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Evidence for a subcircuit in medial entorhinal cortex representing elapsed time during immobility

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Abstract and Figures

The medial entorhinal cortex (MEC) is known to contain spatial encoding neurons that likely contribute to encoding spatial aspects of episodic memories. However, little is known about the role MEC plays in encoding temporal aspects of episodic memories, particularly during immobility. Here using a virtual ‘Door Stop’ task for mice, we show that MEC contains a representation of elapsed time during immobility, with individual time-encoding neurons activated at a specific moment during the immobile interval. This representation consisted of a sequential activation of time-encoding neurons and displayed variations in progression speed that correlated with variations in mouse timing behavior. Time- and space-encoding neurons were preferentially active during immobile and locomotion periods, respectively, were anatomically clustered with respect to each other, and preferentially encoded the same variable across tasks or environments. These results suggest the existence of largely non-overlapping subcircuits in MEC encoding time during immobility or space during locomotion.
Functionally and anatomically clustered populations of neurons in MEC encode space during locomotion and elapsed time during immobile intervals of the Door Stop task a, ΔF/F traces of significant transients (green traces; P < 0.01; see Methods) from individual example rest-selective cells (left) and run-selective cells (right) during running and resting periods (black traces) in Door Stop task. b, Histogram of RRI for all active cells across all FOVs in all mice during Door Stop task; transition periods and reward zone excluded. c, Top: mean ΔF/F vs. time across all correct trials of a single session for 6 individual neurons from the same FOV during the 6-s Door Stop wait interval. Purple dashed lines and arrows indicate transition period. Bottom: ΔF/F vs. time for each correct trial. Scale bars indicate 100% ΔF/F. d, Mean ΔF/F vs. time across all correct trials in a single session for all time-encoding cells (each row represents an individual neuron mean ΔF/F value) in a single FOV during the 6-s Door Stop wait interval. Mean ΔF/F is normalized to peak for each neuron (each row). e, Histogram of RRI for all time-encoding cells (red) and all space-encoding cells (blue) across all FOVs in all mice during Door Stop task; transition periods and reward zone excluded. f, MEC FOVs of GCaMP6f-labeled populations (top) colored red or blue to indicate cells encoding time or space, respectively (bottom). g, Mean pairwise distance (left) or fold-change (right) between neurons in various groups. All space- or time-encoding cells from all mice in Door Stop task. Black lines connect measures (dots) from same FOV; thick lines are means across all FOVs (n = 11 imaging fields from 7 mice; repeated-measures ANOVA, F = 11.8, P < 0.0001; between time-encoding cells vs. between all cells, P < 0.001 Tukey’s post hoc test with Bonferroni correction; between time-encoding cells vs. between time- and space-encoding cells, P < 0.01 Tukey’s post hoc test with Bonferroni correction.) Notably, spatial cells were not significantly clustered compared to all cells, although we previously found grid cells clustered compared to nongrid cells²³. This difference is likely due to the heterogeneous spatial cell population defined here, which likely includes grid, border, and spatially selective nongrid cells. ***P < 0.001, **P < 0.01; N.S., nonsignificant.
Sequence progression across time-encoding MEC cells correlates with animal wait time a, Velocity leading into (time < 0 s), during (0 s < time < 6 s) and after 6-s Door Stop wait interval for all short wait (pink) and long wait (green) correct trials (dark line, mean; shading, s.e.m.). b, Examples of normalized ΔF/F sequence for (top) all time-encoding cells from an individual trial (same cell-ordering and same session in left and right; short wait = 6.1 s, long wait = 8.0 s), (middle) across all trials from an individual FOV (same cell-ordering and same session in left and right; mean short wait = 6.3 ± 0.3 s (mean ± s.d.), mean long wait = 8.3 ± 0.7 s (mean ± s.d.)) and (bottom) for all time-encoding cells across all FOVs (same cell ordering, includes multiple sessions in left and right; mean short wait = 6.5 ± 0.3 (mean ± s.d.), mean long wait = 8.0 ± 0.7 (mean ± s.d.)), short waits (6–7 s; left) and long waits (7–9.5 s; right). Cells were ordered according to each cell’s mean center of mass across all short wait trials (earliest mean center of mass at top, latest at bottom). Pink and green lines are linear fits of short (pink, left) and long (green, right) wait sequences. c, Plot of slopes (from linear fits of cell activations per second) as a function of animal wait time for all individual trials (each circle represents a single trial, as in top panel of b). Cells were ordered according to each cell’s mean center of mass across all correct (6–9.5 s) trials (earliest mean center of mass at top, latest at bottom; n = 73 wait trials from 4 imaging fields in 3 mice).
The temporal representation formed by populations of time-encoding cells in MEC is present from the first moments of new experiences a, Views of linear tracks mice navigated in during environment-switch experiments. b, Bottom: ΔF/F vs. time for each voluntary rest period (wait trial) of a single session for 4 individual neurons from 2 different mice during the first session in the novel linear track (see a). Rest period 1 was the first time the mice stopped to rest in the novel track (orange trace). Top, mean ΔF/F (red) and velocity (black) vs. time across all rest periods; ΔF/F (orange) and locomotion velocity (gray) from first rest period. Note that negative deflection in mouse velocity trace reflects backwards movements on the treadmill. c, Pearson’s correlation between the calcium transients during each rest period and the mean timing field over all periods (y axis) as a function of the number of wait periods in novel environment (x axis). Gray, mean across all cells in a single FOV in a single session; black, mean ± s.e.m. across all cells in all sessions; n = 5 imaging fields across 3 mice. d, Cumulative distribution of the trial number on which a transient first occurred in the significant timing field (P < 0.05 from bootstrapping; see Methods) in the novel session across all time encoding cells. e, Mean fraction of trials with transients occurring within the significant timing field across all cells for the first half of wait trials in the session versus the second half of wait trials in the session (n = 5 imaging fields from 3 mice; P = 0.1875, two-sided paired Wilcoxon signed-rank test). Black circles indicate means for each session; red circles indicate means across all sessions.
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Department of Neurobiology, Northwestern University, Evanston Illinois, Evanston, IL, USA. *e-mail:
Over the past 50 years, research from humans and animal
models have implicated the medial temporal lobe, which
includes the hippocampus and MEC, in the formation
of personal memories of events that occur at specific places and
involve specific time intervals1,2. While a vast amount of research
has uncovered cellular substrates in the hippocampus and MEC
that likely make up the spatial representation required for these epi-
sodic memories38, our understanding of the temporal representa-
tion is substantially less advanced and has focused mostly on the
hippocampus911. Time-related neurons were first demonstrated in
the hippocampus using studies in which rodents were moving to
some degree, either in a running wheel12, on a treadmill13, or in a
small box14. Notably, one study found hippocampal time-related
activity during immobility15. These so-called hippocampal ‘time
cells’ fire briefly and consistently at specific times during the task,
such that behavioral time periods are tiled by a sequence of brief
neuronal activations. Strikingly, specialized circuitry representing
spatial information during immobility has also been demonstrated
in the hippocampus16,17. This suggests that separate circuitry within
the medial temporal lobe might be used to encode behaviorally rel-
evant variables between mobile and immobile periods, though it is
unclear from these studies whether the representation of elapsed
time maps onto a particular circuit(s).
In MEC, one study18 found that MEC grid cells can provide timing-
related information during treadmill running, and a separate study
found MEC neurons that were more active at low running speeds
rather than high speeds during locomotion19. Inactivation of MEC
during such mobile periods was found to produce deficits in encod-
ing memories across trace periods20,21, produce deficits in a temporal
memory task, and cause instability in downstream hippocampal time
cells22. These studies suggest that a code for elapsed time may exist
in MEC during locomotion, but it is currently unknown whether the
neural circuitry in MEC forms a representation of elapsed time during
immobility, when sensory cues may not change in a temporally infor-
mative manner. Furthermore, if such a representation exists in MEC, it
is unknown how the neural circuitry might be organized to generate it.
To explore these ideas, we used our previously developed functional
two-photon imaging methods23 to optically record from popula-
tions of layer II MEC neurons (Fig. 1a and Supplementary Fig. 1)
during mouse navigation in a novel virtual Door Stop task. The
Door Stop task combines both a locomotion-dependent virtual
navigation phase and an explicit instrumental timing phase that
was separated in time and location from reward delivery (Fig. 1b
and Supplementary Fig. 2a). Mice were trained to run down a lin-
ear track to a specific location where they encountered an invisible
door, which they could not run past, though they could still run
on the treadmill. At the door location, the mice were required to
stop and wait for at least 6 s (an auditory click signaled the start of
the 6-s interval once the treadmill velocity fell below a threshold;
see Methods); if the mice began running on the treadmill before
the expiration of the 6 s interval, the mice could not progress past
the closed door and the trial would start over (signaled by another
click). After the 6-s interval, the door would open and the mice
could run down the remaining length of the track to the reward
zone. After 6–8 weeks of training, mice ran to the invisible door and
stopped on their first attempt for the full 6-s wait period on 55.1%
of trials (Fig. 1c), referred to as ‘correct trials’. To easily compare
neural activity during immobile timing periods and neural activity
during locomotion periods, we excluded a transition zone between
these periods and excluded the reward zone when behavior was
more ambiguous (Fig. 1e, Supplementary Fig. 2a, and see Methods).
During the wait periods, mice mostly sat immobile with essentially
0 velocity with small jerky movements occurring during 12.9% of
the wait period to maintain balance on the treadmill (velocity over
wait periods = 0.33 ± 1.00 cm/s (mean ± s.d.); Fig. 1d,e). All of the
data presented in Figs. 24 using the (invisible door) Door Stop task
come only from these correct trials (see Supplementary Fig. 2b–f
for velocity on all trials). Since the mice could not see the invisible
door opening at the end of the 6-s interval, this Door Stop task
therefore required an internal temporal representation for efficient
Evidence for a subcircuit in medial entorhinal
cortex representing elapsed time during
JamesG.Heys and DanielA.Dombeck *
The medial entorhinal cortex (MEC) is known to contain spatial encoding neurons that likely contribute to encoding spatial
aspects of episodic memories. However, little is known about the role MEC plays in encoding temporal aspects of episodic
memories, particularly during immobility. Here using a virtual ‘Door Stop’ task for mice, we show that MEC contains a repre-
sentation of elapsed time during immobility, with individual time-encoding neurons activated at a specific moment during the
immobile interval. This representation consisted of a sequential activation of time-encoding neurons and displayed variations
in progression speed that correlated with variations in mouse timing behavior. Time- and space-encoding neurons were prefer-
entially active during immobile and locomotion periods, respectively, were anatomically clustered with respect to each other,
and preferentially encoded the same variable across tasks or environments. These results suggest the existence of largely non-
overlapping subcircuits in MEC encoding time during immobility or space during locomotion.
NATURE NEUROSCIENCE | VOL 21 | NOVEMBER 2018 | 1574–1582 |
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... The hippocampus (HIPP) has been considered critical for memory of elapsed time (Meck et al., 1984Jacobs et al., 2013;Yin and Meck, 2014;Sabariego et al., 2019). Recent evidence shows that neural activity in the HIPP and its major input region, the medial entorhinal cortex (MEC), encodes elapsed time through the sequential firing of time-sensitive neurons, or time cells, at specific moments of delay periods (Gill et al., 2011;Kraus et al., 2013;MacDonald et al., 2013;Kraus et al., 2015;Salz et al., 2016;Heys and Dombeck, 2018;Umbach et al., 2020). This sequential organization of firing patterns in the HIPP is likely dependent on intact MEC inputs (Schlesiger et al., 2015;Robinson et al., 2017; but see Sabariego et al., 2019), which are critical to holding information across delays (Sauvage et al., 2010;Suh et al., 2011;Kitamura et al., 2014). ...
... At this stage, rats had no access to external cues guiding the waiting period and had to retrieve a previously memorized target time to successfully complete the task. Comparable to previous work with mice in a virtual reality head-fixed preparation (Heys and Dombeck, 2018), waiting behavior in the freely moving rat became highly stereotyped, consisting of a well defined period of immobility inside the ROI, followed by significant acceleration starting on completion of the target interval (Fig. 1C). This pattern suggested a similar strategy across distinct intervals, tuning to the target interval in the absence of external temporal cues, and showed that the interval was memorized. ...
... Using rats trained in an explicit goal-directed timing task, we show that MEC is necessary for accurate online estimation of time. Interestingly, MEC time cells were previously found to correlate with elapsed time during immobility when animals needed to report an interval by waiting (Heys and Dombeck, 2018). This earlier study showed that sequences of putatively excitatory MEC time cells progressed slower or faster when mice reported longer or shorter intervals, respectively. ...
The hippocampal region has long been considered critical for memory of time, and recent evidence shows that network operations and single-unit activity in the hippocampus and medial entorhinal cortex (MEC) correlate with elapsed time. However, whether MEC activity is necessary for timing remains largely unknown. Here we expressed DREADDs (designer receptors exclusively activated by designer drugs) under the CaMKIIa promoter to preferentially target MEC excitatory neurons for chemogenetic silencing, while freely moving male rats reproduced a memorized time interval by waiting inside a region of interest. We found that such silencing impaired the reproduction of the memorized interval and led to an overestimation of elapsed time. Trial history analyses under this condition revealed a reduced influence of previous trials on current waiting times, suggesting an impairment in maintaining temporal memories across trials. Moreover, using GLM (logistic regression), we show that decoding behavioral performance from preceding waiting times was significantly compromised when MEC was silenced. In addition to revealing an important role of MEC excitatory neurons for timing behavior, our results raise the possibility that these neurons contribute to such behavior by holding temporal information across trials.
... For example, outside the NAc, synaptic eligibility traces have been found to have longer time scales of 5 s in the neocortex 30 and 10 min in the hippocampus 31 . In addition to synaptic eligibility traces, persistent activities that store eligible events in working memory can also associate temporally separated events 32 . ...
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Reward reinforces the association between a preceding sensorimotor event and its outcome. Reinforcement learning (RL) theory and recent brain slice studies explain the delayed reward action such that synaptic activities triggered by sensorimotor events leave a synaptic eligibility trace for 1 s. The trace produces a sensitive period for reward-related dopamine to induce synaptic plasticity in the nucleus accumbens (NAc). However, the contribution of the synaptic eligibility trace to behaviour remains unclear. Here we examined a reward-sensitive period to brief pure tones with an accurate measurement of an effective timing of water reward in head-fixed Pavlovian conditioning, which depended on the plasticity-related signaling in the NAc. We found that the reward-sensitive period was within 1 s after the pure tone presentation and optogenetically-induced presynaptic activities at the NAc, showing that the short reward-sensitive period was in conformity with the synaptic eligibility trace in the NAc. These findings support the application of the synaptic eligibility trace to construct biologically plausible RL models.
... It is of some significance that the medial temporal lobe which encodes episodic memories also carries information about the temporal context of objects (Hsieh et al., 2014;MacDonald et al., 2011). A subcircut in the entorhinal cortex in that lobe has been shown to represent elapsed time in immobile animals (Heys & Dombeck, 2018) and correlated ramping activity in humans (Umbach et al., 2020). ...
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Many comparisons involve sequentially presented stimuli, as perforce the case in comparisons of temporal intervals. Interactions of such stimuli are as inevitable as the spatial interactions that yield color and brightness contrast. A memory-trace theory of perception (TToP) is developed and applied to time perception. Duration is estimated based on the memorial strength of the stimuli that signal the initiation of an interval at the time of its termination. Memorial persistence depends on modality and character of the signals, which condition the response to them. When the constant difference limen on the memorial continuum is back-translated to the temporal one it yields a generalized Weber function. Memory traces interact as a function of generalization gradients: Memories of stimuli that are similar enough are aggregated-feature-bound-some veridically, others as illusory conjunctions. The resulting representations may then be judged in a discrimination paradigm, or translated back to the physical domain as reproductions of the intervals. The presentation of a standard stimulus affects the perception of the comparison stimulus, warping the ruler by which it is measured. Complementary effects are predicted for discrimination and adjustment paradigms. Thus configured, the TToP accounts for multiple special effects, variously referred to as distortions, anomalies, and illusions, that are observed with classical psychophysical methods: Scalar and nonscalar timing, modality effects, time-order errors, masking, time warping, lengthening, and Vierordt's law. Similar processes affect the perception of nontemporal stimuli whenever they are presented in sequential proximity to one another.
... Neural correlates of interval timing in the range of seconds have been found in several brain areas (Merchant et al., 2013;Paton and Buonomano, 2018;Issa et al., 2020), including prefrontal cortex (Genovesio et al., 2006;Kim et al., 2013;Xu et al., 2014;Emmons et al., 2017;Tiganj et al., 2017), pre-/supplementary motor cortex (Mita et al., 2009;Merchant et al., 2011), hippocampus (MacDonald et al., 2011, entorhinal cortex (Heys and Dombeck, 2018), and striatum (Gouvêa et al., 2015;Mello et al., 2015;Bakhurin et al., 2017;Emmons et al., 2017). What distinguishes our experiments from previous studies is twofold: (1) we tested time intervals on a continuous range and (2) we combined timing of an external event (measurement phase) and timing own behavior (reproduction phase), linking sensory and motor timing (Paton and Buonomano, 2018). ...
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As we interact with the external world, we judge magnitudes from sensory information. The estimation of magnitudes has been characterized in primates, yet it is largely unexplored in non-primate species. Here we use time interval reproduction to study rodent behavior and its neural correlates in the context of magnitude estimation. We show that gerbils display primate-like magnitude estimation characteristics in time reproduction. Most prominently their behavioral responses show a systematic overestimation of small stimuli and an underestimation of large stimuli, often referred to as regression effect. We investigated the underlying neural mechanisms by recording from medial prefrontal cortex and show that the majority of neurons respond either during the measurement or the reproduction of a time interval. Cells that are active during both phases display distinct response patterns. We categorize the neural responses into multiple types and demonstrate that only populations with mixed responses can encode the bias of the regression effect. These results help unveil the organizing neural principles of time reproduction and perhaps magnitude estimation in general.
... Researchers studied people with neurological disorders to elucidate the relationship between the performance of time perception and specific brain areas. They have found that the hippocampus, entorhinal cortex, prefrontal lobe cortex, insula, cerebellum, and basal ganglia are related to time perception, though the mechanisms involved remain somewhat unclear (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11). ...
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Background: Time perception is a subjective experience or sense of time. Previous studies have shown that Alzheimer's dementia (AD) patients have time perception deficits compared to a cognitively unimpaired control group (CU). There are only a few studies on dementia with Lewy bodies (DLB) patients' time perception in comparison with CU and AD patients. Early intervention and prescription of the right medicine may delay the deterioration of AD and DLB, moreover, knowing how prodromal AD (prAD) and prodromal DLB's (prDLB) time perception differ from each other might be helpful for future understanding of these two dementias. Therefore, the purpose of this study is to explore the difference in time perception performance between prodromal AD and prodromal DLB. Methods: We invited people diagnosed with prAD, prDLB, and CU to participate in this study. Tests of verbal estimation of time and time interval production were used to assess their time perception. We analyzed the average time estimation (ATE), absolute error score (ABS), coefficient of variance (CV), and subjective temporal unit (STU) within the three groups. Results: A total of 40 prAD, 30 prDLB, and 47 CU completed the study. In the verbal estimation test, the CV for the prAD was higher than both prDLB and CU at the 9 s interval, and the CV of prAD was higher than CU at the 27 s interval. In the time interval production test, the subjective time units of prDLB were higher than prAD at the 10 s interval, while those of both prDLB and CU were higher than prAD at the 30 s interval. The percentage of subjects with STU < 1.0 s, indicating overestimation, was higher in prAD than both prDLB and CU. Conclusion: Time perception of prAD patients showed imprecision and overestimation of time, while prDLB tended to underestimate time intervals. No significant difference was found in accuracy among the three groups. It is speculated that the clinical and pathological severity of the two prodromal dementia stages may be different, and some patients have not yet had their time perception affected.
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The relation between the processing of space and time in the brain has been an enduring cross-disciplinary question. Grid cells have been recognized as a hallmark of the mammalian navigation system, with recent studies attesting to their involvement in organization of conceptual knowledge in humans. To determine whether grid-cell-like representations support temporal processing, we asked subjects to mentally simulate changes in age and time-of-day, each constituting a trajectory in an age-day space, while undergoing fMRI. We found that grid-cell-like representations supported trajecting across this age-day space. Furthermore, brain regions concurrently coding past-to-future orientation positively modulated the magnitude of grid-cell-like representation in the left entorhinal cortex. Our findings suggest that temporal processing may be supported by spatially modulated systems, and that innate regularities of abstract domains may interface and alter grid-cell-like representations, similarly to spatial geometry.
An immune molecule has an unexpected role in memory formation — specifically, in limiting the window of time in which newly forming memories can be contextually linked. The protein CCR5 limits neuronal co-allocation to memory networks.
How do we recollect specific events that have occurred during continuous ongoing experience? There is converging evidence from non-human animals that spatially modulated cellular activity of the hippocampal formation supports the construction of ongoing events. On the other hand, recent human oriented event cognition models have outlined that our experience is segmented into discrete units, and that such segmentation can operate on shorter or longer timescales. Here, we describe a unification of how these dynamic physiological mechanisms of the hippocampus relate to ongoing externally and internally driven event segmentation, facilitating the demarcation of specific moments during experience. Our cross-species interdisciplinary approach offers a novel perspective in the way we construct and remember specific events, leading to the generation of many new hypotheses for future research.
Our memory for time is a fundamental ability that we use to judge the duration of events, put our experiences into a temporal context, and decide when to initiate actions. The medial entorhinal cortex (MEC), with its direct projections to the hippocampus, has been proposed to be the key source of temporal information for hippocampal time cells. However, the behavioral relevance of such temporal firing patterns remains unclear, as most of the paradigms used for the study of temporal processing and time cells are either spatial tasks or tasks for which MEC function is not required. In this study, we asked whether the MEC is necessary for rats to perform a time duration discrimination task (TDD), in which rats were trained to discriminate between 10-s and 20-s delay intervals. After reaching a 90% performance criterion, the rats were assigned to receive an excitotoxic MEC-lesion or sham-lesion surgery. We found that after recovering from surgery, rats with MEC lesions were impaired on the TDD task in comparison to rats with sham lesions, failing to return to criterion performance. Their impairment, however, was specific to the longer, 20-s delay trials. These results indicate that time processing is dependent on MEC neural computations only for delays that exceed 10 seconds, perhaps because long-term memory resources are needed to keep track of longer time intervals.
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During our daily life, we depend on memories of past experiences to plan future behaviour. These memories are represented by the activity of specific neuronal groups or 'engrams'1,2. Neuronal engrams are assembled during learning by synaptic modification, and engram reactivation represents the memorized experience 1 . Engrams of conscious memories are initially stored in the hippocampus for several days and then transferred to cortical areas 2 . In the dentate gyrus of the hippocampus, granule cells transform rich inputs from the entorhinal cortex into a sparse output, which is forwarded to the highly interconnected pyramidal cell network in hippocampal area CA3 3 . This process is thought to support pattern separation 4 (but see refs. 5,6). CA3 pyramidal neurons project to CA1, the hippocampal output region. Consistent with the idea of transient memory storage in the hippocampus, engrams in CA1 and CA2 do not stabilize over time7-10. Nevertheless, reactivation of engrams in the dentate gyrus can induce recall of artificial memories even after weeks 2 . Reconciliation of this apparent paradox will require recordings from dentate gyrus granule cells throughout learning, which has so far not been performed for more than a single day6,11,12. Here, we use chronic two-photon calcium imaging in head-fixed mice performing a multiple-day spatial memory task in a virtual environment to record neuronal activity in all major hippocampal subfields. Whereas pyramidal neurons in CA1-CA3 show precise and highly context-specific, but continuously changing, representations of the learned spatial sceneries in our behavioural paradigm, granule cells in the dentate gyrus have a spatial code that is stable over many days, with low place- or context-specificity. Our results suggest that synaptic weights along the hippocampal trisynaptic loop are constantly reassigned to support the formation of dynamic representations in downstream hippocampal areas based on a stable code provided by the dentate gyrus.
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Musicians can perform at different tempos, speakers can control the cadence of their speech, and children can flexibly vary their temporal expectations of events. To understand the neural basis of such flexibility, we recorded from the medial frontal cortex of nonhuman primates trained to produce different time intervals with different effectors. Neural responses were heterogeneous, nonlinear, and complex, and they exhibited a remarkable form of temporal invariance: firing rate profiles were temporally scaled to match the produced intervals. Recording from downstream neurons in the caudate and from thalamic neurons projecting to the medial frontal cortex indicated that this phenomenon originates within cortical networks. Recurrent neural network models trained to perform the task revealed that temporal scaling emerges from nonlinearities in the network and that the degree of scaling is controlled by the strength of external input. These findings demonstrate a simple and general mechanism for conferring temporal flexibility upon sensorimotor and cognitive functions.
Hippocampal place cell ensembles form a cognitive map of space during exposure to novel environments. However, surprisingly little evidence exists to support the idea that synaptic plasticity in place cells is involved in forming new place fields. Here we used high-resolution functional imaging to determine the signaling patterns in CA1 soma, dendrites, and axons associated with place field formation when mice are exposed to novel virtual environments. We found that putative local dendritic spikes often occur prior to somatic place field firing. Subsequently, the first occurrence of somatic place field firing was associated with widespread regenerative dendritic events, which decreased in prevalence with increased novel environment experience. This transient increase in regenerative events was likely facilitated by a reduction in dendritic inhibition. Since regenerative dendritic events can provide the depolarization necessary for Hebbian potentiation, these results suggest that activity-dependent synaptic plasticity underlies the formation of many CA1 place fields.
The hippocampus is famous for mapping locations in spatially organized environments, and several recent studies have shown that hippocampal networks also map moments in temporally organized experiences. Here I consider how space and time are integrated in the representation of memories. The brain pathways for spatial and temporal cognition involve overlapping and interacting systems that converge on the hippocampal region. There is evidence that spatial and temporal aspects of memory are processed somewhat differently in the circuitry of hippocampal subregions but become fully integrated within CA1 neuronal networks as independent, multiplexed representations of space and time. Hippocampal networks also map memories across a broad range of abstract relations among events, suggesting that the findings on spatial and temporal organization reflect a generalized mechanism for organizing memories.
Network activity is strongly tied to animal movement; however, hippocampal circuits selectively engaged during locomotion or immobility remain poorly characterized. Here we examined whether distinct locomotor states are encoded differentially in genetically defined classes of hippocampal interneurons. To characterize the relationship between interneuron activity and movement, we used in vivo, two-photon calcium imaging in CA1 of male and female mice, as animals performed a virtual-reality (VR) track running task. We found that activity in most somatostatin-expressing and parvalbumin-expressing interneurons positively correlated with locomotion. Surprisingly, nearly one in five somatostatin or one in seven parvalbumin interneurons were inhibited during locomotion and activated during periods of immobility. Anatomically, the somata of somatostatin immobility-activated neurons were smaller than those of movement-activated neurons. Furthermore, immobility-activated interneurons were distributed across cell layers, with somatostatin-expressing cells predominantly in stratum oriens and parvalbumin-expressing cells mostly in stratum pyramidale. Importantly, each cell’s correlation between activity and movement was stable both over time and across VR environments. Our findings suggest that hippocampal interneuronal microcircuits are preferentially active during either movement or immobility periods. These inhibitory networks may regulate information flow in “labeled lines” within the hippocampus to process information during distinct behavioral states.
Recent studies have shown that hippocampal “time cells” code for sequential moments in temporally organized experiences. However, it is currently unknown whether these temporal firing patterns critically rely on upstream cortical input. Here we employ an optogenetic approach to explore the effect of large-scale inactivation of the medial entorhinal cortex on temporal, as well as spatial and object, coding by hippocampal CA1 neurons. Medial entorhinal inactivation produced a specific deficit in temporal coding in CA1 and resulted in significant impairment in memory across a temporal delay. In striking contrast, spatial and object coding remained intact. Further, we extended the scope of hippocampal phase precession to include object information relevant to memory and behavior. Overall, our work demonstrates that medial entorhinal activity plays an especially important role for CA1 in temporal coding and memory across time.
The medial entorhinal cortex (mEC) has been identified as a hub for spatial information processing by the discovery of grid, border, and head-direction cells. Here we find that in addition to these well-characterized classes, nearly all of the remaining two-thirds of mEC cells can be categorized as spatially selective. We refer to these cells as nongrid spatial cells and confirmed that their spatial firing patterns were unrelated to running speed and highly reproducible within the same environment. However, in response to manipulations of environmental features, such as box shape or box color, nongrid spatial cells completely reorganized their spatial firing patterns. At the same time, grid cells retained their spatial alignment and predominantly responded with redistributed firing rates across their grid fields. Thus, mEC contains a joint representation of both spatial and environmental feature content, with specialized cell types showing different types of integrated coding of multimodal information.
The spatial receptive fields of neurons in medial entorhinal cortex layer II (MECII) and in the hippocampus suggest general and environment-specific maps of space, respectively. However, the relationship between these receptive fields remains unclear. We reversibly manipulated the activity of MECII neurons via chemogenetic receptors and compared the changes in downstream hippocampal place cells to those of neurons in MEC. Depolarization of MECII impaired spatial memory and elicited drastic changes in CA1 place cells in a familiar environment, similar to those seen during remapping between distinct environments, while hyperpolarization did not. In contrast, both manipulations altered the firing rate of MEC neurons without changing their firing locations. Interestingly, only depolarization caused significant changes in the relative firing rates of individual grid fields, reconfiguring the spatial input from MEC. This suggests a novel mechanism of hippocampal remapping whereby rate changes in MEC neurons lead to locational changes of hippocampal place fields.
The neural representation of space relies on a network of entorhinal-hippocampal cell types with firing patterns tuned to different abstract features of the environment. To determine how this network is set up during early postnatal development, we monitored markers of structural maturation in developing mice, both in naïve animals and after temporally restricted pharmacogenetic silencing of specific cell populations. We found that entorhinal stellate cells provide an activity-dependent instructive signal that drives maturation sequentially and unidirectionally through the intrinsic circuits of the entorhinal-hippocampal network The findings raise the possibility that a small number of autonomously developing neuronal populations operate as intrinsic drivers of maturation across widespread regions of cortex.
Our sense of time is far from constant. For instance, time flies when we are having fun, and it slows to a trickle when we are bored. Midbrain dopamine neurons have been implicated in variable time estimation. However, a direct link between signals carried by dopamine neurons and temporal judgments is lacking. We measured and manipulated the activity of dopamine neurons as mice judged the duration of time intervals. We found that pharmacogenetic suppression of dopamine neurons decreased behavioral sensitivity to time and that dopamine neurons encoded information about trial-to-trial variability in time estimates. Last, we found that transient activation or inhibition of dopamine neurons was sufficient to slow down or speed up time estimation, respectively. Dopamine neuron activity thus reflects and can directly control the judgment of time.