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Adaptive stimulus selection for consolidation in the hippocampus


Associative memories guide behavioural adaptation by binding together outcome-predictive sensory stimuli1,2. However, in a feature-rich environment, only a subset of stimuli may predict a desired outcome3,4. How neural circuits enable behavioural adaptation by selectively and durably representing subsets of sensory stimuli that are pertinent to a specific outcome is not known. We investigated this feature selection process in the hippocampus during memory acquisition and subsequent consolidation. Two-photon calcium imaging of CA3 axonal projections to CA1 combined with simultaneous local field potential recordings revealed that CA3 projections that encode behaviourally informative sensory stimuli were selectively recruited during the memory replay events that underlie hippocampal memory consolidation5. These axonal projections formed sequential assemblies that conjunctively link sensory features to spatial location and thus reward proximity. By contrast, axons encoding uninformative, peripatetic sensory cues were notably suppressed during memory replay. Thus, while the hippocampus comprehensively encodes the real-time sensory environment, it implements a flexible filtering mechanism to maximize the utility of memories destined for long-term storage. We propose that utility-dependent recruitment of sensory experience during memory consolidation is a general coding principle for associative learning.

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Fig. 1: Rapid reorganization of CA3SC activity during sensory experience.
Fig. 2: Suppressed reactivation of task-irrelevant sensory information during SWRs.
Fig. 3: Adaptive switch in reactivation of cue-CA3SCs during SWRs.
Fig. 4: Replay of sensory cue-driven cognitive maps in CA3SCs.

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Data availability

Datasets included in this study are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

Custom Matlab code supporting this study is available at Source data is provided with this paper.


  1. Eichenbaum, H. A cortical–hippocampal system for declarative memory. Nat. Rev. Neurosci. 1, 41-50, (2000).

    Article  CAS  Google Scholar 

  2. Whittington, J. C. R. et al. The Tolman–Eichenbaum machine: unifying space and relational memory through generalization in the hippocampal formation. Cell 183, 1249-1263, (2020).

    Article  CAS  Google Scholar 

  3. Desimone, R. & Duncan, J. Neural mechanisms of selective visual attention. Ann. Rev. Neurosci. 18, 193-222, (1995).

    Article  CAS  Google Scholar 

  4. Knudsen, E. I. Fundamental components of attention. Ann. Rev. Neurosci. 30, 57-78, (2007).

    Article  CAS  Google Scholar 

  5. Buzsaki, G. Hippocampal sharp wave-ripple: a cognitive biomarker for episodic memory and planning. Hippocampus 25, 1073-1188, (2015).

    Article  Google Scholar 

  6. Kentros, C. G., Agnihotri, N. T., Streater, S., Hawkins, R. D. & Kandel, E. R. Increased attention to spatial context increases both place field stability and spatial memory. Neuron 42, 283-295 (2004).

    Article  CAS  Google Scholar 

  7. Rowland, D. C. & Kentros, C. G. Potential anatomical basis for attentional modulation of hippocampal neurons. Ann. N.Y. Acad. Sci. 1129, 213-224, (2008).

    Article  ADS  Google Scholar 

  8. Aronov, D., Nevers, R. & Tank, D. W. Mapping of a non-spatial dimension by the hippocampal–entorhinal circuit. Nature 543, 719-722, (2017).

    Article  CAS  ADS  Google Scholar 

  9. Constantinescu, A. O., O’Reilly, J. X. & Behrens, T. E. J. Organizing conceptual knowledge in humans with a gridlike code. Science 352, 1464-1468, (2016).

    Article  CAS  ADS  Google Scholar 

  10. Kesner, R. P. & Rolls, E. T. A computational theory of hippocampal function, and tests of the theory: new developments. Neurosci. Biobehav. Rev. 48, 92-147, (2015).

    Article  Google Scholar 

  11. Nakazawa, K. et al. Hippocampal CA3 NMDA receptors are crucial for memory acquisition of one-time experience. Neuron 38, 305-315 (2003).

    Article  CAS  Google Scholar 

  12. Kudrimoti, H. S., Barnes, C. A. & McNaughton, B. L. Reactivation of hippocampal cell assemblies: effects of behavioral state, experience, and EEG dynamics. J. Neurosci. 19, 4090-4101 (1999).

    Article  CAS  Google Scholar 

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

    Article  CAS  ADS  Google Scholar 

  14. Jadhav, S. P., Kemere, C., German, P. W. & Frank, L. M. Awake hippocampal sharp-wave ripples support spatial memory. Science 336, 1454-1458, (2012).

    Article  CAS  ADS  Google Scholar 

  15. Nakashiba, T., Buhl, D. L., McHugh, T. J. & Tonegawa, S. Hippocampal CA3 output is crucial for ripple-associated reactivation and consolidation of memory. Neuron 62, 781-787, (2009).

    Article  CAS  Google Scholar 

  16. Nakashiba, T., Young, J. Z., McHugh, T. J., Buhl, D. L. & Tonegawa, S. Transgenic inhibition of synaptic transmission reveals role of CA3 output in hippocampal learning. Science 319, 1260-1264, (2008).

    Article  CAS  ADS  Google Scholar 

  17. Csicsvari, J., Hirase, H., Mamiya, A. & Buzsaki, G. Ensemble patterns of hippocampal CA3–CA1 neurons during sharp wave-associated population events. Neuron 28, 585-594, (2000).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  19. Danielson, N. B. et al. Sublayer-specific coding dynamics during spatial navigation and learning in hippocampal area CA1. Neuron 91, 652-665, (2016).

    Article  CAS  Google Scholar 

  20. Zaremba, J. D. et al. Impaired hippocampal place cell dynamics in a mouse model of the 22q11.2 deletion. Nat. Neurosci. 20, 1612-1623, (2017).

    Article  CAS  Google Scholar 

  21. 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  Google Scholar 

  22. Hunt, D. L., Linaro, D., Si, B., Romani, S. & Spruston, N. A novel pyramidal cell type promotes sharp-wave synchronization in the hippocampus. Nat. Neurosci. 21, 985-995, (2018).

    Article  CAS  Google Scholar 

  23. Soltesz, I. & Losonczy, A. CA1 pyramidal cell diversity enabling parallel information processing in the hippocampus. Nat. Neurosci. 21, 484-493, (2018).

    Article  CAS  Google Scholar 

  24. Mysore, S. P. & Knudsen, E. I. Reciprocal inhibition of inhibition: a circuit motif for flexible categorization in stimulus selection. Neuron 73, 193-205, (2012).

    Article  CAS  Google Scholar 

  25. Geiller, T. et al. Large-scale 3D two-photon imaging of molecularly identified CA1 interneuron dynamics in behaving mice. Neuron 108, 968-983, (2020).

    Article  CAS  Google Scholar 

  26. 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  Google Scholar 

  27. Wu, C. T., Haggerty, D., Kemere, C. & Ji, D. Hippocampal awake replay in fear memory retrieval. Nat. Neurosci. 20, 571-580, (2017).

    Article  CAS  Google Scholar 

  28. Ahmed, M. S. et al. Hippocampal network reorganization underlies the formation of a temporal association memory. Neuron, (2020).

  29. Liu, Y., Mattar, M. G., Behrens, T. E. J., Daw, N. D. & Dolan, R. J. Experience replay is associated with efficient nonlocal learning. Science 372, (2021).

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

    Article  CAS  ADS  Google Scholar 

  31. Kaufman, A. M., Geiller, T. & Losonczy, A. A role for the locus coeruleus in hippocampal CA1 place cell reorganization during spatial reward learning. Neuron 105, 1018-1026, (2020).

    Article  CAS  Google Scholar 

  32. Kaifosh, P., Lovett-Barron, M., Turi, G. F., Reardon, T. R. & Losonczy, A. Septo-hippocampal GABAergic signaling across multiple modalities in awake mice. Nat. Neurosci. 16, 1182-1184, (2013).

    Article  CAS  Google Scholar 

  33. Lovett-Barron, M. et al. Dendritic inhibition in the hippocampus supports fear learning. Science 343, 857-863, (2014).

    Article  CAS  ADS  Google Scholar 

  34. Pachitariu, M. et al. Suite2p: beyond 10,000 neurons with standard two-photon microscopy. Preprint at bioRxiv, 061507, (2016).

  35. Hainmueller, T. & Bartos, M. Parallel emergence of stable and dynamic memory engrams in the hippocampus. Nature 558, 292-296, (2018).

    Article  CAS  ADS  Google Scholar 

  36. 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  CAS  ADS  Google Scholar 

  37. Rajasethupathy, P. et al. Projections from neocortex mediate top–down control of memory retrieval. Nature, (2015).

  38. Ali, F. & Kwan, A. C. Interpreting in vivo calcium signals from neuronal cell bodies, axons, and dendrites: a review. Neurophotonics 7, 011402, (2020).

    CAS  PubMed  Google Scholar 

  39. Cox, C. L., Denk, W., Tank, D. W. & Svoboda, K. Action potentials reliably invade axonal arbors of rat neocortical neurons. Proc. Natl Acad. Sci. USA 97, 9724-9728, (2000).

    Article  CAS  ADS  Google Scholar 

  40. Petreanu, L. et al. Activity in motor-sensory projections reveals distributed coding in somatosensation. Nature 489, 299-303, (2012).

    Article  CAS  ADS  Google Scholar 

  41. Dugladze, T., Schmitz, D., Whittington, M. A., Vida, I. & Gloveli, T. Segregation of axonal and somatic activity during fast network oscillations. Science 336, 1458-1461, (2012).

    Article  CAS  ADS  Google Scholar 

  42. Williams, A. H. et al. Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor component analysis. Neuron 98, 1099-1115, (2018).

    Article  CAS  Google Scholar 

  43. Skaggs, W. E., Mcnaughton, B. L., Markus, E. J. & Gothard, K. M. in Advances in Neural Information Process Systems (eds Hanson, S. et al.) Vol. 5, 1030–1037 (Morgan Kaufmann, 1993).

  44. Mackevicius, E. L. et al. Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience. eLife 8, (2019).

  45. Malvache, A., Reichinnek, S., Villette, V., Haimerl, C. & Cossart, R. Awake hippocampal reactivations project onto orthogonal neuronal assemblies. Science 353, 1280-1283, (2016).

    Article  CAS  ADS  Google Scholar 

  46. Ecker, A. et al. Hippocampal sharp wave-ripples and the associated sequence replay emerge from structured synaptic interactions in a network model of area CA3. Preprint at bioRxiv, 2021.2002.2018.431868, (2021).

  47. Friedrich, J., Zhou, P. & Paninski, L. Fast online deconvolution of calcium imaging data. PLoS Comput. Biol. 13, e1005423, (2017).

    Article  ADS  Google Scholar 

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S.T. is supported by a JSPS Overseas Fellowship. A.L. is supported by National Institute of Mental Health (NIMH) 1R01MH124047 and 1R01MH124867, National Institute of Neurological Disorders and Stroke (NINDS) 1U19NS104590 and 1U01NS115530, and the Kavli Foundation. B.V. is supported by grants (NIH) T32GM007367 and (NIMH) F30MH125628, J.O. is supported by F32MH118716. Z.L. is supported by NINDS F31NS120783-01 and NIH T32GM007367. We thank S. Fusi, M. Shadlen, J. Gottlieb, F. Polleux and I. Soltesz, as well as members of the Losonczy laboratory and D. Hadjiabadi (Soltesz laboratory), for their invaluable comments on previous versions of the manuscript.

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Authors and Affiliations



S.T. and A.L. designed the project and experiments. S.T. performed the experiments with help from T.G. and B.V. S.T. analysed the data with help from Z.L., J.O. and T.G. All authors wrote the manuscript.

Corresponding author

Correspondence to Attila Losonczy.

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The authors declare no competing interests.

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Peer review information Nature thanks Aman Saleem and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Labeling of CA3 Schaffer Collaterals for in vivo two-photon calcium imaging and identification of ROIs.

a, Representative coronal slice of the dorsal hippocampus of a Grik4-Cre mouse showing Cre-dependent rAAV-driven GCaMP6f expression restricted to CA3. The CA3 pyramidal cell layer is outlined with the dotted white contour, and the approximate locations of the CA3 subregions (CA3a-c) are shown. b, Quantification of relative fluorescence intensity along the pyramidal cell layer of CA3 subregions (mean±s.e.m.). Fluorescence intensity at 10 locations along the CA3 pyramidal cell layer was measured, starting from the hilus of the dentate gyrus (x=0 μm) to the approximate CA2/3 border (x=900 μm). c, Example in vivo two-photon field of view (FOV) in CA1 SR from one representative mouse (n=6 in this study). Imaging focal plane was located 50-150 μm below CA1 SP. Right, the same FOV after ROI segmentation with the Suite2p analysis package (middle, overlapping display). d, Distributions of correlation coefficients between pairs of CA3SC ROIs during spontaneous activity for each individual mouse (black lines) and mean for all mice (green). e, Estimation of number of unique CA3 units recorded using hierarchical clustering and silhouette score (Methods). Top, silhouette values as number of clusters K (to the total number of detected ROIs) in an example FOV (green: average, pink: median, light blue: 75th percentile, red: 25th percentile). Bottom, marginal change in silhouette values each time K is incremented by 1. The estimated number of unique CA3 units recorded on each FOV was defined as the number of clusters that maximized the rate of change (528 clusters in this case, and 644±91 clusters, mean ± s.e.m.). f, Left, correlation matrix of ROI signals from the example FOV shown in (c). Right top, zoom: example clusters of putative same-axon ROIs (red), and non-clustered ROIs, likely recorded from distinct axons (blue). g, Median silhouette values over percentage of putative distinct CA3 axons as a proportion of total number of ROIs detected in the FOVs (color scheme as in e). The pink shaded area indicates the ranges between the mice. Dashed lines indicate the cluster numbers that maximized rate of change in 25th (red shaded area on right) and 75th percentiles (blue shaded area on left). h, Boxplots of within-cluster correlations of ROI signals all together (left, n=175 ROIs) and by mouse (right, 29.17±9.5 ROIs, mean±s.e.m.). The whiskers indicate maxima and minima. i, Fraction of ROIs putatively originating from the same cell across mice (n=6810 ROIs from 6 mice). The whiskers indicate maxima and minima, outliers excluded.

Source data

Extended Data Fig. 2 Stability of two-photon imaging.

a, Top, cue onsets in a representative RC-NS session (orange: odor; green: visual; light blue: reward). Bottom, trial-averaged FOVs corresponding to each odor trial from one representative mouse (n=6 in this study). Trial averages were computed from the frame of odor stimulus onset through the last frame 5 s prior to the next stimulus onset. b, Mean pixel-wise correlations between all pairs of trial-averaged FOVs from the above session. c, Mean pixel-wise correlations of trial-averaged FOVs with whole-session time-averaged FOVs in RC-NS sessions. Colors correspond to individual mice. Correlations were recalculated with whole-session time-averaged FOVs on different mice (shuffled) and plotted in black (mean, shaded area: 90% confidence interval). d, Left, empirical cumulative distribution function (CDF) of mean squared error (MSE) between each frame and time-averaged FOVs from corresponding sessions (green: all frames, orange: intra-trial frames, black: comparison to other FOVs). Middle, violin plot of data shown in CDFs. Right, MSEs between each frame and trial-averaged FOVs. MSEs were averaged every 10 trials and shaded areas indicate percentiles from 5% to 95%. ei, Same as (a) for each individual mouse.

Source data

Extended Data Fig. 3 Reward-predictive behavior during sensory-place associative paradigm.

a, Schematic of sensory stimulation paradigm and belt types (Methods). b, Examples of velocity in individual laps under different conditions. Each shaded color area corresponds to textural elements of the spatial belt where the sensory stimulations were presented and dashed lines indicate cue onsets. c, Representative lap-by-lap (top) and mean (bottom) velocity during FC-S. d, Velocity as predicted by generalized linear model (GLM, Method). Lines indicate mean GLM-predicted velocity and shaded areas indicate 25th and 75th percentiles across mice. e, Comparison of velocities before (black) and after (green) the onset of visual stimulus. Top, cumulative histograms of mean velocities 50 cm before and after visual stimulus. Bottom, velocity modulation by visual stimulus calculated with real pairs of velocities (green line) vs null distribution of scores calculated on 1000 random permutations of the onset ID (black histogram). Dotted lines indicate significance thresholds corresponding to p-values of 0.05 and 0.01. f, Examples of cue-CA3SC ROIs showing the different trial-by-trial transitions (left and middle; odor-, right; multi-sensory CA3SC ROIs). Heatmap shows ΔF/F0 by trial, chunked by cue modality and centered on cue onset with mean values plotted on right. PSTHs calculated on subsets of trials are shown below. g, Heatmap of mean cue-CA3SC activity centered on running bout initiations during random cue/non-spatial paradigm. h, Top, velocity (mean±s.e.m.) from all running bout initiations. Bottom, mean cue-CA3SC activity shown in (a). i, Fraction of significantly running-modulated CA3SCs during RC-NS (Other: n=5,836; Cue: n=1,219 ROIs from 6 mice). 1000-time shuffling test with Bonferroni correction was used to obtain significance thresholds for P < 0.05 in each ROIs (two-tailed test, α=2.5% on each side). j, Correlation between cue response and running speed during trial periods represented as boxplots with median and interquartile range (Up: n=276 odor, n=143 visual, n=203 reward; Down: n=136 odor, n=77 visual, 78 reward; Other: n=41 odor, n=196 visual, n=69 reward). The whiskers indicate maxima and minima, outliers excluded. The red plus symbols indicate outliers.

Source data

Extended Data Fig. 4 Multi-axis dimensionality reduction of trial-by-trial changes in cue-CA3SC activity via Tensor Component Analysis.

a, Schematic of tensor component analysis (TCA). Imaging data were arranged into a 4th order tensor with dimensions N×T×K×C where axes correspond to the number of CA3SC ROIs, within-trial temporal dynamics, evolution over multiple trials, and cue modality. b, Reconstructed (top) and original (bottom) ΔF/F0 heatmaps, centered on cue onset, for cue-CA3SCs in response to their respective 1st preferred (left), 2nd preferred (middle), and least preferred (right) cues. A 10-component TCA model, shown in (e), was used for reconstruction. c, Error plot showing normalized reconstruction error for TCA (inset) and reduction in error with each additional component until 50 for 1st preferred (cyan), 2nd preferred (pink), and least preferred (black) cue. d, Median coefficients (R2)±s.d. of reconstruction for each cue-CA3SC with the number of components for TCA ranging from 1 to 20 (n=1219 ROIs from 6 mice).e, Extracted tensor components (TCs) from the 5, 10, 15-component models. Columns show temporal factor (left), trial factor (middle left), cue factor (middle right), and ROI factor (right). Cyan trial factor: TCs dominated by a single sensory modality (#1-5) Pink trial factor: multimodal TCs (#6-10). Unimodal TCs strongly contributed to reconstructed responses to their 1st preferred cues shown in (b) and represent modality-specific features within and across trials. f, Distributions of averaged ΔF/F0 response after cue onsets, by trial, in each cue-CA3SC cluster.

Source data

Extended Data Fig. 5 Latency and reliability of cue-CA3SC activity across trials.

a, Left, Heatmap activity showing responses triggered by all cue onsets across ROIs from each cluster (Up and Down). Row indicates individual trial. Right, response profiles of each cluster. b, Latency between cue onset and peak time of cue-CA3SC response in each trial. Each symbol indicates mean response latency of all cue-CA3SCs in each cluster (Up (purple): n=276 odor, n=143 visual, n=203 reward; Down (yellow): n=136 odor, n77 visual, 78 reward. Two-way ANOVA with post-hoc Tukey’s tests. For odor-CA3SCs: Up x Down: P =0.74.; main effect of trial: P=3.6x10−14; interaction: P=1.8x10−5; for visual-CA3SCs, Up x Down: P=0.62; main effect of trial: P=5.7x10−5; interaction: P=2x10−4; for reward-CA3SCs, Up x Down: P=0.74; main effect of trial: P=9.6x10−6; interaction: P=0.0193). c, Reliability of cue response, defined as the number of trials with response divided by the total number of trials (10 trials per modality) represented as boxplots with median and interquartile range. Whiskers denote minimum and maximum values. d, Extracted 1st principal component (PC) of the cue response of individual cue-CA3SCs assigned to Up and Down clusters using the TCA/K-means approach. To validate TCA and K-means classification, PCA was applied to K x T (trial x trial time) arrays of individual cue-CA3SCs. The 1st PC of each CA3SC robustly captured the initially-identified cue response. Note that 1st PC coefficients across individual trials represent the trial-by-trial evolution in the cue response of each CA3SC. e, Using K-means clustering, coefficient trends across trials were assigned to Up or Down clusters. Mean coefficients are plotted across trials of classified cue-CA3SCs. f, Fraction of cue-CA3SCs in inferred clusters. g, Overlap rates between TCA and PCA classifications of trial-by-trial trends.

Source data

Extended Data Fig. 6 Additional data on SWR recruitment of cue-CA3SCs in random cue conditions.

a, Top, Example period of velocity and cue onsets. Blue shaded area corresponds to time window in the below heatmap showing examples of peri-SWR CA3SC activity. Bottom: SWR band-filtered LFP is plotted on top with detected CA1 SWRs indicated by black ticks and white dashed lines. b, Top, Average peri-SWR response in each group shown in Fig. 1 for cue-CA3SCs. Bottom, Fraction of SWR recruitments of cue-CA3SCs. Number of ROIs for single-modality: odor: 349, visual: 256, reward: 350. Multi-modality ROIs, n=264; with odor-visual: 74, odor-reward: 30, visual-reward: 107, all-modalities: 53 (two-sided Fisher’s exact test with Bonferroni correction. P=3.7x10−6 for Odor-vs-Reward. P=1.5x10−5 for Visual-vs-Reward. P=0.8967 for Odor-vs-Visual. P=3.7x10−6 for down-Reward-vs-down-Odor, -vs-up-Odor (P=1.6x10−7), vs-down-Visual (P=3.6x10−5), vs-up-Visual (P=3.4x10−5). P=0.0033 for up-Reward-vs-down-Odor, and -vs-up-Odor (P=3.7x10−5). P=0.2418 for up-Reward-vs-down-Visual. P = 0.0985 for up-Reward-vs-up-Visual. c, Top, Cumulative histograms of peri-SWR z-scored ΔF/F0 of all CA3SCs shown in Fig. 2g (RC-S, Day 4. Other: n=3,371; Place: n=1,644; Cue-Place: n=198, Cue: n=501 from 4 mice). Bottom left, fraction of cue-, cue-place, place-, and non-responsive CA3SC ROIs-. Bottom right, fraction of SWR recruitments of CA3SCs (One-way ANOVA with post-hoc Tukey’s test. ROI-type: P<1.3x10−16. d, Left, mean SWR-triggered power spectrogram of contralateral CA1 LFP (black line: an example filtered LFP trace). Right, peri-cue onset time histogram of SWR rate for all mice and average rate stem plot juxtaposed to random, non-cue-centered onset times (‘outside’). Dashed line indicates onset of cue presentation. While SWR events around cue onset were excluded in the following analysis. e, Top, a single-component TCA model fit to cue-CA3SC peri-SWR suppression over individual SWR events in each mouse (n=17.76±1.39 SWRs in each inter-trial-interval, mean ± s.e.m. from 6 mice). Left, sorted ROI factor for each CA3SC. Middle-left and middle-right, temporal factor and trial factor (mean±s.e.m.). Trial factor TC scores were extracted in each SWR event and indexed to inter-trial intervals where these events were observed. Right, ROI-averaged ΔF/F0 indexed to inter-trial intervals (mean±s.e.m.). Bottom, a single-component TCA model of other-CA3SC peri-SWR activation over SWR events. The trial factor TC score is likely to correlate with synchronicity of all ROIs to SWR events. To more clearly isolate any trend in the trial factor other-CA3SCs, we included only other-CA3SCs with strong responses to SWRs for model fitting (exceeding average amplitude shown in Fig. 2c). The error bars indicate s.e.m. f, Left, extracted PCs representing CA3SC suppression and activation driven by SWRs during different task conditions in each class of CA3SCs (cue, place, and other). K-means clustering was applied to the PCA loadings to classify ROIs as “activated” or “suppressed”. Middle, percentages of variance accounted for by each PC (up to 20). Right, mean peri-SWR response of classified CA3SCs (dark green: activated, magenta: suppressed).

Source data

Extended Data Fig. 7 Reproducibility of SWR suppression of cue-responsive CA3SCs with automated and manual sparse ROI segmentation and cell body ROI.

a, Left, representative time-averaged FOV from one representative mouse (n=6 in this study). Right, zoomed-in patches of FOV on left (yellow dashed square) showing original (default Suite2p performance), sparse automated, and sparse manual segmentation (Methods). ROIs overlaid onto FOV in random transparent colors at top with ROI outlines plotted below. b, Cumulative histogram of Euclidean distance between each ROI and its nearest neighboring ROI (black: n=7055 original ROIs; pink: n=2945 sparse automated ROIs). Median values±s.d. are shown in bar plot (inset). c, Density of surrounding ROIs calculated for each ROI as a function of distance. dg, Analysis of automatically-detected CA3SC ROIs including mean cue responses±s.e.m. of cue-CA3SCs during RC-NS (d), mean peri-SWR response±s.e.m. of all cue-CA3SCs (Cue, red) and non-cue CA3SCs (Other, black) recorded (e), fraction of cue-responsive and non-cue CA3SCs (910 cue-CA3SC ROIs from 6 mice) (f), and fraction of SWR recruitment of CA3SCs during RC-NS and POST (Other: n=1,945; Cue: n=910 ROIs from 6 mice. two-sided Fisher’s exact test with Bonferroni correction. P=2.6x10−24 for Cue-vs-All and Cue-vs-Other) (g). hk, Analysis of manually-curated sparse CA3SC ROIs plotted as in (d-g) (Other: n=1,083; Cue: n=255 ROIs from 6 mice. Fisher’s exact test with Bonferroni correction. P=1.2x10−7 for Cue-vs-All and Cue-vs-Other). Fraction of hand-drawn cue-responsive and non-cue CA3SC ROIs. k, Fraction of SWR recruitment. l, Schematic of window implantation above CA3 stratum pyramidale and CA3 pyramidal cells injected with rAAV2/1:Syn(GCaMP7f)Cre in Grik4-Cre mice. A 4-channel linear silicon probe was implanted in contralateral CA1. m, Example motion-corrected, time-averaged two photon imaging FOV from one representative mouse (n=6 in this study). Scale bar=100 μm n, Left, two example Suite2p-detected ROIs from (b). Right, representative GCaMP calcium signals (ΔF/F0) extracted from CA3 pyramidal cell ROIs. o, Mean cue responses±s.e.m. of identified cue-CA3 cells during RC-NS in n=6 mice. p, Mean peri-SWR response±s.e.m. of cue- and non-cue-CA3 cells recorded during RC-NS. q, Fraction of cue-responsive and non-cue CA3 cells in RC-NS. r, Fraction of SWR recruitment during RC-NS and POST (Other: n=238; Cue: n=76 ROIs from 6 mice. two-sided Fisher’s exact test with Bonferroni correction. P=2.7x10−9 for Cue-vs-All and Cue-vs-Other).

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Extended Data Fig. 8 Spatial coding profiles of CA3SCs in FC-S.

a, Design of session block on Days 5 and 6 (Methods). b, Left, diagram of CA3SC ROI classification scheme on Days 5 and 6. Right, Fraction of active CA3SCs on Days 5 and Day 6. Each color indicates classified CA3SCs (Cue: n = 661, Place: n = 1,024, Other: n = 459 from 3 mice). c, Mean responses during RC-NS. Middle, Heatmap of place fields of individual cue-CA3SCs during FC-S with ROIs sorted according to location of peak activity to illustrate place coding (bottom). d, Cumulative histograms of cue-related and spatial information (bits). Mean±s.d. information values are shown in the insets. Cue- (red) and place- (blue) CA3SCs showed higher place information than other- (gray) CA3SCs on Day 5 (Cue: n=470, Place: n=1,170, Other: n = 418 from 3 mice). One-way ANOVA with post-hoc Tukey’s test. Main effect of ROI-type for both FC-S and RC-NS: P =4.6x10−9) and on Day 6 (Cue: n=661, Place: n=1,024, Other: n=459 from 3 mice. One-way ANOVA with post-hoc Tukey’s test. Main effect of ROI-type for both FC-S and RC-NS: P=1.5x10−5). e, peri-SWR activity in PRE and POST (left, mean±s.e.m.) summary of peri-SWR peak activity (right, mean±s.d.) for cue-, place- and other-CA3SCs on Day 6 (Two-way ANOVA with post-hoc Tukey’s test, main effect of RC-vs-FC: P=8.7x10−12; main effect of ROI-type: P=2.3x10−12; interaction: P=7.9x10−10). f, Cumulative distribution plots of peri-SWR, z-scored ΔF/F0 for all CA3SCs shown in (e).

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Extended Data Fig. 9 SWR-associated high-synchrony events containing recurring CA3SC assemblies.

a, Example SWR-associated high-synchrony event (HSE). Top, heatmap showing ΔF/F0 of individual CA3SC ROIs with place fields. Bottom, mean ΔF/F0 and the first principal component (PC) of these ROIs (bottom). 0 point indicates onset of an SWR event. Red line indicates duration of the detected HSE with threshold (Methods). b, Distribution of HSE peak latencies and co-occurrence rate (inset) with SWRs (n=500 HSEs from 4 mice). Red center line in the box plot indicates the median, bottom and top edges of the box denote 25th and 75th percentiles. The whiskers indicate maxima and minima. c, Distribution of individual CA3SC latencies within SWR-associated HSEs. d, Left, normalized cross-correlation (CC) at zero lag between all pairs of CA3SC ROIs replotted from Fig. 4b. Purple dashed line denotes result including all detected ROIs for comparison. Middle, distribution of correlation coefficients between ROIs putatively from the same cells (black: n=139 ROIs, all together; each color: n = 34.75±13.84 clustered ROIs, Mean±s.e.m, individual mice). Red center line in the box plot indicates the median, bottom and top edges of the box denote 25th and 75th percentiles. The whiskers indicate maxima and minima. Right, fraction of clustered ROIs putatively originating from common cells (n=3315 ROIs from 4 mice, Methods). Red center line in the box plot indicates the median, bottom and top edges of the box denote 25th and 75th percentiles. The whiskers indicate maxima and minima. e, Left, similarity matrix of all HSEs containing recurring assemblies in a representative session. Middle, assembly recruitment for each identified HSE cluster. Colored lines and traces with ID numbers correspond to identified HSE clusters and recurring assemblies in the similarity map, respectively. Right, example plots showing participation of 2 recurring assemblies across identified HSE clusters in Fig. 4d (activation quantified as normalized covariance; clusters represented by different colors). f, Boxplot of the number of CA3SC ROIs in each identified recurring assembly (median with 25th and 75th percentiles from n=4 mice). The whiskers indicate maxima and minima. g, Distribution of correlation coefficients between place fields within the recurring assemblies (magenta, mean with 25th and 75th percentiles), and all cue-place CA3SC ROIs in a field of view (black, mean of 4 mice). Pairs with coefficients >0.7 are considered as ‘Near’ fields, and coefficients <0 are considered as ‘Other’ field pairs. Middle, boxplots of correlation coefficients between the distribution within assemblies (n=28 HSE clusters from 4 mice, median with 25th and 75th percentiles). Right, fractions of ‘Other’ fields and ‘Near’ fields pairs within assemblies. Right, scatter plot based on these fractions (magenta circles for each assembly, black cross for mean value of all ROIs from each mouse). Notably, the lack of bias for recurring assemblies to comprise CA3SCs representing similar spatial locations suggests that individual structured replay sequences of CA3SC activity represent past experience in a relatively unbiased manner. Red center lines in the box plots indicate the median, bottom and top edges of the boxes denote 25th and 75th percentiles. The whiskers indicate maxima and minima, outliers excluded.

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Extended Data Fig. 10 Validation of convNMF on simulated CA3 replay sequences.

a, Randomly, recurrently-connected spiking network model (pyramidal cells, blue triangles; interneurons, red circles) of CA3 used to generate synthetic replay sequences (Methods). b, Replay occurs spontaneously at rest in the simulated CA3 network (n=8000 simulated pyramidal cells, 150 inhibitory interneurons): 10 s simulation epoch, sampled at 10 kHz. Cell x time raster. c, Simulated spike rasters from (b) are re-binned to the calcium sampling rate (30 Hz). d, Re-binned spikes are then convolved with a calcium kernel (tau=0.65 s), with added white Gaussian noise to simulate experimental conditions. e, Schematic of convNMF technique used to detect sequential reactivation events, vs standard NMF (Methods). The convNMF approximates the cell x time raster as the sum of K matrices, each which can be decomposed into the convolution of a rank-1 temporal component with cell x lag matrix. Unlike standard NMF, convNMF extracts recurring, localized sequential activity patterns. f, High-fidelity reconstruction of the simulated replay/calcium dataset in (b) is achieved using the convNMF-identified replay ensembles (left panels) and temporal factors (top traces). Each cell-component (left panel) represents a recurring sequential pattern of cell activations detected in the network. g, Slopes and intercepts were calculated for inferred replay events which were then compared to ground truth. ConvNMF accurately identifies replay events and extracted sequences of replaying cells. Left, scatterplot of true (x-axis) vs inferred (y-axis) replay slopes (a.u.). Note that this simulation also demonstrates that forward and reverse replay events are reliably differentiated by convNMF. Right, distribution of true (x-axis) vs inferred (y-axis) replay intercepts. h, ROC curve of sensitivity/specificity of replay event detection by convNMF. Specificity is prioritized over sensitivity. 20-60% of “true” replay events were detected at an 80% specificity threshold.

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Terada, S., Geiller, T., Liao, Z. et al. Adaptive stimulus selection for consolidation in the hippocampus. Nature 601, 240–244 (2022).

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