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Retuning of hippocampal representations during sleep

Abstract

Hippocampal representations that underlie spatial memory undergo continuous refinement following formation1. Here, to track the spatial tuning of neurons dynamically during offline states, we used a new Bayesian learning approach based on the spike-triggered average decoded position in ensemble recordings from freely moving rats. Measuring these tunings, we found spatial representations within hippocampal sharp-wave ripples that were stable for hours during sleep and were strongly aligned with place fields initially observed during maze exploration. These representations were explained by a combination of factors that included preconfigured structure before maze exposure and representations that emerged during θ-oscillations and awake sharp-wave ripples while on the maze, revealing the contribution of these events in forming ensembles. Strikingly, the ripple representations during sleep predicted the future place fields of neurons during re-exposure to the maze, even when those fields deviated from previous place preferences. By contrast, we observed tunings with poor alignment to maze place fields during sleep and rest before maze exposure and in the later stages of sleep. In sum, the new decoding approach allowed us to infer and characterize the stability and retuning of place fields during offline periods, revealing the rapid emergence of representations following new exploration and the role of sleep in the representational dynamics of the hippocampus.

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Fig. 1: Bayesian learning of hippocampal spatial tunings during offline states.
Fig. 2: Bayesian LTs during MAZE and offline states.
Fig. 3: Stability of LTs during ripples in PRE and POST.
Fig. 4: Spatial representations are randomized over the course of sleep.
Fig. 5: POST ripple tunings predict future PFs on maze re-exposure.
Fig. 6: Ensemble patterns during awake θ-oscillations and ripples and a diversity of pre-existing representations affect the tunings in POST sleep.

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Data availability

The Grosmark dataset is publicly available at https://doi.org/10.6080/K0862DC5. The Miyawaki and Giri datasets are available upon request from the corresponding author.

Code availability

Custom-written MATLAB and python code supporting this study is available at https://github.com/diba-lab/Maboudi_et_al_2022.

References

  1. Mankin, E. A. et al. Neuronal code for extended time in the hippocampus. Proc. Natl Acad. Sci. USA 109, 19462–19467 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  2. Klinzing, J. G., Niethard, N. & Born, J. Mechanisms of systems memory consolidation during sleep. Nat. Neurosci. 22, 1598–1610 (2019).

    Article  CAS  PubMed  Google Scholar 

  3. Havekes, R. & Abel, T. The tired hippocampus: the molecular impact of sleep deprivation on hippocampal function. Curr. Opin. Neurobiol. 44, 13–19 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Hebb, D. O. The Organization of Behavior (Wiley, 1949).

  5. O’Keefe, J. & Dostrovsky, J. The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain Res. 34, 171–175 (1971).

    Article  PubMed  Google Scholar 

  6. Zhang, K., Ginzburg, I., McNaughton, B. L. & Sejnowski, T. J. Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells. J. Neurophysiol. 79, 1017–1044 (1998).

    Article  CAS  PubMed  Google Scholar 

  7. Frank, L. M., Stanley, G. B. & Brown, E. N. Hippocampal plasticity across multiple days of exposure to novel environments. J. Neurosci. 24, 7681–7689 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Dong, C., Madar, A. D. & Sheffield, M. E. J. Distinct place cell dynamics in CA1 and CA3 encode experience in new environments. Nat. Commun. 12, 2977 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. Alme, C. B. et al. Place cells in the hippocampus: eleven maps for eleven rooms. Proc. Natl Acad. Sci. USA 111, 18428–18435 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ziv, Y. et al. Long-term dynamics of CA1 hippocampal place codes. Nat. Neurosci. 16, 264–266 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. van der Meer, M. A. A., Kemere, C. & Diba, K. Progress and issues in second-order analysis of hippocampal replay. Philos. Trans. R. Soc. B 375, 20190238 (2020).

    Article  Google Scholar 

  12. Dragoi, G. & Tonegawa, S. Preplay of future place cell sequences by hippocampal cellular assemblies. Nature 469, 397–401 (2011).

    Article  ADS  CAS  PubMed  Google Scholar 

  13. Cirelli, C. & Tononi, G. The why and how of sleep-dependent synaptic down-selection. Semin. Cell Dev. Biol. 125, 91–100 (2022).

    Article  CAS  PubMed  Google Scholar 

  14. Giri, B., Miyawaki, H., Mizuseki, K., Cheng, S. & Diba, K. Hippocampal reactivation extends for several hours following novel experience. J. Neurosci. 39, 866–875 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Grosmark, A. D., Sparks, F. T., Davis, M. J. & Losonczy, A. Reactivation predicts the consolidation of unbiased long-term cognitive maps. Nat. Neurosci. 24, 1574–1585 (2021).

    Article  CAS  PubMed  Google Scholar 

  16. Pettit, N. L., Yap, E. L., Greenberg, M. E. & Harvey, C. D. Fos ensembles encode and shape stable spatial maps in the hippocampus. Nature 609, 327–334 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. Wiskott, L. Lecture Notes on Bayesian Theory and Graphical Models. Institut für Neuroinformatik https://www.ini.rub.de/PEOPLE/wiskott/Teaching/Material/Bayes-LectureNotesPublicVideoAnnotated.pdf (2013).

  18. Diba, K. & Buzsaki, G. Forward and reverse hippocampal place-cell sequences during ripples. Nat. Neurosci. 10, 1241–1242 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Dragoi, G. & Buzsaki, G. Temporal encoding of place sequences by hippocampal cell assemblies. Neuron 50, 145–157 (2006).

    Article  CAS  PubMed  Google Scholar 

  20. Tirole, M., Huelin Gorriz, M., Takigawa, M., Kukovska, L. & Bendor, D. Experience-driven rate modulation is reinstated during hippocampal replay. eLife 11, e79031 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Siclari, F. et al. The neural correlates of dreaming. Nat. Neurosci. 20, 872–878 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Vertes, R. P. Memory consolidation in sleep; dream or reality. Neuron 44, 135–148 (2004).

    Article  CAS  PubMed  Google Scholar 

  23. Miyawaki, H. & Diba, K. Regulation of hippocampal firing by network oscillations during sleep. Curr. Biol. 26, 893–902 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Norimoto, H. et al. Hippocampal ripples down-regulate synapses. Science 359, 1524–1527 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  25. Kinsky, N. R., Sullivan, D. W., Mau, W., Hasselmo, M. E. & Eichenbaum, H. B. Hippocampal place fields maintain a coherent and flexible map across long timescales. Curr. Biol. 28, 3578–3588 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Geva, N., Deitch, D., Rubin, A. & Ziv, Y. Time and experience differentially affect distinct aspects of hippocampal representational drift. Neuron 111, 2357–2366 (2023).

    Article  CAS  PubMed  Google Scholar 

  27. Khatib, D. et al. Active experience, not time, determines within-day representational drift in dorsal CA1. Neuron 111, 2348–2356 (2023).

    Article  CAS  PubMed  Google Scholar 

  28. Drieu, C., Todorova, R. & Zugaro, M. Nested sequences of hippocampal assemblies during behavior support subsequent sleep replay. Science 362, 675–679 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  29. Liu, C., Todorova, R., Tang, W., Oliva, A. & Fernandez-Ruiz, A. Associative and predictive hippocampal codes support memory-guided behaviors. Science 382, eadi8237 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Roux, L., Hu, B., Eichler, R., Stark, E. & Buzsaki, G. Sharp wave ripples during learning stabilize the hippocampal spatial map. Nat. Neurosci. 20, 845–853 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Yagi, S., Igata, H., Ikegaya, Y. & Sasaki, T. Awake hippocampal synchronous events are incorporated into offline neuronal reactivation. Cell Rep. 42, 112871 (2023).

    Article  CAS  PubMed  Google Scholar 

  32. Liu, K., Sibille, J. & Dragoi, G. Preconfigured patterns are the primary driver of offline multi-neuronal sequence replay. Hippocampus 29, 275–283 (2019).

    Article  PubMed  Google Scholar 

  33. Farooq, U., Sibille, J., Liu, K. & Dragoi, G. Strengthened temporal coordination within pre-existing sequential cell assemblies supports trajectory replay. Neuron 103, 719–733 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Grosmark, A. D. & Buzsaki, G. Diversity in neural firing dynamics supports both rigid and learned hippocampal sequences. Science 351, 1440–1443 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  35. Foster, D. J. Replay comes of age. Annu. Rev. Neurosci. 40, 581–602 (2017).

    Article  CAS  PubMed  Google Scholar 

  36. Silva, D., Feng, T. & Foster, D. J. Trajectory events across hippocampal place cells require previous experience. Nat. Neurosci. 18, 1772–1779 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hasselmo, M. E. What is the function of hippocampal theta rhythm?–Linking behavioral data to phasic properties of field potential and unit recording data. Hippocampus 15, 936–949 (2005).

    Article  PubMed  Google Scholar 

  38. Siegle, J. H. & Wilson, M. A. Enhancement of encoding and retrieval functions through theta phase-specific manipulation of hippocampus. eLife 3, e03061 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Ujfalussy, B. B. & Orban, G. Sampling motion trajectories during hippocampal theta sequences. eLife 11, e74058 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Olypher, A. V., Lansky, P. & Fenton, A. A. Properties of the extra-positional signal in hippocampal place cell discharge derived from the overdispersion in location-specific firing. Neuroscience 111, 553–566 (2002).

    Article  CAS  PubMed  Google Scholar 

  41. Monaco, J. D., Rao, G., Roth, E. D. & Knierim, J. J. Attentive scanning behavior drives one-trial potentiation of hippocampal place fields. Nat. Neurosci. 17, 725–731 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Gupta, A. S., van der Meer, M. A., Touretzky, D. S. & Redish, A. D. Hippocampal replay is not a simple function of experience. Neuron 65, 695–705 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Mattar, M. G. & Daw, N. D. Prioritized memory access explains planning and hippocampal replay. Nat. Neurosci. 21, 1609–1617 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Cheng, S. & Frank, L. M. New experiences enhance coordinated neural activity in the hippocampus. Neuron 57, 303–313 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Wilson, M. A. & McNaughton, B. L. Reactivation of hippocampal ensemble memories during sleep. Science 265, 676–679 (1994).

    Article  ADS  CAS  PubMed  Google Scholar 

  46. Bittner, K. C., Milstein, A. D., Grienberger, C., Romani, S. & Magee, J. C. Behavioral time scale synaptic plasticity underlies CA1 place fields. Science 357, 1033–1036 (2017).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  47. Geiller, T. et al. Local circuit amplification of spatial selectivity in the hippocampus. Nature 601, 105–109 (2022).

    Article  ADS  CAS  PubMed  Google Scholar 

  48. Vaidya, S. P., Chitwood, R. A. & Magee, J. C. The formation of an expanding memory representation in the hippocampus. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/2023.02.01.526663v1 (2023).

  49. Tingley, D. & Peyrache, A. On the methods for reactivation and replay analysis. Philos. Trans. R. Soc. B 375, 20190231 (2020).

    Article  Google Scholar 

  50. Krause, E. L. & Drugowitsch, J. A large majority of awake hippocampal sharp-wave ripples feature spatial trajectories with momentum. Neuron 110, 722–733 (2022).

    Article  CAS  PubMed  Google Scholar 

  51. Stella, F., Baracskay, P., O’Neill, J. & Csicsvari, J. Hippocampal reactivation of random trajectories resembling Brownian diffusion. Neuron 102, 450–461 (2019).

    Article  CAS  PubMed  Google Scholar 

  52. Diba, K. Hippocampal sharp-wave ripples in cognitive map maintenance versus episodic simulation. Neuron 109, 3071–3074 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Genzel, L., Kroes, M. C., Dresler, M. & Battaglia, F. P. Light sleep versus slow wave sleep in memory consolidation: a question of global versus local processes? Trends Neurosci. 37, 10–19 (2014).

    Article  CAS  PubMed  Google Scholar 

  54. Poe, G. R., Nitz, D. A., McNaughton, B. L. & Barnes, C. A. Experience-dependent phase-reversal of hippocampal neuron firing during REM sleep. Brain Res. 855, 176–180 (2000).

    Article  CAS  PubMed  Google Scholar 

  55. Zielinski, M. C., Shin, J. D. & Jadhav, S. P. Hippocampal theta sequences in REM sleep during spatial learning. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/2021.04.15.439854v1.full (2021).

  56. Louie, K. & Wilson, M. A. Temporally structured replay of awake hippocampal ensemble activity during rapid eye movement sleep. Neuron 29, 145–156 (2001).

    Article  CAS  PubMed  Google Scholar 

  57. Boyce, R., Glasgow, S. D., Williams, S. & Adamantidis, A. Causal evidence for the role of REM sleep theta rhythm in contextual memory consolidation. Science 352, 812–816 (2016).

    Article  ADS  CAS  PubMed  Google Scholar 

  58. Hobson, J. A. REM sleep and dreaming: towards a theory of protoconsciousness. Nat. Rev. Neurosci. 10, 803–813 (2009).

    Article  CAS  PubMed  Google Scholar 

  59. Colgin, L. L., Kubota, D., Jia, Y., Rex, C. S. & Lynch, G. Long-term potentiation is impaired in rat hippocampal slices that produce spontaneous sharp waves. J. Physiol. 558, 953–961 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Schacter, D. L., Addis, D. R. & Buckner, R. L. Episodic simulation of future events: concepts, data, and applications. Ann. N. Y. Acad. Sci. 1124, 39–60 (2008).

    Article  ADS  PubMed  Google Scholar 

  61. Siegle, J. H. et al. Open Ephys: an open-source, plugin-based platform for multichannel electrophysiology. J. Neural Eng. 14, 045003 (2017).

    Article  ADS  PubMed  Google Scholar 

  62. Yger, P. et al. A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo. eLife 7, e34518 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Rossant, C. et al. Spike sorting for large, dense electrode arrays. Nat. Neurosci. 19, 634–641 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Bartho, P. et al. Characterization of neocortical principal cells and interneurons by network interactions and extracellular features. J. Neurophysiol. 92, 600–608 (2004).

    Article  PubMed  Google Scholar 

  65. Petersen, P. C., Siegle, J. H., Steinmetz, N. A., Mahallati, S. & Buzsaki, G. CellExplorer: a framework for visualizing and characterizing single neurons. Neuron 109, 3594–3608 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Grosmark, A. D., Long, J. D. & Buzsáki, G. Recordings from hippocampal area CA1, PRE, during and POST novel spatial learning. CRCNS.org https://doi.org/10.6080/K0862DC5 (2016).

  67. Skaggs, W., McNaughton, B. & Gothard, K. An information-theoretic approach to deciphering the hippocampal code. In Advances in Neural Information Processing Systems 5 (eds Hanson, S., Cowan, J. & Giles, C.) 1030–1037 (Morgan Kaufmann Publishers Inc., 1992).

  68. Wen, H. & Liu, Z. Separating fractal and oscillatory components in the power spectrum of neurophysiological signal. Brain Topogr. 29, 13–26 (2016).

    Article  PubMed  Google Scholar 

  69. Bokil, H., Andrews, P., Kulkarni, J. E., Mehta, S. & Mitra, P. P. Chronux: a platform for analyzing neural signals. J. Neurosci. Methods 192, 146–151 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Buzsaki, G. Theta oscillations in the hippocampus. Neuron 33, 325–340 (2002).

    Article  CAS  PubMed  Google Scholar 

  71. Park, M., Weller, J. P., Horwitz, G. D. & Pillow, J. W. Bayesian active learning of neural firing rate maps with transformed Gaussian process priors. Neural Comput. 26, 1519–1541 (2014).

    Article  MathSciNet  PubMed  Google Scholar 

  72. Davidson, T. J., Kloosterman, F. & Wilson, M. A. Hippocampal replay of extended experience. Neuron 63, 497–507 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Quirk, M. C. & Wilson, M. A. Interaction between spike waveform classification and temporal sequence detection. J. Neurosci. Methods 94, 41–52 (1999).

    Article  CAS  PubMed  Google Scholar 

  74. Schmitzer-Torbert, N., Jackson, J., Henze, D., Harris, K. & Redish, A. D. Quantitative measures of cluster quality for use in extracellular recordings. Neuroscience 131, 1–11 (2005).

    Article  CAS  PubMed  Google Scholar 

  75. Murphy, K. P. Machine Learning: a Probabilistic Perspective (MIT Press, 2012).

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Acknowledgements

We thank A. Amarasingham, T. Abel, G. Mashour, M. van der Meer, N. Kinsky, P. Hale and R. Wahlberg for comments on the manuscript. This work was financed by the US National Institute of Neurological Disorders and Stroke (R01NS115233) and the US National Institute of Mental Health (R01MH117964).

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Contributions

K.D. and K.M. conceived the analytical approach. B.G. conceived and carried out sleep and reMAZE experiments for the Giri dataset. H.M. carried out maze and sleep recordings for the Miyawaki dataset. K.M. carried out all analyses. K.D. supervised the research with input from C.K. K.D. and K.M. wrote the manuscript with input from C.K. and H.M.

Corresponding author

Correspondence to Kamran Diba.

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Extended data figures and tables

Extended Data Fig. 1 The factors that impact learned tunings.

In three sample maze sessions we calculated LTs by (a) randomly varying the number and subset of awake ripple events, or (b), varying the number of units included, and tested the effects on the quality of LTs, as reflected by PF fidelity. Samples that yielded with significant median LT fidelities (r(LT, PF), p < 0.01 relative to unit ID shuffle) are represented in gray dots, whereas non-significant samples are indicated by empty circular markers. The median LT fidelity increases as more ripples and units are incorporated. Based on these results, we estimate that approximately 40 simultaneously recorded units are needed to obtain quality LTs, whereas the minimum number of ripples could vary across sessions, with as few as five ripples needed to generate LTs with significant PF fidelities in some cases. (c) The distribution of PF fidelities corresponding to quiet wake (QW), or slow-wave sleep (SWS), when the number of firing time bins utilized for calculation of each unit’s LT were matched between PRE and POST via subsampling the firing time bins, indicated significant difference between PRE and POST within each event category (QW: P = 2.0 × 10−35; SWS: P = 2.7 × 10−43, two-sided Wilcoxon signed-rank tests with no correction for multiple comparisons). (d) Left, the distribution of the mean sharpness of the posterior probability distribution over position (quantified by the Gini coefficient, see Methods) used in LT calculations for units in each event category. There was a significant effect of event category (p = 1.5 × 10−151, Friedman’s test) with both QW and SWS had higher median Gini coefficients (i.e. sharper posteriors) than REM (QW versus REM: P <  4.3 × 10−98; SWS versus REM: P = 3.8 × 10−97, two-sided WSRT with no correction for multiple comparisons). The overlaid lines (dots for sessions with no REM in PRE) connect median values corresponding to individual sessions. PRE and POST also exhibited slightly different Gini coefficients during QW (P = 5.0 × 10−11) or REM (P = 8.6 × 10−11) but not during SWS (P = 0.95), though the effect sizes of the difference were small (QW: 0.20; SWS: 0.04; REM: −0.29). Right, the correlation between the PF fidelity and posterior Gini coefficient for PRE (top) and POST (bottom) by pooling across all event categories (best linear fits in black with 95% confidence intervals in shaded gray) was weak in both PRE and POST, and significant during POST (P = 0.004) but not PRE (P = 0.72), indicating that the sharpness of posteriors was not a major driver of differences between PRE and POST LTs. (e) The distributions of the number of bins with spikes used to calculate LTs in QW ripples, SWS ripples, and REM sleep during PRE or POST epochs in Fig. 2a. The overlaid lines (dots for sessions with no REM in PRE) connect median values corresponding to individual sessions. (f) The distributions of the average number of units that cofired with the learning unit when calculating LTs during each epoch. (g) Population vector correlation matrices (top) and cumulative distributions of PF fidelity (bottom) for SWS and QW LTs during POST, recalculated following subsampling of each unit’s SWS and QW firing bins to match the number of firing bins during the corresponding REM periods.

Extended Data Fig. 2 The impact of sleep oscillations on LT quality.

(a) Top row, the fraction of each sleep state in 2-minute sliding windows during POST from a sample session. Middle row, the power of δ-oscillations (1–4 Hz) in 2-second sliding windows (gray) across POST. Filled and empty dots indicating SWS ripple events with high (≥ median) or low (< median) δ-oscillation power for that session. Bottom row, similar to the middle panel, but for spindle power (9–18 Hz) calculated in 500 ms sliding windows (gray). (b) In POST, but not PRE, SWS ripples separated into those occurring during high vs. low δ-oscillation power (left) and high vs. low spindle power (middle) resulted in higher fidelity when the oscillations were present at higher power. When we split each 2-s δ-oscillation window into two 500 ms windows with higher and two 500 ms windows with lower spindle power, to isolate the impact of spindles at each level of δ-oscillation power, we observed higher fidelity LTs for ripples that occurred during the high spindle power subset. (c) LTs calculated based on events with high (≥ median) ripple (120–250 Hz) amplitude, multi-unit firing rate, unit participation rate, or ripple event duration, all exhibited significantly higher PF fidelity compared to those with low (< median) values in POST. In all comparisons in (b) and (c), LTs were calculated based on matched number of bins and p-values (inset) were derived from Wilcoxon signed-rank tests. (d) Distributions of the duration of ripple events obtained from each session in each dataset. (e) The distribution of θ-oscillation amplitude (z-scored over REM, left) and frequency (right) during the REM periods in POST (n = 813613 20-ms time bins pooled across all sessions), with individual session medians (dots) and interquartile ranges (horizontal lines) superimposed. (f) PF fidelity of LTs in POST REM calculated based on distinct subsets of 20-ms time bins separated according to high and low θ-oscillation amplitude (left panel) or frequency (middle panel) separated by the median values in each session. There was a significant effect of frequency (p = 0.02, two-sided WSRT, n = 660). Similarly, REM LTs calculated based on separating windows according to θ phase (right panel) into trough (−π/4 – π/4), ascending (π/4 – 3π/4), peak (3π/4 – 5π/4) and descending (5π/4-7π/4) phase. Median PF fidelities significantly differed across θ phase (P = 0.0015, Friedman’s test, n = 660) (g) We tested the effect of different sized time bins on REM LT-PF fidelities in PRE (left) and POST (right). While the effect was subtle and not significantly different across different sized bins (Friedman’s test), LTs using 125 ms and 250 ms bin durations exhibited significantly aligned LT-PF fidelities (median fidelities compared (one-sided) to null distributions obtained from 104 unit identity shuffles without multiple comparison corrections). (h) The posteriors used to calculate LTs exhibit greater sparsity for larger bin sizes in both PRE and POST REM. This is because larger bins result in more active neurons within each bin, producing increasingly sharper posteriors (see equation (9), Methods).

Extended Data Fig. 3 Additional examples of the evolution of LTs from PRE to POST.

Similar to Fig. 3a, heat maps of ripple LTs in sliding 15 min windows from PRE through MAZE to POST (maze place fields in gray on right) for 6 sample units from each of 5 different sessions (hypnogram on top left indicating the brain state, quiet wake (QW), active wake (AW), rapid eye movement (REM) sleep, and slow-wave sleep (SWS) at each timepoint).

Extended Data Fig. 4 Place field fidelities do not strictly correlate with replay score.

(a) Distribution of replay scores in the different datasets calculating as percentile against time shuffled bins. Median scores for different epochs are shown with dashed lines (chance median score = 50; see Methods). (b) Ripple events were divided into quartiles according to replay score. Top panels show the place fields and sets of LTs calculated based on low and high quartile replay score events within PRE, MAZE, and POST. Bottom panels show population vector (PV) correlations between position bins in the PFs versus different sets of LTs. (c) Distribution of PF fidelity for each ripple subset. Median PF fidelities were significantly greater compared (one-sided) against surrogate distributions (from 104 unit identity shuffles, without multiple comparison corrections) in all subsets during MAZE and POST but not during PRE (PRE; P = 0.86, P = 0.67, P = 0.49, P = 0.06 for first to forth quartiles, respectively. MAZE and POST: P < 10−4 for all quartiles). (d) Place fields of participating units in replays show differing amounts of overlap with the decoded posteriors. Example events with high replay scores in PRE and POST, and low replay scores in POST showing posterior probability matrices and corresponding spike rasters of units sorted by place field order. The middle row depicts the mean correlation of the participating units’ place fields with the decoding posterior in each time bin. The bottom panels show the place fields and decoded positions of participating units for example time bins. Note that even bins with poor place-field coherence display sharp posteriors, because of the multiplication rule in Bayes formula, whereby spatial tunings of participating units are multiplied by each other. (e) Mean posterior correlation of PFs and decoded positions show increased place-field overlap in both low and high score replays in POST compared to PRE. Low and high replay score events in PRE did not differ significantly (PRE low versus high: P = 0.36; POST low versus high: P = 1.8 × 10−66; POST high versus PRE high: P = 1.1 × 10−282; POST low versus PRE high: P = 1.1 × 10−59; two-sided Mann Whitney U Test). ***P < 0.001.

Extended Data Fig. 5 Additional details and variations on MAZE/reMAZE analyses.

(a) Place fields (PFs) from MAZE and reMAZE for all units used for analyses in Fig. 5. (b) PF peak firing rates during MAZE and reMAZE and their marginal histograms. Despite an apparent modest decrease in peak firing rates during reMAZE and disappearance or appearance of a small subset of units (orange dots), peak firing rates in reMAZE and MAZE remained significantly correlated. In this and subsequent panels, best linear fit and 95% confidence intervals are overlaid with black line and shaded gray, respectively. (c) The same as (b) but for spatial information between MAZE and reMAZE spatial tunings across units. (d). POST LT fidelity to MAZE PFs (left) is correlated with the similarity to the reMAZE PF. Likewise, POST LT similarity to reMAZE PFs (right) is correlated with the similarity between MAZE and reMAZE PFs. (e) MAZE/reMAZE similarity correlation with POST PF fidelity (as in Fig. 5f) separately for units with lower (left) or higher (right) PF stabilities relative to each session’s median. Higher POST fidelity was predictive of greater MAZE/reMAZE similarity in both sets. (f) Multiple regression separately for each panel directly above in (e) The regressors were more predictive (higher R2) for units with more stable MAZE PFs, but POST LTs beta coefficients were similar between units with lower or higher PF stabilities (both p values = 0.02). P values were obtained by comparing (one-sided) the R2 and each coefficient against surrogate distributions from 104 unit-identity shuffles of reMAZE PFs. (g) PF fidelities of POST LTs calculated exclusively based on slow-wave sleep (SWS; left) or quiet wake (QW; right) ripple events both predicted similarity between MAZE and reMAZE place fields. However, a stronger correlation was observed for SWS LTs. (h) The same multiple regression analysis for modeling reMAZE PFs as in Fig. 5g but with the inclusion of POST SWS LTs (left panel), POST QW LTs (middle panel), or both (right panel), as regressors. While both SWS and QW POST LTs were predictive of reMAZE (P < 10−4 and P < 0.01, P values obtained by comparing (one-sided) the R2 and each coefficient against surrogate distributions from 104 unit-identity shuffles of reMAZE PFs), the POST SWS LTs offered the stronger prediction. (i) The Gini coefficients of POST LT’s (measuring sparsity, i.e. sharpness of tuning) were significantly correlated with their similarity to reMAZE place fields. This demonstrates that sparser (as opposed to more diffuse) POST LTs display higher similarity with the upcoming place fields during maze re-exposure. (j) Similar to Fig. 5f & 5g, but using tunings learned during θ-oscillations (active periods) on MAZE and reMAZE. This analysis also allowed us to add data from an additional session (from Rat S) for which video tracking was lost during the reMAZE epoch). Left panel, the similarity of POST LTs with MAZE θ-oscillation LTs predicted the similarity between MAZE and reMAZE θ-oscillation LTs. Right panel, POST LTs remained predictive of reMAZE θ-oscillation LTs in this control comparison. (k) The stability of POST LTs (z-scored against unit-id shuffles, as in Fig. 3d) for units with MAZE PF peak firing rate < 1 Hz (threshold used in this paper) were not significantly > 0. (l) In the same set of units, the POST LTs did not display a significant correlation with reMAZE PFs (left) but still showed a significant correlation with reMAZE θ-oscillation LTs (right). (m) The correlation with reMAZE θ-oscillation LT was absent for latePOST LTs. (n) Multiple regression analyses for modeling the reMAZE PFs (left) or reMAZE θ-oscillation LTs (right) for these low-firing units both resulted in significant regression coefficients for POST LTs. *P < 0.05, **P < 0.01, ***P < 0.001.

Extended Data Fig. 6 Additional examples of the evolution of LTs from PRE to reMAZE.

Similar to Fig. 5h, heat maps of ripple LTs in sliding 15 min windows from PRE through MAZE, POST, and latePOST for sample units from 4 different sessions (hypnogram on top left indicate the brain state, quiet wake (QW), active wake (AW), rapid eye movement (REM) sleep, and slow-wave sleep (SWS) at each timepoint). MAZE and reMAZE place fields and LTs during PRE, POST, and latePOST are plotted on the right of each panel, except for Rat S for which we plot reMAZE θ-oscillation LTs (rather than reMAZE PFs) because video tracking was lost during reMAZE for this session.

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 (1st column) (P = 0.01) or frequency (2nd column) (P = 7.9 × 10−16). The impact of θ phase (3rd 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 (4th 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.

Extended Data Fig. 8 Unit stability and isolation.

(a) Sample units (from Rat U) depicting mean spike waveform and unit stability assessed by spike amplitude, isolation distance, and firing rate over three sleep epochs (PRE, POST, and latePOST). Inclusion thresholds for isolation distance and firing rates are shown with dashed lines. (b) The distribution of unit stability measures by pooling across all units in sessions shown for each dataset. See Methods for further details on unit inclusion criteria. Within-group comparisons used two-sided Wilcoxon Signed Rank Tests. (c) The L-ratio was used to quantify the degree of overlap in the spike feature space between each pair of units. Each scatterplot (top row) shows the spikes of the reference unit #20 (black) and other units (colored) recorded on the same shank in an example recording session from the Giri dataset. The axes in each scatterplot correspond to the spike amplitude on two channels with maximal distinction between the pairs, showing a range of overlap with unit #20. For example, unit #30 on the leftmost inset showed almost no overlap, whereas unit 19 on the rightmost inset significantly overlapped. The L ratio (e.g. between unit #20 and the other units) was obtained by calculating the probability of spikes from the second unit belonging to the reference unit. An L ratio threshold of 10−3 was applied to include only isolated units for determining the LTs of each reference cell. Corresponding mean spike waveforms (bottom row) provided for each pair of units across recording electrodes. (d) The cumulative distributions of L ratios for this example session and across all sessions (top) (n = 40207 unit pairs). The L-ratios for each individual session (bottom), showing mean (dots), the full range (whiskers) and interquartile range (boxes).

Supplementary information

Supplementary Information

Supplementary text regarding representations during high-score preplays that do not align with maze PFs, Supplementary References and Tables 1–3.

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Maboudi, K., Giri, B., Miyawaki, H. et al. Retuning of hippocampal representations during sleep. Nature 629, 630–638 (2024). https://doi.org/10.1038/s41586-024-07397-x

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