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Nested compressed co-representations of multiple sequential experiences during sleep

Nature Neuroscience volume 27, pages 1816–1828 (2024)Cite this article

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A Publisher Correction to this article was published on 15 August 2024

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Abstract

Animals encounter and remember multiple experiences daily. During sleep, hippocampal neuronal ensembles replay past experiences and preplay future ones. Although most previous studies investigated p/replay of a single experience, it remains unclear how the hippocampus represents many experiences without major interference during sleep. By monitoring hippocampal neuronal ensembles as rats encountered 15 distinct linear track experiences, we uncovered principles for efficient multi-experience compressed p/replay representation. First, we found a serial position effect whereby the earliest and the most recent experiences had the strongest representations. Second, distinct experiences were co-represented in a multiplexed, flickering manner during nested p/replay events, which greatly enhanced the network’s representational capacity. Third, spatially contiguous and disjunct track pairs were bound together into contiguous conjunctive representations during sleep. Finally, sequences spanning day-long multi-track experiences were p/replayed at hyper-compressed ratios during sleep. These coding schemes efficiently parallelize, bind and compress multiple sequential representations with reduced interference and enhanced capacity during sleep.

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Fig. 1: Ensemble activity of hippocampal CA1 place cells across 15 linear tracks and different brain states.
Fig. 2: Representation of individual and multiple tracks during sleep frames.
Fig. 3: Nested parallel co-representation of multiple tracks within sleep frames.
Fig. 4: Multiplexed parallel co-representation of two contexts within the same sleep frame.
Fig. 5: Representation of serial track pair concatenation in sleep frames.
Fig. 6: Representation of sequences of DTE within sleep frames.

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Data availability

Data used in this study are available from the corresponding author upon reasonable request. The reported data are archived on file servers at the Yale Medical School. The very large size of the raw data prohibits their public availability. No clinical datasets or genetics data were used in this study.

Code availability

The custom codes specific to this study that are needed to interpret, verify and extend the research in the article will be uploaded and made available after publication of the paper via GitHub at https://github.com/GDYlab/GDYlabcode. Additional codes are available from the corresponding author upon reasonable request.

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Acknowledgements

We thank the Dragoi laboratory members for helpful discussions. This work was supported by National Institutes of Health grants R01NS104917, R01MH121372 and R35NS132342, to G.D.

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Authors and Affiliations

  1. Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA

    Kefei Liu, Jeremie Sibille & George Dragoi

  2. Bioscience and Biomedical Engineering Thrust, Systems Hub, Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

    Kefei Liu

  3. Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA

    George Dragoi

  4. Wu Tsai Institute, Yale University, New Haven, CT, USA

    George Dragoi

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  1. Kefei Liu
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Contributions

K.L. and G.D. analyzed the data. J.S. and G.D. collected the data. G.D. conceived and designed the study. G.D. and K.L. wrote the paper.

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Correspondence to George Dragoi.

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Extended data

Extended Data Fig. 1 Experimental maze design.

a, Cartoon illustrating the 15 tracks distributed in two different mazes placed in 2 different rooms/compartments (Top, red lines highlighted track) and the configuration of the two mazes and rooms/compartments (Bottom; highlighted: track 13 in Maze2 in Room2, red lines indicates barriers). b, Properties of cells and recordings (43, 51, 76, 48, and 62 pyramidal neurons from 5 rats, 4 sleeps, 15 tracks). Top row: total number of pyramidal cells in each animal; number of place cells on each track; number of tracks that a place cell had a field on; number of subfields in each place cell; number of place cells on each track; number of place cells per track in each rat; Middle row: number of cells in each sleep frame; number of frames in each sleep; proportion of frames co-occur with SWR; single track place cell participation in frames; the distribution of isolation distance of sorted units; the distribution of L-Ratio of sorted units. Bottom row: examples of day-long single unit stability of spike amplitude from the beginning (first ten minutes) to the end (last ten minutes) of the recording. c, Correlation of single place cells place fields across different tracks measuring remapping. d, Correlation of place fields from the first half of run sessions and the corresponding second half of sessions measuring within-session stability. e, Animal velocity, theta/delta ratio and spikes during NREM and REM sleep time periods. f, Power of reconstructed EMG from LFP signals during different sleep sessions (Kruskal-Wallis test, P=0.7, 20 sleep sessions). Box plot shows median as center, 25th and 75th percentile as box bounds, minima and maxima excluding outliers as whiskers.

Extended Data Fig. 2 Track discrimination estimated by Bayesian decoding probability.

a, Place maps across the 15 tracks used for Bayesian decoding in one example rat. Neurons were ordered based on the location of their peak firing on track 6. Place maps were normalized for each neuron based on its activity on the concatenated 15 tracks. b, Example of Bayesian decoding of animal position based on place cells spikes during one lap of animal run on one track. c, Distribution of Bayesian decoding error of animal position on individual tracks during run based on the activity of the place cells on the corresponding tracks. Black line indicates the mean error value. d, Confusion matrix of decoded locations and actual rat locations during run. e, Distribution of correlation coefficients between the overall decoded posterior probabilities within a track and the sequence score for the corresponding track across all sleep sessions (Pearson’s correlation, R=0.02, P<0.0002, N=20). f-g, Cosine angle (f) and Euclidian distance (g) between the population vectors of place cell activity across different tracks during run and the corresponding neuronal activity across different frames during sleep (Wilcoxon signed rank test); (f): P = 0.14; 9×10-5; 0.33; (g) P=0.04; 9×10-5; 0.97; n=20 decoding sessions. Blue lines depict average values of corresponding data in grey. One run session connects to corresponding 4 sleep sessions. h, Signal to noise ratio of track discrimination using decoded probability on tracks during run and during sleep (all animals). Wilcoxon signed rank test, P= 9×10-5; n=20 decoding sessions. Blue lines depict average values. i, Cross correlation of sequence scores for the same tracks across temporally nearby sleep frames as a function of temporal lag between them. j, Proportion of significant sleep frames as a function of the temporal difference between the run session and the sleep session when the frames were restricted to those co-occurring with sharp wave ripples. Data are represented as mean±SEM. ***P<0.005. *P<0.05. ns=not significant.

Extended Data Fig. 3 Nested parallel co-representation of multiple tracks within sleep frames estimated by Bayesian decoding.

a-b, Example multi-track sleep frames analyzed by the Bayesian decoding method. Red outline indicates tracks that were significantly represented (for example, tracks 2&10 in a and 3&4 in b) in the corresponding sleep frame shown on the far left. Decoded probabilities were normalized across all tracks (top) or within each track (bottom). c, Proportion of sleep frames grouped by the number of significantly represented tracks estimated by Bayesian decoding in each sleep session and corresponding shuffle sleep. Pie charts summarize the proportions of frames with 0 (that is, no), 1, 2 and >2 tracks represented significantly in parallel within the same frame. d, Multiple-track parallel co-representation in sleep frames across sleep sessions (that is, from pre- to post-experience sleep) and in corresponding shuffle sleep expressed as proportions of multi-track frames among all frames (left) or among significant p/replay frames (right). e, The minimum total number of tracks investigated after which the average or peak of the distribution of multi-track co-representing sleep frames exceeded those of sleep frames representing single tracks, across all sleep sessions. f-g, Proportion of sleep frames when the threshold of significance was set to 0.002 (f, Bonferroni correction) or frame significance was adjusted by Benjamini-Hochberg correction (g). Left: Pie plots of proportions of the sleep frames grouped by the number of significantly represented (and co-represented) tracks estimated by Bayesian decoding across all sleep sessions (data and shuffle sleep). (f) Wilcoxon signed rank test, two-sided, P=0; n=300 tracks; (g) Wilcoxon signed rank test, two-sided, P=0; n=300 tracks. Middle: Cumulative proportions of significant sleep frames expressed as a function of the number of tracks explored. Right: the difference of cumulative proportion of significant sleep frames between data and shuffle sleep. Data are represented as mean±SEM. ***P<0.005.

Extended Data Fig. 4 Features of nested parallel co-representation of track pairs in sleep frames.

a, Proportion of track-pair co-representation across spiking frames out of all significant frames across sleep sessions (left), corresponding shuffle sleep (middle) and their difference (right). b, Incidence of track-pair co-representation (ratio) when the frames were restricted to those co-occurring with sharp wave ripples; Data>shuffle sleep, P=0, Wilcoxon signed-rank test, ***P<0.005. c, Incidence of track-pair co-representation when place cells with fields across the intersecting corner of two connected tracks were excluded; Data>shuffle sleep, P=0, Wilcoxon signed-rank test, ***P<0.005. d, Correlation between the slope of decoded trajectory from significant tracks in the multi-track nested frames across sleep sessions; Average correlation coefficient 0.18±0.0093, n=525 track pairs. e, Top: Contribution of maze and track geometric features to track-pair co-representation in multi-track frames estimated using a GLM with geometric features of tracks as variables and track co-representation (co-rep) ratio, quantified by Bayesian decoding, in sleep frames as predictor. Bottom: Contribution of maze and track geometric features to place field similarity across tracks quantified by population vector correlation (Wilcoxon signed rank test, ***P<0.005. **P<0.01. *P<0.05). f, Contribution of geometric features to track-pair parallel co-rep in multi-track frames across sleep sessions quantified by Bayesian decoding. Note changes in contributions across sessions (Wilcoxon signed rank test, **P<0.01. *P<0.05). g, Correlations between the co-representation ratio of track pairs and the average rank of track pairs estimating whether the track pairs were explored early or late in the day (Pearson’s correlation). Data are represented as mean±SEM.

Extended Data Fig. 5 Dynamics between nested tracks parallel co-representation within multi-track sleep frames.

a-i, Examples (n=9, 5 rows each) of dynamics between track-pair co-representation in 9 decoded frames. First row displays the spiking activity in the frames with nested track-pair co-representation. Second row (orange mask) shows the decoded probabilities of the 2 individual tracks normalized within the tracks. The decoded probabilities normalized across the track pair (3rd row, green mask) were used to extract the posterior decoded probabilities on the fitted line (POL) (4th row, pink mask). Transparent lines (4th row) were copied from the other track of the pair for illustration purpose only. POL of two tracks were compared to obtain the difference curve (5th row).

Extended Data Fig. 6 Representation of multiple tracks in sleep frames.

a, Relative proportions of the three types of co-representation dynamics when the frames were restricted to those co-occurring with sharp wave ripples. Wilcoxon signed rank tests, Dominant, P<10-7, n=20; Biphasic, P=0.04, n=20; Flicker, P<10-7, n=20. b, Participation of each pyramidal cell in the three types of co-represented frames, sorted according to flicker frames. c, Left: Probability of the decoded track representation crossing between the co-represented tracks or staying within track in multi-track frames of sleep and shuffle sleep. Black lines indicate mean value. Kruskal-Wallis test, pairwise, post hoc Tukey’s HSD correction, P=0; 0, n= 25762; 9832 for data and shuffle. Right: difference between probabilities of crossing-between and staying-within tracks. Wilcoxon rank sum test, two-sided P=0. d, Duration of bouts of relative representation dominance in the three types of temporal dynamics of nested parallel co-representation of track pairs. Black lines indicate mean value. Kruskal-Wallis test, post hoc Tukey’s HSD correction, P=1×10-105; 3×10-135; 2×10-77 for 3,843 Dominant, 8,606 Biphasic and 14,098 Flicker frames. e, Diagram of all serial combinations of directions of an example track pair (concatenation). Solid line is the entry direction (direction first taken by the animal), dashed line is the return direction. f, Proportion of significant frames representing the concatenation of adjacent and non-adjacent track pairs when sleep frames were restricted to those co-occurring with sharp wave ripples. g, Proportion of significant frames of concatenated track pairs when place cells with fields covering the intersecting corner of two connected tracks were removed. h, Proportions of sleep (data) and shuffle sleep (control) frames significant for specific orientation/direction combination of track pairs (as in Fig. 5h) in Sleep2 and Sleep3 sessions. Kruskal-Wallis test, post hoc Tukey’s HSD correction, **P<0.01; *P<0.05; comparisons without any * mark not significant, 7,760 Non-adj, 60 Adj real, 420 Adj virtual and 160 Adj-barrier tracks pairs. Data are represented as mean±SEM. ***P<0.005.

Extended Data Fig. 7 Representation of DTE in sleep frames.

a, Proportion of significant frames representing DTE sequences when sleep frames were restricted to those co-occurring with sharp wave ripples. b, Forward/reverse decoded representations of trajectories on single tracks in the shuffle sleep sessions. c, Proportion of significant frames representing trajectories on individual tracks across the 4 sleep sessions among frames grouped according to their significance or non-significance for track trajectories (overlapping lines are not individually visible). d, Number of individual tracks covered by the extended range (left) and duration (right) of significant sequential DTE sleep frames compared to shuffle sleep. Wilcoxon rank-sum tests, P=7×10-108; 3×10-17 for number of tracks in DTE and duration of frames, n=26,547 decoded frames. Vertical bars are mean group values. e, Compression ratio of DTE frames (number of tracks/sec) across sleep sessions as a function of run experience. Black lines indicate mean value. Kruskal-Wallis test, post hoc Tukey’s HSD correction, P=4×10-5, n=7,866 frames. Data are represented as mean±SEM. ***P<0.005.

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Liu, K., Sibille, J. & Dragoi, G. Nested compressed co-representations of multiple sequential experiences during sleep. Nat Neurosci 27, 1816–1828 (2024). https://doi.org/10.1038/s41593-024-01703-6

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