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The Journal of Neuroscience, April 15, 2003, 23(8):3478
Rapid Spatial Reorientation and Head Direction Cells
Michaël B.
Zugaro,
Angelo
Arleo,
Alain
Berthoz, and
Sidney I.
Wiener
Centre National de la Recherche Scientifique-Collège de
France, Laboratoire de Physiologie de la Perception et de l'Action,
75231 Paris Cedex 05, France
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ABSTRACT |
It is surprising how quickly we can find our bearings when suddenly
confronted with a familiar environment, for instance when the lights
are turned on in a dark room. Subjectively, this appears to occur
almost instantaneously, yet the neural processes permitting this rapid
reorientation are unknown. A likely candidate is the head direction
(HD) cell system. These limbic neurons found in several brain regions,
including the thalamus and the hippocampus, discharge selectively when
the head of an animal is oriented in a particular ("preferred")
direction. This neuronal activity is independent of position and
ongoing behavior and is thus likely to constitute a physiological basis
for the sense of direction. Remarkably, although the HD cell system has
properties resembling those of a compass, it is independent of
geomagnetic fields. Rather, the preferred directions of the HD cells
are strongly anchored to visual cues in the environment. Here, we bring
evidence for the first time that a fundamental component of the
capacity to rapidly reorient in a familiar environment may be brought
about by updating of HD cell responses as rapidly as 80 msec after
changes in the visual scene. Continuous attractor networks have been
used successfully to model HD cell ensemble dynamics. The present
results suggest that after large rotations of the surrounding
landmarks, activity in such networks may be propagated in abrupt jumps
rather than in a gradually progressive manner.
Key words:
anterodorsal thalamic nucleus; update latency; spatial memory; landmark; visual orientation; attractor network
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Introduction |
Head direction (HD) cells discharge
selectively when the head of a monkey, rat, mouse, or chinchilla is
oriented in a particular direction of the environment, which is
referred to as the preferred direction (Ranck, 1984 ; Knierim et al.,
1998; Taube, 1998; Blair et al., 1999; Robertson et al., 1999; Khabbaz
et al., 2000; Muir and Taube, 2002). Although HD cell responses can be
influenced by various multisensory and motor signals (Blair and Sharp,
1996; Stackman and Taube, 1997; Goodridge et al., 1998; Zugaro et al., 2001b), the preferred directions are primarily updated on the basis of
visual landmarks (Taube, 1995; Zugaro et al., 2001a). But how rapidly
are preferred directions updated after rotation of visual landmarks?
Although this question was addressed briefly in previous studies
(Knierim et al., 1998; Zugaro et al., 2000), the experimental protocols
could not measure updates occurring faster than 15 sec, because those
procedures required recordings while the head of the animal was
reoriented in many different directions. However, neural network
simulations predict that preferred direction updates could be as rapid
as a few hundred milliseconds (Zhang, 1996; Redish, 1999). The
experimental protocol described here was developed to test this prediction.
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Materials and Methods |
Electrode implantation. Four adult male Long-Evans
rats (200~250 gm; CERJ, Le Genest-St-Isle, France) were tranquilized
with xylazine, deeply anesthetized with pentobarbital (40 mg/kg), and then surgically implanted with electrodes in the anterodorsal nucleus
of the thalamus (anteroposterior, approximately  1.6-2.0 mm;
mediolateral, ±1.2 mm relative to bregma; 3.8 mm ventral to brain
surface). The electrode assembly consisted of bundles of eight, single
wire electrodes (Formvar-coated nichrome wires: diameter, 25 µm;
impedance, 200-800 k ) inserted in a 30 gauge stainless steel
cannula and mounted on an advanceable connector assembly (Wiener,
1993). The headstage was permanently fixed to the skull with dental
acrylic and tiny screws. All animal care and experimental protocols
were in accord with institutional and international standards and legal
regulations (Certificate 7186, Ministère de l'Agriculture et de
la Pêche).
Data acquisition. During the recording sessions, electrode
signals passed through field effect transistors and were differentially amplified (10,000×) and filtered (300 Hz to 5 kHz, notch at 50 Hz).
The signals were acquired on a DataWave Discovery system (Longmont,
CO). Two small infrared light-emitting diodes (10 cm separation)
mounted above the headstage were detected by a video camera, and their
moment-to-moment positions were stored by the data acquisition system
for off-line analyses [for details, see Zugaro et al. (2001b)].
Apparatus. The experimental setup consisted of a black
cylinder (diameter, 76 cm; height, 50 cm) with a white card attached to
its inner wall. This subtended 90° and served as the principal visual
cue. Water could be delivered to a small reservoir at the center of the
cylinder to keep the lightly water-deprived rats immobile without
applying physical restraint (Zugaro et al., 2001b), which is known to
depress directional responses (Taube, 1995). The 3 × 3 × 3 m recording chamber was surrounded by black curtains suspended
from the ceiling along the four walls. Illumination was provided by a
40 W overhead lamp on the ceiling that diffused light evenly within the
cylinder. All electronic instruments and computers were situated
outside of the curtains, and the entire experimental room was
phonically isolated from the rest of the building.
Behavioral task. At the beginning of each recording session,
the preferred directions of the HD cells were determined as the rat
foraged for small food pellets distributed sparsely onto an elevated
circular platform over a 5 min period. Then, the unrestrained rat
remained immobile with its head oriented in the preferred direction
while receiving small drops (~30 µl) of water at 0.5-1 sec
intervals from the reservoir at the center of the platform. The rats
had previously been trained to remain immobile at the water spout with
a behavioral shaping procedure: water delivery was triggered only when
the rat was positioned at the reservoir at the appropriate orientation,
and water was ceased as soon as it moved away from the preferred
direction. (The solenoid valve that released the water made a distinct
clicking sound that likely served as a cue.) After a stable recording
of the directional response had been established (Fig.
1A), the room light was turned off and
the card was manually rotated along the cylinder wall by 90° to a new
orientation (Fig. 1B). This was done as rapidly and
silently as possible to keep the rat from detecting the rotations. The
light was then turned back on (Fig. 1C). This was intended to trigger an update in the HD system. Because the preferred directions of the HD cells are anchored to visual cues (Taube, 1998), the previously active cells would stop firing, whereas others would become
active. After a short delay, the light was turned off again, and the
card was rotated back to the initial position (Fig.
1D). The light was then turned back on (Fig.
1E) to retrigger the initial cell responses. We
measured the latencies of the cell responses to the light onset with
the card in each position (referred to as a "reorientation
event").
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Figure 1.
Experimental procedure. A, Because
the rat remains immobile oriented in the (previously determined)
preferred direction while drinking water from the reservoir (left),
full response curves cannot be sampled. Rather, only the cell responses
corresponding to this particular head direction can now be recorded
(black circle in right panel). B, The light is turned
off, and the card is rotated by 90° along the cylinder wall.
C, The light is turned back on. This triggers a shift in
the directional response curve of the neuron because this activity is
anchored to visual cues (right panel). Accordingly there should be a
marked decrease in firing rate (compare the filled circles in
B and C, right panels). D,
The light is turned off again, and the card is returned to the standard
position. E, The light is turned back on. The preferred
direction shifts back to its initial orientation (right panel). This
corresponds to a marked increase in discharge frequency (compare the
filled circles in D and E, right panels).
Steps B through E are repeated until the
rat is satiated and no longer remains immobile at the center.
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Data analysis. For each reorientation event, the response of
each HD cell was compared 0.5 sec before and 0.5 sec after the light
was turned on (referred to as a "trial"). Two criteria were used to
select trials suitable for analysis: (1) the head of the rat had to
remain close to the preferred direction (i.e., within two SDs of the
Gaussian fit of the response curve around the preferred direction), and
(2) the head direction had to remain stable throughout the whole trial,
i.e., remain within ±15° around its mean value. Two methods were
used to measure latencies of updating of directional responses. We
first pooled all analyzable trials recorded from all cells and computed
a cumulative response histogram. In the cumulative response histogram,
the change point in firing rate was determined by computing the best
fit slopes before and after each 10 msec bin within 250 msec after
light onset. The bin corresponding to the smallest square error was
then selected as the estimated update latency (Friedman and Priebe,
1998). The second method examined firing rate changes on a trial by
trial basis. Only trials with a minimum of 10 action potentials were
selected for this analysis. The mean interspike intervals were compared
for 500 msec intervals before and after each spike occurring within 250 msec after light onset. A maximum likelihood estimator was then used to
determine the transition point.
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Results |
Twenty-three HD cells were recorded in 15 sessions (including 7 sessions in which two or more cells were recorded simultaneously). Turning the light off did not appear to alter the cell responses. The
head of the rat remained stable in 261 of 496 (53%) trials. Perhaps
because of drift or attentional factors, the preferred directions of
the HD cells were not always updated after the light was turned on.
Trials were rejected as unusable if the number of spikes emitted was
unchanged in the 500 msec before versus 500 msec after light
onset [the criterion was
| spikesbefore spikesafter | /(spikesbefore + spikesafter)  0.2)], or if the total
number of spikes in these periods was <5. With these additional criteria, 129 of 496 (26%) trials were retained. In 51 trials, perhaps
because of drift or updating of the preferred directions, the cells
started firing when the light went on with the card in its rotated
position or stopped firing when the light went on with the card in its
initial position.
Figure 2 shows raster plots of the HD
cell responses to the light going on with the cue card in the new
orientation. The corresponding peri-event histograms and cumulative
spike count histograms are also shown. The figure includes the plots of
the best fit models of the latter histograms used to compute response
latencies (Friedman and Priebe, 1998). The new preferred directions
were established at a latency of 80 ± 10 msec, when the newly
activated HD cells arrived at their maximum firing rate (Fig.
2A). However, the cessation of activity after the cue
card was shifted away from its original orientation occurred at a
slower rate: the return to baseline occurred only after 140 ± 10 msec (Fig. 2B).
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Figure 2.
Latency of preferred direction updates in HD
cells. Raster plots (above), peri-event histograms (middle), and
cumulative histograms (below) (bin width = 10 msec) of action
potentials recorded from all of the HD cells analyzed. Time 0 indicates
when the lights were turned on again. After light onset, the preferred
directions return to their initial orientations
(A) or shift to the rotated (nonpreferred)
orientations (B). To determine the average
latency of the preferred direction update, least-squares estimates were
computed from the cumulative histograms using the first 250 msec of
data after light onset (thick curves) (Friedman and Priebe, 1998).
Transition points are at 80 ± 10 msec (A)
for returns to the preferred orientation and 140 ± 10 msec
(B) for shifts to the nonpreferred orientation.
Brackets indicate trials from the same cell within a given session; the
variations in spike density among the rows of rasters reflect
differences in peak and background firing rates among the
neurons.
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We then examined the time course of firing rate changes on a trial by
trial basis, using the maximum likelihood estimator of Seal et al.
(1983). As shown in the example in Figure
3, there was moderate variability across
trials within the same session. No systematic trend was evident,
indicating that it is unlikely that learning occurred during the course
of the experiment. Also, update latencies appeared reproducible across
sessions and animals in the total of 75 trials used for this analysis
(Table 1). This was tested with separate
ANOVAs for the increasing and decreasing firing rate conditions. For
trials in which the firing rates increased after light onset, there
were no significant differences among animals (F = 0.09; df = 2; NS) or sessions (F = 0.07; df = 4; NS). One outlier was removed from these analyses. For this session, the average estimated latency was 167 msec (Table 1) (the least-square estimator described above yielded an update latency of only 70 msec).
Similarly, for trials in which the firing rates decreased after the
light was turned on, no significant differences were found among
animals (F = 1.18; df = 2; NS) or sessions
(F = 0.53; df = 4; NS).
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Figure 3.
A typical analysis of update latency on a trial by
trial basis with the method of Seal et al. (1983). Raster plots show
action potentials recorded from a single HD cell during a single
session, when the firing rate increases (A) or
decreases (B) after card rotation. Light onset
occurs at time 0. For each trial, the update latency is computed as the
maximum likelihood estimator of the change point in the mean interspike
interval (thick vertical bars).
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Discussion |
These results show that in HD cells of the anterodorsal thalamic
nucleus, preferred direction updates, a likely basis for reorientation
processes, benefit from very rapid processing of visual signals. Neural
network simulations (Zhang, 1996) predict that after changes in the
visual scene, the firing rates of the newly activated cells reach their
maximum at latencies on the order of several hundreds of milliseconds.
The present results reveal markedly briefer delays. However, all
existing models of HD cells use rate code neurons rather than spiking
neurons which would provide more veridical models of the dynamical
properties of biological neural circuits. In neural networks with
spiking neurons, state transitions can occur almost instantaneously
(Brunel et al., 2001), consistent with the present results.
Our work sheds light on the nature of the dynamics of the ensemble
response of HD cells to reorienting stimuli. Neural network models have
described the ensemble activity profile of HD cells as a "hill" of
excitation (attractor state) encoding the current directional heading
(Redish et al., 1996; Zhang, 1996; Goodridge and Touretzky, 2000) (cf.
Droulez and Berthoz, 1991). Whether this activity profile responds to
rapid visual reorientation by traveling toward a new preferred
direction (Fig. 4B) or
by jumping abruptly to it (Fig. 4C) has been unresolved
until now (Taube, 1998). The rapid transient response (80 ± 10 msec for a 90° reorienting signal, including retinothalamic
transmission time) observed in our experiment appears to support the
abrupt shift model rather than the hypothesis of a pulse of activity
moving to the new distal state by passing through all cells selective
for the intermediate heading angles. However, it is likely that the
state transition dynamics depend more generally on the magnitude of the
angle of rotation (Zhang, 1996), as well as on other factors such as
the efficacy of the orienting cue or the complexity of the visual scene. The longer latency observed here when the directional firing rates returned to baseline (140 msec) is consistent with the notion that recurrent inhibition triggering this decrease in firing rates would occur after the increase in overall activity within the HD cell
network (illustrated in Fig. 4C where the left hill
decreases more slowly than the right hill increases).
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Figure 4.
Two possible mechanisms for dynamic updating in
continuous attractor networks. A, Network connectivity.
Each cell (circle) sends excitatory signals (triangles) to its
neighbors and inhibitory signals (bars) to all of the cells in the
network (for clarity, only the connections from one prototypic cell are
shown). B, Progressive updating of the ensemble response
of the HD system. The firing rate of each formal cell is proportional
to the height of the vertical bar. The hill of activity migrates
progressively to the target population. C, Abrupt
updating of the ensemble response. The hill of activity jumps to the
target firing pattern without activation of intermediate neurons.
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Previous neurophysiological studies addressing this question failed to
reveal the striking rapidity reported here because the experimental
protocols did not permit measures of dynamic changes of <1 sec
(Knierim et al., 1998; Zugaro et al., 2000). The very short latencies
observed are consistent with the fact that anterodorsal thalamic
nucleus receives direct projections from the retina (Itaya et al.,
1981; Ahmed et al., 1996) as well as indirect projections from the
visual cortex via the postsubiculum (Vogt and Miller, 1983) and the
retrosplenial cortex (Reep et al., 1994), and that visual stimulation
of the retina evokes field potentials in the primary visual cortex with
delays as brief as 40 msec (Galambos et al., 2000).
These experimental observations and theoretical considerations provide
a plausible mechanism underlying the capacity to rapidly reorient in a
familiar environment. They may also provide a new paradigm to study the
deficits of this capacity in aging or in a number of neurological disorders.
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FOOTNOTES |
Received Sept. 17, 2002; revised Jan. 28, 2003; accepted Jan. 28, 2003.
This work was supported by the Centre National de la Recherche
Scientifique-National Science Foundation cooperation program, Centre National d'Etudes Spatiales, Action Concertée Incitative du
Ministère de la Recherche, Cogniseine, Groupement d'Intérêts Scientifique. M.B.Z. received a grant from the Fondation pour la
Recherche Médicale. We thank N. Brunel for comments on this text,
F. Maloumian for illustrations, M.-A. Thomas and S. Doutremer for histology, and L. Hazan for help with experiments.
Correspondence should be addressed to S. I. Wiener,
CNRS-Collège de France LPPA, 11 place Marcelin Berthelot, 75231 Paris Cedex 05, France. E-mail:
sidney.wiener{at}college-de-france.fr.
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TOP
ABSTRACT
Introduction
