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Contextual and pure time coding for self and other in the hippocampus

Nature Neuroscience volume 26, pages 285–294 (2023)Cite this article

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Abstract

Navigation and episodic memory depend critically on representing temporal sequences. Hippocampal ‘time cells’ form temporal sequences, but it is unknown whether they represent context-dependent experience or time per se. Here we report on time cells in bat hippocampal area CA1, which, surprisingly, formed two distinct populations. One population of time cells generated different temporal sequences when the bat hung at different locations, thus conjunctively encoding spatial context and time—‘contextual time cells’. A second population exhibited similar preferred times across different spatial contexts, thus purely encoding elapsed time. When examining neural responses after the landing moment of another bat, in a social imitation task, we found time cells that encoded temporal sequences aligned to the other’s landing. We propose that these diverse time codes may support the perception of interval timing, episodic memory and temporal coordination between self and others.

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Fig. 1: Time cells in bat hippocampal area CA1.
Fig. 2: Controls for reward and movement.
Fig. 3: Comparing time tuning at different locations.
Fig. 4: Two distinct populations of hippocampal time cells: contextual time cells and pure time cells.
Fig. 5: Time cells for the other bat.

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Data availability

All the behavioral and neural data in this study are available from the authors upon reasonable request and are also accessible online at Zenodo46.

Code availability

All the behavioral and neural data in this study were analyzed using custom code in MATLAB (version 2021b). The code that supports the conclusions of this study is available from the authors upon reasonable request and is also accessible online at Zenodo46.

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Acknowledgements

We thank Y. Dudai, Y. Ziv, S. Ray, S. R. Maimon, G. Ginosar, A. Sarel, T. Eliav, A. Rubin, A. Ravia, S. Palgi and J. Aljadeff for discussions and comments on the manuscript; S. Kaufman, O. Gobi, S. Futerman and E. Solomon for bat training; A. Tuval for veterinary support; C. Ra’anan and R. Eilam for histology; G. Ankaoua and B. Pasmantirer for mechanical designs; and G. Brodsky for graphics. N.U. holds the Barbara and Morris Levinson Professorial Chair in Brain Research. This study was supported by research grants from the European Research Council (ERC-CoG – NATURAL_BAT_NAV) to N.U. and the Israel Science Foundation (ISF 1920/18) to N.U. and L.L. and by the André Deloro Prize for Scientific Research and the Kimmel Award for Innovative Investigation to N.U.

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Authors and Affiliations

  1. Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel

    David B. Omer, Liora Las & Nachum Ulanovsky

  2. Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel

    David B. Omer

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  1. David B. Omer
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Contributions

D.B.O., L.L. and N.U. designed the project. D.B.O. performed the experiments and analyzed the data, with major input from N.U. and L.L. D.B.O. and N.U. wrote the manuscript, with major input from L.L. N.U supervised the project.

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Correspondence to David B. Omer or Nachum Ulanovsky.

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Nature Neuroscience thanks James Heys and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Behavioral setup, conditions, and behavior.

(a) Behavioral setup. Bats flew inside a flight-room (2.35 × 2.69 × 2.56 m, seen here from top side view). The demonstrator bat (blue) was trained to fly from the Start ball, roughly randomly to either landing-ball A or B, and back. The observer bat was trained to watch, remember and imitate the ball-choices of the demonstrator bat. Different trials are shown, one to ball A (trial i) and one to ball B (trial j). Balls and bats are not drawn to scale, for display purposes. (b) The six different conditions which were used to identify and analyze time-cells. Top row: the 3 conditions which were used to analyze self-time-cells. In each of these 3 conditions, the firing activity of cells recorded in dorsal CA1 of the observer bat was aligned to the landing moment of the observer bat on one of the landing-balls (columns). Bottom row: the 3 conditions which were used to analyze time-cells for the other bat. In each condition, the firing activity of cells recorded in the observer’s dorsal CA1 was aligned to the landing moment of the other bat, the demonstrator. To enhance the clarity of reading the main text, we re-plotted the top row of panel b also in main Fig. 1b, and re-plotted the bottom row of panel b also in main Fig. 5a. (c) Two examples of bat behavior from the experiment. For each example: x-axis is the elapsed time in seconds; y-axis shows the distance in meters of each bat (demonstrator in blue, observer in red) from the Start ball. For clarity, the distances during roundtrips to balls A or B were plotted with opposite signs (A – positive distances, B – negative distances). In the top example, the two bats flew alternatingly from the Start ball to ball B and back and then to ball A and back; in the bottom example, the opposite order occurred: they first flew to ball A and then to ball B. (d) Distribution of trial durations in each of the three locations in the room (A, B and Start; shown is the time spent by the observer-bat on each of the landing-balls, from the moment of landing to takeoff); the rightmost bin corresponds to trial-durations ≥ 20 s. The median trial-duration in each location was marked by a red arrowhead. (e) Coronal Nissl-stained section through dorsal hippocampus of one observer bat. Arrowhead, electrolytic lesion at the end of a tetrode-track, located in dorsal CA1.

Extended Data Fig. 2 The duration of time-fields increased with the neuron’s preferred-time; analysis of non time-cells; and control for sharp-wave ripples.

(a-d) The temporal resolution of time-cells deteriorated with the passage of time. (a) Examples: Spike rasters (top), color-coded rasters (middle), and temporal tuning-curves (bottom) for a subset of the time-cells from Fig. 1f. Each column represents one time-cell. Top row – Spike rasters: x-axis, elapsed time from the moment the bat has landed (time 0); y-axis, repeated landings (trials). Each raster corresponds to a single location in the room (indicated above the raster), and each line in the raster shows the spiking activity in a single trial; each tick represents one spike. The trials in each raster were sorted according to trial-duration; the thin gray line denotes the trial-end (shown are only spikes contained within the trial). Middle row – Color-coded rasters: arranged as the spike-rasters above, but showing the instantaneous firing-rate instead of raw spikes (100 ms time-bins). Plotted as in Fig. 1e; color-scale ranges from zero (blue) to the maximal firing-rate in each panel (red; maximal rate indicated). Bottom row – Temporal tuning-curve for each cell (black trace), which is the averaged firing-rate of the neuron (average of the color-coded raster above). The preferred-time is indicated above the peak-firing of each cell (marked also by a vertical red line). Green shading represents statistically-significant time bins (Methods). Red curve, width-at-half-height of the time-field. Note how the width of the time-field (duration of the red curve) increases with a neuron’s preferred-time. (b-c) Plots showing that time-cells are aligned to landing, and not to takeoff. (b) Examples: color-coded rasters for the same cells as in panel a, aligned here to the bat’s takeoff. x-axis, elapsed time until the moment the bat took off (time 0); y-axis, repeated landings (trials). Each line in the raster represents the firing-rate for the cell in a single trial. The trials in each raster were sorted according to trial-duration (same sorting as in panel a). Color-scale ranges from zero (blue) to the maximal firing-rate in each panel (red; maximal rate indicated). Note that the peak firing across trials is diagonally tilted, and is aligned to landing and not to takeoff. (c) Distributions of Spearman correlations between the time of peak-firing in each trial and the trial-number (ordered by trial-duration). Cells whose firing is truly aligned to landing are expected to show zero correlation when the rasters are aligned to landing (as seen in the example rasters in panel a) and a negative correlation when the rasters are aligned to takeoff (as seen in the negative correlations in the examples in panel b). The distributions in the current panel were plotted for all the significant time-cell rasters (n = 274 cells × positions), separately when the rasters are aligned to landing (blue) or aligned to takeoff (pink). Note that, as expected, the distribution for rasters that we aligned to takeoff was significantly shifted towards –1, as compared to the distribution for rasters aligned to landing (two-sided t-test: P = 7.7 × 10–170) – indicating that time-cell rasters show vertical bands when aligned to landing (as in panel a), and are tilted when aligned to takeoff (as in panel b); this means that the time-cells are tuned to the elapsed time from landing, rather than to time-until-takeoff (the small rightward shift in the blue histogram occurs because of late noisy firing in longer trials, as seen for example in panel a, fourth cell, which biases the correlations positively). Furthermore, since the time-cells in this analysis were defined based on the alignment of their firing to landing, we performed an additional analysis without such definition – to test whether takeoff (departure) can also trigger time-sequences, perhaps in a different set of neurons. To this end, we aligned the activity of all the neurons to the takeoff instead of landing, and sought to identify significant responses with this new alignment. We used in this analysis the exact same time-binning and same criteria to detect pure time-cells, contextual time-cells, and social time-cells, as we used for ‘landing-triggered’ time-cells throughout the paper – but now aligned on takeoff. This analysis yielded a substantially lower number of significant time-cells from each class: we found only 13 significant pure time-cells when aligned to takeoff versus 44 pure time-cells when aligned to landing; only 65 contextual time-cells when aligned to takeoff versus 125 contextual time-cells when aligned to landing; and only 28 social time-cells when aligned to takeoff versus 56 social time-cells when aligned to landing (all numbers are cells, not cells × positions). This much-lower percentage of significant cells when aligning to takeoff versus landing, strongly suggests that the relevant trigger for time-cells is landing and not takeoff. (d) Scatter plots of the time-field duration (field width at half-height) versus the preferred time, for all the significant time-fields (dots), in each of the three locations in the room: ball A (left; n = 116 significant time-fields), ball B (middle; n = 98), and Start ball (right; n = 61). All three scatter-plots showed significant positive correlations: ball A: Spearman ρ = 0.41, P = 4.6 × 10–6; ball B: ρ = 0.57, P = 1.3 × 10–9; Start ball: ρ = 0.82, P = 1.1 × 10–15 (two-sided tests) (the significant positive correlations persisted also after eliminating from the correlations those time-cells with preferred time < 0.5-s: ball A: ρ = 0.24, P = 0.01; ball B: ρ = 0.46, P = 2.4 × 10–5; Start ball: ρ = 0.77, P = 4.6 × 10–9). This demonstrates that in each of the 3 locations in the room (A, B, Start), the time-resolution of time-fields deteriorated with the passage of time – as reported also for time-cells in rats7,8,10,17. (e) Distribution of the time differences ΔT between the estimated time of landing from the video data and the estimated time of landing from the accelerometer signal (mean and standard deviation of ΔT: µ = 78.4 ms; σ = 90.7 ms; n = 5695 trials; the video-based landing time [our main estimate of landing-time in this study] was explained in the Methods – and the accelerometer-based landing time was estimated as the peak in the accelerometer signal, which exceeded 1.5 × g (1.5 times the Earth’s gravitational acceleration), and occurred within a time window of ± 300 ms around the video-based landing-time). Note that the standard deviation of this distribution was less than the time-bin resolution (100-ms bins) that we used for computing the temporal tuning-curves of the time-cells – indicating a very precise estimation of the landing-time. (f) Non time-cells. Top row: Temporal firing pattern of all the non-time-cells, plotted as in Fig. 1g: the cells are plotted separately for each of the landing-balls, and are ordered by the time of their peak firing-rate. Bottom row: the distributions of peak z-scores for time-cells (blue curves) and non time-cells (red curves). The firing sequences of non-time cells were clearly very different from the firing sequences of the significant time-cells shown in Fig. 1g: The z-scores were dramatically lower for non time-cells as compared to time-cells. In addition, the sequences of non-time cell tended to fall close to the diagonal in the top row. Both of these differences indicate that non time-cells do not exhibit true temporal tuning. (g-h) Sharp-wave ripples (SWRs) do not generate the temporal responses of time-cells. (g) Examples of two time-cells (rows), showing high similarity when plotted with versus without trials that included SWRs (columns; compare left versus right; example cells are from bat 1 [top row] and bat 2 [bottom row]). (h) Distribution of Pearson correlation coefficients between the temporal tuning-curves of time-cells when computed using all trials versus when computed after removal of trials with SWRs. Blue histogram, correlations for the data for all time cells (n = 274 cells × positions; note that the rate of SWRs was very low and they occurred only on a small subset of the trials: on average 0.97% of the trials). Black line, distribution of correlations for cell-shuffling (correlation between the temporal tuning-curve computed over all trials for cell i and the temporal tuning-curve computed over trials without SWRs for cell j, for i ≠ j). The real data correlations were significantly higher than the shuffles (two-sided t-test with unequal variances: P < 10–300; t = 485.2; df = 7.4 × 104). Inset: enlarged view of the blue histogram (zoom-in on the x-axis between 0.96 – 1). These high correlations indicate that the temporal tuning of time cells could not be explained by the occurrence of sharp-wave ripples.

Extended Data Fig. 3 Additional 20 examples of self time-cells.

For each example cell, the top panel shows the color-coded raster plot: x-axis, elapsed time from the moment the bat has landed (time 0); y-axis, repeated landings (trials); plotted as in main Fig. 1e. The bottom panel shows the temporal tuning-curve (black trace), which is the averaged firing-rate of each cell (average of the color-coded raster above); the preferred-time is indicated above the peak-firing of each cell (marked also by a vertical red line); green shading represents statistically-significant time bins; red curve shows the width-at-half-height of the time-field. Cells were sorted by increased preferred times (from top-left to bottom-right panel).

Extended Data Fig. 4 Time-fields represent an internally-generated signal, not linked to movement.

(a-d) Only 12.0% of the 133 time-cells [cells × positions] that were recorded together with an accelerometer signal (16/133 cells) showed significant correlation between the trial-to-trial variation in firing-rate and the trial-to-trial variation in the acceleration signal. (a-b) Six typical time-cells (columns) that showed no significant correlation between the trial-to-trial variation in firing-rate and the trial-to-trial variation in the acceleration signal. (a) Top: color-coded raster plot, aligned to the moment of landing (t = 0). Trials (y-axis) are sorted according to the trial duration. Plotted as in main Fig. 1f. Middle: temporal tuning-curves – the average firing-rate across all recorded trials, aligned to the moment of landing of the observer bat. Green shading represents statistically-significant time bins. Bottom: acceleration signal, averaged across trials (gray shading, mean ± SEM). The acceleration signal shown here included flight-data for t < 0, while for t > 0 we only included here data recorded when the bat was on the ball (before takeoff). Note the large acceleration signal prior to landing (prior to t = 0) in all cases, which is caused by the bat’s flight – but then during the significant time bins (green shading) there was basically no acceleration signal. In other words, the bats hardly moved during the firing of the time-cells. All three panels for each cell (top, middle, bottom) are aligned to the landing-moment (t = 0) and to each other. (b) Six example scatter plots (for the 6 cells in panel a), showing that there is no significant correlation between the trial-to-trial variation in peak firing-rate and the trial-to-trial variation in the peak acceleration signal (both the peak firing-rate and the peak acceleration signal were measured inside the green rectangles in panel a; we used here a one-sided test for the Pearson correlation, and not two-sided test, because we assumed that only positive correlations are physiologically meaningful). These six examples represent the typical majority of time-cells that we recorded in experiments with accelerometer signal – which showed no trial-to-trial correlation between firing-rate and acceleration. This indicates that time-cells represent an internally-generated signal, unrelated to movement. (c-d) Examples of two rare neurons (columns) which represent the small minority of time-cells that showed a significant correlation between the trial-to-trial variation in firing-rate and the trial-to-trial variation in the acceleration signal. Plotted as in panels a and b. (c) Color-coded rasters, temporal tuning-curves, and acceleration signals – plotted as in panel a. (d) Scatters, plotted as in b. The example cell on the right showed the highest correlation value among all our neurons (r = 0.67); we note, however, that when removing the outlier point, the correlation became non-significant (r = 0.23, P = 0.16). (e-f) The bats did not perform on the balls stereotypical movements that were similar across trials – suggesting that stereotypical movements could not explain the firing of time-cells. (e) Examples: Three acceleration traces recorded on three different trials on the same day, all from the same ball (a significant time-cell was recorded on that day on the same ball). Note that in these three example traces: (i) the acceleration values were extremely low (<0.1 g, where g is the Earth’s gravity), and (ii) the traces were not similar to each other – indicating that this bat did not exhibit stereotypical movements across trials. (f) Population: Distribution of Pearson correlations between the acceleration signals recorded on different trials of the same day, on the same ball (computed from 0.5-s until trial-end; n = 39,323 trial-pairs) – that is, correlations between acceleration-traces as plotted in panel e. The correlation values were pooled across landing balls A and B and across experimental days and bats – only for days and balls on which a significant time-cell was recorded. The correlation of the acceleration signal between the different trials was very low (mean < r > = 0.052) – indicating that there were no stereotypical movements across trials that could explain the firing of time-cells. (g-i) No relation between time of firing and time of reward. (g) A typical example neuron showing no significant correlation between the time of peak neuronal firing (x-axis) and the time of reward delivery after landing (y-axis; extracted from the raw videos), with dots showing individual trials (Pearson r = 0.23; two-sided t-test, P = 0.16; n = 38 trials). Note there was large variability in the time-of-reward (large spread along the y-axis), as compared to the small variability in the neuron’s time of firing across the trials (small spread along the x-axis). (h) Left panel, scatter plot, showing a similar plot as in panel g (with dots showing individual trials), pooled across all the example cells shown in main Fig. 1. Right panel, same scatter as on the left, but here the x-axis data and y-axis data for each neuron were normalized by the mean for that neuron, in order to expose possible correlations which may be masked due to the high variability of preferred-times across different neurons. Both scatters show a lack of significant trial-to-trial correlation between the time of firing for each time-cell and the time of reward on the same trial. In addition, the timing of reward-delivery was highly variable, arguing against a role for reward in the temporal tuning of time-cells. (i) Histogram showing the distribution of Pearson correlation coefficients between the time of peak firing and the time of reward delivery – like the correlation for the cell shown in panel g – plotted here for all the example cells shown in Fig. 1 (in panels h and i, shown are n = 9 cells for which we also recorded raw video movies in addition to the video-tracking; this raw-video footage was used to measure the time of reward). Almost all these cells (except one) showed non-significant correlation (P > 0.05). (j) Raster of the times of ear-movements (x-axis) that were measured across 10 randomly-chosen landing trials (y-axis); the measurements were performed manually from high-speed camera recordings at 100 frames/second. This raster shows that, first, ear movements are generally repetitive – and hence cannot explain the firing of time-cells, which always fire only once per trial, rather than repetitively; and second, ear movements do not show stereotypical structure across trials (note the lack of vertical bands in this raster) – and therefore ear movements cannot underlie the temporally-reproducible, distinct firing of time-cells.

Extended Data Fig. 5 Firing sequences in simultaneously recorded time-cells are similar to the population time-cell sequences pooled across all days; and decoding elapsed-time from time cells.

(a) Temporal tuning curves for all the significant time-cells, pooled across all experimental days and bats (the 3 panels correspond to the 3 different locations in the room: balls A, B and Start). These plots are identical to those shown in main Fig. 1g, and were plotted here again to facilitate comparison with panel b. (b-d) Simultaneously-recorded time-cells. (b) Three examples of internally-generated temporal sequences, for ensembles of neurons that were recorded simultaneously: These examples depict similar sequences (with a similar time-span) to the population in panel a. These 3 ensembles were recorded on 3 different recording-days, in the 3 different locations in the room (balls A, B, Start). We could not obtain larger numbers of simultaneous neurons because of the limited number of tetrodes in this study (n = 4 tetrodes; we obtained up to 12 simultaneously recorded significant time-cells per day). (c) All the days × locations (for all bats) in which we had ≥ 2 simultaneously recorded time-cells (n = 57 days × locations). The 3 panels represent the 3 locations in the room. x-axis, preferred time for each neuron (circles); y-axis, experimental day; horizontal lines in each panel represent groups of simultaneously recorded time-cells. Experimental days are sorted according to the total span of preferred-times for the time-cells recorded on that day. Green: the 3 examples in panel b of internally-generated firing sequences. The red numbers on the right indicate the identity of the bat (no. 1–4) from which the cells were recorded. (d) Distributions of time-differences (∆T) between the preferred-times for all the cell-pairs recorded simultaneously on the same day (gray bars; n = 151, 109 and 23 cell-pairs on landing balls A, B and Start respectively), and all the cell-pairs recorded on different days (black lines; n = 12800, 9288 and 3614 cell-pairs on landing balls A,B and Start respectively), plotted separately for the 3 locations in the room. The gray and black distributions were statistically indistinguishable (two-sided Kolmogorov-Smirnov tests: P = 0.126, P = 0.128 and P = 0.208, for balls A, B and Start, respectively). This demonstrates that the pooled sequences (main Fig. 1g) are reliably representing the within-day sequences – indicating that time-cells in the bat hippocampus form internally-generated firing sequences. (e) Bayesian maximum-likelihood decoding of elapsed time. Left panel: Confusion matrix showing the decoded time (y-axis) versus the actual elapsed time (x-axis), using all the time cells, in all three locations. The probabilities in each time-bin were divided by the uniform chance probability. Right panel: Temporal decoding error for each time bin (200-ms bins were used here), computed between 0–8 s, for three cell groups: red line, all the time-cells (n = 274 cells × positions); peach line, contextual time-cells only (cells that were time-tuned in only one location; n = 123 cells × positions); blue line, pure time-cells only (cells that were time-tuned on both A and B; n = 88 cells × positions). Note the temporal decoding error was < 0.6 s for all the time bins up to 8 s – indicating that these neurons carry robust information about elapsed time, up to 8 s after landing. (f) Cross-decoding of elapsed time: For each trial we trained a decoder on responses at the other location. Only pure time-cells with preferred-time difference of ΔT ≤ 1 s between locations were used to train the decoder. The confusion matrix shows the decoded time (y-axis) versus the actual elapsed time (x-axis); the decoded probabilities in each time-bin were divided by the uniform chance probability. (g) Bayesian maximum-likelihood decoding of the origin of flight history – namely, decoding from where did the bat fly to the Start ball – this decoding was performed based on the firing of time-cells when the bat was on the Start ball. Left panel: the identity of the previous landing ball (ball A or B) can be decoded (classified) above chance level during the first ~4 seconds after landing on the Start ball. To assess the statistical significance of decoding in each time bin, we compared the observed classification accuracy to a shuffle test where we randomly permuted the true identities of balls A and B from which the bat flew. We repeated the shuffling 1,000 times and calculated the classification accuracy for each of the 1,000 shuffle-repeats (permutations) in each time bin. Asterisks denote time bins in which the empirically-observed classification accuracy showed significance at 95% [two-sided] compared to the distribution of classification accuracy of the shuffle tests (the observed classification accuracy was higher than the accuracy of 997.5 of the shuffles – Bonferroni-corrected for multiple comparisons for the number of time bins; P < 0.0025). Right panel: the number of time-cells, in each time bin, which showed significant difference in their firing-rate between trials when the bat flew from ball A to the Start ball versus from ball B to the Start ball. These results support the notion that time-cells encode relevant behavioral information. (h) Violin plots showing the distributions of peak firing-rates for pure time-cells, contextual time-cells, and non-time cells (n = 151, 123, and 603 cells × positions, respectively). Dots, individual neurons (cells × positions); red circles, median for each cell group. Peak firing-rate plotted in this panel is the peak of the temporal response (temporal tuning-curve).

Extended Data Fig. 6 Self time-cells: stability across sessions.

In session 1, the observer bat mimicked the flight-choices of the demonstrator bat; in session 2, the observer mimicked an object (Methods). Session 2 was recorded immediately after session 1. This Extended Data Figure shows that the temporal-tuning was generally conserved between the two sessions; however, it also shows that in session 2, the distribution of preferred-times in the Start location became more similar to that in locations A and B (panels a-b: compare the bottom-left panel in b to the two panels above it and to the bottom-left panel in a; and see also panel e – note in session 2 the distributions of preferred-times became more similar across the 3 landing-balls; see below for Kolmogorov-Smirnov tests). The main change in session 2 was a reduction in the percentage of cells with preferred-times < 1-s on the Start ball (panel f: note in session 2 [S2] the green and purple bars were more similar to each other than in session 1 [S1]). This figure suggests that since a major change between session 1 and session 2 was the presence of the demonstrator bat at the Start location in session 1, versus its absence in session 2, this presence/absence may underlie the observed neural differences in the firing sequences between the Start ball and balls A and B in session 1. (a) The temporal tuning-curve of time-cells was stable across consecutive sessions. Left column, temporal tuning curves of time-cells that were significantly-tuned in session 1. Cells were sorted according to their preferred time of firing. Right column, temporal tuning curves of the same cells which were tuned in session 1, but plotted for session 2; cells were sorted according to their preferred time in session 1. Note the stability of the internally-generated firing sequences across consecutive sessions (compare left and right panels). (b) Same analysis as in panel a, but for significant time-cells in session 2. Panels a and b demonstrate the stability of the sequences over the two sessions. (c) Violin plots of the distributions of Pearson correlations between the temporal tuning-curves in the two sessions; we repeated this calculation for each of the three locations. The correlations between the two sessions were very high at locations A and B (medians: ball A, r = 0.86, n = 51 cells; ball B, r = 0.91, n = 33 cells), and were statistically indistinguishable between balls A and B (two-sided Wilcoxon rank-sum test, P = 0.59; two-sided Kolmogorov-Smirnov test, P = 0.34) – indicating stability of the time representation across the two sessions for balls A and B. By contrast, the across-session correlations for the Start location were significantly lower (median on Start: r = 0.72, n = 17 cells; comparing correlations in A versus Start: two-sided Wilcoxon rank-sum test, P < 0.002; two-sided Kolmogorov-Smirnov test, P < 0.005; comparing correlations in B versus Start: two-sided Wilcoxon rank-sum test, P < 0.002; two-sided Kolmogorov-Smirnov test, P < 0.002) – consistent with the explanation that the presence of the demonstrator bat at the Start location in session 1 was responsible for the difference in the firing sequences in session 1 between the Start ball and the other two locations, A and B; note the demonstrator was removed from the room in session 2. (d) Gray bars, distribution of differences in preferred-times (ΔT) for the same neuron between session 1 and session 2 (at the same location). Plotted for all the time-cells that were significant in session 1; pooled across the 3 balls (n = 174 cells × positions; this number is smaller than the total number of time-cells in this study, because we included here only the significant time-cells where session 2 was run, which was only for a subset of the cells). The sharp peak at ΔT = 0 indicates that the preferred-time of time-cells was stable across sessions. Red line, shuffle distribution (cell shuffling: ΔT for cell i in session 1 minus cell j in session 2, for i ≠ j; two-sided Kolmogorov-Smirnov test of data versus shuffles, P = 6.8 × 10–5). (e) Cumulative distribution functions (CDF) for the preferred-times of the time-cells in each location, for session 1 (left) and session 2 (right) (ball A: yellow; ball B: cyan; Start ball: green). In session 1 the distribution of preferred-times on the Start ball (green) was quite different from those on balls A or B. By contrast, in session 2, the CDF for the Start ball became statistically indistinguishable from the CDFs for balls A and B (two-sided Kolmogorov-Smirnov test on time segments between t = 0 and t = 4 s: Session 1: Start ball versus ball A: P = 0.047; Start ball versus ball B: P = 0.047; ball A versus ball B: P = 0.74; Session 2: Start ball versus ball A: P = 0.15; Start ball versus ball B: P = 0.37; ball A versus ball B: P = 0.74). Note that we removed the demonstrator bat from the room in session 2, so only in session 1 the observer bat was landing next to the demonstrator bat on the Start ball. Taken together, this suggests that the presence of the demonstrator bat at the Start location in session 1 was responsible for the difference in the firing sequences seen in session 1 between the Start ball and the other two locations, A and B – while in session 2, when the demonstrator was removed from the room, the time-cell sequences became more similar to each other. (f) Percentage of time-cells with short preferred-times (< 1 s). Magenta bars, the percentage of time-cells with short preferred-times on balls A and B was statistically indistinguishable between session 1 (S1; n = 213 cells) and session 2 (S2; n = 132 cells) (two-sided log odds ratio test: P = 0.055). Green bars, same for the Start ball: here, the percentage of time-cells with short preferred-times was significantly smaller in session 2 (S2; n = 50 cells) than in session 1 (S1; n = 61 cells) (two-sided log odds ratio test: P < 10–5). Together, panels a-c and e-f suggest that the internally-generated firing sequence on the Start ball became more similar to those on balls A and B during session 2, when the other bat (demonstrator) was absent from the Start-ball and from the room altogether. (g) Venn diagram depicting the distribution of time-cell tuning in the different locations, in session 2. Note that we included in this figure only neurons that were stably spike-sorted across both sessions (see Methods section on ‘Spike sorting’).

Extended Data Fig. 7 Time-cells (as measured during motionless hanging) and place-cells (as measured in-flight) represent a largely overlapping population of cells – but there was no clear relation between their preferred-time and preferred-place.

(a) Top view of the experimental room (2.35 × 2.69 m, with 2.56 m height). Three landing-balls were positioned inside the room, designated as locations ‘Start’, ‘A’ and ‘B’. (b) Five examples of dorsal hippocampal CA1 neurons which were place-cells when the observer bat was flying (left), and were time-cells when the observer bat was motionlessly hanging from one of the landing balls (right). These examples demonstrate two things: First, that time cells and place cells are overlapping populations of cells (see also main Fig. 1h for a population analysis). Second, these examples demonstrate that the place-field and time-field of the same neuron are not necessarily related to each other in a simple way: The top 2 examples are cells whose place-fields were on opposite sides from the location of the time-field; and the bottom 3 examples demonstrate the lack of clear relation between preferred-time and preferred-place – for example late time-field for a cell whose place-field was early along the flight (third from the top), or vice versa (fourth from the top). First example: Left, place-cell firing rate map (top view) for flights from landing balls A and B to the Start ball. Right, time-cell raster for landing ball A. Note that the place-field is located on the flight path from ball B to the Start ball, whereas the time-field is on the other side – on ball A. Second example: Left, place-cell firing rate map for flights from the Start ball to balls A and B. Right, time-cell raster for ball A. Note that the place-field is located on the side of B, while the time-field is on the other side – on ball A. Third example: Left, place-cell firing rate map for flights from the Start ball to balls A and B. Right, time-cell raster for ball B. Note that the place-field is located close to the Start ball – early in the flight to B, while the time-field when the bat was on B occurs late in time. Fourth example: Left, Place-cell firing rate map for flights from landing balls A and B to the Start ball. Right, time-cell raster for ball B. Note that the place-field is located mid-way during the flight from B to Start, while the time-field occurs early in time. Fifth example: Left, place-cell firing rate map for flights from the Start ball to balls A and B. Right, time-cell raster on ball A. Note that the place-field is located mid-way during the flight, while the time-field occurs relatively early in time. (c) Population analysis. No significant correlation was found between the preferred time of firing after landing (x-axis) and the distance of the place-field peak from the takeoff-ball (y-axis) (Pearson r = –0.03, P = 0.64; Spearman ρ = –0.04, P = 0.54; two-sided tests). Plotted here are all the cells which were both significant place-cells when the bat was flying and significant time-cells when the bat was hanging motionlessly on one of the landing balls (n = 135 cells; note the number of dots plotted here [n = 194] is larger than the number of cells [n = 135] because neurons that had significant place-fields in the two flight-directions have contributed two dots to this scatter, and likewise for cells with significant time-fields in multiple locations [multiple balls]). Overall, there was no strong systematic relation between the preferred-time and preferred-place of firing for bat dorsal CA1 neurons.

Extended Data Fig. 8 Time tuning across different individual bats, and analysis of pure time-cells.

(a-d) Data for individual bats. (a) Trial durations at each of the locations in the room (balls A, B and Start), for each of the 4 observer bats which we recorded. Horizontal lines in the box-plots show the median trial duration, boxes show the 25th to 75th percentiles, and vertical lines show the 10th to 90th percentiles. n = 340, 628, 861 and 174 trials on landing ball A, for each of the bats respectively; n = 123, 624, 839 and 119 trials on landing ball B, for each of the bats respectively; n = 439, 861, 1277 and 316 trials on the Start ball, for each of the bats respectively. Mean trial durations for landing ball A: 7.9, 7.7, 7.2, 8.8 s; mean trial durations for landing ball B: 9.1, 6.3, 6.9, 7.1 s; mean trial durations for landing ball Start: 14.5, 10.5, 10.4, 14.7 s. 10th percentile trial duration for landing ball A: 4.0, 4.3, 3.6, 3.8 s; 10th percentile trial duration for landing ball B: 5.4, 3.5, 4.0, 3.9 s; 10th percentile trial duration for the Start ball: 3.9, 3.9, 3.9 4.5 s; 90th percentile trial duration for landing ball A: 12.5, 11.6, 10.9, 14.2 s; 90th percentile trial duration for landing ball B: 14, 9.7, 10.3, 10.6 s; 90th percentile trial duration for the Start ball: 31.7, 17.8, 17.5, 26.1 s. Minimum trial duration for landing ball A: 0.4, 0.3, 0.6, 0.6 s; minimum trial duration for landing ball B: 0.8, 0.3, 0.4, 2.4 s; minimum trial duration for the Start ball: 0.4, 0.6, 0.4, 0.5 s. Maximum trial duration for landing ball A: 23.4, 22.7, 54.2. 59.6 s; Maximum trial duration for landing ball B: 23.3, 19.4, 43.4, 31.3 s; Maximum trial duration for the Start ball: 59.3, 57.1, 56.9, 59.8 s. (b) Cumulative functions showing the cumulative fraction of cells with a particular preferred-time for each of the bats no. 1, 2, 3 (the number of cells from bat 4 was low and hence we omitted it from this panel). (c) Ensemble temporal sequences for the 3 balls (columns), depicted similarly to main Fig. 1g, but plotted here separately for the 4 bats (rows). (d) Venn diagrams showing the distributions and overlap between place-cells and time-cells, separately for each of the 4 observer bats. (e-f) Analysis of pure time cells. (e) Top panel: Scatter plot showing a significant correlation between the preferred-time on landing ball A and the preferred-time on landing ball B, for all the time-cells which were significantly tuned on both A and B in session 1 (Pure time-cells; Spearman rank correlation ρ = 0.33, P = 2.8× 10–2; Pearson correlation r = 0.38, P = 0.011; two-sided tests; n = 44 cells; the correlation remained significant also after removing cells with short preferred-time of less than 0.5-s on both balls A and B: Pearson r = 0.32, P = 0.041). Top right inset, Venn diagram illustrating the cell population analyzed here (pink area, time-cells tuned on A and B: ‘pure time-cells’; n = 44). Bottom panel: Scatter plot for the cell-shuffling of time cells tuned on A and B (‘pure time-cells’; n = 44) – plotting all possible combinations of the preferred-time of cell i on landing-ball A and the preferred-time for cell j on landing-ball B, where i ≠ j, for all the time-cells which were significantly tuned on both A and B in session 1 (dots were slightly jittered for display purposes). The Venn diagram illustrates the cell population for the shuffle: as in the top panel. Note that for the majority of the time-cells shown in the top panel (data), the difference between the preferred-times in locations A and B was < 1 s (61.4% of the cells [27/44] were within ±1 s from the diagonal – marked by the gray shaded area). This percentage is 2–3-fold larger than expected by chance – when compared to 2 types of chance levels: (i) Only 35.2% [333/946] of the shuffles in the bottom panel were inside the gray band, showing preferred-time differences of < 1 s between locations. (ii) Only 22% of the cells are expected to show differences < 1 s, assuming uniform distribution of differences (the gray shaded area divided by the total area of the graph = 22%). (f) Pearson correlations in panel e (top), after uniform subsampling. Shown is the distribution of Pearson correlations for 1,000 subsamples, which was computed as follows: In panel e (top), we binned the preferred times on ball A (x-axis) into 12 uniform time bins, 0.5 seconds each. Then for each subsample we chose randomly one dot from each bin, to form 12 pairs of preferred times on A and B, whose times on A were uniformly-distributed (by construction). We then calculated the Pearson correlation for these 12 dots. This subsampling procedure was repeated 1,000 times; the distribution of Pearson correlations for these 1,000 subsamples is shown here. The mean Pearson correlation of this histogram was < r > = 0.31. We found that 129 correlations out of the total 1,000 correlations showed P-value < 0.05, which amounts to 12.9% of the total subsamples. This fraction of P-values is significantly higher than the fraction of 5% that is expected by chance (one-sided Binomial test: P < 10–300). These results further support the notion that pure time-cells preserved their preferred-time between balls A and B. (g) Analysis of contextual time-cells. Solid purple: distribution (kernel density plot) of the differences in preferred time for contextual time-cells in both experimental sessions 1 and 2, with differences computed within-ball – for both landing-balls A and B; that is, pooling ΔT preferred times for A1 – A2 and B1 – B2 (n = 39 cells × positions). Dashed purple: distribution (kernel density plot) of the shuffled ΔT preferred times between different landing-balls from different sessions: A1 – B2 and B1 – A2. These distributions were very significantly different (two-sided nonparametric F-test [Ansari-Bradley test]: P = 4.0 × 10–17), indicating that contextual time-cells showed stability across sessions, and were more similar between different sessions of the same kind (landing on the same ball) than between different sessions of different kind (landing on different balls). (h-i) Comparing pure time-cells across the two sessions. (h) Distributions (kernel density plots) of the differences in preferred time for the group of 14 cells which were pure time-cells in both session 1 and session 2. Green, distribution of ΔT between preferred times on ball A versus ball B, for session 1 (A1 – B1; n = 14 cells; two-sided nonparametric F-test [Ansari-Bradley test] compared to cell-shuffling [dotted line]: P = 6.5 × 10–2). Yellow, distribution of ΔT between preferred times on ball A versus ball B, for session 2 (A2 – B2; n = 14 cells; two-sided nonparametric F-test compared to cell-shuffling: P = 4.6 × 10–2). The cell shuffling distributions (dotted lines) were calculated as the difference between the preferred times for cells i and j, where i ≠ j. (i) Distributions (kernel density plots) of the differences in preferred time for the group of 14 cells which were significant pure time-cells in both session 1 and session 2. Dark green, distribution of ΔT between preferred times in session 1 versus session 2, for ball A (A1 – A2; n = 14; two-sided nonparametric F-test [Ansari-Bradley test] compared to cell-shuffling [dotted line]: P = 8.0 × 10–4). Light green, distribution of ΔT between preferred times in session 1 versus session 2, for ball B (B1 – B2; n = 14; two-sided nonparametric F-test: P = 2.9 × 10–3). Note that ΔT is strongly and significantly concentrated around ΔT = 0 – suggesting high stability of the pure-time-cell tuning across the two sessions. (j-k) Matching the sample size between sets of neural data or between neural data and shuffles. (j) Distribution of P-values over 1,000 independent two-sided nonparametric F-tests [Ansari-Bradley tests], where each test was done between the distribution of real ΔT differences (ball A – B, in session 1) of the preferred-times for pure-time cells (n = 44) and randomly chosen 44 samples (neurons), taken from the distribution of ΔT of contextual time-cells. In each of the 1,000 tests the sample size of the pure time-cells and the contextual time-cells was thus identical (matched): n = 44. This distribution shows the percentage (y-axis) of the P-values for each of the 1,000 tests (x-axis); red line indicates the P = 0.05 cutoff. The y-axis was clipped at 10% for display purposes only. Note that 96.3% of the tests yielded P-values smaller then 0.05, indicating that the variance of the distribution of ΔT in pure time-cells was significantly smaller than the variance of the distribution in contextual time-cells – consistent with main Fig. 4c. (k) Similar to panel j, but here showing the distribution of P-values between the pure time-cells (n = 44) and 1,000 randomly chosen 44 samples taken from the cells-shuffling distribution of all cells. Note that 96.6% of the tests yielded P-values smaller than 0.05, indicating that the variance of the distribution of ΔT in pure time-cells was significantly smaller than the variance of the distribution for the cells-shuffling – again consistent with main Fig. 4c. (l) Distribution of ∆T for pure time cells on balls A and B, compared to a null distribution of shuffles for ∆T using the preferred times on ball A for even trials minus preferred times on ball A for odd trials (and likewise for ball B); plotted for all the pure time cells which exhibited a difference in preferred time of < 1 sec (n = 27 cells). The shuffle (null) distribution was significantly different from the data (two-sided nonparametric F-test [Ansari-Bradley test]: P = 1.4 × 10–4); but nevertheless, the distributions of data (black) and shuffles (red) were clearly highly similar.

Extended Data Fig. 9 Time cells for the other bat.

(a) Scatter plot of the time-field duration (field width at half-height) versus the preferred time, for all the significant time-fields for the other bat, pooled across all three locations (n = 73 significant fields: blue dots; the number of dots here [73] is larger than the number of significant time-cells for the other bat [n = 56], because if a cell was significantly time-tuned on 2 or 3 landing-balls, it contributed 2 or 3 dots to this scatter). This scatter shows a significant positive correlation: Pearson r = 0.27, P = 0.021; Spearman ρ = 0.25, P = 0.035 (two-sided tests). Thus, the resolution of time-fields deteriorated with the passage of time – as for self time-cells (Extended Data Fig. 2d), and as reported for time-cells in rats7,8,10,17. (b) Firing sequences in simultaneously-recorded time-cells for the other bat are similar to the population time-cell sequences pooled across all days. Distributions of time-differences (∆T) between the preferred-times for all the pairs of significant time-cells for the other, which were recorded simultaneously on the same day (gray bars; n = 28 cell-pairs), and all the cell-pairs recorded on different days (black line; n = 1672 cell-pairs), pooled over the 3 locations in the room. The gray and black distributions were statistically indistinguishable (two-sided Kolmogorov-Smirnov test: P = 0.56). This demonstrates that the pooled sequences for time-cells for the other bat (main Fig. 5c: diagonal panels) are reliably representing the within-day sequences – indicating that time-cells for the other bat form internally-generated firing sequences. (c) Additional 12 examples of time-cells for the other. For each example cell, the top panel shows the color-coded raster plot: x-axis, elapsed time from the moment the bat has landed (time 0); y-axis, repeated landings (trials); plotted as in main Fig. 1e. The bottom panel shows the temporal tuning-curve (black trace), which is the averaged firing-rate of each cell (average of the color-coded raster above); the preferred-time is indicated above the peak-firing of each cell (marked also by a vertical red line); green shading represents statistically-significant time bins; red curve shows the width-at-half-height of the time-field. (d) Venn diagrams showing the distributions and overlap between social place-cells and social time-cells, separately for each of the 4 individual recorded bats. (e) Scatter plot of the time of peak firing of the time-cells for the other bat versus the time of reward (dots show individual trials, pooled across all the example cells shown in main Fig. 5; Pearson r = 0.28; P = 2 × 10–4; two-sided test; n = 174 trials). Note there was large variability in the time-of-reward (large spread along the y-axis: standard deviation = 1.0 s; mean = 3.18 s), which was substantially larger than the variability in the neurons’ time of firing across the trials (small spread along the x-axis: standard deviation = 0.45 s; mean = 1.38 s).

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Omer, D.B., Las, L. & Ulanovsky, N. Contextual and pure time coding for self and other in the hippocampus. Nat Neurosci 26, 285–294 (2023). https://doi.org/10.1038/s41593-022-01226-y

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