Knowledge of the temporal structure of events is central to our experience. We remember how a sequence of events unfolded and can recall when in time events occurred. Emphasizing both when in time and where in space events came to pass, episodic memories typically comprise event information linked to a spatiotemporal context. Space and time have been suggested to constitute fundamental dimensions along which our experience is organized (Konkel and Cohen, 2009; Ekstrom and Ranganath, 2018; Bellmund et al., 2018a). Consistently, the role of the hippocampus — a core structure for episodic memory (Scoville and Milner, 1957; Squire, 1982) — in coding locations in space (O'Keefe and Dostrovsky, 1971; Moser et al., 2017; Epstein et al., 2017) and moments in time (Pastalkova et al., 2008; MacDonald et al., 2011; Eichenbaum, 2014; Ranganath, 2019; Howard, 2018) is well-established. Human memory research has highlighted the role of the hippocampus in the encoding, representation and retrieval of temporal relations (Tubridy and Davachi, 2011; DuBrow and Davachi, 2014; Ezzyat and Davachi, 2014; Hsieh et al., 2014; Jenkins and Ranganath, 2010; Jenkins and Ranganath, 2016; Kyle et al., 2015; Copara et al., 2014). The similarity patterns of mnemonic representations suggest that the hippocampus forms integrated maps reflecting the temporal and spatial structure of event memories (Deuker et al., 2016; Nielson et al., 2015). Consistently, activity in the hippocampal-entorhinal region has been demonstrated to be sensitive to Euclidean distances as well as the lengths of shortest paths to goals during navigation (Spiers and Maguire, 2007; Viard et al., 2011; Sherrill et al., 2013; Howard et al., 2014; Chrastil et al., 2015; Spiers and Barry, 2015).

How do representations of temporal structure arise in the hippocampus? Evidence suggests that neural ensembles in the lateral entorhinal cortex (EC), which is strongly connected to the hippocampus (Witter et al., 2017), carry temporal information in freely moving rodents (Tsao et al., 2018). Specifically, temporal information could be decoded from population activity with high accuracy (Tsao et al., 2018). This temporal information was suggested to arise from the integration of experience rather than an explicit clocking signal (Tsao et al., 2018). Recently, the human anterior-lateral entorhinal cortex (alEC), the homologue region of the rodent lateral entorhinal cortex (Navarro Schröder et al., 2015; Maass et al., 2015), as well as the perirhinal cortex and a network of brain regions including the hippocampus, the medial prefrontal cortex, posterior cingulate cortex and angular gyrus have been implicated in the recall of temporal information (Montchal et al., 2019). These regions responded more strongly for high compared to low accuracy retrieval of when in time snapshots from a sitcom appeared over the course of the episode viewed in the experiment (Montchal et al., 2019). Together, these findings demonstrate that entorhinal population activity carries temporal information in navigating rodents and that its human homologue is activated during temporal memory recall. However, the contents of mnemonic representations in the alEC remains unclear.

We used representational similarity analysis of fMRI multi-voxel patterns in the entorhinal cortex to address the question how learning the structure of an event sequence shapes mnemonic representations in the alEC. Using this paradigm and data, we previously demonstrated that participants can successfully recall spatial and temporal relations of events defined by object encounters in a virtual city and that the change of hippocampal representations reflects an integrated event map of the remembered distance structure (Deuker et al., 2016). Here, we show that the change of multi-voxel pattern similarity through learning in the alEC specifically reflects the temporal structure of the event sequence.

We examined the effect of learning on object representations in the human entorhinal cortex using fMRI. In between two picture viewing tasks during which fMRI data were collected, participants acquired knowledge of temporal and spatial positions of objects in a familiar virtual city. Participants navigated repeated laps of a route along which they encountered chests containing different objects (Figure 1; Figure 1—figure supplement 1). We aimed to test whether entorhinal pattern similarity change from before to after learning related to experienced object relationships. Specifically, we presented object images twelve times in the picture viewing tasks before and after learning, using the same random order in both scanning runs. For both runs, we calculated the similarity of multi-voxel patterns for all object pairs and correlated changes in representational similarity with the temporal and spatial object relationships. The temporal distance structure of the object sequence can be quantified as the elapsed time between object encounters or as ordinal differences between their sequence positions, which are closely related in our task. Spatial distances on the other hand can be captured by Euclidean distances or geodesic distances between positions based on the shortest navigable paths between object positions. Importantly, we dissociated temporal from Euclidean and geodesic spatial object relationships through the use of teleporters along the route (Figure 1—figure supplement 2). Further, object relationships can be quantified by the distance traveled along the section of the route separating their positions (Figure 1—figure supplement 2). To assess whether entorhinal object representations change from before to after learning to map experienced object relationships, we compared changes in neural pattern similarity to the temporal and spatial structure of the task.

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The change in multi-voxel pattern similarity in alEC between pre- and post-learning scans was negatively correlated with the sequence structure (Figure 2A and Figure 2B, T(25)=- 3.75, p=0.001, alpha-level of 0.0125, Bonferroni-corrected for four comparisons), which was quantified as the median elapsed time between objects pairs along the route. After relative to before learning, objects encountered in temporal proximity were represented more similarly compared to object pairs further separated in time (Figure 2C). Pattern similarity change was negatively correlated with temporal distances after excluding comparisons of objects encountered in direct succession from the analysis (T(25)=-2.00, p=0.029, one-sided test, Figure 2—figure supplement 1A), speaking for holistic representations of temporal structure in the alEC and ruling out that the effect we observed is largely driven by increased similarity of temporally adjacent objects. Importantly, the strength of this effect was strongly related to behavior in the post-scan free recall test, where participants retrieved the objects from memory. Specifically, participants with stronger correlations between alEC pattern similarity change and the temporal task structure tended to recall objects together that were encountered in temporal proximity along the route (Pearson r = −0.53, p=0.006, CIs: −0.76,–0.19, Figure 2D).

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Figure 2—source data 1

Z-values of correlations between pattern similarity change in the entorhinal subregions and temporal and Euclidean spatial distances as shown in panel B.

https://doi.org/10.7554/eLife.45333.010

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Figure 2—source data 2

Pattern similarity changes in alEC for object pairs separated by low and high temporal distances as shown in panel C.

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Figure 2—source data 3

Z-values of correlations between alEC pattern similarity change and temporal distances without comparisons of objects encountered in direct succession along the route and Pearson correlation coefficients quantifying temporal clustering during the free recall task (panel D).

https://doi.org/10.7554/eLife.45333.012

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Figure 2—source data 4

Z-value differences quantifying the difference in temporal and spatial mapping in alEC and pmEC as shown in panel E.

https://doi.org/10.7554/eLife.45333.013

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Pattern similarity change in alEC did not correlate significantly with Euclidean spatial distances (T(25)=0.81, p=0.420) and pattern similarity change in posterior-medial EC (pmEC) did not correlate with Euclidean (T(25)=0.58, p=0.583) or temporal (T(25)=1.73, p=0.089) distances. Temporal distances between objects during the first picture viewing task were not related to alEC pattern similarity change (Figure 2—figure supplement 1B; T(24)=-0.29 p=0.776, one outlier excluded, see Materials and methods) and correlations with elapsed time between objects during navigation were significantly more negative (T(24)=-1.76 p=0.045; one-sided test); strengthening our interpretation that pattern similarity changes reflected relationships experienced in the virtual city.

Can we reconstruct the timeline of events from pattern similarity change in alEC? Here, we used multidimensional scaling to extract coordinates along one dimension from pattern similarity change averaged across participants (Figure 3A–D). The reconstructed temporal coordinates, transformed into the original value range using Procrustes analysis (Figure 3A), mirrored the time points at which objects were encountered during the task (Figure 3B, Pearson correlation between reconstructed and true time points, r = 0.56, p=0.023, bootstrapped 95% confidence interval: 0.21, 0.79). Further, we contrasted the fit of the coordinates from multidimensional scaling between the true and randomly shuffled timelines (Figure 3C). Specifically, we compared the standardized sum of squared errors of the fit between the reconstructed and the true timeline, the Procrustes distance, to a surrogate distribution of deviance values. This surrogate distribution was obtained by fitting the coordinates from multidimensional scaling to randomly shuffled timelines of events. The Procrustes distance from fitting to the true timeline was smaller than the 5th percentile of the surrogate distribution generated via 10000 random shuffles (Figure 3D, p=0.026). Taken together, these findings indicate that alEC representations change through learning to reflect the temporal structure of the acquired event memories and that we can recover the timeline of events from this representational change.

Figure 3

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Figure 3—source data 1

True and reconstructed temporal coordinates of object positions as shown in panel B.

https://doi.org/10.7554/eLife.45333.015

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Figure 3—source data 2

Procrustes distance from mapping coordinates from multidimensional scaling based on alEC pattern similarity change to true temporal coordinates and surrogate distribution obtained from fitting to shuffled temporal coordinates (panel D).

https://doi.org/10.7554/eLife.45333.016

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What is the nature of regional specificity within entorhinal cortex? In a next step, we compared temporal and spatial mapping between the subregions of the entorhinal cortex. We conducted a permutation-based two-by-two repeated measures ANOVA (see Materials and methods) with the factors entorhinal subregion (alEC vs. pmEC) and relationship type (temporal vs. Euclidean spatial distance between events). Crucially, we observed a significant interaction between EC subregion and distance type (F(1,25)=7.40, p=0.011, Figure 2B and E). Further, the main effect of EC subregion was significant (F(1,25)=5.18, p=0.029), while the main effect of distance type was not (F(1,25)=0.84, p=0.367). Based on the significant interaction, we conducted planned post-hoc comparisons, which revealed significant differences (Bonferroni-corrected alpha-level of 0.025) between the mapping of temporal and spatial distances in alEC (T(25)=-2.91, p=0.007) and a significant difference between temporal mapping in alEC compared to pmEC (T(25)=-3.52, p=0.001).

Operationalizing the temporal structure in terms of the ordinal distances between object positions in the sequence yielded comparable results since our design did not disentangle time elapsed from ordinal positions as objects were encountered at regular intervals along the route. Pattern similarity change in alEC correlated significantly with ordinal temporal distances (Figure 4, T(25)=-3.37, p=0.002), an effect further qualified by a significant interaction in the two-by-two repeated measures ANOVA contrasting the effects of ordinal temporal and Euclidean spatial distances in the entorhinal subregions (interaction: F(1,25)=7.11, p=0.012; main effect of EC subregion: F(1,25)=4.97, p=0.033; main effect of distance type: F(1,25)=0.84, p=0.365). Alternative to the quantification of spatial relationships as Euclidean distances we calculated geodesic distances between object positions (Figure 1—figure supplement 2B–E). Entorhinal pattern similarity change was not correlated with geodesic distances based on shortest paths between locations using all positions not obstructed by buildings or other obstacles (Figure 2—figure supplement 2A, alEC: T(25)=0.82, p=0.436, pmEC: T(25)=0.73, p=0.479) or based on shortest paths using only the street network (Figure 2—figure supplement 2B, alEC: T(25)=0.36, p=0.715, pmEC: T(25)=0.92, p=0.375). Furthermore, the interaction of the two-by-two repeated measures ANOVA with the factors entorhinal subregion and distance type remained significant when using geodesic spatial distances based on shortest paths using all non-obstructed positions (interaction: F(1,25)=6.96, p=0.014; main effect of EC subregion: F(1,25)=5.18, p=0.031; main effect of distance type: F(1,25)=0.99, p=0.330) or the street network only (interaction: F(1,25)=4.30, p=0.048; main effect of EC subregion: F(1,25)=6.68, p=0.017; main effect of distance type: F(1,25)=0.81, p=0.376). Spatial and temporal signal-to-noise ratios did not differ between alEC and pmEC (Figure 2—figure supplement 3), ruling out that differences in signal quality might explain the observed effects.

Figure 4

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Figure 4—source data 1

Z-values of correlations between pattern similarity change in the entorhinal subregions and ordinal temporal and Euclidean spatial distances.

https://doi.org/10.7554/eLife.45333.018

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Does the presentation of object images during the post-learning picture viewing task elicit reactivations of similar representations from the pre-scan? For example, associations might be formed between objects encountered in succession along the route, which might result in the reactivation of neighboring objects after learning. To test this notion, we trained pattern classifiers to distinguish object representations on the pre-learning scan and tested these classifiers on the post-learning scan (see Materials and methods). We observed classifier accuracies exceeding chance levels in the lateral occipital cortex (LOC, Figure 2—figure supplement 4, T(25)=7.54, p<0.001, see Materials and methods) — known to be involved in visual object processing (Grill-Spector et al., 2001). In the entorhinal cortex, classification accuracies did not exceed chance levels (alEC: T(25)=-0.08, p=0.941; pmEC: T(25)=0.53, p=0.621). Next, we examined classifier predictions as a function of lag along the sequence. If effects in the alEC are driven by the reactivation of objects at neighboring sequence positions, then one might expect systematic classification errors, where an object might likely be confused with preceding or successive objects. In the entorhinal cortex, classifier evidence did not exceed chance levels for the three objects preceding (Figure 2—figure supplement 4B, alEC: most extreme T(25)=1.13; minimum p=0.270; pmEC: most extreme T(25)=1.00; min. p=0.332) or following (alEC: most extreme T(25)=-2.07; min. p=0.055; pmEC: most extreme T(25)=-0.83; min. p=0.414) an object. We also did not observe above-chance classifier evidence for nearby objects in the LOC, but rather classifier evidence was below chance levels for some lags, potentially due to high classification accuracies at no lag (preceding objects: most extreme T(25)=-2.51; min. p=0.018; successive objects: most extreme T(25)=-4.09; min. p<0.001).

Collectively, our findings demonstrate that, within the EC, only representations in the anterior-lateral subregion changed to resemble the temporal structure of the event sequence and that this mapping was specific to the temporal rather than the spatial dimension.

We examined the similarity of multi-voxel patterns to demonstrate that alEC representations reflect the experienced temporal event structure. Despite being cued in random order after learning, these representations related to a holistic temporal map of the sequence structure. Moreover, entorhinal pattern similarity change correlated with participants’ recall behavior and we recovered the timeline of events during learning from these representations.

Our hypothesis for temporal mapping in the alEC specifically was based on a recent finding demonstrating that population activity in the rodent lateral EC carries information from which time can be decoded at different scales ranging from seconds to days (Tsao et al., 2018). Time could be decoded with higher accuracies from the lateral EC than the medial EC and hippocampal subfield CA3. During a structured task in which the animal ran repeated laps on a maze separated into different trials, neural trajectories through population activity space were similar across trials, illustrating that the dynamics of lateral EC neural signals were more stable than during free foraging (Tsao et al., 2018). Consistently, temporal coding was improved for time within a trial during the structured task compared to episodes of free foraging. These findings support the notion that temporal information in the lateral EC might inherently arise from the encoding of experience (Tsao et al., 2018). In our task, relevant factors contributing to a similar experience of the route on each lap are not only the encounters of objects in a specific order at their respective positions, but also recognizing and passing salient landmarks as well as traveled distance and navigational demands in general. Changes in metabolic states and arousal presumably varied more linearly over time. Slowly drifting activity patterns have been observed also in the human medial temporal lobe (Folkerts et al., 2018) and EC specifically (Lositsky et al., 2016). A representation of time within a known trajectory in the alEC could underlie the encoding of temporal relationships between events in our task, where participants repeatedly navigated along the route to learn the positions of objects. Hence, temporal mapping in the alEC as we report here might help integrate hippocampal spatio-temporal event maps (Deuker et al., 2016).

Our findings demonstrate that alEC representations reflect the temporal structure of events after learning. This finding further dovetails with a recent fMRI study (Montchal et al., 2019), in which participants indicated when a still frame was encountered over the course of an episode of a sitcom. The alEC activated more strongly for the third of trials in which participants recalled temporal information most accurately compared to the third of trials in which temporal precision was lowest (Montchal et al., 2019). Going beyond the relationship of univariate activation differences to the precision of temporal memory recall, we focused on the content of alEC activation patterns and demonstrate that the alEC represents the temporal structure of events after learning.

One possibility for why alEC multi-voxel patterns resemble a holistic temporal map of the event structure in our task is the reactivation of temporal context information. If alEC neural populations traverse similar population state trajectories on each lap, they would carry information about time within a lap. A given object would be associated with a similar alEC population state on each lap. Associations with temporally drifting signals during the learning task would result in representational changes relative to the baseline scan that, if reactivated in the post-learning picture viewing task, reflect the experienced temporal structure of object encounters. This might explain the observed pattern similarity structure with relatively increased similarity for objects encountered in temporal proximity during learning and decreased similarity for items encountered after longer delays. While this interpretation is in line with data from rodent electrophysiology (Tsao et al., 2018) and the framework proposed by the temporal context model (Howard and Kahana, 2002; Howard et al., 2005) as well as evidence for neural contiguity effects in image recognition tasks (Howard et al., 2012; Folkerts et al., 2018), we cannot test the reinstatement of specific activity patterns from the learning phase directly since fMRI data were only collected during the picture viewing tasks in this study.

An alternative explanation for how the observed effects might arise is through associations between the objects. During learning, an object might become associated with preceding and successive objects, with stronger associations for objects closer in the sequence (Metcalfe and Murdock, 1981; Lewandowsky and Murdock, 1989; Jensen and Lisman, 2005). In this framework, the reactivation of associated objects during the post-learning picture viewing task could drive similarity increases for objects close together in the sequence. We tested for stable object representations from before to after learning and assessed classifier predictions to test the hypothesis that — if object reactivations underlie the effects — we might observe biased classifier evidence for the objects preceding or following a given object in the sequence. However, using classifiers trained on the picture viewing task before learning, we did not observe evidence for stable object representations in the entorhinal cortex or above-chance classifier evidence for objects nearby in the sequence after learning. Object representations in lateral occipital cortex (LOC) were stable between the picture viewing tasks. Previous studies have observed evidence for cortical reinstatement during memory retrieval (Nyberg et al., 2000; Wheeler et al., 2000; Polyn et al., 2005), modulated by hippocampal-entorhinal activity (Bosch et al., 2014). We did not observe classification accuracies exceeding chance levels for objects from nearby sequence positions in LOC, which one would expect if associative retrieval of objects accompanied by cortical reinstatement were to underlie our effects. Hence, these results fail to provide evidence for the notion that the reactivation of object representations drove our effects.

Importantly, the highly-controlled design of our study supports the interpretation that alEC representations change through learning to map the temporal event structure. The order of object presentations during the scanning sessions was randomized and thus did not reflect the order in which objects were encountered during the learning task. Since the assignment of objects to positions was randomized across participants and we analyzed pattern similarity change from a baseline scan, our findings do not go back to prior associations between the objects, but reflect information learned over the course of the experiment. Further, we presented the object images during the scanning sessions not only in the same random order, but also with the same presentation times and inter-stimulus intervals; thereby ruling out that the effects we observed relate to temporal autocorrelation of the BOLD-signal. Taken together, the high degree of experimental control of our study supports the conclusion that alEC representations change to reflect the temporal structure of acquired memories.

The long time scales of lateral EC temporal codes differ from the observation of time cells in the hippocampus and medial EC, which fire during temporal delays in highly trained tasks (Pastalkova et al., 2008; MacDonald et al., 2011; Eichenbaum, 2014; Kraus et al., 2015; Mau et al., 2018; Heys and Dombeck, 2018). Time cell ensembles change over minutes and days (Mau et al., 2018), but their firing has been investigated predominantly in the context of short delays in the range of seconds. One recent study did not find evidence for time cell sequences during a 60s-delay (Sabariego et al., 2019). In our task, one lap of the route took approximately 4.5 min on average; comparable to the 250s-duration of a trial in Tsao et al. (2018). How memories are represented at different temporal scales, which might be integrated in hierarchically nested sequences such as different days within a week, remains a question for future research.

Our assessment of temporal representations in the antero-lateral and posterior-medial subdivision of the EC was inspired by a recent report of temporal coding during free foraging and repetitive behavior in the rodent EC, which was most pronounced in the lateral EC (Tsao et al., 2018). In humans, local and global functional connectivity patterns suggest a preserved bipartite division of the EC, but along not only its medial-lateral, but also its anterior-posterior axis (Navarro Schröder et al., 2015; Maass et al., 2015). Via these entorhinal subdivisions, cortical inputs from the anterior-temporal and posterior-medial memory systems might converge onto the hippocampus (Ranganath and Ritchey, 2012; Ritchey et al., 2015). In line with hexadirectional signals in pmEC during imagination (Bellmund et al., 2016), putatively related to grid-cell population activity (Doeller et al., 2010), one might expect the pmEC to map spatial distances between object positions in our task. However, we did not observe an association of pattern similarity change in the entorhinal cortex with Euclidean or geodesic distances based on shortest paths between object positions. One potential explanation for the absence of evidence for a spatial distance signal in pmEC might be the way in which we cued participants’ memory during the picture viewing task. The presentation of isolated object images probed locations in their stored representation of the virtual city. Due to the periodic nature of grid-cell firing, different locations might not result in diverging patterns of grid-cell population activity. Hence, the design here was not optimized for the analysis of spatial representations in pmEC, if the object positions were encoded in grid-cell firing patterns as suggested by models of grid-cell function (Fiete et al., 2008; Mathis et al., 2012; Bush et al., 2015).

Our findings are in line with the role of the hippocampus in the retrieval of temporal information from memory (Copara et al., 2014; DuBrow and Davachi, 2014; Kyle et al., 2015; Nielson et al., 2015). Hippocampal pattern similarity has been shown to scale with temporal distances between events (Deuker et al., 2016; Nielson et al., 2015) and evidence for the reinstatement of temporally associated items from memory has been reported in the hippocampus (DuBrow and Davachi, 2014). Already at the stage of encoding, hippocampal and entorhinal activity have been related to later temporal memory (DuBrow and Davachi, 2014; DuBrow and Davachi, 2016; Ezzyat and Davachi, 2014; Jenkins and Ranganath, 2010; Jenkins and Ranganath, 2016; Lositsky et al., 2016; Tubridy and Davachi, 2011). For example, increased pattern similarity has been reported for items remembered to be close together compared to items remembered to be far apart in time, despite the same time having elapsed between these items (Ezzyat and Davachi, 2014). Similarly, changes in EC pattern similarity during the encoding of a narrative correlated with later duration estimates between events (Lositsky et al., 2016). Complementing these reports, our findings demonstrate that entorhinal activity patterns carry information about the temporal structure of memories at retrieval. Furthermore, the degree to which EC patterns reflected holistic representations of temporal relationships related to recall behavior characterized by the consecutive reproduction of objects encountered in temporal proximity; potentially through mental traversals of the route during memory recall. The central role of the hippocampus and entorhinal cortex in temporal memory (for review see Davachi and DuBrow, 2015; Howard, 2018; Ranganath, 2019; Wang and Diana, 2017) dovetails with the involvement of these regions in learning sequences and statistical regularities in general (Barnett et al., 2014; Garvert et al., 2017; Hsieh et al., 2014; Kumaran and Maguire, 2006; Schapiro et al., 2012; Schapiro et al., 2016; Thavabalasingam et al., 2018).

In this experiment, the paradigm was designed to disentangle temporal distances from Euclidean spatial distances between objects (Deuker et al., 2016), resulting also in a decorrelation of temporal distances and geodesic distances based on shortest paths between object positions. Since objects were encountered at regular intervals along the route, temporal distances quantified as elapsed time in seconds or, on an ordinal level of measurement, as the difference in sequence position were highly correlated measures of the sequence structure. To partially decorrelate elapsed time from ordinal temporal distances and distance traveled along the route, future studies could vary the speed of movement between different sections of the route. This might allow the investigation of the level of precision at which the hippocampal-entorhinal region stores temporal relations, in line with evidence for the integration of duration information in the representations of short sequences (Thavabalasingam et al., 2018; Thavabalasingam et al., 2019). Interestingly, a multi-scale ensemble of successor representations was recently suggested to estimate sequences of anticipated future states, including the order and distances between states (Stachenfeld et al., 2017; Momennejad and Howard, 2018); consistent with the sensitivity of neurons (Sarel et al., 2017; Gauthier and Tank, 2018; Qasim et al., 2018) and BOLD-responses (Spiers and Maguire, 2007; Viard et al., 2011; Sherrill et al., 2013; Howard et al., 2014; Chrastil et al., 2015) in the hippocampal-entorhinal region to distances and directions to navigational goals. Related to the effects of circumnavigation on travel time and Euclidean distance estimates (Brunec et al., 2017), experimental manipulations of route tortuosity could shed additional light on how, in the context of navigation, spatio-temporal event structures shape episodic memory.

In conclusion, our findings demonstrate that activity patterns in alEC, the human homologue region of the rodent lateral EC, carry information about the temporal structure of experienced events. The observed effects might be related to the reactivation of temporal contextual tags, in line with the recent report of temporal information available in rodent lateral EC population activity and models of episodic memory.

Source data files have been provided for Figures 2, 3 and 4. The virtual city Donderstown is available at https://osf.io/78uph/.

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Author details

1. Jacob LS Bellmund

   1. Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
   2. Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway
   3. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands

   Contribution

   Conceptualization, Formal analysis, Investigation, Visualization, Writing—original draft, Writing—review and editing

   For correspondence

   bellmund@cbs.mpg.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2098-4487

2. Lorena Deuker

   Department of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany

   Contribution

   Conceptualization, Formal analysis, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4939-5862

3. Christian F Doeller

   1. Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
   2. Kavli Institute for Systems Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing—review and editing

   For correspondence

   doeller@cbs.mpg.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4120-4600

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