Patterns across multiple memories are identified over time



Memories are not static but continue to be processed after encoding. This is thought to allow the integration of related episodes via the identification of patterns. Although this idea lies at the heart of contemporary theories of systems consolidation, it has yet to be demonstrated experimentally. Using a modified water-maze paradigm in which platforms are drawn stochastically from a spatial distribution, we found that mice were better at matching platform distributions 30 d compared to 1 d after training. Post-training time-dependent improvements in pattern matching were associated with increased sensitivity to new platforms that conflicted with the pattern. Increased sensitivity to pattern conflict was reduced by pharmacogenetic inhibition of the medial prefrontal cortex (mPFC). These results indicate that pattern identification occurs over time, which can lead to conflicts between new information and existing knowledge that must be resolved, in part, by computations carried out in the mPFC.

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Figure 1: Passage of time improves behavioral match to pattern.
Figure 2: Pattern matching improves over time with alternate distribution and measures.
Figure 3: Sensitivity to difference between pattern consistency versus conflict increases over time.
Figure 4: Time-dependent sensitivity to pattern conflict with alternate distribution.
Figure 5: CNO inhibits spiking activity in hM4Di infected neurons in the mPFC.
Figure 6: mPFC inhibition impairs learning of conflicting platform after 30-d delay.


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This work was supported by grants from the Canadian Institutes of Health Research to P.W.F. (MOP-77561), S.A.J. (MOP-74650) and M.A.W. (MOP-123466). B.A.R. received financial support from a Banting Postdoctoral Fellowship via the Natural Sciences and Engineering Research Council of Canada. B.A.R., F.X. and J.H. received financial support from the Canadian Institutes of Health Research Sleep and Biological Rhythms Training Program. A.S. received financial support from the Hospital for Sick Children. We thank G. Hinton and M. Moscovitch for comments on earlier drafts of this manuscript.

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B.A.R., S.A.J. and P.W.F. conceived and designed the experiments. B.A.R., F.X., A.S. and J.H. performed the behavioral experiments. B.A.R. and M.A.W. performed the patch clamp experiments. B.A.R. conducted the analyses. B.A.R., S.A.J. and P.W.F. wrote the paper.

Corresponding author

Correspondence to Paul W Frankland.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Platform selection for training and testing.

(a) Platforms were selected using normal distributions over the platform distance from the center of the pool, d, and Von Mises distributions over the platform angle, θ. The platforms for the first set of experiments (Fig. 1) are shown here. (b) Platforms from the weighted bimodal distribution experiments (Fig. 2) are shown. (c) Platforms from the first set of Consistent vs. Conflict experiments (Fig. 3) are shown. (d) Platforms from the second set of Consistent vs. Conflict experiments (Fig. 4) and the mPFC inhibition experiments (Fig. 6) are shown.

Supplementary Figure 2 Behavioral experiment timelines.

(a) Mice were trained to locate each platform for 4 trials a day, with start locations randomly selected from the 4 cardinal coordinates (N, E, S, W). The start locations are shown. Mice were given a 1 d or 30 d delay after training. The time line for the first experiments (Fig. 1) is shown. (b) Time line for the weighted bimodal distribution experiments (Fig. 2) is shown. (c-d) Time lines for the Consistent vs. Conflict experiments (Fig. 3, Fig. 4 and Fig 6) are shown.

Supplementary Figure 3 Calculating the Kullback-Leibler divergence (DKL).

The platform distribution (Pplatform) was a kernel density estimate based on the training platform locations with a bandwidth selected by a likelihood cross-validation optimization algorithm. The search distributions, Psearch, were estimated via a kernel density estimate using the mouse's location in the pool at each time step with a 5 cm bandwidth. To calculate the DKL between Pplatform and Psearch the natural logarithm of the ratio between the two distributions was taken and multiplied by Pplatform at each location across the pool. The sum across the pool then gave the DKL value in nats. Two example paths and the resulting DKL values are shown here.

Supplementary Figure 4 Escape latencies during training in pattern-matching experiments.

Times to find the platform for (a) the first distribution (Fig. 1), (b) the single platform (Supplementary Fig. 5) and (c) the weighted bimodal distribution (Fig. 2) are shown. Data shown is mean ± s.e.m. Source data

Supplementary Figure 5 Reduction in DKL does not occur spontaneously.

(a) To determine whether mice might naturally gravitate towards improved matching to the distribution used in the first set of experiments (Fig. 1), we performed a set of control experiments. Mice were trained to locate a single platform for 9 days, located at the mean of the original distribution, then probed after a 1 d or 30 d delay. Example paths from a probe 1 d and 30 d later are shown. (b) The DKL between mouse search paths and the original distribution showed a trend towards an increase at the 30 d delay (Mann-Whitney U-test: 1 d n = 11, 30 d n = 12, ranksum = 118, P = 0.41). This was in the opposite direction to the first experiments (Fig. 1d), and indicates that the reduction in DKL depended on exposure to the original platform distribution and did not emerge spontaneously. (c) Mice exhibited decreased accuracy in their memory for the single platform location after a 30 d delay, as shown by the number of platform crossings during the probe (Mann-Whitney U-test: ranksum = 169.5, P = 0.02). (d) The decrease in accuracy did not reflect an increase in the spread of their search paths, as evidenced by the lack of difference in search path entropy (t-test: t21 = 1.68, P = 0.11). Likewise, animals trained on a distribution did not show an increase in entropy after a 30 d delay (t-test 1 d vs. 30 d: t90 = 1.35, P = 0.18). (e) To determine whether the observed decrease in accuracy of a single platform memory could account for a reduced DKL over time if extrapolated to a multiple-platform memory situation, we conducted a Monte Carlo simulation. Simulated probe search path distributions were constructed using kernel density estimation with the 9 platform locations from the first set of experiments (Supplementary Fig. 1a) used as the kernel centers. The kernels were circular Gaussians with standard deviations based on the search paths of the single platform mice (σ = 19 cm). The kernel locations were randomly shuffled in space to simulate memory error. The memory error was sampled from a normal distribution with mean 0 and different standard deviations. 100 samples were drawn for each level of standard deviation, and for each sample the DKL between the simulated path and the actual platform distribution was calculated. As the standard deviation in memory error increased, the DKL also increased. Therefore, an observed change in single platform memory accuracy would not account for a drop in DKL when applied to a multi-platform situation. Data shown is mean ± s.e.m. (*: P < 0.05) Source data

Supplementary Figure 6 Thigmotaxis cannot account for improved pattern matching over time.

(a) In the first set of experiments (Fig. 1), mice spent more time in the 10 cm outer ring of the pool during the probe test following a 30 d delay (Mann-Whitney U-test: ranksum = 2345, P = 9.7 x 10–5). (b) This did not account for the reduction in DKL observed in these groups, though, because there was no correlation between time spent in the outer 10 cm zone and DKL for either the 1 d (Pearson's: r = –0.16, P = 0.26) or 30 d (Pearson's: r = 0.15, P = 0.37) groups and there was no difference between the groups in this level of correlation (z-test: z = 0.31, P = 0.32). (c) Thigmotaxis was further dissociated from DKL by the fact that the mice trained on a single platform also showed increased time spent in the outer 10 cm ring of the pool (t-test: t21 = –3.67, P = 0.001), yet they did not show any reduction in DKL (Supplementary Fig. 5b). (d) Thigmotaxis could also not account for the results in the weighted bimodal distribution experiments (Fig. 2). While mice showed improved pattern matching at the 30 d delay, there was no significant difference in thigmotaxis in the probe tests 1 d vs. 30 d after training (Mann-Whitney U-test: ranksum = 125, P = 0.16). Data shown is individual mice or mean ± s.e.m. (**: P < 0.005) Source data

Supplementary Figure 7 Effects of platform recency on search path.

(a) In the first set of experiments (Fig. 1) there was no significant correlation between platform recency and the time the mice spent in the 10 cm zones surrounding the platforms during the probe test, for either the 1 d delay (Pearson's: r = 0.49, P = 0.18) or 30 d delay (Pearson's: r = 0.16, P = 0.68) groups. Furthermore, these correlations were not different between the 1 d delay and 30 d delay groups (z-test: z = 0.37, P = 0.65). (b) Both groups searched a fair amount in the last platform location, though, which corresponded to the mean of the distribution. (c) In the weighted bimodal distribution experiments (Fig. 2) there was also no significant correlation between platform recency and time spent in platform zone for either the 1 d delay (Pearson's: r = 0.52, P = 0.15) or 30 d delay (Pearson's: r = 0.03, P = 0.93) groups. There was no difference between the groups in this correlation either (z-test: z = 0.55, P = 0.5). However, in these experiments only the 1 d delay groups searched a great deal for the last platform, which was located in a lower probability zone within the distribution (Supplementary Fig. 1b). Data shown is mean ± s.e.m. Source data

Supplementary Figure 8 Escape latencies during training in new platform experiments.

Data shown is escape latencies (time to find the platform) for mice in the Consistent vs. Conflict new platform experiments, including (a) the second set of experiments (Fig. 4), (b) the 1 d delay groups in the mPFC inhibition experiments (Fig. 6a-c) and (c) the 30 d delay groups in the mPFC inhibition experiments (Fig. 6d-f). Data shown is mean ± s.e.m. Source data

Supplementary Figure 9 Complete timeline for mPFC inhibition experiments.

All animals were given at least 1 week to recover from viral microinfusion surgery. The time between the microinfusion and the new platform training (when animals received CNO injections) was kept constant (45 days) to ensure equivalent levels of virus expression in all groups.

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Richards, B., Xia, F., Santoro, A. et al. Patterns across multiple memories are identified over time. Nat Neurosci 17, 981–986 (2014).

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