note the stronger correlation for SWR_pupilL (*p = 0.031, one-way ANOVA with post hoc test for linear trend; n = 10 PRE-POST session pairs).

Fig. 5|. Distinct input and local circuit properties in small and large pupil substates.

a, Left, averaged CSD and LFP depth profile for SWRs in a representative session. Right, CSD magnitude revealed stronger CA3 and entorhinal inputs during SWR_pupilS (n.s., p = 0.80, ***p = 0.0002 and 0.0007 for rad and l-m, respectively, rank-sum tests compared to 0). Box plots show median, 75th (box), and 90th (whiskers) percentile. b, Firing rate difference between CA1 INTs and PYRs during SWRs in different pupil substates (**p = 0.0068, one-way ANOVA with post hoc test for linear trend; n = 17 sessions from 4 animals). c, CCGs for a representative pre-/post-synaptic PYR-INT pair during small, middle and large pupil substates. Dashed line shows 0 ms lag from the reference spike. d, CA1 PYR-INT spike transmission probability was stronger in large pupil substates (*p = 0.028, paired t-test). e,f, Optogenetic induction of ripples at different pupil sizes. e, Examples of optogenetically induced ripples in CA1 at the corresponding pupil sizes shown on the left. A spontaneous ripple is shown for comparison (indicated by the triangle). Note the smaller ripple amplitude induced at the larger pupil size (trace b), with the same stimulation intensity. Cyan vertical line: light stimulation pulse onset. f, Amplitude of optogenetically induced (opto; dark red) and spontaneous (spont; light red) ripples from the same sleep sessions over pupil size sextiles (**p = 0.014, ****p = 4e-15, one-way ANOVA with post hoc test for linear trend. n = 9 sessions from 6 animals). Data are shown as mean ± sem in b, d and f.