Previous Article | Next Article 
The Journal of Neuroscience, September 15, 2001, 21(18):7284-7292
Dentate Gyrus and CA1 Ensemble Activity during Spatial Reference
Frame Shifts in the Presence and Absence of Visual Input
Katalin M.
Gothard1,
Kari L.
Hoffman2,
Francesco
P.
Battaglia2, and
Bruce L.
McNaughton2
1 Department of Psychiatry, California Regional Primate
Research Center, University of California Davis, Davis, California
95616, and 2 Arizona Research Laboratory Division of
Neural Systems, Memory and Aging, University of Arizona, Tucson,
Arizona 85724
 |
ABSTRACT |
In rats shuttling between a variably placed landmark of origin and
a fixed goal, place fields of hippocampal CA1 cells encode location in
two spatial reference frames. On the initial part of the outbound
journey, place fields encode location with respect to the origin while
on the final segment, place fields are aligned with the goal (Gothard
et al., 1996b
). An abrupt switch of reference frame can be
induced experimentally by shortening the distance between the origin
and the goal. Two linked hypotheses concerning this effect were
addressed: (1) that the persistent, landmark-referenced firing results
from some internal dynamic process (e.g., path integration or
"momentum") and is not a result of maintained sensory input from
the landmark of origin; and (2) that this hypothetical process is
generated by connections either within CA3 or between CA3 and CA1, in
which case the effect might be absent from the dentate gyrus. Neuronal
ensemble recordings were made simultaneously from CA1 and the dentate
gyrus as rats shuttled on a linear track between a variably located box
and a goal, under light or dark conditions. The box-referenced firing
persisted significantly longer in the dark in both hippocampal
subfields, suggesting a competitive interaction between an internal
dynamic process and external sensory cues. The similarity between
reference frame transitions in the dentate gyrus and the CA1 region
suggests that this process probably occurs before CA3, possibly in the
entorhinal cortex or subiculum.
Key words:
multisite recording; path integration; spatial memory; navigation; neural ensembles; population vector; granule cells
 |
INTRODUCTION |
Both environmental and self-motion
cues contribute to place-specific firing in the rat hippocampus. In
certain tasks, visual cues control the location of CA1 place fields
(O'Keefe and Conway, 1978
; Hill and Best, 1981
; Muller and Kubie,
1987
; Knierim et al., 1995
; O'Keefe and Burgess, 1996
). Nevertheless,
place fields can be generated and maintained in darkness (O'Keefe,
1976
; McNaughton et al., 1989
; Quirk et al., 1990
; Markus et
al., 1994
). In darkness, self-motion cues (vestibular and motor
efference signals) are sufficient to update the angular component of
hippocampal location information via a process called path integration
(Mittelstaedt and Mittelstaedt, 1982; Etienne et al., 1996
; Golob and
Taube, 1999
). The spatial information conveyed by visual and
self-motion cues is normally redundant; however, an experimentally
induced conflict between these two types of cues can disambiguate their relative contribution to place cell activity (Gothard et al., 1996a
,b
;
Bures et al., 1997
; Knierim et al., 1998
). Rats trained to shuttle on a
linear track between a start box and a fixed goal at the opposite end
of the track experience an inconsistency between their location
relative to the fixed cues and the shifted box. Under these conditions,
at least on the initial segment of the journey, place cells fire with
an invariant spatial relationship to the box (Gothard et al.,
1996b
). This strongly suggests that, on the initial segment of a
journey, the surrounding visual cues do not provide the major driving
force for place cell firing; rather, location is updated by either
self-motion signals or intrinsic dynamics of the hippocampal and
related circuitry. A study by Gothard et al. (1996b)
, however, left
open the possibility that the maintained alignment of the place fields
with respect to the start box was a result of the continued
availability of visual information about the rat's position relative
to the box. A primary goal of this study was, therefore, to determine
whether place fields remain aligned with the box in the dark and
whether the dynamic interaction between linear self-motion signals and
external sensory cues in the dark is biased in favor of self-motion signals.
Little is known about how and where in the brain the path integration
operation takes place. Lesion studies (Alyan and McNaughton, 1999
;
Maaswinkel et al., 1999
) have led to conflicting conclusions as to
whether the hippocampus performs this computation or whether it occurs
elsewhere and the hippocampus merely receives the result (Cooper and
Mizumori, 1999
; Redish, 1999
). One possibility is that the effect
occurs directly within CA3 recurrent collaterals (Samsonovich and
McNaughton, 1997
), in which case one might predict differences
in the behavior of CA1 pyramidal cells and dentate gyrus granule cells.
The secondary goal of this study was, therefore, to compare place field
shifts in the dentate gyrus and CA1 induced by systematically shifting
the point of departure of the journey. If the updating of the
representation based on self-motion signals involves a computation that
takes place primarily within the CA3 region, and the result is not fed
back to the hippocampal input (i.e., open loop processing), then one
might expect differences in place field shifts between the dentate
gyrus and CA1.
 |
MATERIALS AND METHODS |
Surgery, electrode assembly, and recording
technology. Surgery was conducted according to National Institutes
of Health guidelines for rodents. Seven adult male Fisher 344 rats were
implanted under pentobarbital anesthesia with a circular array of 14 separately moveable microdrives. This device and the parallel recording
technique were described first by Wilson and McNaughton (1993)
and in
more detail by Gothard et al. (1996a)
. Briefly, each microdrive
consisted of a drive screw coupled by a nut to a guide cannula. Each
guide cannula contained a tetrode, a four-channel electrode constructed by twisting together four strands of insulated 13 µm nichrome wire
(H. P. Reid, Inc., Neptune, NJ). A full turn of the screw advanced
the tetrode 318 µm. The stereotaxic coordinates for the placement of
the electrode array were 1.8 or 2.0 mm lateral and 3.8 mm posterior to
bregma on the right hemisphere. The tetrodes were lowered gradually
after surgery into the hippocampus and allowed to stabilize above the
CA1 layer or the upper blade of the dentate gyrus. Two of the tetrodes
served as reference electrodes. The four channels of each tetrode were
attached to a 50-channel unity-gain head stage (Neuralynx Inc., Tucson
AZ). The rat's position was recorded by tracking two clusters of
infrared diodes. A large cluster of diodes was mounted on the head
stage, and a smaller cluster of diodes was mounted on a 14 cm
lightweight aluminum rod attached to the back of the head stage to
monitor head orientation. The position and head orientation of the
animal were recorded with a sampling frequency of 20 Hz.
A multiwire cable connected the head stage to a commutator (Biela Idea
Development, Anaheim, CA) from which the signals were transmitted to
digitally programmable amplifiers (Neuralynx). The signals were
amplified by a factor of 10,000, bandpass-filtered between 600 Hz and 6 kHz, and transmitted to an array of seven 486 computers equipped with
synchronized clocks. Signals crossing a manually set threshold were
digitized at 32 kHz. Spikes were sorted off-line on the basis of the
relative amplitude and other characteristics of action potentials
(e.g., spike width and area under the curve) on different tetrode
channels (McNaughton et al., 1983
; Recce and O'Keefe, 1989
) using
custom software (Wilson and McNaughton, 1993
).
Behavioral apparatus and task. The behavioral apparatus was
a 184 × 8 cm wooden linear track, painted black. The track was placed in the center of a large room and surrounded by black curtains. Large white objects hung in front of the curtains and asymmetrically aligned lights served as additional static background cues. A small
food well was mounted at one end of the track. Attached to the other
end was a 27-cm-high, 32-cm-wide, 27-cm-long box, which could be
repositioned along the track. The box had carpeted floors and a
centrally located food well. Rats were trained to shuttle along the
full length of the track between the two food wells. To vary the
journey length, the box was moved to one of five equally spaced
locations along the track (Fig.
1A). These five
locations are referred to as box 1-box 5, with box 1 out being the
longest box outbound journey (box located at the far end of the track).
The distance from the front edge of the box to the fixed food well
varied from 151 cm for the longest journey to 53 cm for the shortest
journey (in the box 1 position, the back of the box extended beyond the
edge of the track by 5 cm).

View larger version (37K):
[in this window]
[in a new window]
|
Figure 1.
Behavioral apparatus and journey types.
A, A 27-cm-high, 32 × 27 cm moveable cardboard box
was mounted on a 184 × 8 cm linear track. The box was moved to
one of five possible locations along the tack (box 1-box
5). A small food cup was mounted in the box, and another cup
was permanently mounted at the opposite end of the track (goal). The
rat was trained to exit the box and run to the goal (outbound journey).
As the rat approached the goal, the box was shifted to a new location.
After consuming the reward at the goal, the rat returned to the box
(inbound journey). During the subsequent outbound journey, the box was
shifted to a new location. B, The five types of outbound
journeys (box1 out-box5 out). C, The
five types of inbound journeys (box1 in-box5 in).
|
|
All rats were initially trained with the box in position 1. The first
trial of each recording session always started from the box 1 position.
While the rat was traveling from the box to the goal, the box was moved
to one of the five possible locations along the track using a
pseudorandomized blocks schedule (i.e., on each block of five trials,
positions 1-5 were sampled without replacement). The rat ate the food
reward placed in the fixed food well and returned to the box, which by
then had been moved to a new location. Thus, each trial started where
the previous trial had ended, and the position of the box was typically
different at the beginning and the end of each trial. A recording
session consisted of 10-15 blocks of five positions. Event flags,
inserted automatically into the data file, marked the time of each box exit and box entry and also the time of reaching the fixed food well
and departing from it. These event flags permitted the selection and
separate analysis of all five types of outbound and inbound journeys
without including the segments while the rat was eating at either food
well. The apparatus and the randomization method were described in
detail by Gothard et al. (1996b)
.
Training. The food reward was a mixture of mashed rat chow,
applesauce, and a veterinary nutritional supplement (Stat; PRN Pharmacal, Inc.). On the initial 3-5 d of habituation to the room and
the track, the rats were placed inside the box, always in the box 1 position, and allowed to explore. Rats ate drops of food strewn along
the track as they made excursions of increasing length from the box.
Eventually, they traveled the whole length of the track multiple times
in a session. Each rat was trained to run at least 40 consecutive
trials. Four of the seven rats were trained under these conditions,
which henceforth are referred to as the light condition. Three other
rats were also trained to a criterion of 40 trials with the lights
turned off (henceforth the dark condition). The recording room had
black floors, walls and ceiling, and double doors, which prevented
light from entering. Behavioral monitoring by the experimenter was
accomplished using a deep infrared light source and night vision
goggles. When the rats would run >40 consecutive trials in either
condition, they were implanted with the multielectrode device described above.
After recovery from surgery, the rats received additional training, and
the tetrodes were gradually lowered into the CA1 pyramidal layer or the
granular layer of the dentate gyrus. During the entire training period
and during the first one or two recording sessions, the box was always
maintained in its initial position (box 1). At this stage, the start
box, the linear track, the fixed food cup, and the surrounding objects
constituted a single, fixed reference frame. When the box was moved, it
defined a new reference frame, whose alignment with the laboratory
frame was variable.
Cell classification. During the process of optimizing
tetrode placement, the EEG and spontaneous unit activity were monitored on each tetrode. The buildup of clusters of spikes was monitored as
accumulating points in a scatter plot. Each point on the scatter plot
corresponded to the height of the same spike registered on two of the
four channels of the tetrode. Principal cells in the CA1 region were
distinguished from interneurons on the basis of spike width (>250
µsec measured from the negative to the positive peak; mean, 350 µsec), the property of firing complex spikes, and an overall mean
rate of <5 Hz (Ranck, 1973
). CA1 pyramidal cells were recorded from
tetrodes that registered sharp waves and "ripples" (Buzsaki, 1986
)
during sleep and quiet wakefulness.
All tetrodes in the dentate gyrus (DG) registered
oscillations
(30-60 Hz; Stumpf, 1965
; Buzsaki et al., 1983
; Bragin et al.,
1995
). When the electrodes reached the layer at which clear
oscillations were observed, they were advanced in small increments until single-unit activity was observed. The recorded cells were divided, on the basis of firing rates, into two groups. Cells with
firing rates of >2.5 Hz were classified as high-rate interneurons (Jung and McNaughton, 1993
; Freund and Buzsaki, 1996
; Nitz and McNaughton, 1999
). Cells with firing rates of <2.5 Hz were grouped as
a single category. This group may have included not only granule cells
but also some mossy cells and low-rate interneurons. No additional
discrimination between these cells types was possible on the basis of
extracellular recordings; however, granule cells outnumber the other
cell classes in the dentate gyrus by a factor of ~10. Therefore the
term putative granule (PG) cell is used throughout the remainder
of this report with the explicit caveat that the PG group may not have
consisted entirely of granule cells.
High-rate interneurons in the dentate gyrus formed tightly packed,
dense clusters in the spike amplitude scatter plots. Usually more than
one interneuron was recorded on the same tetrode. PG cells discharged
single spikes or small bursts, typically had low average rates (<1
Hz), and generated less compact parameter clusters than high-rate interneurons.
At the end of the experiment, a small electrolytic lesion was made at
the tips of two of the tetrodes. One of these tetrode tips was at a
depth estimated to be in CA1, registering EEG and single-unit signals
characteristic of the CA1 region. The second tetrode was at a depth
estimated to be in the dentate gyrus, having passed through the
estimated CA1 pyramidal layer and just beyond the region of maximal
amplitude. In one rat, a lesion was placed in a region estimated to be
the CA3 pyramidal cell layer between the upper and lower blades of the
dentate gyrus. The lesions were visualized histologically in cresyl
violet-stained 40-µm-thick slices. Each lesion was localized to the
intended area, confirming that the electrophysiological criteria used
to estimate the recording sites were reliable. In one rat, all tetrodes
were medial to the most lateral aspect of the CA3 region (Fig.
2). The low-rate cells from this rat,
which were recorded from depths corresponding to the upper and lower
blades of the dentate gyrus, and all of the cells recorded in the other
rats at sites of dentate gyrus lesions were selected for the
illustrative examples of PG cells. There is no question that these
cells do not include CA3c pyramidal cells. The properties of these
cells were indistinguishable from the remainder of the PG class.

View larger version (145K):
[in this window]
[in a new window]
|
Figure 2.
Histological verification of electrode placement.
A coronal section of the hippocampus stained with cresyl violet is
shown. For each rat, one tetrode, assumed on physiological grounds to
be in the CA1 pyramidal layer, and a second tetrode, assumed to be in
the granular layer of the DG (see Materials and Methods), were
selected. Lesions were produced after the final recording session by
passing 5 µA of cathodal current through a tetrode for 5 sec.
Additional electrode tracks are visible above the two lesions, medial
to the dentate gyrus lesion and lateral to the CA1 lesion. In this rat,
all the electrode tracks were medial to the CA3c region, thus ruling
out misidentification of CA3 pyramidal cells as PG cells.
|
|
Data analysis. The distribution of population activity along
the track was calculated using a sparsity index (based on cells that
fired with a mean rate of >0.35 Hz during track running). For each of
the 64 bins into which the track was subdivided, the sparsity is
defined as:
where fI is the average firing
rate for the Ith cell in that bin, and
Nc is the number of cells. For a
completely distributed representation, i.e., one in which all the cells
fire at the same rate in one location, the sparsity has a value of 1. For the sparsest possible representation, i.e., a representation in
which only one cell fires and the others are silent, the sparsity takes
the minimum allowed value of 1/Nc.
Note, however, that because inactive cells were not included, this
measure is strictly a means of determining whether there were spatial
variations in population activity and cannot be used to compare the
general sparsity of the ensemble representations in the CA1 and DG regions.
To determine whether place cells fire at fixed distances from the
variably placed box, the place field shift was estimated by calculating
the center of mass of the locations at which spikes occurred for each
neuron and each journey type. The center of mass is computed as the
average of all the locations on the rail at which a spike occurred and
provides an estimate of place field location that is independent of
firing rate and place field size or shape. If a cell fired fewer than
three spikes on a journey type, the center of mass was not calculated
for that journey type. If a cell had multiple place fields, the center
of mass will be at the average location of the subfields. For cells
recorded in multiple sessions, only one session was selected for these
calculations. The centers of mass were plotted on a scaled drawing of
the track for all five types of outbound journeys (see Fig. 5). For
each neuron, the centers of mass across journey types were connected by
lines, providing an indication of the degree to which place cells were
aligned with the box or the fixed cues as a function of distance along
the track.
Construction of population vectors and vector correlation
matrices. The 176 cm distance between the food well in box 1 and the fixed food well at the opposite end of the rail (the full extent of
the journey traveled by the rat) was divided into 64 2.75 cm spatial
bins. The firing rate of each cell was computed for each bin, for each
outbound journey type (box 1, box 2 . . . box 5), for each condition
(light vs dark), and for each region (CA1 vs DG). Data from all
available recording sessions were combined for this analysis, but if
the same cell appeared to have been recorded in multiple sessions, data
from only one of these sessions were used. The N-dimensional
vector (where N is the total number of included cells
recorded in dark or light in CA1 or DG) represents the population
firing pattern at a specific location averaged across rats (Fig.
3).

View larger version (52K):
[in this window]
[in a new window]
|
Figure 3.
Construction of population vectors. Population
activity of an ensemble of 130 cells from CA1 in the light condition
along the full-length journey (top panel) and a
shortened journey (bottom panel) is shown. The
x-axis represents the 64 spatial bins in which firing
rates were computed for each cell, marked as distance (in centimeters)
along the rail. Each row on the y-axis
represents the firing rate of a cell in each 2.75 cm spatial bin. The
firing rate scale goes from white (no firing) to
black (high firing). Cells are sorted according to the
firing profile center of mass calculation. A population vector was
defined as the list of the firing rates for the corresponding spatial
bin. For illustration, one such bin is highlighted for each journey
type. The spatial bin size was kept constant across journey
types.
|
|
A population vector for a given spatial bin represents the list of
firing rates for all the recorded cells on that journey type,
condition, and hippocampal subregion. For example, the PG population
vector for the 10th spatial bin (27 cm from the exit of box 1) on the
box 1 outbound journey in the light condition contained a list of
firing rates for that location from all 125 recorded PG cells that
showed outbound activity anywhere on the track. The population vector
for the same spatial bin for the box 2 outbound journeys contained the
firing rates of the same 125 PG cells when the rat was exiting the box
in the box 2 position. The difference between the two conditions under
which these population vectors were recorded is that even though the
rat was in the same spatial bin, during the box 2 outbound journey the
rat was just exiting the box at that location, whereas in the box 1 outbound journey, the rat was 27 cm from the box. A different
population vector was compiled for the same spatial bin from 79 PG
cells recorded in the dark. Vector correlations were used to compare the population-firing pattern at different points on the track, across
different outbound journey types. The correlation coefficient, that is,
the normalized overlap, of each pair of population vectors was
calculated and used as a measure of the similarity of population firing
patterns at different locations on different types of journeys. The
correlation coefficient was defined as:
|
|
where Si(a) is the
firing of the ith cell at location a.
Coherency and reference frame transition analysis. For the
population vector analysis, which provides a means of visualizing the
reference frame switch along the track (see Fig. 6), data were averaged
across multiple trials, recording sessions, and rats. This analysis
does not provide information about the population dynamics on a
single-trial basis, making it difficult to assess the statistical
significance of any effects that may be observed. Redish et al. (2000)
presented a method to compute the extent to which the internal
representation contained in the firing of hippocampal cells is tied to
different reference frames at any given moment in time, in an
experimental context similar to the one used here. With this method, it
is possible to estimate the point at which the representation transits
from the box-related frame to the room frame. The details of this
method have been described by Redish et al. (2000)
. Briefly, a
reconstruction procedure is used to determine the internal
representation of the rat's position contained at any point in time in
the ensemble activity. Given the results of the averaged population
vector analysis, at the beginning of an outbound journey, it could be
expected that the reconstruction of the position in the box frame of
reference would be more accurate than the reconstruction of the
position in the room frame. As the reference frame transition occurs,
the quality of the reconstruction in the box frame will decrease, and the quality of the reconstruction in the room frame will increase until it becomes better than the quality of the reconstruction in the
box frame. The transition point was defined as the location at which
this occurred. If visual information from the fixed reference frame
contributes to the transition, then the transition should occur at a
shorter distance from the origin in the light condition compared with
the dark condition. To test this hypothesis, the distributions of
transition points across trials in light and dark conditions were
computed and compared. To reconstruct the rat's position, the activity
packet defined as:
|
|
was computed (Samsonovich and McNaughton, 1997
), where
Fi(t) is the firing of the
ith cell at time t,
SiC(v)
is the number of spikes fired, over all trials, by cell i, when the
animal was in position v in the reference frame C. Thus, if
all the cells had very small and reliable place fields in one frame,
the activity packet would be confined to a narrow spatial region;
however if place fields were widespread and unreliable, the activity
packet would be wider. The coherency of the activity packet, defined
as:
where Gu(v) is an
idealized gaussian activity packet centered in the rat's actual
position, with a fixed width (9 cm), is a measure of the accuracy of
the reconstruction in the frame C.
For each trial, the coherencies in the two frames were computed at all
the locations sampled by the rat. Each trial was subdivided into 20 time bins, and the coherency of the activity packet was computed for
each time bin if at least one spike occurred in that time bin. To
determine the transition point, the difference of the coherencies in
the room frame and in the box frame were computed. The point at which
the difference crossed zero is defined as the transition point. When
there was more than one transition point, the zero crossing
corresponding to the absolute minimum of the function was taken.
For the transition point analysis, data were used from 11 sessions from
three rats in the dark condition and 17 sessions from five rats in the
light condition. For the light condition, coherency values were
computed for 1911 trials (423 box 1 trials, 383 box 2 trials, 374 box 3 trials, 372 box 4 trials, and 359 box 5 trials). For the dark
condition, coherency values were computed for 518 trials (109 box 1 trials, 101 box 2 trails, 103 box 3 trials, 103 box 4 trails, and 102 box 5 trials). The transition points were computed only for those
trials for which the coherency was defined (see Materials and Methods,
coherency and reference frame transition analysis) in at least
15 of 20 time bins.
A total of 892 transition points were calculated in the light condition
(231 transition points for box 1 trials, 188 for box 2 trials, 156 for
box 3 trials, 177 for box 4 trials, and 140 for box 5 trials). A total
of 180 transition points were computed in the dark condition (52 transition points for the box 1 trials, 43 for the box 2 trials, 34 for
the box 3 trials, 30 for the box 4 trials, and 21 for the box 5 trials).
 |
RESULTS |
All 7 rats acquired the task in ~10-15 training sessions in the
light. An additional 15-20 sessions were required to acquire the task
in the dark. Rats ran slower in the dark (mean ± SD velocity, 29.07 ± 8.42 cm/sec) than in the light (mean velocity, 37.81 ± 10.7 cm/sec; two-tailed t test, p < 0.0001). In the dark, the lower running velocity was not accounted for
by excessive hesitation in the vicinity of the goal. The rats did not
show a tendency to overshoot the goal or to fall off the track. To test
whether the olfactory or tactile cues associated with the baited food cup could account for the precise localization of the goal in the dark,
a probe trial was administered, in which a baited food cup, identical
to the one at the goal, was placed in the middle of the track, 30-50
cm closer to the box than the true goal. The rat ran over, passed the
food cup in this novel location, and stopped at the expected goal location.
When the box was moved for the first time in either condition, the rats
hesitated before entering the box and explored vigorously. In the next
two or three trials, however, they habituated to the manipulation and
showed no additional hesitation to enter the box. Data from sessions
when the box was moved for the first time were excluded from analysis.
The duration of a trial was ~25 sec, including the time that the rat
spent eating in the box and at the fixed food well. In a recording
session of 50-75 trials, each start box location was sampled 10-15
times. The session came to an end when the rats slowed down. The number
of recording sessions per rat varied between 6 and 25; more recording
sessions were possible when each electrode yielded a good-quality
signal in both the CA1 and the dentate region, and fewer sessions were
possible when the electrodes that were aimed at the dentate gyrus did
not encounter recordable signals.
A total of 1821 spike trains, from units considered well isolated, were
recorded from seven rats. From these, 240 spike trains, with average
rates of <0.03 Hz, were eliminated from analysis. Because of
similarities in spike waveform, relative spike height ratio on the four
tetrode channels, and spatial selectivity across sessions, some cells
were assumed to have been recorded from over two or more sessions. For
each of these cells, the session showing the best signal-to-noise ratio
(largest spikes) and cleanest unit isolation was used for the
population vector analysis. After excluding high-rate interneurons (278 spike trains, of which 187 were recorded from CA1 and 91 were recorded
from the dentate gyrus) and cells recorded multiple times (798 spike
trains), a total of 505 cells were analyzed. Of these, 301 cells were
recorded from the CA1 region, 144 of which were recorded in the light
condition and 157 of which were recorded in the dark condition. Of the
204 PG cells, 125 were recorded in the light condition, and 79 were
recorded in the dark condition. The average firing rates of the CA1
pyramidal cells in the light and dark conditions were 0.97 ± 0.74 and 0.95 ± 0.68 Hz, respectively. The average firing rates for PG
cells in the light and dark conditions were 0.73 ± 0.55 and
0.74 ± 0.58 Hz, respectively. For the trial-by-trial analysis of
transition points on the outbound journey, all spike trains with more
than three spikes on the outbound journey were used.
No difference in place field distribution along the track was observed
between cells recorded from the CA1 region and the dentate gyrus, and
no spatial trends in the sparseness of the distributions were observed,
meaning that the place fields were approximately uniformly distributed.
Finally, no significant differences in spike width between CA1 and PG
cells were found (CA1 spike width, 358 ± 47 µsec; DG spike
width, 328 ± 66 µsec).
Comparison of place field shift in CA1 and DG
CA1 pyramidal cells and putative granule cells responded similarly
to the variations in the start box location. In both CA1 and DG, place
fields located immediately after exiting the box remained at fixed
distances from the box (Fig. 4) in both
light and dark conditions. Place fields near the opposite end of the track did not appear to be influenced by the position of the box, remaining fixed in the reference frame of the track and the surrounding static cues. Place fields between these two regions exhibited transitional properties. In this transition zone, especially in the
most shortened journeys, some cells reduced their firing rates or
ceased firing altogether.

View larger version (21K):
[in this window]
[in a new window]
|
Figure 4.
Spatial firing profiles of DG cells and CA1
pyramidal cells on outbound journeys in the dark. Top,
Three firing profiles of CA1 pyramidal cells shown for all five types
of outbound journeys in the dark. Profiles were calculated by averaging
firing rate along the track across all trials of a journey type and
normalizing for occupancy. Note that the firing profiles near the box
remain aligned with the box across most journey types. In contrast, the
firing profiles at the end of the track remain stationary across
journey types. The firing profile in the middle of the box 1 out
journey shifted less than the firing profile in the vicinity of the box
and disappeared in the three shortest journeys. Bottom,
Three firing profiles of DG cells shown for all five types of outbound
journeys in the dark. Similar to the firing profiles seen in CA1, DG
cell profiles in the vicinity of the box shifted with the box, whereas
the profiles at the end of the track remained fixed.
|
|
In the examples shown in Figure 4, both place fields located midway
between the box and the goal were greatly attenuated in the three most
shortened journey types, whereas place fields in the vicinity of the
box were present in all but the most shortened journey types
(box-referenced place fields). In contrast, place fields in the
vicinity of the fixed food well were present on all five journey types
(track- or room-referenced place fields). Some cells had more than one
place field. In these cases, subfields closer to the box shifted more
than fields farther from the box. An example is shown in Figure 4; the
first and last of the firing profiles along the outbound journey in the
bottom panel for DG belong to the same PG cell (in
dark gray). These two profiles show different place field
shifts on the shortened journeys; i.e., although the location of the
first firing profile is strongly dependent on the location of the box,
the location of the second firing profile is stable across journey
types. The place fields in this figure were recorded in the dark. A
similar dependence of place field shift on place field position on the
track was observed in the light condition in this study and in the
study of Gothard et al. (1996b)
.
As described previously (Gothard et al., 1996b
) a comparable pattern of
place field shifts was observed on the inbound journeys (data not
shown), which began when the rat moved away from the fixed food well
(goal) and ended at the food well in the box. On the inbound journeys,
in which the origin of the journey was the fixed food well and the
destination was the shifting box, the transition zone was located
closer to the end of the journey, i.e., immediately before the entrance
to the box. Consistent with the detailed description of directionality
by Gothard et al. (1996b)
and Jung and McNaughton (1993)
, most of the
CA1 and PG cells had directional place fields. Bidirectional fields
occurred no more than expected by chance, given the (low) probability
of multiple, independent fields within the same environment.
The extent to which a place field represents location in the reference
frame of the box or of the track can be expressed as the slope of the
lines in Figure 5, where a slope of 1 corresponds to a place field that maintains constant relationship with
the box, and a slope of 0 corresponds to a place field that is fixed in
the reference frame of the track.

View larger version (47K):
[in this window]
[in a new window]
|
Figure 5.
Place field alignments on outbound
journeys. Centers of mass of firing rate distributions of each
principal cell were calculated separately for the five outbound journey
types. The centers of mass across journey types for each cell are
connected by lines. Consistent with the individual
examples in Figure 4, cellular activity remained aligned with the box
for a large extent of the journey. Some cells active in the midportion
of the journey were silent on the shorter journeys. This shift in
activity occurred in both CA1 and DG in both light and dark conditions.
The top left plot was constructed from 144 CA1 pyramidal
cells from the light condition, and the top right plot
was constructed from 157 CA1 pyramidal cells from the dark condition.
The bottom plots contain the corresponding plots for the
DG light (125 cells) and dark (79 cells) conditions. Note that this
measure does not take into account situations in which a single cell
had more than one place field on the track. As shown previously
(Gothard et al.; 1996 ), in such cases, place fields tended to behave
independently, in a manner that depended on their location on the full
track. Thus, the population vector correlations shown in Figure 6
provide a more robust representation of the coordinate frame shift
dynamics.
|
|
The slope was calculated for those cells that had a place field in at
least three of the five journeys. The mean and SD of the slopes for the
first and second halves of the outbound journeys calculated separately
for the light and dark conditions and for CA1 and DG are presented in
Table 1. The slope values indicate that
on the first half of the journey, fields tended strongly to remain
aligned with the box, whereas on the second half, they tended to align
with the fixed reference frame.
View this table:
[in this window]
[in a new window]
|
Table 1.
Means and SDs of slopes calculated for CA1 and DG place
fields on the first and second halves of the track
|
|
Population vector analysis
The similarity of the population activity across journey types was
quantified by point-by-point correlation of the population vectors
computed for each spatial location (see Materials and Methods). In both
illumination conditions, cells fired in the same spatial order between
the box and the goal; however, on the shortened journeys, some place
fields decreased in size and firing rates, or were entirely absent,
skipping over some cells in the firing sequence. The least variability
was observed near the fixed food well, whereas the maximum variability
was in the intermediate zone. In both CA1 and DG, for both illumination
conditions, ensemble activity near the beginnings of the shortened
journeys was highly correlated with the ensemble activity near the
beginnings of the full-length journey, whereas the activity patterns
near the ends of the shortened journeys were correlated more with the
patterns near the end of the full-length journey. This is indicated by the reference lines in Figure 6.

View larger version (119K):
[in this window]
[in a new window]
|
Figure 6.
Population vector correlations between full-length
and shortened outbound journeys for CA1 and DG cells. For each outbound
journey type, a correlation coefficient was calculated for the
population vector at each location on the shortened journey
(x-axis) with each population vector on the full track
(y-axis), creating five correlation matrices for
each experimental condition and cell class. The four
rows of correlation matrices are constructed from the
activities of 144, 157, 125, and 79 cells, respectively. The Box
B1 matrix is simply the correlation of each population vector on
the full track with every other vector on the full track. The broad
region of higher correlation along the diagonal reflects
the fact that neighboring spatial locations tend to have correlated
firing patterns. The black diagonal line indicates the
box reference frame, and the white line corresponds to
the laboratory reference frame. Note that in all cases, the vector
correlations undergo a transition from the box to the laboratory
reference frames. Only at the end of the journey was the population
activity consistent with absolute position. This indicates that
population activity was aligned with the box well after the rat had
left the box and that in both regions, this maintained alignment
occurred even under dark conditions. The color bar
provides the correlation scale. The distance scale is in
centimeters.
|
|
Comparison of place field shifts in the light and
dark conditions
Although the rats ran slower in the dark than in the
light, no differences in place field size (based on the box 1 trials) and firing rate were observed between these two conditions (two-tailed t test, p > 0.7). The box was not visible
in the dark; nevertheless, during the outbound journeys, place fields
remained aligned with the box for considerable distances (up to 1 m from the box). The fact that fields remained aligned with the box
well beyond the box boundaries under both light and dark conditions is
illustrated in Figures 4 and 5. Place fields in the immediate vicinity
of the fixed food well remained aligned with the track.
Trial-by-trial analysis of the transition points in the
light and dark conditions
Given that place fields of CA1 and DG cells showed a
similar pattern of shift along the track, the spike trains from CA1 and DG were pooled to calculate a coherency measure, i.e., the extent to
which the spatial representation contained in the firing of all
hippocampal cells is tied to one or the other reference frame. On the
basis of the coherence, the transition points between box and track
alignment were computed for each trial (see Materials and Methods). The
distributions of transition points in the dark and light conditions are
shown in Figure 7.

View larger version (23K):
[in this window]
[in a new window]
|
Figure 7.
Distribution of transition points in the
dark and light. The transition from the box reference frame to the
laboratory reference frame was determined using a procedure (Redish et
al., 2000 ; see Materials and Methods) in which the tightness of the
place field distribution in the two reference frames was computed at
each moment in time. The transition was taken as the point where the
difference between the two measures crossed zero. A-E,
Histograms of transition points in the light (solid
lines) and dark (dotted lines) for box 1 out-box 5 out journeys, respectively. The x-axis
represents the track (in centimeters), and the y-axis
represents normalized counts. F shows the location of
the mean transition points on the track. The x-axis
corresponds to the origin of a journey on the track, and the
y-axis represents the location of the mean transition
points (± SEM) in the light (solid lines) and dark
(dotted lines) conditions for each of the five journey
types. Box location and transition point are related linearly, with the
exception of the transition points for the box 5 out journeys in the
dark, for which the transition occurred earlier than expected on the
basis of the first four journey types.
|
|
In the dark condition as in the light condition, for every 27 cm
shift of the box, the transition points shifted ~15 cm farther on the
track. The relationship between the translation of the start box and
the shift of the transition point appeared to be linear (Fig. 7), with
the exception of the box 5 out journeys in the dark. On the shortest,
box 5 out, journey in the dark, the distribution of transition points
overlapped with the distribution of transition points on the box 4 out
journeys. On the contrary, in the light, the transition points shifted
proportionally with each box location; i.e., the transition occurred at
approximately equal increments for each increment in box position (Fig.
7, solid lines). Most importantly, with the exception of the
shortest journeys, the transition points in the dark occurred
significantly farther along the journey than in the light
(p < 0.01), indicating that whatever factor or
process maintained box-referenced firing in the light persisted longer
in the dark.
 |
DISCUSSION |
In both light and dark conditions, place fields of
both CA1 and PG cells exhibited persistent activity in the reference
frame of the moveable box, i.e., the point of departure on each
journey. Consistent with the behavior of CA1 ensembles reported by
Gothard et al. (1996b)
, place fields in both CA1 and DG showed a
dynamic shift from box-aligned ensemble activity to activity aligned to the reference frame of the fixed landmarks in the environment. The
transition between reference frames was nonlinear in the sense that the
ensemble remained aligned with the box for a substantial portion of the
track, despite the fact that the box was well behind the rat (more than
two body lengths including the tail) or invisible to the rat (in the
dark) by the time the transition to the room frame occurred. The shift
appeared in the population as a whole, not independently in different
sets of cells. As suggested by Gothard et al. (1996b)
the maintained
alignment of place fields to a box-related reference frame on the
outbound journeys might be the result of an internal dynamic process
such as path integration. The present results reinforce the conclusion
that the correlation of hippocampal ensemble activity with location can
be accurately updated by path integration (McNaughton et al.,
1989
; Muller et al., 1991
; Gothard et al., 1996a
; Recce and
Harris, 1996
; Touretzky and Redish, 1996
; Samsonovich and McNaughton,
1997
; Whishaw, 1998
; Mittelstaedt, 2000).
The principal finding of this study is that the box-referenced activity
persisted significantly longer in the dark. These results support the
hypothesis that the persistence of box-related activity is not
attributable to visual signals from the box; rather, it is maintained
or updated by self-motion signals. The relative preponderance of
self-motion signals over external sensory signals in the dark, without
loss of accuracy of the population code for place, strongly suggests
that place fields are controlled dynamically by whatever signals are
available and reliable in any situation. Because the box was moved
relative to the rail, there was no possibility that the rat could have
used local sensory cues on the rail to maintain place field alignment
in the box reference frame. An olfactory gradient emanating from the
box or some form of echolocation are also implausible, because the
transition on the return journeys did not occur until the animal was at
or near the threshold of the box (i.e., much closer to the box than on
the outbound journey). Although visual and olfactory cues may not
account for the persistence of activity in the reference frame of the
box, the present data leave open the possibility that this effect is
not a consequence of path integration per se. The persistence of
ensemble activity aligned with the box might reflect the replay of a
sequence of states that was learned during the animals' initial
training in the light with the box in a fixed location. Thus, the
context of leaving the box may be represented in the hippocampus by
some time-dependent sequence of states that is not tightly bound to any
physical location. Redish et al. (2000)
have obtained similar results
(in the light) under conditions in which the rat never received initial
training with the box at a fixed location, making an explanation based
on previously learned sequences of external inputs unlikely; however,
the existence of some strictly time-dependent sequence cannot be
entirely ruled out with the present data.
The fact that the transition in the dark nevertheless occurred before
the rat reached the fixed goal at the track end probably reflects a
combination of experience with the task and the presence of local cues
on the track, which were fixed in the laboratory reference frame. It is
plausible that during the 1000-1500 training trials that preceded most
recording sessions, the rats learned the expected distances between all
the possible box locations and the goal. This possibility is supported
by the rats' behavior during a probe trial in the dark, when a baited
food cup, identical to the fixed food cup at the goal, was moved
halfway between the box and the goal. The rat ran over the food cup,
ignoring the olfactory and tactile cues associated with it, and stopped
at the end of the track to eat the bait from the fixed food cup.
Finally, the similarity between the place field shifts in the CA1 and
dentate gyrus ensembles rules out any simple, open-loop, sequential
processing hypothesis in which the path integration operation is
performed in CA3, and the result is transferred unidirectionally to CA1
and projected out of the hippocampus without recirculating via the
entorhinal cortex or other intrinsic connections. Indeed, the present
result raises the possibility that the dentate gyrus itself may be
capable of updating the hippocampal ensemble on the basis of
self-motion information. Verification of this hypothesis would require
comparison of the behavior of PG cells and projection cells from
entorhinal layer II.
Other possibilities also remain. The simplest explanation for these
effects is that the hippocampus does not take part in the path
integration computation at all but merely receives path integration
information as a component of its input (Touretzky and Redish, 1996
;
Arbib, 1997
; Cooper and Mizumori, 1999
; Sharp, 1999
). The nature and
site of implementation of the path integration computations remain to
be elucidated. At the behavioral level, there are mixed results in the
literature concerning the necessity of an intact hippocampus for path
integration (Whishaw and Jarrard, 1996
; Alyan and McNaughton, 1999
;
Maaswinkel et al., 1999
); however, it is known that the output of CA1
feeds back to the entorhinal cortex and also that CA3 sends some
recurrent excitatory projections directly back to the dentate gyrus
(Penttonen et al., 1997
). It may well be that these longer-range
recurrent connections serve to maintain a consistent representation
throughout all principal hippocampal subfields.
 |
FOOTNOTES |
Received Aug. 24, 2000; revised June 15, 2001; accepted July 5, 2001.
This work was supported by National Institutes of Health Grant
NS20331 (K.M.G. and B.L.M.), by Human Frontier Science Program Grant LT0150/1999-B (F.P.B.), and by a National Science Foundation predoctoral fellowship (K.L.H.). We thank Shanda Roberts and Karen Weaver-Sommers for help with recording and James. J. Knierim and A. David Redish for helpful comments on this manuscript.
Correspondence should be addressed to Dr. Bruce L. McNaughton, 384 Life
Sciences North, University of Arizona, Tucson, AZ 85724. E-mail:
Bruce{at}nsma.arizona.edu.
 |
REFERENCES |
-
Alyan S,
McNaughton BL
(1999)
Hippocampectomized rats are capable of homing by path integration.
Behav Neurosci
113:19-31[ISI][Medline].
-
Arbib MA
(1997)
From visual affordances in monkey parietal cortex to hippocampo-parietal interactions underlying rat navigation.
Philos Trans R Soc Lond [Biol]
352:1429-1436[ISI][Medline].
-
Bragin A,
Jando G,
Nadasdy Z,
van Landeghem M,
Buzsaki G
(1995)
Dentate EEG spikes and associated interneuronal population bursts in the hippocampal hilar region of the rat.
J Neurophysiol
73:1691-1705[Abstract/Free Full Text].
-
Bures J,
Fenton AA,
Kaminsky Y,
Zinyuk L
(1997)
Place cells and place navigation.
Proc Natl Acad Sci USA
94:343-350[Abstract/Free Full Text].
-
Buzsaki G
(1986)
Hippocampal sharp waves: their origin and significance.
Brain Res
398:242-252[ISI][Medline].
-
Buzsaki G,
Leung LW,
Vanderwolf CH
(1983)
Cellular bases of hippocampal EEG in the behaving rat.
Brain Res
287:139-171[Medline].
-
Cooper BG,
Mizumori SJ
(1999)
Retrosplenial cortex inactivation selectively impairs navigation in darkness.
NeuroReport
10:625-630[ISI][Medline].
-
Etienne AS,
Maurer R,
Seguinot V
(1996)
Path integration in mammals and its interaction with visual landmarks.
J Exp Biol
199:201-209[Abstract].
-
Freund TF,
Buzsaki G
(1996)
Interneurons of the hippocampus.
Hippocampus
6:347-470[ISI][Medline].
-
Golob EJ,
Taube JS
(1999)
Head direction cells in rats with hippocampal or overlying neocortical lesions: evidence for impaired angular path integration.
J Neurosci
19:7198-7211[Abstract/Free Full Text].
-
Gothard KM,
Skaggs WE,
Moore KM,
McNaughton BL
(1996a)
Binding of hippocampal CA1 neural activity to multiple reference frames in a landmark-based navigation task.
J Neurosci
16:823-835[Abstract/Free Full Text].
-
Gothard KM,
Skaggs WE,
McNaughton BL
(1996b)
Dynamics of mismatch correction in the hippocampal ensemble code for space: interaction between path integration and environmental cues.
J Neurosci
16:8027-8040[Abstract/Free Full Text].
-
Hill AJ,
Best PJ
(1981)
Effects of deafness and blindness on the spatial correlates of hippocampal unit activity in the rat.
Exp Neurol
74:204-217[ISI][Medline].
-
Jung MW,
McNaughton BL
(1993)
Spatial selectivity of unit activity in the hippocampal granular layer.
Hippocampus
3:165-182[ISI][Medline].
-
Knierim JJ,
Kudrimoti HS,
McNaughton BL
(1995)
Place cells, head direction cells, and learning of landmark stability.
J Neurosci
15:1648-1659[Abstract].
-
Knierim JJ,
Kudrimoti HS,
McNaughton BL
(1998)
Interactions between idiothetic cues and external landmarks in the control of place cells and head direction cells.
J Neurophysiol
80:425-446[Abstract/Free Full Text].
-
Maaswinkel H,
Jarrard LE,
Whishaw IQ
(1999)
Hippocampectomized rats are impaired in homing by path integration.
Hippocampus
9:553-561[ISI][Medline].
-
Markus EJ,
Barnes CA,
McNaughton BL,
Gladden VL,
Skaggs WE
(1994)
Spatial information content and reliability of hippocampal CA1 neurons: effects of visual input.
Hippocampus
4:410-421[ISI][Medline].
-
McNaughton BL,
Barnes CA,
O'Keefe J
(1983)
The contributions of position, direction, and velocity to single unit activity in the hippocampus of freely-moving rats.
Exp Brain Res
53:41-49.
-
McNaughton BL,
Leonard B,
Chen L
(1989)
Cortical-hippocampal interactions and cognitive mapping: a hypothesis based on re-integration of the parietal and inferotemporal pathways for visual processing.
Psychobiology
17:230-235[ISI].
-
Muller RU,
Kubie JL
(1987)
The effects of changes in the environment on the spatial firing of hippocampal complex-spike cells.
J Neurosci
7:1951-1968[Abstract].
-
Muller RU,
Kubie JL,
Saypoff R
(1991)
The hippocampus as a cognitive graph (abridged version).
Hippocampus
3:243-246.
-
Nitz DA,
McNaughton BL
(1999)
Hippocampal EEG and unit activity responses to modulation of serotonergic median raphe neurons in the freely behaving rat.
Learn Mem
6:153-167[Abstract/Free Full Text].
-
O'Keefe J
(1976)
Place units in the hippocampus of the freely moving rat.
Exp Neurol
51:87-109.
-
O'Keefe J,
Burgess N
(1996)
Geometric determinants of the place fields of hippocampal neurones.
Nature
381:425-428[Medline].
-
O'Keefe J,
Conway DH
(1978)
Hippocampal place units in the freely moving rat: why they fire where they fire.
Exp Brain Res
31:573-590[ISI][Medline].
-
Penttonen M,
Kamondi A,
Sik A,
Acsady L,
Buzsaki G
(1997)
Feed-forward and feed-back activation of the dentate gyrus in vivo during dentate spikes and sharp wave bursts.
Hippocampus
7:437-450[ISI][Medline].
-
Quirk GJ,
Muller RU,
Kubie JL
(1990)
The firing properties of hippocampal place cells in the dark depends on the rat's recent experience.
J Neurosci
10:2008-2017[Abstract].
-
Ranck Jr JB
(1973)
Studies on single neurons in dorsal hippocampal formation and septum in unrestrained rats.
Exp Neurol
41:461-531[Medline].
-
Recce M,
Harris KD
(1996)
Memory for places: a navigational model in support of Marr's theory of hippocampal function.
Hippocampus
6:735-748[ISI][Medline].
-
Recce ML,
O'Keefe J
(1989)
The tetrode: an improved technique for multi-unit extracellular recording.
Soc Neurosci Abstr
15:1250.
-
Redish AD
(1999)
In: Beyond the cognitive map: from place cells to episodic memory. Cambridge, MA: MIT.
-
Redish AD,
Rosenzweig ES,
Bohanick JD,
McNaughton BL,
Barnes CA
(2000)
Dynamics of hippocampal ensemble activity realignment: time versus space.
J Neurosci
20:9298-9309[Abstract/Free Full Text].
-
Samsonovich A,
McNaughton BL
(1997)
Path integration and cognitive mapping in a continuous attractor neural network model.
J Neurosci
17:5900-5920[Abstract/Free Full Text].
-
Sharp PE
(1999)
Comparison of the timing of hippocampal and subicular spatial signals: implications for path integration.
Hippocampus
9:158-172[ISI][Medline].
-
Stumpf C
(1965)
Drug action on the electrical activity of the hippocampus.
Int Rev Neurobiol
8:77-138[Medline].
-
Touretzky DS,
Redish AD
(1996)
Theory of rodent navigation based on interacting representations of space.
Hippocampus
6:247-270[ISI][Medline].
-
Whishaw IQ
(1998)
Place learning in hippocampal rats and the path integration hypothesis.
Neurosci Biobehav Rev
22:209-220[Medline].
-
Whishaw IQ,
Jarrard LE
(1996)
Evidence for extrahippocampal involvement in place learning and hippocampal involvement in path integration.
Hippocampus
6:513-524[ISI][Medline].
-
Wilson MA,
McNaughton BL
(1993)
Dynamics of the hippocampal ensemble code for space.
Science
261:1055-1058[Abstract/Free Full Text].
Copyright © 2001 Society for Neuroscience 0270-6474/01/21187284-09$05.00/0
This article has been cited by other articles:

|
 |

|
 |
 
A. Kemp and D. Manahan-Vaughan
The Hippocampal CA1 Region and Dentate Gyrus Differentiate between Environmental and Spatial Feature Encoding through Long-Term Depression
Cereb Cortex,
April 1, 2008;
18(4):
968 - 977.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
S. Leutgeb and J. K. Leutgeb
Pattern separation, pattern completion, and new neuronal codes within a continuous CA3 map
Learn. Mem.,
November 15, 2007;
14(11):
745 - 757.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
V. Ramirez-Amaya, D. F. Marrone, F. H. Gage, P. F. Worley, and C. A. Barnes
Integration of New Neurons into Functional Neural Networks.
J. Neurosci.,
November 22, 2006;
26(47):
12237 - 12241.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
A. Terrazas, M. Krause, P. Lipa, K. M. Gothard, C. A. Barnes, and B. L. McNaughton
Self-Motion and the Hippocampal Spatial Metric
J. Neurosci.,
August 31, 2005;
25(35):
8085 - 8096.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
J. Qian and J. L Noebels
Visualization of transmitter release with zinc fluorescence detection at the |