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

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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.
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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).
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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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Articles
https://doi.org/10.1038/s41593-018-0252-8
Department of Neurobiology, Northwestern University, Evanston Illinois, Evanston, IL, USA. *e-mail: d-dombeck@northwestern.edu
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.
Results
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
completion.
Evidence for a subcircuit in medial entorhinal
cortex representing elapsed time during
immobility
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 | www.nature.com/natureneuroscience
1574
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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. ...
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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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