Hippocampal place cells are key to episodic memories. How these cells participate in memory retrieval remains unclear. After rats acquired a fear memory by receiving mild footshocks in a shock zone on a track, we analyzed place cells when the animals were placed on the track again and displayed an apparent memory retrieval behavior: avoidance of the shock zone. We found that place cells representing the shock zone were reactivated, despite the fact that the animals did not enter the shock zone. This reactivation occurred in ripple-associated awake replay of place cell sequences encoding the paths from the animal's current positions to the shock zone but not in place cell sequences within individual cycles of theta oscillation. The result reveals a specific place-cell pattern underlying inhibitory avoidance behavior and provides strong evidence for the involvement of awake replay in fear memory retrieval.
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We thank all the members of the Ji lab for helpful discussions. This study was supported by grants from National Institutes of Health (R01MH106552) and Simons Foundation (#273886) to D.J.
The authors declare no competing financial interests.
Integrated supplementary information
Supplementary Figure 1 Animals moved more slowly and spent more time facing the SZ after the shock experience.
(a) Average speed of each animal (o) before (Pre/Day 1) and after (Post) the shocks. *P = 0.01, t3 =5.5, paired t-test. N = 4 animals.(b) Same as a, but for the proportion of time each animal spent facing the SZ. **P = 0.002, t3 = −10.4.
(a) An example animal’s trajectory in re-exposure was plotted as in Fig. 1b. The animal was first placed at the SZ (▲). Asterisks denote when the animal was manually placed back to the SZ.(b) Average occupancy (mean ± s.e.m.) across all animals along the track in re-exposure.(c) Occupancy within the SZ for each animal (o) in re-exposure.
(a) Coronal brain sections showing the dorsal (left) and ventral (right) hippocampus of a sham-lesioned rat that was injected with vehicle.(b) Same as (a), but for a lesioned rat injected with NMDA. The arrows demarcate the area with most cell loss in the dorsal CA1. Note that the ventral hippocampus remained intact.(c, d) Occupancy time (mean ± s.e.m.) along the track in Pre and Post for animals injected with vehicle (c, N = 9) or NMDA (d, N = 9). At least one week (>7 days) after the injection, both the vehicle and NMDA groups were tested in the linear IA task with a Pre and Post session, each 10-minutes long, as shown in Figure 1a. Note that the average occupancy time along the track was significantly correlated between Pre and Post in the NMDA group (***P = 2.6 x 10−7, Pearson’s r), but not in the vehicle group (n.s.: P = 0.73), suggesting little behavioral change from Pre to Post in the NMDA group.(e) The occupancy time within the SZ (SZ occupancy) in Pre was not different between the two groups. Each dot is a rat. n.s.: P = 0.14, t16 = −1.6, t-test.(f) SZ occupancy (bar) in Post was greater in the NMDA group than that in the vehicle group. ***P = 3.6 x 10−4, t16 = −4.5.
Supplementary Figure 4 Strong ripple oscillations in LFPs accompanied PBEs and replay events, and theta power was low prior to SZ- and LE-avoiding turns.
(a) Left: cumulative distribution of peak amplitudes of LFP ripple oscillations within all PBEs across all recording sessions and animals. LFPs at the CA1 pyramidal layer were filtered within the ripple band (100 - 250 Hz). The peak amplitude within each PBE was expressed as a z-score relative to the mean and standard deviation of ripple amplitudes of the entire session. The vast majority of events (90%) had increased ripple power from the mean by 3 standard deviations. Right: Similar to Left, but for those PBEs identified as replay events. Here, 93% of the replay events had z-scored peak ripple amplitudes greater than 3, indicating that replay events occurred together with strong ripple oscillations.(b) Average spectrogram of raw LFPs triggered by peak times of multiunit activity for all PBEs. Power at each frequency in each 100 ms sliding window (with a step of 10 ms) was normalized as a z-score relative to the mean and standard deviation of the power values at the frequency across all time windows of a session. Note the high power centered at trigger time 0 and at ~180 Hz, indicating that, again, replays occurred within periods of strong ripple oscillations.(c) LFP power in the theta band (6 - 12 Hz) around LE-avoiding turns. Left: Average theta power (mean ± s.e., z-scored to the mean and standard deviation of theta powers of the entire session), within a [−14 3] s window around LE-avoiding turns. ▴: turning time. Black line: mean; gray line: standard error. Right: average theta power during pausing prior to LE-avoiding turns (Pausing) and during a 3-s window after the turning time (Turning). **P = 0.001, t128= 3.3, t-test. Note that the theta power was significantly reduced during pausing than turning. N = 65 LE-avoiding turns.(d) Similar to (c) but for SZ-avoiding turns. *P = 0.016, t72 = 2.47. N = 37 SZ-avoiding turns.
(a) Firing rate curves of all active cells in Pre for an example rat, as the animal moving along the track on two different directions (from the SZ to LE, left; or LE to SZ, right). Firing rates of each cell were normalized by their maximum rate on both directions. The cells were defined as bi-directional (blue) or unidirectional (red). The latter could be either active (peak rate >1.5 Hz) on only one direction but not on the other, or had uncorrelated firing rate curves on the two directions (denoted by +). Numbers are peak rates. Note that here most cells (29 out of 40) were bi-directional.(b) An example of bitemplate replay event. Using the rate curves on each direction as a separate template (unidirectional template), the activity pattern (raster) of this event replayed both templates (bitemplate replay) with similar replay trajectories (solid lines; arrows mark the replay end). The replay trajectories of this event started close to the animal’s current position and ended further away from the animal. We also define this event as an outward replay (otherwise as an inward replay). In a minority of cases (see below), an event only replayed one uni-directional template, but not the other. In this case, we further defined it as a forward or reverse replay, if its replay trajectory pointed toward (from start position to end position) the same or reverse direction of the template it replayed, respectively.(c) Proportions of forward, reverse and bitemplate replays among all replays on Day 1 and Day 2 for each animal (o) and for all animals combined (bar). The majority of replays (70%) were bitemplate replays. The proportions of forward and reverse replays were not different (n.s.: P = 0.46, binomial test). N = 583 replay events.(d) Mean replay trajectory overlap for bitemplate replays on Day 1 and Day 2. For each bitemplate replay, we decoded two replay trajectories, each based on one of the two unidirectional templates. The overlap between the two replay trajectories was computed. Note the large mean overlap (75%) between two replay trajectories.(e) Proportion of inward, outward replays in Pre/Day 1 and Post for each animal (o) and for all animals combined (bar). Note that most replay events were outward (Pre/Day 1: 88%; Post: 89%).
Supplementary Figure 6 Replay trajectories of all replay events and the animal's actual moving trajectories for each rat.
(a) The trajectories during Run 1/Run 2 on Day 1. Red: replay trajectories; red arrow head: end of replay trajectories; blue: animal’s actual moving trajectory; ◂, ▸: turning time of LE-avoiding and SZ-avoiding turns, respectively; dashed line: SZ boundary.(b) The trajectories during Pre/Post on Day 2.
Supplementary Figure 7 The increase in SZ-pointing replays before SZ-avoiding turns in Post could not be explained by changes in the animals’ head direction or position.
(a) Replay trajectories while animals were facing the SZ in Pre/Day 1 and Post. Here, we plot a random sample of 30 examples each, out of 113 replays in Pre/Day 1 and out of 210 in Post.(b) Percentages of replay trajectories that pointed toward the SZ among all replay events while animals were facing the SZ in Pre/Day 1 and in Post, plotted for each rat (o, N = 4) and for all animals combined (bars). Whereas 81.4% of replays in Post pointed to the SZ, only 52.2% of those in Pre/Day 1 did so (***P = 3 x 10−8, binomial test; N = 113 replays in Pre/Day 1, 210 replays in Post). The result suggests that facing the SZ itself did not bias replay trajectories toward the SZ and that the biased direction of replay trajectories toward the SZ only occurred in Post after the shock experience.(c) Same as in (b), but including only the replays that occurred when animals were in the light or dark segment of the track. Again, there was a significant increase in the fraction of replays pointing to the SZ from Pre/Day 1 to Post when animals in the dark (Pre/Day 1: 49.4%, Post: 83.1%, ***P = 2 x 10−5, binomial test; N = 83 replays in Pre/Day 1, 65 replays in Post), or light (Pre/Day 1: 60.0%, Post: 80.7%, *P = 0.014; N = 30 replays in Pre/Day 1; 145 replays in Post) segment. The result suggests that while animals were facing the SZ in either the dark or light segment, the fraction of replay trajectories pointing toward the SZ was increased after the shocks.
Supplementary Figure 8 SZ cells were preferentially reactivated within PBEs during pausing prior to SZ-avoiding turns in Post.
(a) Activation probability and mean spike count of SZ cells within the PBEs during pausing before SZ-avoiding turns (SZ-avoiding) and within other PBEs in the rest of the Post session (Post other). *P = 0.0054 (activation probability), 0.0086 (mean spike count), signrank test. N = 26 SZ cells.(b) Same as (a), but for non-SZ cells (NSZ cells), which had place fields outside the SZ. n.s.: P = 0.36 (activation probability), 0.08 (mean spike count). N = 85 NSZ cells.This result indicates that SZ cells, but not NSZ cells, were preferentially reactivated during pausing prior to SZ-avoiding turns than the rest of the Post session.
Supplementary Figure 9 Effects of animals’ physical location on place cell activity and coactivity within PBEs.
(a) Place cell activity within PBEs in track sessions (Day 1 and Day 2) was biased by animals’ current locations. Activation probability and mean spike count within PBEs for place cells that had place fields peaked 35 cm within animals’ current locations (Local) and for other place cells (Remote). Note the significantly greater Local than Remote activity in both measures within PBEs. ***P = 1.5 x 10−23 (activation probability), 5 x 10−40 (mean spike count). N = 2209 PBEs.(b, c) Changes in activation probability (b) and mean spike count (c) for SZ cells and NSZ cells, excluding PBEs in Pre that occurred inside the SZ. n.s.: P = 0.80 (b), 0.97 (c), Wilcoxon rank-sum test; N = 26 SZ cells, 85 NSZ cells. Note the similar changes in activation probability (b) and mean spike count (c) between SZ and NSZ cells.(d) Changes in coactivity for those vicinity pairs with average peak locations near the SZ (SZ pairs) and other vicinity pairs (NSZ pairs), excluding PBEs in Pre that occurred inside the SZ. ***P = 1.8 x 10−4, Wilcoxon rank-sum test; N = 80 SZ pairs, 363 NSZ pairs. Note that the results were similar to Fig. 5f.(e) Changes in coactivity for each vicinity pair, plotted against the average of the pair’s peak firing locations, excluding PBEs in Pre that occurred inside the SZ. Note the similar result to Fig. 5g (significant correlation between the coactivity change and average peak location). N = 443 vicinity pairs.
(a, b) Proportion of replays among candidate events in track sessions on Day 1 (a) and Day 2 (b), plotted for each rat (o) and for all animals combined (bars). n.s.: P = 0.11 (a), P = 0.09 (b), binomial test; N = 194 (Run 1), 273 (Run 2), 226 (Pre), 434 (Post) candidate events. Note the similar proportions between Run 1 and Run 2 on Day 1 and between Pre and Post on Day 2.(c) Proportion of replays among candidate events on Day 1 and Day 2, analyzed separately for candidate events with different mean firing rate of place cells. Note that the proportion was not significantly correlated with mean firing rate of place cells (P = 0.49, Pearson’s r), suggesting that higher firing rates within candidate events did not necessarily result in higher detection of replays.
For each rat, theta trajectories (red) were plotted together with the animal’s actual trajectory (blue) along the track (x-axis) in Post. Red arrow heads: ends of theta trajectories; dashed line: SZ boundary.
Supplementary Figure 12 Firing rate curves of place cells for three more rats (Rat 2–Rat 4), in addition to the one shown in Figure 7a.
(a) Firing rate curves in Run 1/Run 2 on Day 1. Firing rates of each cell are normalized to its maximum rate between the two sessions of the same day. The cells are ordered by their peak firing locations in Run 1 or Pre along the track (x-axis).(b) Firing rate curves in Pre/re-exposure on Day 2. Cells are ordered by their peak firing locations in Pre.
Supplementary Figure 13 Partial remapping occurred in Post with additional remapping in re-exposure.
Cumulative distributions of PV correlation between sessions on Day 1 (Run 1 vs. Run 2) and on Day 2 (Pre vs. re-exposure, Pre vs. Post, Post vs. re-exposure) are plotted. Here PV correlations on Day 2 were computed only for those positions on the track where animals had visited in Post.The median correlation of Pre vs. re-exposure was close to that of Pre vs. Post (P = 0.08, Wilcoxon rank-sum test), but significantly different from others (P ≤ 0.0083, with Bonferroni correction). In addition, the median correlation of Post vs. re-exposure was greater than that of Pre vs. Post, with the difference close to the significant level (P = 0.009). However, the median correlation of Post vs. re-exposure remained significantly smaller than that of Run 1 vs. Run 2 (P = 5 x 10−7). N = 216 (Day 1), 195 (Day 2) PV correlations. The result suggests that much of the partial remapping occurred in Post after the shocks, with additional remapping occurring in re-exposure.
Supplementary Figure 14 Using templates made of place cell activities in re-exposure produced replay results in Post similar to those produced by using templates in Pre.
(a) Distributions of R2 values for all PBEs in Post, using Pre (blue) or re-exposure (red) templates. P = 0.47, Kolmogorov-Smirnov test; N = 882 PBEs. R2 quantifies how well the decoded positions within a PBE fit into a linear trajectory on the track. The similar distributions indicate that the firing order of place cells in PBEs was unaffected by using Pre or re-exposure templates.(b) Proportion of detected replays among all Post candidate events using Pre or re-exposure templates, for each rat (o) and for all rats combined (bar). P = 0.68, binomial test. The result indicates that the proportion of replays detected was also unaffected by which templates to use. N= 434 (Pre), 410 (re-exposure) candidate events.(c - j) Using templates in re-exposure produced similar results on replay trajectories. (c, d) As in Fig. 3c,d, but analyzed using the templates in re-exposure. **P = 1 x 10−3, ***P = 6 x 10−5 (χ2 test across all 3 types: P = 1 x 10−4); N = 15 (LE-avoiding), 37 (SZ-avoiding), 182 (Post other). (e - j) As in Fig. 4b-g, but analyzed using the templates in re-exposure. (f) *P = 0.014, t205 = −2.5; **P = 0.004, t217 = 2.9; t-test (ANOVA across all 3 types: P = 0.0135, F2, 323 = 4.4); N = 107 (Pre/Day 1 near), 100 (Post near), 119 (Post other). (g) ***P = 4 x 10−8, **P = 0.0015 binomial test (χ2 test among all 3 types: P = 3 x 10−7). (h) **P = 0.001, *** P = 8 x 10−4 (χ2 test among all 3 types: P = 0.0015); N = 70 (Pre/Day 1 near), 27 (Post near), 57 (Post other). (i) *** P = 8 x 10−11 (between Future and Past in Pre/Day 1), 5 x 10−31 (between Pre/Day 1 and Post for future overlap), Wilcoxon rank-sum test. Two-way ANOVA among all 4 overlaps: P = 4 x 10−11 (Future vs. Past), 9 x 10−41 (Pre/Day 1 vs. Post); F1, 1, 1125 = 44.7, 193.8, respectively; N = 345 (Pre/Day 1), 219 (Post). (j) ***P = 1 x 10−23, binomial test; N = 323 (Pre/Day 1), 168 (Post).
Supplementary Figure 15 Using place cell templates in re-exposure produced results on theta sequences similar to those achieved by using templates in Pre.
(a) – (d) are plotted as in Fig. 6b-e, respectively.(a) Theta trajectories (left) and replay trajectories (right) in Post, plotted against animals' positions on the track. All identified theta trajectories pointing to the SZ from 4 animals are plotted (N = 84). For replay trajectories, we plot a random sample of 84 out of 174 SZ-pointing replays. Dashed line: SZ boundary.(b) Trajectory lengths of theta and replay trajectories in Post. ***P = 1.6 x 10−9, Wilcoxon rank-sum test; N = 165 theta sequences, 219 replays.(c) Percentages of theta/replay trajectories that ended or started near the SZ among all theta/replay trajectories in Post, plotted for each rat (o) and for all animals combined (bars). ***P = 2.2 x 10−16, binomial test; N = 165 theta sequences, 219 replay events.(d) Activation probability and mean spike count of SZ cells (N = 20) defined by templates in re-exposure within theta sequences and replays in Post. ***P = 2.5 x 10−4 (activation probability), 3.4 x 10−4 (mean spike count), Wilcoxon signed-rank test.
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Wu, C., Haggerty, D., Kemere, C. et al. Hippocampal awake replay in fear memory retrieval. Nat Neurosci 20, 571–580 (2017). https://doi.org/10.1038/nn.4507
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