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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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... Consider a TS group S whose group operator S(α) ∈ S will map a time scale t (the scale of the time axis) into a new scale t, Figure 2: (A-B) A disentangled neural circuit that represents the sequence's time and pattern information in the recurrent and feedforward circuits respectively. The recurrent circuit generates a neural sequence embedding the "time" manifold z (B top, adapted from [19]) regardless of the sequence pattern, and then the feedforward circuit maps the neural sequence into sequences with arbitrary patterns (B bottom). A control input (green circle) is supposed to modulate the temporal scale of the neural sequences generated by the recurrent circuit. ...
Time perception is fundamental in our daily life. An important feature of time perception is temporal scaling (TS): the ability to generate temporal sequences (e.g., movements) with different speeds. However, it is largely unknown about the mathematical principle underlying TS in the brain. The present theoretical study investigates temporal scaling from the Lie group point of view. We propose a canonical nonlinear recurrent circuit dynamics, modeled as a continuous attractor network, whose neuronal population responses embed a temporal sequence that is TS equivariant. We find the TS group operators can be explicitly represented by a time-invariant control input to the network, whereby the input gain determines the TS factor (group parameter), and the spatial offset between the control input and the network state on the continuous attractor manifold gives rise to the generator of the Lie group. The recurrent circuit's neuronal responses are consistent with experimental data. The recurrent circuit can drive a feedforward circuit to generate complex sequences with different temporal scales, even in the case of negative temporal scaling ("time reversal"). Our work for the first time analytically links the abstract temporal scaling group and concrete neural circuit dynamics.
... Individual neurons peaked during the start, middle and end of the interval, providing a fair match to the firing patterns in dorsal striatum found by Gouvêa et al. (2015). This time-cell activity by the LDN was first shown by Voelker and Eliasmith (2018) and is similar to time-cell activity found in hippocampus (Eichenbaum, 2014), entorhinal cortex (e.g., Heys & Dombeck, 2018), prefrontal cortex (e.g., Tiganj et al., 2017) and striatum (e.g., Mello et al., 2015). ...
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Time is a central dimension against which perception, action, and cognition play out. From anticipating when future events will happen to recalling how long ago previous events occurred, humans and animals are exquisitely sensitive to temporal structure. Empirical evidence seems to suggest that estimating time prospectively (i.e., in passing) is qualitatively different from estimating time in retrospect (i.e., after the event is over). Indeed, computational models that attempt to explain both prospective and retrospective timing assume a fundamental separation of their underlying processes. We, in contrast, propose a new neurocomputational model of timing, the Unified Temporal Coding (UTC) model that unifies prospective and retrospective timing through common principles. The UTC model assumes that both stimulus and timing information are represented inside the same rolling window of input history. As a consequence, the UTC model explains a wide range of phenomena typically covered by specialized models, such as conformity to and violations of the scalar property, neural responses underlying timing, timing behavior under normal and distracting conditions, common capacity limits in timing and working memory, and how timing depends on attention. Strikingly, by assuming that prospective and retrospective timing rely on the same principles and are implemented in the same neural network, a simple attentional gain mechanism can resolve the apparently paradoxical effect of cognitive load on prospective and retrospective timing.
The hippocampus is a higher-order brain structure responsible for encoding new episodic memories and predicting future outcomes. In absence of external stimuli, neurons in the hippocampus express sequential activities which have been proposed to support path integration by tracking elapsed time, distance traveled, and other idiothetic variables. On the other hand, with sufficient external sensory inputs, hippocampal neurons can fire with respect to allocentric cues. Previously, these idiothetic codes have been described in conditions where running speed is clamped experimentally. To this day, the balance of idiothetic and allocentric representations in freely moving conditions remains unclear. Additionally, whether CA1 and CA3 temporal and distance codes are transmitted downstream to the lateral septum has not been established. Here, we develop an unsupervised model trained to compress neural information with minimal loss, and find that we can efficiently decode elapsed time and distance travelled from low-dimensional embeddings of neural activity in freely moving mice. We also developed unbiased information metrics that are minimally sensitive to quantization parameters and enable comparisons across modalities and brain regions. In more than 30,000 CA1 pyramidal neurons, we show that spatiotemporal information is represented as a mixture of self-motion, idiothetic as well as allocentric information, the balance of which is dictated by task demand and environmental conditions. In particular, we find that a subset of CA1 pyramidal neurons encode the spatiotemporal distance relative to rewards. Single cell and population statistics across the hippocampal-septal circuit reveal that idiothetic variables emerge in CA1 and are integrated postsynaptically in the lateral septum. Finally, we implement a computational model trained to replicate real world neural activity, and find that grid cells could provide a plausible input for CA1 representations of time and distance. Altogether, our results suggest that hippocampal CA1 continuously integrates both idiothetic and allocentric signals depending on task demand and available cues, and these high-level representations are effectively transmitted to downstream regions.
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The ability of rodents to use visual cues for successful navigation and goal-directed behavior has been long appreciated, although the neural mechanisms supporting sensory representations in navigational circuits are largely unknown. Navigation is fundamentally dependent on the hippocampus and closely connected entorhinal cortex, whose neurons exhibit characteristic firing patterns corresponding to the animal's location. The medial entorhinal cortex (MEC) receives direct projections from sensory areas in the neocortex, suggesting the ability to encode sensory information. To examine this possibility, we performed high-density recordings of MEC neurons in awake, head-fixed mice presented with simple visual stimuli and assessed the dynamics of sensory-evoked activity. We found a large fraction of neurons exhibited robust responses to visual input that shaped activity relative to ongoing network dynamics. Visually responsive cells could be separated into subgroups based on functional and molecular properties within deep layers of the dorsal MEC, suggesting diverse populations within the MEC contribute to sensory encoding. We then showed that optogenetic suppression of retrosplenial cortex afferents within the MEC strongly reduced visual responses. Overall, our results demonstrate the the MEC can encode simple visual cues in the environment that can contribute to neural representations of location necessary for accurate navigation.
Discriminating between temporal features in sensory stimuli is critical to complex behavior and decision-making. However, how sensory cortical circuit mechanisms contribute to discrimination between subsecond temporal components in sensory events is unclear. To elucidate the mechanistic underpinnings of timing in primary visual cortex (V1), we recorded from V1 using two-photon calcium imaging in awake-behaving mice performing a go/no-go discrimination timing task, which was composed of patterns of subsecond audiovisual stimuli. In both conditions, activity during the early stimulus period was temporally coordinated with the preferred stimulus. However, while network activity increased in the preferred condition, network activity was increasingly suppressed in the nonpreferred condition over the stimulus period. Multiple levels of analyses suggest that discrimination between subsecond intervals that are contained in rhythmic patterns can be accomplished by local neural dynamics in V1.
It is well-accepted in neuroscience that animals process time internally to estimate the duration of intervals lasting between one and several seconds. More than 100 years ago, Henri Bergson nevertheless remarked that, because animals have memory, their inner experience of time is ever-changing, making duration impossible to measure internally and time a source of change. Bergson proposed that quantifying the inner experience of time requires its externalization in movements (observed or self-generated), as their unfolding leaves measurable traces in space. Here, studies across species are reviewed and collectively suggest that, in line with Bergson's ideas, animals spontaneously solve time estimation tasks through a movement-based spatialization of time. Moreover, the well-known scalable anticipatory responses of animals to regularly spaced rewards can be explained by the variable pressure of time on reward-oriented actions. Thus, instead of considering time as static information processed by the brain, it might be fruitful to conceptualize it as a kind of force to which animals are more or less sensitive depending on their internal state and environment.
In mammals, the activity of neurons in the entorhinal-hippocampal network is modulated by the animal's position and its movement through space. At multiple stages of this distributed circuit, distinct populations of neurons can represent a rich repertoire of navigation-related variables like the animal's location, the speed and direction of its movements, or the presence of borders and objects. Working together, spatially tuned neurons give rise to an internal representation of space, a cognitive map that supports an animal's ability to navigate the world and to encode and consolidate memories from experience. The mechanisms by which, during development, the brain acquires the ability to create an internal representation of space are just beginning to be elucidated. In this review, we examine recent work that has begun to investigate the ontogeny of circuitry, firing patterns, and computations underpinning the representation of space in the mammalian brain.
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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.
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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.
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.
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 lights go on in order Grid cells and place cells in the brain function as part of a circuit that helps us figure out where we are in our physical world. Donato et al. examined how that circuit develops in the brains of mice. Expression patterns of doublecortin and parvalbumin revealed that neurons in the circuit mature in the order in which information flows. Maturation of each piece of the circuit depends on excitatory neuronal activity from the preceding portion. Stellate cells, in contrast, follow an endogenous maturation program. The stellate cells are responsible for initiating the circuit's developmental progression. Science , this issue p. eaai8178
Time is a subjective experience Time, like space, is one of the fundamental dimensions of all our experiences. However, organisms do not work like clocks, and our judgment about the passage of time is variable, depending on circumstances. Soares et al. systematically investigated midbrain dopaminergic neurons during timing behavior in mice (see the Perspective by Simen and Matell). When measuring and manipulating mouse activity, the authors observed that dopaminergic neurons controlled temporal judgments on a time scale of seconds. Science , this issue p. 1273 ; see also p. 1231