The ability to recall the timing of events is an important feature of long-term memory. Episodic memory, the mental account of “what” happened, “where” and “when”, depends on a region of a brain called the hippocampus. Certain neurons in the hippocampus, called place-cells, are known to capture information about the locations an animal has visited so that a specific pattern of place cell activity marks each location an animal visits. However, it is not clear how the brain can mark the relationship between the timing of different events.

Some studies have documented gradual changes in the activity patterns of the place cells over time, which could help mark time. If these changes are specific to a particular environment then they would not allow animals to associate in memory events that occurred close in time (for instance, in the same day) if these events occurred in different environments. To do that, a certain component of the changes in the activity patterns would have to be independent of any specific environment or context in which events occur.

Now, Rubin, Geva et al. have captured time-lapse images of the activity of thousands of hippocampal cells in mice as they explored two different environments on repeated occasions over a two-week period. The environments had different shapes, textures, visual cues, and odors. The mice were allowed to explore each environment daily for more than a week prior to the time-lapse filming so that they would be very familiar with the two environments. During the filming portion of the experiments, the mice visited one environment in the morning, and then the other in the afternoon.

The analysis of the images revealed what appeared to be unique patterns of cell activity for specific days, which gradually changed over the course of the experiment. The patterns persisted even when the animals switched to a new environment during the same day, but were different for visits to the same environment on different days. Next, Rubin, Geva et al. used the patterns of activity collected from the mice while they were in one environment to create a timeline of events. From this timeline, it was possible to accurately deduce which day each visit to the other environment occurred based on the patterns of hippocampal cell activity alone. One challenge that stems from this work is to understand the biological mechanisms that drive the patterns in neuronal activity over timescales that are relevant for long-term memory.

The capacity to remember temporal relationships between different events is essential to our autobiographical memory and sense of self. Autobiographical or episodic memory relies on the hippocampus, whose neurons are thought to encode information about where and when events have occurred (Davachi and DuBrow, 2015; Eichenbaum, 2014; Howard et al., 2014; Rolls, 2010; Shapiro, 2014; Tulving, 2002). Hippocampal place cells encode the spatial location of an animal through localized firing patterns, and have long been considered a substrate for long-term memory of the location in which events occurred (O'Keefe and Dostrovsky, 1971; O'Keefe, 1978). Whereas ample knowledge exists regarding the encoding of location, relatively little is known about the neural mechanisms that enable the encoding of the time in which events occur. Recent work has revealed that in familiar environments hippocampal place cell activity is dynamic over timescales that range from minutes to weeks (Howard and Kahana, 2002; Mankin et al., 2015; Mankin et al., 2012; Manns et al., 2007; Ziv et al., 2013). For timescales that are greater than one day, these dynamics primarily result from ongoing changes in the subsets of place cells that are active during repeated visits to the same fixed environment (Ziv et al., 2013). Such dynamics may contribute information about the temporal relationship between events by providing a unique code that functions as a ‘timestamp’. If such timestamps exist, they would likely aid long-term memory by reducing interference between traces of events that occur at different times at the same place, or that are similar in that they share contextual components such as sensory experience and behavior. Moreover, to support the formation of a mental timeline of experienced events in long-term memory, and the capacity to mentally ‘time-travel’ during memory recall (Kragel et al., 2015; Nyberg et al., 2010), timestamps should change gradually and continuously with time. Such gradual changes in the ensembles of place cells active during similar events on different days have been recently reported, but the extent that these dynamics actually carry temporal information remains unclear (Mankin et al., 2012; Ziv et al., 2013). We consider two alternative hypotheses regarding the possible contribution of the observed dynamics to coding of time. According to one hypothesis, the dynamics in the ensemble activity over days is unique to the environment in which it is observed, and independent from the dynamics in other, dissimilar environments. In this case, the dynamics may contribute ordinal information about different events that occur within a given environment, but will not contribute to associations in memory between events that happen close in time if these events occurred in different or dissimilar environments. An alternative hypothesis asserts that certain aspects of the days-scale dynamics in the ensemble activity are common to different environments. Such environment-nonspecific dynamics could support a linkage in long-term memory between dissimilar events that occur at temporal proximity. If this is the case, we would expect the hippocampal representations of events that occur in different spatial environments but in temporal proximity (e.g. the same day) to share common time-varying components.

To test these alternative hypotheses we investigated hippocampal neuronal representations of different spatial contexts over multiple days and weeks. We combined head-mounted miniaturized fluorescence microscopes (Ghosh et al., 2011; Ziv et al., 2013), chronic microendoscopy (Barretto et al., 2011), and viral-vector based expression of a genetically encoded Ca²⁺ indicator (Chen et al., 2013), to longitudinally image the Ca²⁺ dynamics of large populations (> 1,000 per mouse) of hippocampal CA1 pyramidal cells in freely behaving mice that repeatedly explored two familiar environments (Figure 1A). To avoid circadian effects we alternated the two environments between AM and PM sessions, 4–5 hr apart. Each session consisted of five 3-min trials. To maximize the perceived differences between the environments, we constructed linear tracks (environments A and B) that differed in shape, floor texture, surrounding proximal and distal visual cues, odor, and flavor of the water reward (see Materials and methods). To uncover the time-dependent coding dynamics, while minimizing changes in place codes induced by learning, we familiarized the mice with the two environments before starting the experiment (Figure 1A). During pre-training, the mice ran on each linear track for 15 min per day for 8–11 days, until they performed at least 60 laps in each environment for two consecutive days. Time-lapse imaging began two days after the last pre-training session, and was performed every other day for two weeks (days 1–15, Figure 1B). To confirm that changes in the representations of the two environments are also evident within a single session, we performed on days 16–17 a ‘remapping test’ by shuttling the mice back and forth between environments within the same session, keeping the interval between environments < 10 min. This test confirmed that CA1 neuronal ensembles were differentially and selectively active in the two environments (Figure 1—figure supplement 1), forming place field maps that were markedly different (Figure 1C), in agreement with past studies of place cell remapping (Leutgeb et al., 2004; Muller and Kubie, 1987).

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For longitudinal analysis of the cell activity, we revalidated a previously established routine for aligning cell locations across sessions (Ziv et al., 2013) and applied it to our study (Figure 1—figure supplement 2). Consistent with a recent study using GCaMP3 (Ziv et al., 2013), we found that only a small portion of the cells were active on all days of the experiment (380 and 364 cells, corresponding to 7.4 and 7.2% of the cells for environment A and B respectively; in either environment ~20% of these cells were also place coding on all days). Cells that were place coding on more than one day typically had place fields at the same spatial location (Figure 1D–F).

Despite extensive pre-training in both environments, changes over days in the place codes that are unique to one environment or common to both environments may have resulted from a continuous refinement of the representation due to learning or familiarization (Karlsson and Frank, 2008; Lever et al., 2002). However, this is unlikely to be the case here due to several factors: the correlations between the place field maps of the two environments (Figure 1G), the difference in cell activity rates between the two environments (Figure 1—figure supplement 3A), and the distance between the cell’s peak activity in each environment (Figure 1—figure supplement 3B) all did not change over time, indicating a stable spatial remapping. Furthermore, the fractions of cells that were place coding in both environments (Figure 1H), the average number of Ca²⁺ events in a session, the amplitude of the Ca²⁺ events, and the fraction of cells that were place coding on different days of the experiment in each environment, remained similar between environments throughout the experiment (Figure 1—figure supplement 3C-E). Two-photon imaging of the CA1 before and after the experiment showed no indications of cell morbidity, such as time-dependent differences in the cells’ morphology or Ca²⁺ indicator expression patterns (Tian et al., 2009) (Figure 1—figure supplement 3F). Our findings that the functional properties of CA1 neurons remained stable throughout the experiment, argues against the possibility that the changes in spatial representation we observed result from pathological processes (e.g. due to overexpression of the Ca²⁺ indicator or photo-toxicity). Also, the number of sessions in which cells were active, and the fraction of cells with place fields for left and right directions of the linear tracks remained similar for both environments and GCaMP6 variants (Figure 1—figure supplement 4A,B). Overall, our finding of stable remapping despite fluctuating place cell ensembles on different days of the experiment are consistent with previous reports of place cell remapping between different environments within the same day (Leutgeb et al., 2004; Muller and Kubie, 1987), and within a single fixed environment across days and weeks (Ziv et al., 2013).

In our experiment, place cells constituted 46% of the neurons active in either of the environments, and their activity during locomotion contributed 33% of the Ca²⁺ transients we recorded (Figure 1I,J). Non-place cells generated almost half of the Ca²⁺ transients (46%), mostly during resting or reward consumption at the ends of the linear tracks. Thus, neuronal activity during an episode on the linear track was much richer than the part of the data that is limited to place codes. Furthermore, the same cells can be place coding in one environment but active and not place coding in another, or alternatively place coding on one day and active but not place coding on other days. Thus, we sought in our subsequent analyses to account for the entire episodic representations. We therefore opted to include in the following analyses all the active cells and their Ca²⁺ events, regardless of their spatial tuning properties.

We quantified the correlations between the ensemble cell activity patterns in different sessions, in either the same or different environments for pairs of sessions separated by different intervals (Figure 2A,B). For the same environment, both the degree of overlap between populations of cells active on different days, and correlations in their activity patterns gradually declined as a function of elapsed time (Figure 2B, green data). This gradual decay in the correlations at the ensemble level cannot be explained by monotonic changes in event rates at the single cell level (Figure 2—figure supplement 1A,B). Notably, the correlations in cell activity patterns between environments, and the probability of cells recurring within the subset that was active in both environments declined with elapsed time (Figure 2B, red data). Thus, hippocampal representation of the two environments co-evolved with time.

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To what extent can the dynamics in the representations of the two environments infer the time in which episodes occurred? To address this question while accounting for the neuronal activity at the ensemble level, we constructed three types of neuronal ‘time decoders’. The decoders are based on the knowledge that the similarity between ensemble activity patterns of different episodes decays as a function of elapsed time (Figure 2A,B). We first quantified whether the coding dynamics are informative about the temporal relationships between different events that occurred days to weeks apart within either one or both of the environments. We used an ‘ordinal-time decoder’ to determine if days-scale changes in CA1 ensemble activity are informative about the order of experienced events. This decoder examines data from all days and orders them by maximizing the mean correlation between cells’ firing patterns on neighboring days (Figure 2C and Figure 2—figure supplement 2A). We tested the decoder’s performance on entire sessions, (each 15 min long) for each environment separately and for both environments together, and contrasted the results with those obtained using data in which the day labels of each cell were randomly shuffled. When data from only one of the environments were included in the analysis, performance was significantly superior compared with chance levels for accurate ordinal sorting and the performance of the decoder on shuffled data (Figure 2C, blue data points). Notably, when data from both environments were included in the analysis, the decoder performed perfectly, accurately ordering all sessions along the experiment in all five mice (Figure 2C, red data points, and Figure 2—figure supplement 2A). Thus, day-to-day differences in the activity patterns of CA1 neurons carry ordinal information. This result argues against the possibilities that the observed differences in neuronal activity between sessions are merely the result of inherently unreliable neuronal responses, or due to fluctuations of cell excitability over minutes to hours. Such alternative explanations are unlikely to support perfect ordinal sorting over multiple days.

Days-scale changes in ensemble activity may facilitate the binding in memory of fractions of events within temporal clusters. Accordingly, we expected that different trials or fractions of trials from the same session would be similarly time-stamped. To determine the consistency of such timestamps within the same environment, we devised a decoder that estimates the day in which an episode was recorded based on the correlations between different trials or fractions thereof (‘within-environment time decoder’). Using single 3-min trials as test data, this decoder accurately inferred the days from which the test data was taken (98 ± 1% of the cases). To determine to what degree temporal information is carried in the changes in neuronal firing rates we performed the analysis again, this time relying only on cells that were active on all days. This decoder was accurate in 80% of the cases, significantly higher than chance (z-test, p < 7 · 10^–48, Figure 2D). The decoder’s performance on shuffled test data was essentially identical to chance levels (12.5% for accurate decoding). Remarkably, even data from very short, several-seconds-long fractions of trials, were sufficient for correct decoding of the day of experiment from which the test data was taken (Figure 2E). Thus, a unique timestamp of events arises from changes in the cell activity levels over time. Our data suggest that such timestamps are not only captured within the CA1 representations of complete episodes but are also highly consistent within short fractions of an episode.

If hippocampal dynamics timestamp different events that occur close in time, as our analysis indicates, then such an aspect of the neural code should also infer the time of an event irrespective of a specific environment or a behavioral context. To test this idea we constructed an ‘across-environment time decoder’ that quantifies the degree to which the time of events in one environment can be inferred by a decoder that is trained on data from another environment. We tested this decoder’s ability to correctly identify from which day of the experiment the test data was taken (the test data consisted of entire sessions or single 3-min-long trials). Despite marked differences in the spatial representation of the two environments (Figure 1) and an inter-session interval of 4–5 hr for sessions taking place on the same day, the across-environment time decoder was able to infer the days from which the test data was taken in 65% and 58% of the cases for sessions and trials respectively, significantly higher than chance (z-test, p < 6 · 10^–7 and 4 · 10⁻²², Figure 2F,G, and Figure 2—figure supplement 2B,C). The decoder’s performance was also essentially identical to chance level when tested on data in which the day labels of the cells were randomly shuffled (Figure 2F,G). Note that the decoder’s errors were mostly in proximal days. Indeed, when excluding the training data from the same day of the tested trials or sessions, the decoders inferred the neighboring days in the vast majority of cases (Figure 2—figure supplement 3). These results are consistent with our finding of a gradual co-evolution of the CA1 ensemble representation of both environments (Figure 2A,B). Furthermore, both place codes and non-place codes carried significant information about time (Figure 2—figure supplement 4), suggesting that time stamping is not intrinsically linked to specific aspects of the episodic representation such as place coding.

Here, we exposed a facet of episodic coding dynamics that has thus far remained hidden: the binding and separation of CA1 episodic codes over timescales of days–weeks and across environments. We capitalized on an imaging approach that allows the tracking of Ca²⁺ dynamics in large populations of hippocampal neurons over timescales of weeks (Ziv et al., 2013). Our results rely on our capability to accurately register the neural data across sessions. Re-analyzing the data using a distance-based (rather than correlation based) registration routine (described in Figure 1—figure supplement 2) yielded highly similar results with regard to the performances of the three time decoders (Figure 2—figure supplement 5A–E). Furthermore, the performance of these decoders was robust to alterations in either the thresholds of correlation or distance used by each of the registration routines (Figure 2—figure supplement 5F,G). Overall, these independent analyses confirm that our findings do not stem from a bias intrinsic to our choice of the across-session registration method or threshold.

The ability to place different experienced events on a personal mental timeline is a central feature of episodic memory. Several recent studies have provided important insights into the hippocampal function with respect to coding of time in memory, suggesting that multiple time-coding mechanisms exist to cover different timescales and mnemonic needs (Mankin et al., 2015; Mankin et al., 2012; Manns et al., 2007; Navratilova and Battaglia, 2015). For instance, ‘time cells’, hippocampal pyramidal cells that fire during a particular moment within a temporally structured episode (Eichenbaum, 2014; Kraus et al., 2013; MacDonald et al., 2011; Modi et al., 2014), may contribute to a representation of sequences of events over timescales of tens of seconds. Due to the seconds to minutes-scale nature of their activity, time cells are less likely to place individual uniquely experienced episodes on a linear temporal axis extending over days. Pyramidal neurons in the hippocampal area CA2 (upstream to CA1) were found to display representations that are similar for different contexts but highly variable upon repeated exposures over timescales of hours. It was suggested that such CA2 dynamics, when combined with spatial information from CA3, could jointly reflect spatial and temporal information in CA1 (Mankin et al., 2012, 2015). However, it was argued that the rapid dynamics in CA2 could support encoding a temporal order between events over timescales that range from hours to one day (Navratilova and Battaglia, 2015), whereas many events in our episodic memory are ordered over much longer timescales.

Our present findings of coding dynamics over timescales of days are consistent with a key feature of episodic memory—that each experience is uniquely encoded. This allows for ‘single trial’ learning and events that occur close in time to be linked in long-term memory. This idea is also consistent with previous reports of changes in CA1 neuronal activity over timescales of seconds-hours (Manns et al., 2007), hours-days (Mankin et al., 2012) and days-weeks (Ziv et al., 2013). Such ensemble dynamics may be regarded as a form of ‘remapping’ in the time domain. Spatial remapping is thought to reflect a mechanism for pattern separation, which could help prevent interference between representations of similar places (Colgin et al., 2008). Likewise, we propose that ‘remapping in time’ may be a reflection of a pattern separation mechanism that could mitigate interference between memories of similar events that occurred at different times.

Another aspect of our findings that is key to episodic memory is the co-evolution of the CA1 ensemble representations across spatial environments. While previous work showed that the CA1 place code for a familiar environment changes over timescales of days–weeks (Mankin et al., 2015; Mankin et al., 2012; Ziv et al., 2013) it has remained unclear whether such evolution in the representation of a given environment is independent of the evolution of the representations of other environments. Our findings demonstrate that the representations of two environments were highly distinct, yet sufficiently co-evolved with time to allow us to infer the day in which events occurred in one environment using reference data from the other environment. We propose that such co-evolution may reflect a neural mechanism for binding in long-term memory events that occurred at temporal proximity, even if these events occurred in different contexts and hours-days apart.

Importantly, our data show that such binding/separation of temporally proximal/distal events does not interfere with the ability to distinguish between the two environments; although the spatial representation of each environment changed with time, within environment spatial correlations from sessions that are two weeks apart remained higher than across-environment spatial correlations from the same day (Figure 2—figure supplement 6A,B).

What are the mechanisms that drive ensemble dynamics and time coding over timescales of days? One potential mechanism at the network level is adult neurogenesis in the dentate gyrus (two synapses upstream to CA1) which takes place over timescales of weeks and has been suggested to support time coding via ongoing modification of the hippocampal circuitry by newborn neurons (Aimone et al., 2006, 2009). Another possible mechanism is the recently found days-scale turnover of dendritic spines in basal CA1 dendrites, which could potentially drive coding dynamics at the cellular level (Attardo et al., 2015).

A closely related issue is the role of the interactions between the absolute passage of time and the experience itself on the observed ensemble dynamics. Previous work found no effect for the number of context exposures on the rate of change in CA1 codes over 30 hours (Mankin et al., 2012), but it is possible such an effect could be observed over longer timescales. Our findings are consistent with reports showing that changes in neuronal excitability govern which neurons will be recruited to code and support a memory of a given experience (‘memory allocation’) (Rogerson et al., 2014; Yiu et al., 2014). Repeated training episodes may alter cell excitability via consolidation or reconsolidation processes involving the expression of immediate early genes and their downstream effectors (Dudai, 2012; Lee, 2008; Nader and Hardt, 2009). Such processes may change episodic representations irrespective of the amount of time that has passed between repeated events. Thus, while we have shown in this study that CA1 ensemble dynamics carry temporal information over days, further work is needed to differentiate between the contributions and functional roles of experience-dependent and experience-independent processes that take place in the brain during the passage of time.

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Author details

1. Alon Rubin

   Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel

   Contribution

   AR, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Nitzan Geva

   Competing interests

   No competing interests declared.

2. Nitzan Geva

   Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel

   Contribution

   NG, Conception and design, Acquisition of data, Drafting or revising the article

   Contributed equally with

   Alon Rubin

   Competing interests

   No competing interests declared.

3. Liron Sheintuch

   Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel

   Contribution

   LS, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

4. Yaniv Ziv

   Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel

   Contribution

   YZ, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   yaniv.ziv@weizmann.ac.il

   Competing interests

   YZ: Has ownership interests at Inscopix Inc.

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