Integrating new memories into the hippocampal network activity space

Abstract

By investigating the topology of neuronal co-activity, we found that mnemonic information spans multiple operational axes in the mouse hippocampus network. High-activity principal cells form the core of each memory along a first axis, segregating spatial contexts and novelty. Low-activity cells join co-activity motifs across behavioral events and enable their crosstalk along two other axes. This reveals an organizational principle for continuous integration and interaction of hippocampal memories.

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Fig. 1: New CPP memory reorganizes pre-existing hippocampal co-firing topology.
Fig. 2: Mnemonic information spans multiple network co-firing axes.
Fig. 3: High- and low-activity cells contribute differentially to network co-firing axes.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The software used for data acquisition and analysis are available using the web links mentioned in the Methods.

References

  1. 1.

    Andersen, P., Morris, R. G. M., Amaral, D., Bliss, T. & O’Keefe, J. The Hippocampus Book (Oxford University Press, 2006).

  2. 2.

    Moser, E. I., Moser, M.-B. & McNaughton, B. L. Spatial representation in the hippocampal formation: a history. Nat. Neurosci. 20, 1448–1464 (2017).

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Buzsáki, G. & Llinás, R. Space and time in the brain. Science 358, 482–485 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  4. 4.

    Kubie, J. L., Levy, E. R. J. & Fenton, A. A. Is hippocampal remapping the physiological basis for context? Hippocampus 30, 851–864 (2020).

    PubMed  Article  Google Scholar 

  5. 5.

    O’Neill, J., Senior, T. J., Allen, K., Huxter, J. R. & Csicsvari, J. Reactivation of experience-dependent cell assembly patterns in the hippocampus. Nat. Neurosci. 11, 209–215 (2008).

    PubMed  Article  CAS  Google Scholar 

  6. 6.

    Bassett, D. S. & Sporns, O. Network neuroscience. Nat. Neurosci. 20, 353–364 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Humphries, M. D. Dynamical networks: finding, measuring, and tracking neural population activity using network science. Netw. Neurosci. 1, 324–338 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Mizuseki, K., Diba, K., Pastalkova, E. & Buzsáki, G. Hippocampal CA1 pyramidal cells form functionally distinct sublayers. Nat. Neurosci. 14, 1174–1181 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Buzsáki, G. & Mizuseki, K. The log-dynamic brain: how skewed distributions affect network operations. Nat. Rev. Neurosci. 15, 264–278 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  10. 10.

    Rich, P. D., Liaw, H.-P. & Lee, A. K. Large environments reveal the statistical structure governing hippocampal representations. Science 345, 814–817 (2014).

    CAS  PubMed  Article  Google Scholar 

  11. 11.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Cembrowski, M. S. & Spruston, N. Heterogeneity within classical cell types is the rule: lessons from hippocampal pyramidal neurons. Nat. Rev. Neurosci. 20, 193–204 (2019).

    CAS  PubMed  Article  Google Scholar 

  15. 15.

    Oliva, A., Fernández-Ruiz, A., Buzsáki, G. & Berényi, A. Spatial coding and physiological properties of hippocampal neurons in the cornu Ammonis subregions. Hippocampus 26, 1593–1607 (2016).

    CAS  PubMed  Article  Google Scholar 

  16. 16.

    Navas-Olive, A. et al. Multimodal determinants of phase-locked dynamics across deep-superficial hippocampal sublayers during theta oscillations. Nat. Commun. 11, 2217 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Gauthier, J. L. & Tank, D. W. A dedicated population for reward coding in the hippocampus. Neuron 99, 179–193.e7 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Valero, M. et al. Determinants of different deep and superficial CA1 pyramidal cell dynamics during sharp-wave ripples. Nat. Neurosci. 18, 1281–1290 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    McKenzie, S. et al. Hippocampal representation of related and opposing memories develop within distinct, hierarchically organized neural schemas. Neuron 83, 202–215 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    Tse, D. et al. Schemas and memory consolidation. Science 316, 76–82 (2007).

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    van de Ven, G. M., Trouche, S., McNamara, C. G., Allen, K. & Dupret, D. Hippocampal offline reactivation consolidates recently formed cell assembly patterns during sharp wave-ripples. Neuron 92, 968–974 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  22. 22.

    Csicsvari, J., Hirase, H., Czurkó, A., Mamiya, A. & Buzsáki, G. Oscillatory coupling of hippocampal pyramidal cells and interneurons in the behaving rat. J. Neurosci. 19, 274–287 (1999).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Trouche, S. et al. A hippocampus-accumbens tripartite neuronal motif guides appetitive memory in space. Cell 176, 1393–1406.e16 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Csicsvari, J., Hirase, H., Czurko, A. & Buzsáki, G. Reliability and state dependence of pyramidal cell–interneuron synapses in the hippocampus. Neuron 21, 179–189 (1998).

    CAS  PubMed  Article  Google Scholar 

  25. 25.

    Harris, K. D., Henze, D. A., Csicsvari, J., Hirase, H. & Buzsáki, G. Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. J. Neurophysiol. 84, 401–414 (2000).

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Kadir, S. N., Goodman, D. F. M. & Harris, K. D. High-dimensional cluster analysis with the masked EM algorithm. Neural Comput. 26, 2379–2394 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Pachitariu, M., Steinmetz, N. A., Kadir, S. N., Carandini, M. & Harris, K. D. Fast and accurate spike sorting of high-channel count probes with KiloSort. Adv. Neural Inform. Process. Syst. 30, 4455–4463 (2016).

    Google Scholar 

  28. 28.

    Magland, J. F. et al. SpikeForest: reproducible web-facing ground-truth validation of automated neural spike sorters. eLife 9, e55167 (2020).

  29. 29.

    Csicsvari, J., Hirase, H., Czurko, A., Mamiya, A. & Buzsáki, G. Fast network oscillations in the hippocampal CA1 region of the behaving rat. J. Neurosci. 19, 1–4 (1999).

    Article  Google Scholar 

  30. 30.

    Onnela, J.-P., Saramäki, J., Kertész, J. & Kaski, K. Intensity and coherence of motifs in weighted complex networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 71, 065103 (2005).

    PubMed  Article  CAS  Google Scholar 

  31. 31.

    Costantini, G. & Perugini, M. Generalization of clustering coefficients to signed correlation networks. PLoS ONE 9, e88669 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  32. 32.

    Saramäki, J., Kivelä, M., Onnela, J.-P., Kaski, K. & Kertész, J. Generalizations of the clustering coefficient to weighted complex networks. Phys. Rev. E 75, 027105 (2007).

    Article  CAS  Google Scholar 

  33. 33.

    Floyd, R. W. Algorithm 97: shortest path. Commun. ACM 5, 345 (1962).

    Article  Google Scholar 

  34. 34.

    Roy, B. Transitivité et connexité. C. R. Acad. Sci. Paris 249, 216–218 (1959).

    Google Scholar 

  35. 35.

    Warshall, S. A theorem on Boolean matrices. J. ACM 9, 11–12 (1962).

    Article  Google Scholar 

  36. 36.

    Dupret, D., O’Neill, J., Pleydell-Bouverie, B. & Csicsvari, J. The reorganization and reactivation of hippocampal maps predict spatial memory performance. Nat. Neurosci. 13, 995–1002 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    McNamara, C. G., Tejero-Cantero, Á., Trouche, S., Campo-Urriza, N. & Dupret, D. Dopaminergic neurons promote hippocampal reactivation and spatial memory persistence. Nat. Neurosci. 17, 1658–1660 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Zhang, S., Schönfeld, F., Wiskott, L. & Manahan-Vaughan, D. Spatial representations of place cells in darkness are supported by path integration and border information. Front Behav. Neurosci. 8, 222 (2014).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Butts, D. A. How much information is associated with a particular stimulus? Network 14, 177–187 (2003).

    PubMed  Article  Google Scholar 

  40. 40.

    Cover, T. M. & Thomas, J. A. Elements of Information Theory (Wiley, 1991).

  41. 41.

    Shannon, C. & Weaver, W. The Mathematical Theory of Communication (Univ. of Illinois, 1949).

  42. 42.

    Panzeri, S. & Treves, A. Analytical estimates of limited sampling biases in different information measures. Netw. Comput. Neural Syst. 7, 87–107 (1996).

    Google Scholar 

  43. 43.

    Moakher, M. A differential geometric approach to the geometric mean of symmetric positive–definite matrices. SIAM J. Matrix Anal. Appl 26, 735–747 (2005).

    Article  Google Scholar 

  44. 44.

    Venkatesh, M., Jaja, J. & Pessoa, L. Comparing functional connectivity matrices: a geometry-aware approach applied to participant identification. Neuroimage 207, 116398 (2020).

    PubMed  Article  Google Scholar 

  45. 45.

    Arsigny, V., Fillard, P., Pennec, X. & Ayache, N. Log-Euclidean metrics for fast and simple calculus on diffusion tensors. Magn. Reson. Med. 56, 411–421 (2006).

    PubMed  Article  Google Scholar 

  46. 46.

    Harris, K. D., Hirase, H., Leinekugel, X., Henze, D. A. & Buzsáki, G. Temporal interaction between single spikes and complex spike bursts in hippocampal pyramidal cells. Neuron 32, 141–149 (2001).

    CAS  PubMed  Article  Google Scholar 

  47. 47.

    Ranck, J. B. Studies on single neurons in dorsal hippocampal formation and septum in unrestrained rats. I. Behavioral correlates and firing repertoires. Exp. Neurol. 41, 461–531 (1973).

    PubMed  Article  Google Scholar 

  48. 48.

    Ho, J., Tumkaya, T., Aryal, S., Choi, H. & Claridge-Chang, A. Moving beyond P values: data analysis with estimation graphics. Nat. Methods 16, 565–566 (2019).

    CAS  PubMed  Article  Google Scholar 

  49. 49.

    Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  50. 50.

    Hagberg, A. A., Schult, D. A. & Swart, P. J. Exploring network structure, dynamics, and Function using NetworkX. in Proc. 7th Python in Science Conference 11–15 (ScyPi, 2008).

  51. 51.

    Ince, R. A. A., Petersen, R. S., Swan, D. C. & Panzeri, S. Python for information theoretic analysis of neural data. Front. Neuroinform. https://doi.org/10.3389/neuro.11.004.2009 (2009).

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Acknowledgements

We would like to thank J. Csicsvari and R. Lambiotte for commenting on a previous version of the manuscript; J. Janson for technical assistance; and H. C. Barron and all members of the Dupret and Schultz labs for discussions and feedback during the course of this project. This work was supported by the Engineering and Physical Sciences Research Council UK Centre for Doctoral Training in Neurotechnology (EP/L016737/1 to S.R.S.), the Biotechnology and Biological Sciences Research Council UK (award BB/N002547/1 to D.D.) and the Medical Research Council UK (Programmes MC_UU_12024/3 and MC_UU_00003/4 to D.D.).

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Contributions

All authors contributed to the preparation of the manuscript. G.P.G., S.R.S. and D.D. designed the study, developed the methodology and analyzed the data. S.B.M., L.L., V.L-.d-.S., M.E. S.T. and D.D. acquired the data and helped with data analysis. S.R.S. and D.D. acquired funding.

Corresponding authors

Correspondence to Simon R. Schultz or David Dupret.

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The authors declare no competing interests.

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Peer review information Nature Neuroscience thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Behavioural performance and hippocampal co-firing graphs in the CPP task.

a, Additional example trajectories for four mice. Each day (one day per row), mice explored the same (circular-walled) familiar enclosure before (exposure) and after (re-exposure) a 1-day 4-session CPP task (Fig. 1a). The CPP apparatus was formed by two novel compartments on each day. Numbers indicate place preference scores for pre-test and CPP test sessions, as the time in sucrose-paired compartment (+Suc.) minus that in water-paired compartment (+Wat.) divided by the sum. b, Place preference scores. A negative score indicates that mice spent less time in the compartment paired with sucrose during conditioning. Note that during CPP test, mice successfully changed their preference for the compartment recently paired with sucrose, as indicated by the positive score. Scores are presented using a Gardner-Altman estimation plot to visualise the effect size. The left panel shows the distribution of raw data points for the pre-test and test sessions. The right panel displays the difference between CPP test and pre-test, computed from 5,000 bootstrapped resamples and with difference-axis origin aligned to the mean of the pre-test session distribution. Black-dot, mean difference; black-ticks, 99% confidence interval; gray-filled-curve: sampling-error distribution. c, The adjacency matrices (top row) of the pairwise correlation coefficients measuring principal cells’ co-firing for days shown in (a), with the corresponding co-firing graphs (bottom row). Each node represents one principal cell. Each edge represents the co-firing association of one cell pair, color-coded according to their correlation’s sign and width proportional to the edge’s absolute value.

Extended Data Fig. 2 Graph topology during exploration of a novel enclosure, spontaneous novel place preference and exploration of a familiar enclosure with or without reward.

a,b, “Novel context only” task. Behavioural protocol (a) and topology of hippocampal co-firing graphs (b) for unrewarded exploration of a novel enclosure (n = 585 total principal cells; 45.0±13.7 principal cells per day; 13 days from 5 mice yielding n=78 graphs; 28,192 co-firing pairs). Each day, mice explored the familiar enclosure before (exposure) and after (re-exposure) exploring a novel enclosure over four sessions, without sucrose reward. The session timeline matches that of the CPP task. (a) An example mouse trajectory for each session is shown for one day below the schematic of in-use enclosures. Additional examples shown below for two other days (one day per row). (b) The topological changes in clustering (top), geodesic path length (middle) and single-neuron cumulative co-firing strength (bottom) of co-firing graphs. For each measure, the entire “Novel context only” dataset is presented using a Cumming estimation plot to visualize the effect size. Each upper panel shows the distribution of raw data points (one point represents one principal cell) for each color-coded session (with the gapped lines on the right as mean (gap) ± SD (vertical ends) for each session). Each lower panel displays the difference between a given session and exposure, computed from 5,000 bootstrapped resamples and with difference-axis origin aligned to the median of the exposure distribution. For each session: black-dot, median; black-ticks, 99% confidence interval; filled-curve: sampling-error distribution. Note that exploring a novel context causes topological deviations from the co-firing graph that had featured exposure, indicating that the hippocampal network “learns” about the novel spatial layout. These deviations no longer occurred during re-exposure. c-e, “Spontaneous Place Preference” (SPP) task. Behavioural protocol (c) and performance (d) along with the graph topology (e) (n=640 total principal cells; 49.2±17.5 principal cells per day; 13 days from 6 mice yielding n=78 graphs; 34,838 co firing pairs). Each day, mice explored the familiar arena before (exposure) and after (re-exposure) exploring a CPP-like apparatus formed by two novel compartments (Nov 1 and Nov 2) connected with a bridge (pre-test). Having identified the preference of each mouse for one of the two novel compartments, the bridge was next removed; each mouse explored its non-preferred compartment, and then its preferred compartment, as in the CPP task but without sucrose nor water. One hour after, the bridge was re-inserted to test place preference (SPP test). The session timeline matches that of the CPP task. c, Example mouse trajectories as in (a). Numbers indicate place preference scores for pre-test and SPP test, as the time in the non-preferred compartment minus that in the preferred compartment over the sum. Note that mice did not changed their preference, as indicated by the negative scores. d, Scores presented using a Gardner-Altman estimation plot (as in Extended Data Fig. 1b). Note that co-firing topology also deviates during SPP sessions from that featuring exposure. Topological deviations no longer marked re-exposure. f,g, “Familiar context with reward” task. Behavioural protocol (f) and graph topology (g) (n=517 total principal cells; 57.4±12.2 principal cells per day; 9 days from 3 mice yielding n=54 graphs; 30,512 co firing pairs). Each day, mice explored the first familiar arena before (exposure) and after (re-exposure) exploring a second familiar enclosure during four sessions. The timeline matches that of the CPP task. Drops of sucrose (+Suc.) and water (+Wat.) were provided during Fam 2b and Fam 2c sessions, respectively. Example mouse trajectories from three days (f) shown as in (a). g, Similar to the unrewarded exploration of novel enclosures (b,e), co-firing graph topology deviated during exploration of the second familiar arena when paired with reward. These deviations no longer marked re-exposure to the first familiar enclosure. h,i, “Familiar context only” task. Behavioural protocol (h) and network topology measured by co-firing strength (i) during exploration of a familiar enclosure without reward (n=658 total principal cells; 59.3±15.8 principal cells per day; 11 days from 3 mice yielding n=44 graphs; 40,744 co firing pairs). Each day, mice explored a familiar enclosure (Fam 1 or Fam 2) over four sessions, to match the four sessions used between exposure and re-exposure in the other tasks (Fig. 1a and Extended Data Fig. 2a–g). h, Example mouse trajectories shown as in (a). i, Co-firing strength did not deviate during repeated familiar explorations. j, Topological changes between exposure and re-exposure are compared across tasks. Note the sustained deviations (topological “hysteresis”) following CPP.

Extended Data Fig. 3 Topological changes between exposure and re-exposure in the CPP task do not relate to differences in spatial exploration or mere fluctuations in co-firing.

a, Spatial occupancy during exposure and re-exposure for example CPP days from four mice (one mouse day per row; using examples shown in Extended Data Fig. 1). Shown for each day, from left to right: (i) animal’s trajectory for each familiar session, (ii) map of pixel-wise dwell time difference across the two sessions (for each pixel, the time spent in that pixel during exposure minus re-exposure), (iii) distribution of pixel-wise dwell time differences for all pixels covering the familiar enclosure, showing no significant difference of the mean from 0 (all Ps>0.05), and (iv) corresponding Gardner-Altman estimation plot to visualize the effect size of the pixel-wise dwell time difference across the two sessions. For each Gardner-Altman plot: left panels show the raw data points for exposure (grey) and re-exposure (green), with each point representing the dwell time in a given pixel; right panel: mean (black-dot), 95% confidence interval (black-ticks) and sampling-error distribution (filled-curve) of the difference between re-exposure and exposure, computed from 5,000 bootstrapped resamples and with the difference-axis (dashed-line) origin aligned to the mean of the exposure distribution. b, Pixel-wise dwell time difference in spatial occupancy across the two sessions for all CPP days, as in (a). Average time difference distribution (left) not significantly different from 0 (1-sample t-test, p=0.53, t=0.62, df=1461; in sec per spatial bin: mean difference=0.05±0.08; 95% confidence interval=[0.106, -0.205]; interquartile range=[1.306, -0.870]), as also shown in the corresponding Gardner-Altman plot (right). c, Cumming estimation plots showing the absolute number of pixels visited in each CPP task session (top; each dot representing one mouse CPP day) and the fraction of visited pixels in each enclosure (bottom). Note that the animal’s coverage was not significantly different between exposure and re-exposure. The higher number of pixels visited during pre-test and CPP test merely reflects the higher dimension of the whole-CPP apparatus. d, Example time course of a mouse instantaneous speed during exposure (top) and re-exposure (bottom) in one CPP day. e, Instantaneous speed across the six sessions for all CPP days (bar charts: mean±SEM; with each superimposed dot representing one mouse CPP day). No significant differences across sessions with respect to exposure (P values: pre-text=0.25; +Suc.=0.38; +Wat.=0.76; CPP test=0.82; Re-exposure=0.98; all 2-sample Kolmogorov-Smirnov tests). f, Average speed across the six CPP task sessions (top) along with the total distance travelled (bottom; calculated for the first 15 min of each session). Note that the significant increased speed and distance travelled during pre-test (when the mouse is exposed for the first time to the novel CPP apparatus) do not translate in topological differences (Fig. 1f). These analyses (a-f) show that the topological hysteresis during re-exposure compared to exposure (Fig. 1f) does not reflect non-specific changes in spatial exploration. g, Topology alterations of hippocampal graphs in re-exposure (Fig. 1f) do not reflect mere fluctuations in co-firing. To control for natural variations in co-firing graphs, we split both exposure and re-exposure in two sections (1 and 2) with equal duration of active exploration (speed>2 cm/sec; exposure: 6.89±0.13 versus 6.97±0.10 min; re-exposure: 6.99±0.30 versus 6.85±0.31 min, all Ps>0.05; Wilcoxon signed-rank test) and quantified topological changes across. As for all topological analyses, sharp-wave/ripples were excluded (though their occurrence did not differ between exposure and re-exposure; t=-1.55, p=0.14, paired t-test). For each measure: the top panel shows the raw data points for each (color-coded) section (with the gapped lines on the right as mean (gap) ± SD (vertical ends)); the bottom-left panel shows the difference between the bootstrapped distribution with respect to first section of exposure; the bottom-right panel shows the difference between the distribution of the second compared to the first section within each familiar session. Note that co-firing topology did not significantly change during each individual familiar exploration. For each section: black-dot, median; black-ticks, 95% confidence interval; filled-curve: bootstrapped sampling-error distribution.

Extended Data Fig. 4 Familiar map-representations are largely reinstated during re-exposure after CPP but include edits not explained by mere fluctuations.

a,b, Cumming estimation plots showing the effect size for changes in the similarity of both single-neuron (a) and population (b) spatial maps across CPP task sessions, with respect to (w.r.t.) exposure. For the single-neuron map similarity analysis (a), each data point represents the Pearson correlation using the firing rate of an individual neuron between the spatial bins of its map during exposure matched to those in a subsequent session. Note that single-neuron familiar maps are well reinstated during re-exposure following their reorganization during CPP sessions. For the population-level map similarity analysis (b), each data point represents the extent to which on a given CPP day pairs of cells that jointly represented a location during exposure continued to show spatially overlapping firing fields in subsequent sessions. This indicates that the combination of cell pairs sharing place fields during exposure largely re-emerges during re-exposure after their re-organization in the CPP enclosure, consistent with the remapping of hippocampal maps across spatial contexts (for example, Muller, R. U. & Kubie, J. L., J. Neurosci. 1987; Wilson, M. A. & McNaughton, B. L., Science 1993; Leutgeb, S. et al, Science 2004; Wills, T. J. et al, Science 2005; Colgin, L. L. et al, Trends in Neurosciences 2008). c-g, The strength of familiar map reinstatement from exposure to re-exposure was compared to non-specific fluctuations in firing activity over time. (c) With respect to the first section (half) of exposure, shown is the effect size for changes in single-neuron map similarity during the second section of the exposure, the 4 CPP sessions and the two (first and second) sections of re-exposure. d, Same as (c) but for the population map similarity. e,f, To contrast the effect of CPP on familiar map reinstatement against within-session variations in single-neuron (c) and population (d) map similarity, we compared the spatial correlation of hippocampal maps between exposure and re-exposure (Re-exposure across) with that between the two sections of the exposure (Exposure within) and of the re-exposure (Re-exposure within). The across-session fluctuations were quantified by comparing maps of each of the two re-exposure half sections (computed as in (c,d)) with those of the two exposure half sections, taking the mean of the four resulting similarity scores. The within-session fluctuations were obtained by comparing maps of the two sections of exposure or re-exposure, as indicated. Note that both single-neuron (e) and population-level (f) map fluctuations are significantly smaller within an exploration session of the familiar enclosure than across, even though single-neuron map variations (e) within re-exposure are markedly larger than those within exposure before CPP. g, In addition, the effect of CPP on familiar map reinstatement was compared to familiar map reinstatement between exposure and re-exposure in the other tasks: unrewarded exploration of a novel enclosure (Novel only), spontaneous place preference for a novel place (SPP) and reward experience in another familiar enclosure (Familiar with reward). Altogether, these analyses show that while spatial maps expressed during re-exposure following CPP are strongly correlated with those initially seen during exposure before CPP (a,b), these reinstated maps nevertheless differ across these two sessions in the familiar enclosure of the CPP task more than changes expected from non-specific fluctuations occurring within a given exploration of the familiar enclosure or those due to the temporal gap between exposure and re-exposure (c-g). These results indicate a crosstalk between the new CPP memory and the prior hippocampal representation of the familiar enclosure. Subsequent analyses in this study relate such a crosstalk to changes in firing activity of low rate principal cells (see Extended Data Figs. 710). For each Cumming estimation plot: black-dot, median or mean as indicated; black-ticks, 95% confidence interval; filled-curve: bootstrapped sampling-error distribution.

Extended Data Fig. 5 Visualizing directions of hippocampal graph transformations in the network co-firing space.

a, Computing the topological distances that separate co-firing graphs across the six task sessions. The co-firing graph of each task session was used to define a 6-dimensional vector of topological Riemannian Log-Euclidean distances to the other co-firing graphs obtained that day (for example here illustrated re-exposure versus exposure), including itself (see Methods). For each 6-session task day, this procedure thus gives a 6 x 6 matrix of topological distances. All distance matrices were then stacked together to form a 6 x N matrix (with N the number of total graphs, that is N = 17 days x 6 sessions = 102) onto which we apply a dimensionality reduction technique (PCA, ICA or MDS). b,c, Segregation of co-firing graphs using Independent Components Analysis (ICA; b) or Multidimensional Scaling (MDS; c). (b) ICA applied to the same matrices of topological distances used with PCA in the CPP dataset (Fig. 2) as another dimensionality reduction method to visualize axes explaining across-session variance in co-firing motifs. Note that co-firing graphs computed for the exposure and the re-exposure overlap on the first two independent components (IC) as they do along the first principal component (Fig. 2c–e); co-firing graphs are separated across the 4 CPP task events, as they are along the second principal component (Fig. 2c–e). (c) MDS also applied to the same matrices of topological distances used with PCA in the CPP dataset (Fig. 2); this method preserves the six-dimensional distances between co-firing graphs and maps them onto a 2D plane. Note that the co-firing graphs computed for each task session of each CPP day are well separated, indicating the existence of multiple axes along which co-firing patterns change across CPP task events. d,e, Here the Pearson correlation coefficient is used instead of the Riemannian Log-Euclidean distance to compute the topological distance between co-firing graphs across the six CPP task sessions. In (d) the average topological distance matrix (left) and its first three PCs (right) are shown. In (e) the segregation of the six CPP task sessions is shown using the PCs shown in (d). Note that for both the Riemannian Log-Euclidean distance (Fig. 2c–e) and the Pearson correlation coefficient (d,e) approaches, the PCA of the CPP dataset reveals that the variance in hippocampal co-firing segregated the familiar enclosure from the whole CPP test apparatus along PC1. Surprisingly, PC1 did not segregate the two compartments (Nov1 and Nov2) that formed the CPP apparatus on each day. This suggests that these compartments were treated together as one spatial continuum along PC1 because when the animal first encountered the CPP apparatus, these two compartments were equally novel and physically connected by the bridge during the pre-test. Therefore, a refined interpretation why along PC1 the two CPP compartments are clustered together (and different from the familiar enclosure) is that neuronal co-firing along this axis also accounts for spatial familiarity versus novelty. f,g, PCA with the two compartments of the CPP apparatus considered separately during both pre-test (f) and CPP test (g) sessions. Here, we computed a co-firing graph using the spike trains associated with the visits of each CPP compartment during both test sessions, thus obtaining one co-firing graph per individual compartment (Nov1 versus Nov2) during each test session. Projecting the resulting four co-firing graphs (two for pre-test and two for CPP test) onto the PCA axes obtained when considering the CPP apparatus as a whole entity (Fig. 2e) shows that PC2 segregates co-firing patterns related to each CPP compartment during test compared to pre-test. Likewise, PC2 segregated the two CPP compartments when explored separately during the conditioning sessions where we removed the bridge (Fig. 2e). h-j, The topological distance projections of the co-firing graphs from the other 6-session tasks. As for the CPP dataset, multiple PCs accounted for the variance in co-firing across the 6 sessions of each task. Note, however, that co-firing variance relates to the specifics of each task, and so is each set of PCs and their interpretation. For each task, we used the PCs explaining at least 80% of the total variance to project the co-firing graphs topological distance. The variance explained by these PCs is: “Novel context only” PC1=57%, PC2=19% and PC3=8%; “Spontaneous Place Preference” PC1=52%, PC2=18% and PC3=16%; “Familiar context with reward experience” PC1=69%, PC2=15%; and “CPP” PC1=48%, PC2=19% and PC3=14% (Fig. 2e).

Extended Data Fig. 6 High and low rate CA1 principal cells are skewed towards deep and superficial pyramidal sublayers, respectively.

a-d, For each CPP task day, we estimated the position (depth) of individual tetrode-recorded principal cell soma by leveraging silicon probe recordings with known spacing between the recording sites along a linear shank (25-μm steps). From these silicon probe recordings, we first computed the laminar profile of sharp-wave/ripples (SWRs) detected in the local field potentials (LFPs) along the radial axis of the dCA1 hippocampus (a). We used the peak of the corresponding depth profile of the ripple-band (110-250Hz) power to estimate the centre (middle) of the pyramidal (pyr.) layer (b; red cross). Using the average LFP waveform of the SWR events detected in these silicone probe recordings, we then established a SWR template where we reported the distance relative to the estimated centre of the pyramidal layer, knowing the precise distance between the recording sites on the linear shank (c). Next, we computed the individual SWR profile of each tetrode for CPP recording days finishing with a sleep session, this way estimating the depth of each individual tetrode (and thus that of the somas of its recorded neurons) by positioning its SWR profile within the silicone probe SWR template (the ground-truth vertical depth; c). Shown in (d) are examples of single-tetrode SWR profiles and their estimated depth. (e) Left: the estimated depth of principal cells as a function of the average firing rate measured in the CPP task. Each data point represents one principal cell. Right: the same data plotted as a firing rate probability distribution per estimated depth. f, Same data as in (e) but plotted as a ridge plot to better visualise the relation between firing rate of principal cells and depth of their recording tetrodes. Note that principal cells in the deep sublayer of the dCA1 pyramidal layer (closer to stratum oriens) show higher firing rates than those in the superficial sublayer (closer to stratum radiatum) (line of best fit: y=23.34log(x)-74.7; p=10−30; two-sided Wald Test). g,h, The same analyses shown in (e,f) but performed for the bursting index. Note that principal cells of the deep dCA1 sublayer show higher spike bursting compared to those of the superficial sublayer (line of best fit: y=47.43log(x)-15.0; p=10-64; two-sided Wald Test). (i) Relative prevalence (left) and cumulative distribution (right) of low (blue) and high (red) rate principal cells along the dCA1 radial axis. Low and high activity cells are skewed towards superficial and deep dCA1 pyramidal sublayers, respectively (p=0.016, Kolmogorov-Smirnov 2-sample test).

Extended Data Fig. 7 Low activity principal cells show increased firing activity during re-exposure to the familiar enclosure following CPP learning.

a, Example spike trains of low and high activity cells during exposure and re-exposure to the familiar environment. For each of the eight example cells, shown are two raster plots (60-s rows; vertical ticks representing spike times) for exposure (upper plot) and re-exposure (lower plot). For clarity, only the first 5 min of each session are presented. Note the increased spike burst discharge by low activity cells during re-exposure compared to exposure. b, Cumming estimation plot used to visualize for low (blue colours) and high (red colours) activity cells the effect size for changes in spike bursting between re-exposure and exposure. Upper panel: distributions of raw data points (each color-coded point represents one cell from the CPP task: n=272 low activity cells and 272 high activity cells) during exposure and re-exposure in light and dark colours, respectively; with the gapped lines on the right as mean (gap) ± SD (vertical ends) for a given subpopulation in a given session. Middle panel: the median (black-dot), 95% confidence interval (black-ticks) and sampling-error distribution (filled-curve) of the difference between (low or high activity) cells in a given session and the low activity cells in exposure, computed from 5,000 bias-corrected bootstrapped resamples and with the difference-axis (dashed-line) origin aligned to the median of low activity cells in exposure. Lower panel: similarly, compares the distribution in re-exposure to that in exposure within each subpopulation. Note that low, but not high, activity cells significantly discharge more bursts of spikes during re-exposure following CPP compared to exposure. The lack of increased bursting of the high activity cells during re-exposure compared to exposure does not reflect a ceiling effect given that the burst spiking distribution of this subpopulation had an inter-quartile range=0.07–0.18, a median=0.12 and a mean=0.15 throughout the CPP task sessions (see also the corresponding raw data points in Extended Data Fig. 9d and the coherence-percentiles’ mean in Extended Data Fig. 9h), showing that this subpopulation could have discharged more bursts during re-exposure. c, Cumming estimation plots to visualize the change in firing rate across CPP task sessions for the low (left panel) and the high (right panel) activity cell subpopulations. Note that firing rate of low activity cells increased during CPP sessions and stayed at higher values during re-exposure, not resetting to the values seen in exposure. d-i, Bursting index (d-f) and firing rate (g-i) of high and low activity principal cells in the exposure and re-exposure to the familiar enclosure during the other tasks: exploration of a novel enclosure only (d,g), spontaneous place preference for a novel place (e,h) and exploration of another familiar enclosure with reward (f,i). Each (color-coded) data point represents one cell (“Novel context only” task: n=214 low activity cells and 128 high activity cells; “Spontaneous Place Preference” task: n=188 low activity cells and 152 high activity cells; “Familiar context with reward experience” task: n=148 low activity cells and 96 high activity cells). All differences presented using Cumming estimation plots as in (b). Note that in these three other tasks, low activity cells did not show significant changes in burst spiking and firing rate during re-exposure compared to exposure.

Extended Data Fig. 8 Low activity cells with increased burst spiking following CPP exhibited stronger place field coherence and spatial information content.

a,b, Cumming estimation plots showing for low (blue colours) and high (red colours) activity cells the effect size in changes of place field coherence (a) and spatial mutual information (b) between exposure and re-exposure in the CPP task. The most burst spiking cells of both subpopulation (top 40% of bursting index distribution) in the re-exposure session are considered. Note that low, but not high, activity cells have significantly higher spatial coherence (a) and mutual information (b) during re-exposure after CPP. c-h, Similarly, the place field coherence (c,e,g) and the spatial mutual information (d,f,h) of low and high activity principal cells is shown for the other tasks including exposure and re-exposure sessions. Low and high activity cells selected in the same way as in the CPP task dataset (a,b) to allow for comparison. For all Cumming estimation plots: black-dot, median (for skewed distributions) or mean (for normal distributions) as indicated; black-ticks, 95% confidence interval; filled-curve: bootstrapped sampling-error distribution.

Extended Data Fig. 9 Relation between spatial field coherence during exposure and subsequent changes in both burstiness and spatial information for low and high activity cells in the CPP task.

a,b, The spatial mutual information of low and high activity cells is plotted against their respective spatial coherence for each of the six CPP task sessions. Each data point represents one cell. Comparing with exposure, note the right-shift towards higher spatial information for a set of low activity cells during the CPP sessions, which then remained at higher values during re-exposure (a). c,d, Similarly, the bursting index of low and high activity cells is plotted against their spatial coherence for each CPP task session. Also comparing with exposure, note the right-shift towards higher spike bursting in the distributions of low activity cells during CPP sessions, remaining higher during re-exposure (c). e-h, For each of the six CPP task sessions, both low (e,f) and high (g,h) activity cell subpopulations were binned in 20th-percentiles according to the spatial coherence of each cell’s place field during the exposure session. Then, the average spatial mutual information (e,g) and bursting index (f,h) were computed for each 20th-percentile bins across all task sessions. For clarity, the bins of the re-exposure session marked with a white star (*) have significant changes in either measure (spatial information or bursting index) compared to the corresponding bins in the exposure session. Note that during re-exposure, low activity cells show significant increase in spatial information following CPP compared to exposure (e). This was not the case for high activity cells (g). Moreover, low activity cells with the least (0th–60th percentiles) spatial coherence during exposure increased their spike bursting during the CPP sessions and thereafter during re-exposure (f; with statistically different percentile bins marked with a white star compared to their corresponding bins in exposure). This was not seen for high activity cells (h). i-l, Cumming estimation plots showing the effect size for changes in spatial information (i,k) and bursting index (j,l) for the low (i,j) and the high (k,l) activity cell subpopulations, binned as 20th-percentiles according to the spatial coherence of each cell’s place field during exposure. For further clarity, the black stars above the green filled-curves in re-exposure indicate significant changes compared to exposure. Note that across all percentile bins, low activity cells show significant increase in spatial information in re-exposure compared to exposure (i). This was not seen for high activity cells (k). Moreover, low activity cells with the least (0th–60th percentiles) spatial coherence during exposure thereafter discharged more spike bursts during the CPP sessions and continued to exhibit significant higher burstiness during re-exposure compared to exposure (f,j; see stars). High activity cells maintained the same burstiness throughout all task sessions (h,l). For each Cumming estimation plot: black-dot, mean; black-ticks, 95% confidence interval; filled-curve: bootstrapped sampling-error distribution. Altogether, these analyses show that CPP learning subsequently affected low activity cells during re-exposure to the familiar environment. Notably, these results show that the dCA1 network gained in spatial information content during re-exposure (compared to exposure) from both: (1) low activity cells that were already spatially tuned during exposure and then exhibited higher spatial informativeness following CPP (e,i), and (2) low activity cells that were not spatially tuned during exposure but were de-novo recruited during CPP, after which they stayed more active during re-exposure (f,j). These results are in line with the increase in spatial coherence observed for the most burst spiking of the low, but not high, activity cells during re-exposure compared to exposure (Extended Data Fig. 8a), and the gradual engagement of low activity cells in whole-network co-firing motifs (see Fig. 3c and Extended Data Fig. 10d).

Extended Data Fig. 10 High and low activity principal cells make distinct contributions to network co-firing motifs.

a, Cumming estimation plots showing CPP task-related changes in topological clustering (top), geodesic path length (middle) and single-neuron cumulative co-firing strength (bottom) of co-firing graphs (with respect to exposure) for low and high activity cells. Black-dots, median; black-ticks, 95% confidence interval; filled-curve: sampling-error distribution. b, Top, schematic showing the location of the containers for the sucrose and water drops in an example CPP enclosure. Bottom, distribution of firing rate changes (scores) between the +Suc and pre-test sessions for low and high activity cells (low activity cells: p=2.68x10-5, t=4.279, df=251; high activity cells: p=0.25, t=1.160, df=271; 1-sample t-tests against 0 mean). For every cell that fired at least 100 spikes in either session, a score is obtained by taking the difference between its mean firing rate at the containers during +Suc and pre-test sessions, dividing by the sum. c, The change in firing rate at the containers from pre-test to +Suc. session correlated with the change in co-firing strength from exposure to re-exposure for the low (regression line y=0.21x-0.11; p=0.018, Wald test) but not the high (regression line y=0.12x-0.23; p=0.51, Wald test) activity cells. Together with the topological deviations that feature the low activity cell co-firing graphs (a), this result supports the idea that during the mnemonic update of a newly encountered place with reward experience, a change in the firing activity of low activity cells allows a cross-talk between the new CPP memory and the prior representation of the familiar enclosure, as reported along PC3 (Fig. 3d). d, Contribution of low and high activity cells to network co-firing motifs, as measured by the proximity between the high (right) and low (left) activity sub-networks to the whole network (that is, containing the full distribution of all recorded neurons) in the topological distance space across the six CPP task events (w.r.t. low activity cells in exposure). Black-dot, median or mean as indicated; black-ticks, 99% confidence interval; filled-curve: sampling-error distribution. e, For low and high activity cells, firing rate changes during sharp-wave/ripples (SWRs) detected in periods of immobility (speed<2cm/sec) of exposure and re-exposure sessions. For every cell, the change in SWR firing is measured as the difference between its mean firing rate during SWRs in the exposure and re-exposure sessions divided by the subpopulation’s average firing rate during SWRs of exposure. f, Change in co-firing during SWRs between exposure and re-exposure for low-low, low-high and high-high activity cell pairs. SWR co-firing computed during SWRs detected in periods of immobility (speed<2cm/sec) of a given exploration session in the familiar enclosure. The change in co-firing between re-exposure and exposure was then divided by the average subpopulation’s co-firing. Note the increased SWR co-firing between low-low and low-high activity cells during the re-exposure session following CPP learning.

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Gava, G.P., McHugh, S.B., Lefèvre, L. et al. Integrating new memories into the hippocampal network activity space. Nat Neurosci (2021). https://doi.org/10.1038/s41593-021-00804-w

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