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The Journal of Neuroscience, May 15, 1999, 19(10):4090-4101
Reactivation of Hippocampal Cell Assemblies: Effects of
Behavioral State, Experience, and EEG Dynamics
Hemant S.
Kudrimoti,
Carol A.
Barnes, and
Bruce L.
McNaughton
Arizona Research Laboratories-Neural Systems Memory and Aging,
University of Arizona, Tucson, Arizona 85724
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ABSTRACT |
During slow wave sleep (SWS), traces of neuronal activity patterns
from preceding behavior can be observed in rat hippocampus and
neocortex. The spontaneous reactivation of these patterns is manifested
as the reinstatement of the distribution of pairwise firing-rate
correlations within a population of simultaneously recorded neurons.
The effects of behavioral state [quiet wakefulness, SWS, and rapid eye
movement (REM)], interactions between two successive spatial
experiences, and global modulation during 200 Hz
electroencephalographic (EEG) "ripples" on pattern reinstatement
were studied in CA1 pyramidal cell population recordings. Pairwise
firing-rate correlations during often repeated experiences accounted
for a significant proportion of the variance in these interactions in
subsequent SWS or quiet wakefulness and, to a lesser degree, during SWS
before the experience on a given day. The latter effect was absent for novel experiences, suggesting that a persistent memory trace develops with experience. Pattern reinstatement was strongest during sharp wave-ripple oscillations, suggesting that these events may reflect system convergence onto attractor states corresponding to previous experiences. When two different experiences occurred in succession, the
statistically independent effects of both were evident in subsequent
SWS. Thus, the patterns of neural activity reemerge spontaneously, and
in an interleaved manner, and do not necessarily reflect persistence of
an active memory (i.e., reverberation). Firing-rate correlations during
REM sleep were not related to the preceding familiar experience,
possibly as a consequence of trace decay during the intervening SWS.
REM episodes also did not detectably influence the correlation
structure in subsequent SWS, suggesting a lack of strengthening of
memory traces during REM sleep, at least in the case of familiar experiences.
Key words:
hippocampus; memory consolidation; sleep; synaptic
plasticity; REM; neural ensembles
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INTRODUCTION |
The hippocampus is thought to play
an important role in the acquisition and consolidation of certain forms
of memory. Lesions of the hippocampus lead to a temporally graded
retrograde amnesia, suggesting that the hippocampus plays a role in the
initial encoding of a memory but that, with time, the memory becomes
independent of the hippocampus (Scoville and Milner, 1957 ; McGaugh and
Herz, 1972; MacKinnon and Squire, 1989; Kim and Fanselow, 1992; for review, see Zola-Morgan and Squire, 1993; but see Nadel and Moscovitch, 1997). Associative synaptic plasticity (Hebb, 1949) may result in the
formation of "cell assemblies" or attractors within the hippocampal
formation (Marr, 1971; McNaughton and Morris, 1987; Treves and Rolls,
1994). Although long-term potentiation, a form of artificially
induced plasticity, has been demonstrated in the hippocampus and
possesses the necessary associative properties (e.g., Bliss and
Gardner-Medwin, 1973; Bliss and Lømo, 1973; McNaughton et al.,
1978), evidence to show that synaptic connectivity among hippocampal
neurons is altered in the course of information storage is mostly indirect.
Rat hippocampal neurons show spatially selective firing (e.g., O'Keefe
and Dostrovsky, 1971; Muller and Kubie, 1987; O'Keefe and Speakman,
1987; Quirk et al., 1990; Gothard et al., 1996; Samsonovich and
McNaughton, 1997), suggesting a role for the hippocampus in spatial
memory encoding. It has been conjectured that firing patterns during
behavior may form labile memory traces in the CA3 region of the
hippocampus. At the termination of exploration, during states of
immobility or sleep, the spontaneous reactivation of these traces may
somehow orchestrate the process of memory consolidation in neocortical
circuits (Marr, 1971; McNaughton, 1983; Buzsáki, 1989; Chrobak
and Buzsáki, 1994; McClelland et al., 1995), possibly by
providing a spatial contextual code that serves to bind together the
diverse neocortical components of the experience (Nadel et al., 1985;
Teyler and Discenna, 1986; McNaughton et al., 1996). This transfer may
be facilitated during 200 Hz network oscillations (ripples) in CA1,
initiated by synchronized bursts of co-operative CA3 cellular discharge
(McNaughton, 1983; Buzsáki, 1986; Wilson and McNaughton, 1994;
Ylinen et al., 1995; Shen and McNaughton, 1996).
In support of these ideas, Pavlides and Winson (1989) demonstrated that
hippocampal CA1 neurons that were highly active during behavior
exhibited increased firing rates during subsequent sleep, relative to
previous sleep. At the network level, both the spatial (Wilson and
McNaughton, 1994) and temporal (Skaggs and McNaughton, 1996) structures
of neuronal firing-rate correlations that appear in a hippocampal
neural ensemble during behavior are significantly preserved in
subsequent slow wave sleep (SWS), at least for periods of ~30 min. A
similar effect is observed in neocortical-hippocampal neuronal-firing
interactions (Qin et al., 1997). These results are consistent with the
hypothesis that there occurs a repetitive broadcast or replay by the
hippocampal networks to the neocortex during "off-line" states such
as SWS of information acquired during behavior. This replay may
constitute an essential element of the process of memory consolidation
in the neocortex by a slowly developing synaptic reorganization
(McClelland et al., 1995).
The present studies addressed several additional questions about the
reactivation phenomenon. Is there a more quantitative statistical
estimator of pattern reactivation than the mean correlation measure
reported by Wilson and McNaughton (1994), which involved an arbitrary
partition of the correlation data into two sets based on their
magnitudes? Are the activity patterns in sleep specifically related to
the most recent experience, or can multiple experiences be reactivated
in the same sleep episode? How persistent is the pattern reactivation
phenomenon? Is the strength of reactivation different for novel than
for familiar experiences? Given the evidence that rapid eye movement
(REM) may play a role in memory consolidation (Fishbein and Gutwein,
1977; McGrath and Cohen, 1978; Bloch et al., 1979; Karni et al., 1994;
for review, see Smith, 1995), is there a replay of previous waking
patterns during REM sleep? Finally, is the activity during REM related
in any way to the previous episode of SWS, and does it have any effect
on subsequent episodes?
Parts of this paper have been published previously (Kudrimoti et al.,
1995, 1996, 1997).
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MATERIALS AND METHODS |
Experimental animals
Fourteen adult, male retired-breeder Fischer 344 rats (Charles
River Laboratories, Wilmington, MA), with a mean age of 12.5 months at
surgery, were at 80% of their weights maintained ad libitum. The rats were housed individually and maintained on a reversed 12 hr light/dark cycle throughout the training and recording period. During this time, they had access to water ad
libitum and were handled and weighed daily. Animal care, surgical
procedures, and killing were performed according to National Institutes
of Health guidelines for the use of vertebrate animals in research.
Training and recording environments
Eight animals were used in the main experiments. Three wooden
tracks were used to train these animals. A triangular track (each side,
75 cm long and 8 cm wide) was used to train three rats. For two of
these animals, the same triangular track was used during recording,
whereas for the third rat, a linear track (180 cm long and 6 cm wide)
was used for the recording sessions. For the remaining five animals,
the training and recording environment consisted of a "digital
8"-shaped track (width = 6 cm). This track could be thought of
as consisting of two rectangular tracks of dimensions 93 cm × 43 cm with one of the longer sides shared between the two. Tracks with
linear configurations were used to ensure repeated sampling of all the
locations on the track as the animals performed stereotyped
trajectories. During the training and recording sessions, these tracks
were placed on a table 1 m from the floor in the center of a
moderately illuminated 3.9 m × 3.9 m × 2.5 m room with
a black floor, a black ceiling, and multiple visual cues distributed
around the perimeter.
Training protocol
Pretraining. Five of the eight rats underwent daily
food-rewarded training on linear tracks, 90 cm long, 13 cm wide, and
1 m above the floor, in a different room. The goal of this
pretraining was to familiarize the animals with the sequence that they
would experience during the training and recording sessions, which
consisted of a pre-track epoch (PRE), track running (RUN), and a
post-track epoch (POST). Each rat was allowed to sleep or rest quietly
in a "nest" for ~30 min (PRE) before the experimenter placed the rat on the track. The rat was then trained to run back and forth along
the track, receiving a food reward at both ends (RUN). After ~30 min
of track running, the animal was placed back into the nest and allowed
to rest for 30 min (POST). The animals learned this task within 1 week
and ran on the rectangular digital 8-shaped track during subsequent
training and recording sessions. The other three animals did not
undergo a formal pretraining protocol; however, they underwent
extensive training on the apparatuses on which subsequent recording
sessions were performed. All training and recording sessions were
performed in the light phase of the animals' diurnal cycle, to
maximize sleep duration during recording.
Training. For 1 week after surgical implantation of
recording electrodes, the five rats pretrained on the linear track
underwent daily food-rewarded training on one-half of the digital
8-shaped track so that they had to traverse a rectangular path. A
wooden partition 60 cm high was placed along one side of the long arm of the rectangular track, so that the animal would have visual and
physical access only to the one-half of the track on which it was
trained. The rat was placed into the nest on the training apparatus and
connected to a recording cable. As the rat rested, multiple single-unit
and electroencephalographic (EEG) activity was monitored as the
electrodes were gradually and intermittently advanced toward the
hippocampal CA1 layer. After ~1 hr, the experimenter placed the rat
on the track and trained it to traverse the track in a clockwise
direction, rewarding it with a mixture of chocolate and rat chow at the
two corners farthest away from the partition. After track running,
which lasted for 20-30 min, the rat was placed back into its nest and
was allowed to sleep for ~1 hr. Two of the remaining three animals
were extensively trained (3-4 weeks) to run alternately in clockwise
and counterclockwise directions on the triangular track and received
"sleep training" before and after track running similar to that
given to the other rats. The last animal was trained for 10 d on
the triangular track and subsequently on the linear track on which data
were collected. Each rat received one training session per day.
Additional animals
Data from an additional six animals, which were prepared, using
identical procedures, for different studies, were included in the
analysis presented under experiment 2 to confirm the result obtained
therein with an independent sample and to increase the sample size in
certain analyses. These animals were pretrained on the linear track
described above, and then recordings were conducted while they ran for
the first and/or second times on either circular, triangular, or
rectangular tracks similar if not identical to those described above.
Electrode microdrive construction and surgery
To acquire extracellular spike signals from a large number of
single units in parallel and to collect EEG data, a multielectrode microdrive (Wilson and McNaughton, 1993) was implanted unilaterally above the hippocampus of each rat. The methods for the electrode microdrive construction and surgical implantation of the drives have
been described in detail by Gothard et al. (1996). Briefly, 14 bundles
of four nichrome wire electrodes ["tetrodes" (McNaughton et al.,
1983b; O'Keefe and Recce, 1993); total diameter of ~40 µm] were
mounted in 14 independent microdrives, such that turning a drive screw
allowed manipulation of each tetrode up to a distance of 5-7 mm within
a circle 1.5 mm in diameter. The microdrive array was stereotaxically
positioned 3.8 mm posterior and 2.5 mm lateral to bregma, above the CA1
region of the hippocampus (Paxinos and Watson, 1982).
Electronics and recording
Twelve of the 14 tetrodes served for unit recording, and the two
remaining tetrodes served as reference or EEG electrodes or both. Each
electrode within a tetrode was connected to an independent, unity-gain
field effect transistor preamplifier. The outputs of the
preamplifiers were fed via a multiwire cable to a 64 channel commutator
and then amplified (× 10,000) and bandpass filtered (600 Hz to 6 kHz),
before being fed into analog-to-digital converter cards housed in eight
80486 personal computers equipped with programmable 10 kHz time-stamp
clocks, giving a resolution of 100 µsec. Signals from each tetrode
were sampled at 32 kHz using Advanced Discovery data-acquisition
software (DataWave, Longmont, CO), and whenever the amplitude of the
signal exceeded a preset voltage threshold, a 1 msec sample of the data
was time stamped and stored on disk. Signals from one channel from each
of 12 tetrodes (generally including the one placed near the hippocampal
fissure) were used to acquire continuous EEG data. These signals were
amplified 2000 times, bandpass filtered between 1 and 100 Hz or between
1 Hz and 3 kHz, and then sampled at 200 Hz or 1 kHz, respectively. The
200 Hz-sampled data were used to capture the theta rhythm (6-10 Hz)
in the EEG during locomotor behavior (Vanderwolf et al., 1975) and REM
sleep (Winson, 1972) and "sharp waves" during SWS (Buzsáki,
1986). The 1 kHz-sampled data were used to identify 100-200 Hz
"ripples" in the EEG during SWS (O'Keefe, 1976; Buzsáki et
al., 1992; Ylinen et al., 1995).
Light-emitting diodes (LEDs) mounted on the ends of a 15 cm lightweight
aluminum rod affixed to the headstage served as markers for position
and head direction during data acquisition. Signals emitted by the LEDs
were received by a video camera mounted on the ceiling above the
recording apparatus, at a resolution of ~2.3 pixels/cm, and were
sampled at 20 frames/sec by a tracking device (Dragon Tracker, Model
SA2, Boulder, CO). The rat's behavior could also be observed on a
television monitor in the computer room.
Recording protocol
Identification of units. After surgery, 12 of the 14 tetrodes were lowered gradually into the hippocampal CA1 layer over the course of 5-7 d. One of the remaining tetrodes remained fixed in the
neocortex near the corpus callosum, serving as a reference electrode,
whereas the last tetrode was positioned ~300 µm below the CA1 cell
body layer, near the hippocampal fissure, and served as an EEG
electrode optimized to record the hippocampal theta rhythm. Proximity
to the CA1 cell body layer was identified by the presence of
ripple-sharp wave complexes, which reverse polarity ~50 µm below
the CA1 layer. The cell body layer itself was characterized by the
appearance of multiple complex-spike cells (Ranck, 1973; Fox and
Ranck, 1981; McNaughton et al., 1983a).
Sleep scoring. After stable hippocampal cell activity was
obtained across at least several tetrodes, the experimenter left the
recording room and started data acquisition. Sleep scoring was done
using a combination of behavioral and EEG criteria. The experimenter
observed the rat's behavioral state on the television monitor, while
writing down the EEG state four to five times per minute and
concurrently listening to an audio monitor for ripples. Baseline data
were collected for 20-60 min (PRE). This period consisted
predominantly of "large irregular activity" (LIA, sharp wave-ripple complexes heard on an audio monitor or seen in the EEG;
the rat apparently sleeping or falling asleep), SWS (the rat clearly
sleeping; but no theta rhythm in the EEG; onset often associated with
obvious neocortical spindle activity picked up by the callosal
reference electrode), and REM (theta in the hippocampal EEG; the rat
sleeping). The animal was then placed on the track, and single-unit and
EEG data were acquired (RUN) as the animal traversed the track. After
track running, the animal was replaced into its nest, and data were
collected for a further 60-180 min (POST) as the animal rested quietly
and/or slept. This experimental protocol constituted experiment 1.
After two or three such recording sessions on consecutive days, a
different protocol was introduced. After PRE, the animals trained on
the rectangular track first ran on the familiar half of the track, as
in experiment 1. When the rat was on the central arm, the partition was
lifted and placed on the other side of the rat, giving the rat access
to the novel half (which also included the central arm). The rat was
allowed to traverse the track in the novel configuration. Finally, the
original configuration was restored, and the animal ran again on the
familiar half. There were no pauses between the three track sessions.
The rat was then placed back in the nest as before (POST). This
protocol was performed with three issues in mind: (1) to confirm that
there were no significant changes in the place field configuration
during the first and second traversals on the familiar half of the
track, thus establishing that the same cells were recorded during the
entire track-running period, (2) to determine whether and how the
spatial representations of two different environments were reactivated
concurrently in sleep after these experiences, and (3) to determine
whether the reactivation after novel environments was qualitatively
and/or quantitatively different as compared with familiar ones.
Questions 2 and 3 formed the basis for experiment 2. Because the sleep
periods before and after the familiar and novel halves of the track
were the same, nonspecific effects because of sharp wave-induced
dynamics in SWS were controlled for in comparisons between the
reactivations for each half. This protocol was repeated on the next
day. In general, the protocol used in experiment 2 was considered to be a novel experience for the animal, although it was obviously somewhat less novel on the second day. To confirm that the hippocampal representations of the two halves of the track were different, smoothed
firing-rate maps of the neurons with place fields on the two halves
were constructed by binning the environments (bin size, 1.7 cm × 1.7 cm) (Skaggs at al., 1993). Correlations were computed between the
rate maps to assess similarities in spatial representation.
Off-line data analysis
Cell identification. Single units were isolated on
each tetrode using a multidimensional "cluster-cutting" technique
(McNaughton et al., 1989; Wilson and McNaughton, 1993). This method
uses the relative peak amplitudes of extracellular spikes, from several cells recorded simultaneously on the four closely spaced electrodes within a tetrode, to distinguish between the spike generators (McNaughton et al., 1983b).
An isolated unit was classified as a pyramidal cell if (1) it had fired
complex spike bursts during SWS, (2) it had been recorded on the same
tetrode with other complex spike cells in the CA1 layer, (3) it had a
spike width (peak to trough) of at least 300 µsec, and (4) it had a
mean firing rate of <5 Hz during track running. Only those pyramidal
cells that were stable across PRE, RUN, and POST were included in the
analysis. A pyramidal cell was considered to have significant
location-specific firing based on a measure that quantifies the amount
of information that the occurrence of a single spike conveys regarding
the location of the rat (Skaggs et al., 1993). Thus, spatially active
single units were pyramidal cells with significant
(p < 0.01) spatial specificity (place fields)
during track-running behavior. Spatially inactive units were pyramidal
cells that did not have place fields on the track and fired very few or
no spikes during track running. Theta cells (putative interneurons)
were excluded from the analysis.
EEG analysis. EEG analysis served two purposes. First, it
allowed a differentiation of sleep states. The experimenter selected two raw EEG traces with the largest overall theta rhythm amplitudes, sampled at 200 Hz and 1 kHz, respectively, and visually inspected each
trace from the start to the end of both prebehavior and postbehavior sleep. The sleep-scoring notes were matched with the EEG trace and used
in conjunction with the EEG waveform to identify periods of REM
activity, LIA and SWS states, and awake theta (AW ). The start and
end times of each of these states were flagged and later used to
analyze correlations separately during REM and SWS periods (Fig.
1A,B).
Second, the 1 kHz-sampled EEG traces were used to identify the start
and end times of ~200 Hz oscillations (ripples) in the EEG recorded
near the pyramidal layer and associated with sharp wave (SPW) activity
deeper in the stratum radiatum (Ranck, 1973; O'Keefe and Nadel, 1976;
Buzsáki, 1986) (Fig. 1B). The traces were
digitally bandpass filtered between 100 and 300 Hz, and the times of
the ripples were identified by an algorithm that detected a ripple when
the amplitude of the filtered EEG crossed a set threshold and remained
above the threshold for at least 25 msec. Durations of the ripples and
inter-ripple intervals were used in comparing the relative strengths of
the pattern reactivation during these periods.
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Figure 1.
A, B, EEG traces and
concurrent hippocampal single-unit activity during 2 sec periods of REM
and SWS in POST. In both A and B, the
top trace is the raw EEG waveform acquired by sampling
at 200 Hz (A) and 1 kHz
(B). The second and third
waveforms are the EEG data bandpass filtered between 6 and 10 Hz and between 100 and 300 Hz, respectively. In A, the
100-300 Hz bandwidth was achieved by filtering the 1 kHz-sampled raw
data. At the bottom in A and
B are rasters of 36 pyramidal neurons of the CA1 region
that fired on the track (not all were active in the time windows
shown). Each vertical tick mark represents one neuronal
action potential, and each row of tick
marks represents the activity of one neuron over 2 sec. Theta
rhythm during REM sleep is shown in A. The filtered
waveform in A shows a large 7-8 Hz component compared
with that in the corresponding filtered waveform in B.
The third waveform shows very small amplitude 100-300
Hz components in these data. Slow wave sleep is illustrated in
B. There is an absence of theta modulation in the EEG;
however, characteristic large amplitude 200 Hz ripples are clearly
observed in the raw data as well as in the 100-300 Hz-filtered waveform. Coincident with the ripples is a
burst of activity of the CA1 pyramidal cells, which do not fire in the
same manner between ripples. C, A raster plot of
population activity with respect to ripples. In the figure, 475 ripples
occurred in the 10 min epoch of slow wave sleep represented. The start
of each ripple oscillation is aligned to the vertical
line at 0. Each row of tick marks
represents the firing of the population (i.e., it is a combination of
spike trains of all of the 36 cells) in a 200 msec window centered
around the start of a ripple. It is clearly seen that there is an
increase in population discharge during the ripple oscillations, as
compared with that in the periods just before the ripples.
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Statistical analysis of firing-rate correlation
distributions. The spike trains of n pyramidal
neurons in a data set were binned into T (100 msec)
intervals to obtain sequences of spike counts
(fi[t]). The choice of
100 msec was based on previous studies (Wilson and McNaughton, 1994).
To check the robustness of the effects described below, four data sets
from four different rats were also analyzed using bin sizes of 50 and
200 msec, with comparable results (data not shown). A Pearson's
correlation coefficient (Cij, hereafter
referred to as a correlation) was calculated between each pair
(ij) of spike rate sequences using the formula:
where µi and
 i are the mean and SD of
fi[t], respectively (Perkel
et al., 1967; Gerstein and Perkel, 1969). Ensemble recording from
n neurons over T sampling intervals (e.g., 100 msec in the present analysis) generates an n × T matrix (Q) in which the rows are spike
rate sequences (spike "trains," in the limit of short intervals)
and the columns are "population vectors" (McNaughton, 1999).
Cij can be defined as the cosine of the
angle between two row vectors in Q, after subtracting their respective means. It is a real number between  1 and +1 and is a
normalized measure of the tendency of the firing rates of both cells to
covary on the time scale in question. When
Cij is computed for all neuron pairs, a
unique n × n matrix R of
pairwise activity correlations is generated. The present study was
designed to assess the similarities of the R matrices over
different behavioral epochs. R is symmetric, and hence only
the lower diagonal portion of it needs to be considered. In addition,
only correlations between cells on different tetrodes were used in the
analyses. This eliminated the possibility of spurious overlap because
of the incomplete isolation of single units.
Experiences presumably are encoded as population vectors in the
n dimensional space of neural firing, and a memory trace is presumably a reinstantiation of some approximation of a previously occurring population vector or sequence thereof. The structure of the
zero-lag firing-rate correlation matrix R depends entirely and solely on the specific set of population vectors contained in the
sample, irrespective of their temporal order (i.e., the state-space
occupancy distribution). The assumption made here is that the
similarity of the R matrices for two sampling epochs is a
reflection of the similarity of the corresponding state-space occupancy
distributions (i.e., the similarity of the two sets of population
vectors). To the extent that the state-space occupancy distributions
differ between two epochs, the R matrices will be less
similar. This could occur either because recall is noisy, because
different events (i.e., events not in the test set) are being recalled,
or because some states are spontaneous and do not reflect recall of any
previous input. These cannot be distinguished in the present analysis.
In this approach, it is the relative similarities of correlations and
not their magnitudes that are critical. In general, the spread of
values in R is expected on statistical grounds to tend
toward zero as the number of different population vectors in the sample
increases. Thus, correlation magnitudes per se are not of much
significance in the assessment of reinstantiation of memory traces.
Approximately speaking, the R matrix potentially contains
contributions from two sources, one global and one specific to
individual cell pairs. Global changes in correlations can arise from
any fluctuation or nonstationarity in the net activity of the system
over the sampling interval. These could be caused by fast or slow
external modulation, for example, changes in circulating hormone levels
or in the activity of subcortical inputs such as the septohippocampal
GABAergic and cholinergic systems or the median raphe serotonergic
system, or by intrinsic dynamics such as intrinsic oscillations, sharp
waves, etc. (see Fig. 1C). Changes in global modulation may
be expected to change the mean values of the correlations, without much
change in their relative magnitudes, at least within some range.
The present study was designed to investigate the dynamics of memory
trace reactivation during sleep and/or quiet waking. We approach this
by quantifying how much of the variance in the elements of R
during sleep can be explained (statistically speaking) by their
relative magnitudes during a previous active waking experience, after
taking into account their relative magnitudes in sleep before the
experience. This enables the comparison across time and behavioral state of the effects of previous experience on the microstructure of
the firing-rate correlation pattern. The explained variance (EV) of the
correlation pattern in sleep (POST), caused by a track-running experience (RUN), was computed using the square of partial correlation coefficients in a multiple correlation analysis (Kleinbaum et al.,
1988) in which the lower diagonal elements of the corresponding ensemble firing-rate correlation matrices, for the periods PRE, RUN,
and POST, were entered as variables:
where rRUN,POST|PRE reflects the
effects of the maze experience (RUN) on the activity correlations in
POST, after controlling for any preexisting relationship that might
have been present in PRE. The analysis typically used correlations
during the last 10 min of baseline SWS (PRE) and track running (RUN) as
independent variables and the correlations in three 10 min epochs in
postbehavior SWS (POST) as dependent variables. The relationship
between RUN and POST correlations was thus measured after controlling
for the linear effects of PRE correlations on both of these variables.
For experiment 2, the relationship between the activities in the two
different halves of the maze was also controlled for, enabling an
assessment of the independent contributions of the two experiences to
the correlation variance during POST. In sessions in which REM occurred
during postbehavior sleep, the correlation during the REM episode was
also used as a dependent variable. In some analyses, SWS epochs were
parsed into SPW and inter-SPW segments, and the magnitudes of the EV
for these two conditions were compared.
A typical example of the distribution of pairwise rate correlations
during track-running behavior and during SWS and REM sleep states is
shown in Figure 2A. An
illustrative example of a simple linear regression fit is shown in
Figure 2B. For the actual RUN-POST comparison, a partial correlation coefficient controlling for the
linear effects of PRE on both the variables was used. For the
PRE-RUN comparison, a simple correlation coefficient was
computed. For illustrative purposes and for comparison with the results of Wilson and McNaughton (1994), some data were sorted into cell pairs
that were relatively highly correlated during track running (HICOR) and
cell pairs that were relatively uncorrelated during track running
(UNCOR) using a cutoff of +0.01. Although this cutoff is arbitrary, it
was a reasonably consistent value by which cells with completely or
partially overlapping spatial fields could be distinguished from those
with nonoverlapping fields. The multiple correlation approach provides
a more quantitative estimate of the true strength of trace reactivation
because it takes into account all of the variance in the data, rather
than partitioning the data arbitrarily into two groups. It also
provides a means of estimating explicitly the magnitude of the
contribution of recent experience to the correlations, by removing the
contributions of preexisting correlation structure in the network. Most
statistical analyses were performed using the SPSS (Chicago, IL)
statistical package.
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Figure 2.
A, Example distributions of the
pairwise correlations from one recording session (familiar track).
Histograms show distributions of correlations during SWS PRE (last 10 min), SWS POST (first 10 min), track-running behavior (RUN), and REM
POST. B, Data from the same recording session showing
the relationship between pairwise correlations during RUN and SWS PRE
(left) and the relationship between correlations during
RUN and SWS POST (right). Each data point in the
scatterplots corresponds to one cell pair, both members of which had
place fields on the track. For illustrative purposes, the relationship
between track and sleep correlations has been shown using a simple
linear regression fit. In the actual analysis of POST versus RUN, a
multiple correlation model that controlled for the effects of PRE was
used.
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To verify that the partial regression analysis was indeed capable of
removing the effects of any preexisting similarity between R
during POST and R during RUN, tests were performed in which one-half of the RUN period was substituted for PRE in the analysis of
the relationship between the other one-half of the RUN period and POST.
As expected, this resulted in an EV for the RUN-POST comparison of
~0.
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RESULTS |
The results are based on 42 recording sessions from 14 rats. A
recording session consisted of data collection during a prebehavior sleep period (PRE), spatial behavior on one or more tracks (RUN), and a
postbehavior sleep period (POST). From 16 to 63 pyramidal cells were
recorded per session for an average of ~32 cells/session. In this
study, a total of 18,051 correlations were computed from 1335 pyramidal
cell unit recordings. Although it is likely that some cells were
recorded from more than once, most of the results were observed
independently in each session.
Mean firing rates and average correlation of the population before
and after track running
Using data collected in experiment 1 (highly familiar track), we
computed the firing rates of cells during PRE and POST for two cell
groups: cells with fields on the track (n = 423; mean firing rate during RUN, 1.03 ± 0.03 Hz) and cells inactive on the
track (n = 237). There was only a slight
(nonsignificant) increase in firing rates from PRE to POST of the cells
that had fields on the track (Fig.
3A; difference of ~14%;
p > 0.05). Cells that did not fire during behavior
also showed no change in their mean firing rates
(p > 0.05). The small increase in firing rates of the active population decreased rapidly with a time constant of
~20 min. Pairwise correlations between cells on different tetrodes were computed during PRE, RUN, and POST. The mean correlation of the
population of cells with fields increased significantly from PRE to
POST but rapidly decayed (Fig. 3B;  ~ 12 min). In addition, the correlations during PRE and POST were sorted by whether
the cell pairs were coactive, i.e., had overlapping fields during RUN
(Fig. 3C; C  0.01, HICOR),
or were active but uncorrelated during RUN (C < 0.01, UNCOR). Correlations of the HICOR group increased from PRE to
POST but rapidly decayed ( ~18 min). Thus, the
behavior-related elevations of firing rates and pairwise correlations decayed rapidly during POST.
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Figure 3.
A, Histograms of the mean firing
rates of pyramidal neurons during pre-behavior SWS (PRE) and three 10 min intervals in post-behavior SWS (POST), computed by averaging across
22 track-running sessions from seven rats, are shown. The firing rates
of cells with place fields on the track (n = 423)
increased marginally (p > 0.05, ANOVA) and
returned rapidly toward their PRE levels with a time constant of 20 min. For all periods, the firing rates of cells with fields on the
track were significantly higher than were the firing rates of cells
inactive during track running (n = 237;
p < 0.05, ANOVA). B, Mean
correlations of cell pairs (using 100 msec bins) during the same time
periods described in A are shown. The mean correlation
of cells active on the track increased from PRE to POST
(p < 0.05), but the increase rapidly
declined over the 30 min period ( = 12 min). The mean correlations
of cells inactive on the track did not change significantly
(p > 0.05, ANOVA). C, In the
cell group with fields on the track, correlation values were sorted
into HICOR (correlation 0.01) and UNCOR (correlation < 0.01) sets. The mean correlation of the HICOR set was significantly
higher compared with the mean correlation of the UNCOR set
(p < 0.05, ANOVA). This difference, present
in 6 of 22 sessions in PRE, was enhanced in all 22 sessions during POST
(significant interaction on a repeated-measure ANOVA) as the mean
correlation of the HICOR set (mean correlation of 0.10 ± 0.003 on
track) increased from a PRE value of 0.04 ± 0.002 to a POST value
of 0.07 ± 0.002 (p < 0.05, ANOVA).
This increase decayed rapidly ( = 18 min). The mean correlation of
the UNCOR set (mean correlation of 0.02 ± 0.000 on track)
increased from a PRE value of 0.02 ± 0.001 to 0.03 ± 0.000 in POST (p > 0.05, ANOVA). It is
theoretically possible that the differences in the mean correlations
between the two groups in the histogram could be attributable to
differences in the firing rates of the neurons in the two groups;
however, the majority of cells were members of pairs in both groups,
and there were no significant differences in the mean firing rates
between the HICOR and UNCOR cells during POST (19 of 22 sessions;
p > 0.05). D, Explained correlation
variance (EV) during the same time periods described in
A are shown. For PRE, EV was computed using the square
of the simple correlation coefficient
rRUN-PRE (solid bar on
left). For POST, EV was computed using the square of the
partial correlation coefficient
rRUN-POST|PRE (3 bars on
right). The EV for all periods of SWS was significantly
>0 (p < 0.001, ANOVA) and increased
significantly after behavior (p < 0.05, ANOVA). The decay time constant (baseline = 0) was 30 min,
suggesting that the similarity of the correlation matrices for the RUN
and POST periods outlasts changes in the average magnitude per se. Note
that the baseline for the POST EV is 0, not the PRE value, which is
removed by the partial correlation procedure (see Materials and
Methods).
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Correlation patterns during sleep were partly explained by the
patterns during previous track running
A multiple correlation analysis was performed on the activity
correlations during RUN, PRE, and POST. There was a significant relationship (rRUN-POST|PRE,
p < 0.0001) between the correlations during RUN and
those during POST after controlling for the linear effects of PRE
correlations. These effects were significant in all data sets. The
activity patterns of hippocampal pyramidal cells during behavior in a
highly familiar environment explained ~15% of the variance in the
firing-rate correlations among these cells during sleep immediately
after behavior (i.e., Fig. 3D; EV = 0.15).
Because the partial coefficient rRUN-POST|PRE
controls for the linear effects of PRE, the baseline level of EV can
thus be taken to be 0. Using the 30 min of POST, we fitted an
exponential to the decay of EV and obtained a slower time constant of
~30 min (compared with the faster time constants of the firing rates
and correlation magnitudes), suggesting that the similarities of the
correlations outlast the changes in the overall mean values of the
correlations or the changes in firing rates per se.
Correlation patterns during behavior on a familiar track can
sometimes be predicted from the patterns during prebehavior sleep
If activity patterns persist in the hippocampus, then, for a
familiar experience that is repeated daily, some of the variance in the
correlations during PRE should also be explainable by the pattern
during behavior. In all 22 sessions for experiment 1, the environment
was already highly familiar. In 6 of these sessions (from three
different rats), there was a significant relationship between the RUN
correlation structure and that during PRE (p < 0.00001 in three sessions; 0.003  p 0.006 in
the other three sessions). In the remaining 16 sessions,
rRUN-PRE was positive, indicating a significant
trend. In experiment 2 (see below), PRE effects were seen for the
familiar track in two of six data sets, consistent with the proportion
seen in experiment 1. Overall, the activity patterns of hippocampal
pyramidal cells during behavior in a highly familiar environment
accounted for, on average, ~5% of the variance in the coactivity
patterns of these cells during sleep immediately before RUN (Fig.
3D).
The foregoing PRE effects might reflect residual traces of memory for
familiar experiences, lasting at least until the next day. This
hypothesis predicts that, for novel environments, there should be no
relationship between correlations during PRE and RUN and a possibly
weaker relationship between the correlations during RUN and POST as
compared with highly familiar experiences. These questions are
addressed in experiment 2.
Trace reactivation in SWS occurs most robustly during ripples
During SWS and quiet wakefulness (LIA), pyramidal cells in the
hippocampus fire intermittently in synchronized bursts, associated with
~200 Hz ripples near the cell bodies and simultaneous SPW in the
stratum radiatum (see Fig. 1). Because ripples were identified on the
basis of EEG traces, only those data sets with robust ripple activity
in the EEG record (i.e., those with optimal positioning of the EEG
electrodes) were considered for analysis of activity during ripples and
inter-ripple intervals. Using this criterion, we selected 12 track-running sessions, in which the postbehavior sleep period had 10 min of continuous SWS and/or LIA activity in the first 30 min of sleep.
Correlations were computed separately for spike rates during the ripple
periods and during the inter-ripple intervals.
For a set of random variables, the variance of their correlation
distribution is inversely proportional to the square of the length of
the sampled epoch. Because a major fraction of SWS is spent in the
inter-ripple state (mean duration of 1.2 sec for inter-ripple intervals
vs 73 msec for ripples), it is necessary to equate the total sampling
time for the two states. Therefore, a period in each inter-ripple
interval midway between the two ripples bounding it and of duration
equal to that of the preceding ripple was selected. The ripple and
inter-ripple segments were then pooled for correlation analysis.
Mean firing rates of the population during ripples increased slightly
from PRE to POST (p < 0.05), but this increase
decayed rapidly with a time constant of ~13 min (Fig.
4A). Mean correlations during ripples did not change from PRE to POST (Fig.
4B). Although the mean firing rates were
significantly different (p < 0.05, ANOVA)
between ripples and inter-ripple intervals, the mean correlations were
not different during POST (Fig. 4B; p > 0.05). The mean ripple duration decreased from PRE to POST (Fig.
4C; p < 0.05), but the number of ripples
increased (Fig. 4D; p < 0.05). A
ripple index, representing a ratio of the total number of spikes
emitted during ripples to the number during inter-ripple intervals,
increased from PRE to POST, presumably because of the increase in
firing rates during ripples (Fig. 4E;
p < 0.05). There was a significant relationship
(rRUN-POST|PRE, p < 0.001)
between the correlations during RUN and those during both ripples and
inter-ripple intervals in POST. As shown in the three sets of
bars on the right in Figure 4F,
the correlations during behavior explained a significant proportion of
the correlation variance during ripples and during inter-ripple
intervals after the behavior (p < 0.05, t tests; chance = 0%). During the first 10 min of SWS,
EV was significantly higher during ripples than during inter-ripple
intervals (p < 0.05, ANOVA). The decay time
constant for the EV during ripples was 30 min.
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Figure 4.
Ripple analysis for a subset of data from
experiment 1 (12 recording sessions; familiar environment).
A, Histograms show the mean firing rates of the
population with place fields (n = 258), during
ripples and during inter-ripple intervals. For all periods, the firing
rates during ripples were significantly higher than were the firing
rates during inter-ripple intervals of comparable duration
(p < 0.05). The firing rates during ripples
increased from PRE to POST (p < 0.05) and
decayed over a 30 min period with a time constant of ~13 min. The
firing rates during inter-ripple intervals did not change
(p > 0.05). B, During POST,
mean correlations during ripples and inter-ripple intervals were not
different (p > 0.05). The mean correlation
of the population during ripples did not change from PRE to POST. The
mean correlation during inter-ripple intervals increased from PRE to
POST but decayed rapidly ( = 8 min) to PRE levels. C,
The average duration of ripples decreased from PRE to POST
(p < 0.05) but remained constant during the
30 min of SWS in POST (p > 0.05).
D, The number of ripples increased from PRE to POST
(p < 0.05) but did not decrease
significantly over the 30 min interval (p > 0.05). E, Histograms show a ripple index that was
computed as: (percent time in the ripple state × the mean firing
rate of the population during ripples)/(percent time in the
inter-ripple state × the mean firing rate of the population
during inter-ripple intervals). The index increased from PRE to POST
(p < 0.05), and there was a decreasing
trend in POST ( = 28 min). F, For PRE (ripple and
inter-ripple periods), EV was computed using the square of the simple
correlation coefficient rRUN-PRE
(bars on left). For POST (ripple and
inter-ripple periods), EV was computed using the square of the partial
correlation coefficient rRUN-POST|PRE (3 sets of bars on right). EV during ripples
and inter-ripple intervals in POST was significantly above chance
(p < 0.05) and increased significantly
after behavior (p < 0.05, ANOVA). During
the first 10 min of SWS, the EV was greater during ripples
(p < 0.05). The decay time constant for the
explained correlation variance during ripples was 30 min. (Note again
that the baseline for the POST EV is 0, not the PRE value, which is
subtracted by the partial correlation procedure).
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Effects of multiple experiences on pattern reactivation
In experiment 2 (2 consecutive days for each of three rats), the
animal ran first on the familiar configuration (F1) of the figure-8
track and then on the unfamiliar configuration (N), followed by another
session on the original configuration (F2). Because the second
configuration was relatively novel on both days, data from both days
were considered together. Although the two halves of the track were
very similar in shape and alignment with respect to extratrack cues,
the place fields of the same cells were mostly different in the two
regions. For example, 18 of the 58 cells recorded on the first day of
experiment 2 did not have fields on the novel half. For cells that had
fields on both halves, the firing-rate map correlations between fields
of the same cell had a mean value of 0.30 ± 0.06. For comparison,
the corresponding rate map correlations for F1 versus F2 were 0.76 ± 0.02, indicating highly similar fields during two episodes in the
familiar environment. On the second day of experiment 2, 17 of 73 neurons with place fields on the familiar half did not fire on the
novel half, and the spatial firing patterns between the two halves had
a lower correlation (0.17 ± 0.06). Similarly, the temporal
activity correlation patterns for the two environments were somewhat
correlated (r = 0.3 and 0.5 on days 1 and 2, respectively). Some of the correlation between the spatial and temporal
activity patterns in the two environments may have been attributable to
the fact that the central arm of the track (i.e., approximately
one-third of the track) was common to both N and F configurations. In
any case, this correlation was controlled for in estimating the
independent effects of both halves of the maze during POST. Thus,
rN-POST|PRE,F2 was computed for the novel half
(N) of the track, and rF2-POST|PRE,N was
computed for the familiar half (F2) for subsequent 10 min intervals in
postbehavior sleep as in experiment 1.
The correlation structures for two sequentially visited
environments both contributed to the correlation structure in sleep
As in experiment 1, the mean correlations of the population
increased from PRE to POST but decayed rapidly with time constants of
36 min (N) and 27 min (F2) on day 1 and with time constants of <10 min
on day 2. rN-POST|PRE,F2 and
rF2-POST|PRE,N were significant (p < 0.001). In POST, the EV for both N and F2 was above chance (Fig.
5; p < 0.05, t tests), and the EV for N was significantly smaller than
that for F2 (Fig. 5; p < 0.05). This was observed in
spite of the fact that mean correlations were somewhat higher for N
(0.0677 ± 0.002) than for F2 (0.0558 ± 0.002) in the first
10 min of POST (p < 0.05) and were not
significantly different from those for F2 in all other periods
(p > 0.05). The combined independent
contributions of N and F2 accounted for ~30% of the variance of the
correlation distributions during POST. Examination of the ripple
dynamics in the manner illustrated in Figure 4 did not reveal any
differences between experiments 1 and 2 that might have accounted for
the increased EV.
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Figure 5.
In experiment 2, the animals traversed the
familiar half of the digital 8-shaped track first and then ran on the
second, novel half of the track before running on the familiar half
again. During POST, the independent contributions of both regions to
the explained variance (i.e., after controlling for any correlation
between regions) were significantly above chance. Also, the EV for the
familiar half was significantly above that for the novel half
(p < 0.05, Mann-Whitney U
test). For both halves, the explained variance had slower time
constants of decay (~43 min), in comparison with that of the
familiar-only experiments.
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Correlations in PRE did not predict those in RUN for a
novel experience
If the significant EV in PRE attributable to RUN and observed in
experiment 1 was caused by a memory effect lasting from one day to the
next, there should be no such effect for a novel environment. Consistent with this prediction, in experiment 2, rPRE-N|F2 was not significant (data not
shown; i.e., there was no significant relationship between the
correlation patterns during PRE and RUN for the novel experience in any
of the six data sets). Significant PRE effects were seen for the
familiar half in two of six data sets, consistent with the proportion
seen in experiment 1. The magnitude of the PRE effect for the familiar
environment was significantly larger than that for the novel (Wilcoxon
signed rank test, p < 0.043). To address this issue
further using independent data, we compared data from nine recording
sessions in a separate experiment, in which six animals ran on entirely
novel mazes, with the familiar environment data from experiment 1. There was a significantly larger PRE effect for familiar environments
(Mann-Whitney U test, p < 0.005; mean EV,
5.7 ± 2.7 for familiar and 0.37 ± 0.23 for novel).
Correlation structure during REM
In several recordings, REM sleep was observed in post-behavior
sleep (see Fig. 1A). In sessions in which POST data
were collected for >1 hr, the animals had a few episodes (up to four)
of REM sleep (average duration, 1.85 ± 0.17 min). In most of the
data sets, however, only one REM episode occurred during POST. To test whether reactivation of activity patterns occurs during REM sleep and
whether a REM episode has any relationship with the correlations during
the SWS before and after it, we computed correlations between hippocampal pyramidal cell pairs in postbehavior sleep (seven recording
sessions from four rats). The data were separated into the following
epochs (Fig. 6, top): a 3 min
SWS interval starting 10 min before a REM episode (block
1), a 3 min SWS period just before the REM episode
(block 2), the REM episode (REM), and
a 3 min SWS interval just after the REM episode (block 3).
An ANOVA revealed no significant differences (p > 0.05) between the mean firing rates in the REM period (mean
firing rate, 0.48 ± 0.04 Hz) and in the SWS episodes that
preceded (block 2; mean firing rate, 0.61 ± 0.04 Hz) and
followed it (block 3; mean firing rate, 0.56 ± 0.05 Hz). Between
blocks 1 and 2, there was a decreasing trend in the EV.
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Figure 6.
Top, Correlations were computed in
POST during the first and last 3 min of a 10 min SWS period
(blocks 1, 2) before the first REM
episode and during a 3 min period after the REM episode (block
3). Bottom, The EV during REM was significantly
smaller than that in the preceding SWS (blocks 1,
2; p < 0.05, Mann-Whitney
U test) and was not significantly >0
(p > 0.05). The EV for block
3 was significantly lower than that in block 1
(p < 0.05), as expected (e.g., Fig.
3D). Between blocks 2 and
3, a decreasing trend in ripple count was observed
(196 ± 15 in block 2 compared with 133 ± 13 in block 3; p < 0.05). The firing
rates during ripples showed a trend toward an increase (block
2, 2.06 ± 0.17 Hz; block 3, 2.23 ± 0.19 Hz; p < 0.06). During inter-ripple intervals,
the firing rates showed a net decrease (block 2,
0.57 ± 0.05 Hz; block 3, 0.50 ± 0.06 Hz;
p < 0.05).
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In the familiar-only condition (experiment 1), there was no
significant effect of the waking experience on the correlation
structure during REM
Although the EV for blocks 1 and 2 of SWS was significantly >0,
the EV was not above chance (Fig. 6; p > 0.05) for a
comparable period of REM immediately after block 2; nor, however, was
the EV for the next SWS block different from 0. It is thus not clear whether the lack of significant EV during REM was caused by the ongoing
decay of the EV per se or by some intrinsic difference in reactivation
dynamics during REM. The mean correlations during the REM episode
(0.0095 ± 0.014) were significantly lower than the correlations
during SWS (block 1, 0.031 ± 0.014; block 2, 0.022 ± 0.011;
and block 3, 0.031 ± 0.016; p < 0.05).
There was a significant relationship between the correlations
during REM and the correlations during SWS before REM
Correlations during REM were related to the correlations during
block 2 (r = 0.2 ± 0.02; p < 0.0001) and block 1 (r = 0.16 ± 0.02;
p < 0.0001); however, there was no relationship
between the correlations during the REM episode and block 3, controlling for block 2 effects (r = 0.03 ± 0.02;
p > 0.05). Thus the REM episode and its associated
theta activity did not appear to affect the pattern in SWS in a manner
comparable with the effects of a previous waking theta episode. In two
rats, three additional REM episodes were present during postbehavior
sleep. In five of six of these episodes, correlations during the REM
episode were related to the correlations during SWS just before the
episode (p < 0.05).
For the familiar condition, after a REM episode, the mean
correlation in SWS increased to levels observed before REM, but there
was no effect on the correlation patterns
The mean correlation of the population of cells increased in block
3 immediately after the REM episode to the block 1 value (p > 0.05). However, in spite of the increase
in mean correlation, the reduction in EV between blocks 1 and 3 was not
substantially different from that expected from the spontaneous decay
rate (Fig. 3D), indicating a lack of an effect of the REM
episode on the memory trace for the familiar experience. There was
insufficient REM data in experiment 2 for analysis.
Sleep per se is unnecessary for trace reactivation
Hippocampal EEG and population discharge characteristics in the
quiet waking state are essentially indistinguishable from that of SWS,
being characterized by the same sharp wave-ripple complexes and
associated complex spike bursts. This raises the question of whether
sleep per se is an essential prerequisite for the reexpression of
preceding correlation patterns. This question was addressed in one
recording session. After RUN, an experimenter sat inside the recording
room and intermittently stroked the rat's fur to prevent it from
falling asleep. Behaviorally, the rat was in a relatively motionless
but alert state, with eyes mostly open and with LIA being observed in
the EEG and audible ripple bursts on the audio monitor. In this data
set (28 cells), there was a significant dependency of the RUN
correlation structure on that during PRE
(rRUN-PRE = 0.13 ± 0.05;
p < 0.02; EV = 2%) and of the POST structure on
that during RUN (rRUN-POST|PRE = 0.35 ± 0.05; p < 0.0001; EV = 12%). These values are
comparable with the effects observed during SWS. Thus, it seems that
the presence of ripples may be a sufficient condition for pattern reactivation.
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DISCUSSION |
This study extends our understanding of the basic characteristics
of off-line reactivation of activity patterns in hippocampal neuronal ensembles (Pavlides and Winson, 1989; Wilson and McNaughton, 1994; Skaggs and McNaughton, 1996; Qin et al., 1997). An
experience-specific pattern of firing correlations persists during
subsequent SWS and quiet wakefulness and was shown here to be
quantifiable in terms of explained (in the statistical sense) variance
(EV) in the correlation structure of the ensemble during these states.
During SWS and quiet wakefulness, the hippocampus exhibits irregularly
timed burst activity accompanied by SPW-ripple oscillations in the
EEG. Although there is significant EV because of the preceding experience in the inter-ripple intervals, the magnitude of the EV is
substantially larger during the ripple events (see also Skaggs and
McNaughton, 1998). This is consistent with the idea that these events
reflect convergence of the network onto "attractor" states
representing stored memories (McNaughton, 1983; Chrobak and
Buzsáki, 1994; Wilson and McNaughton, 1994; Shen and McNaughton, 1996); however, this conjecture requires verification from further analysis. It is possible, for example, that the reduced EV in the
inter-ripple intervals reflects mostly statistical error, related to
the interaction between the lower firing probabilities in these periods
and the minute sample size (compared with the total number of neurons).
It is also possible, however, that the reactivation occurs exclusively
during ripples and that the EV during the inter-ripple intervals
reflects measurement error in the detection of small ripple events.
Although ripple dynamics were observed to change over the course of a
sleep episode, the time course of the EV itself was somewhat more
persistent on average.
The EV attributable to one experience is still present, although
possibly attenuated (see Fig. 5), in SWS, even if a different experience (and hence a different activity pattern) intervenes before
sleep onset. This observation is consistent with the notion that the EV
reflects spontaneous retrieval of stored memory traces and not merely
the persistence of recent activity such as is commonly associated with
"working memory" (e.g., Fuster and Alexander, 1971; Kubota and
Niki, 1971; Goldman-Rakic, 1987). It cannot be ruled out that multiple
independent reverberatory traces may be maintained elsewhere in the
brain and imposed on the hippocampus during SWS; however, the probable
origination of the ripple events in CA3 (Buzsáki, 1986; Chrobak
and Buzsáki, 1994) would weigh against this hypothesis. Moreover,
significant traces of familiar experiences were occasionally detected
during SWS 24 hr after the last episode, in a few cases rather robustly
(EV of 30-60%). Overall, the magnitude of the PRE effects for
familiar environments, although usually small, was significantly >0
and significantly larger than for novel ones. This pattern suggests
that the memory traces for repeated experiences may be quite well
preserved, even though they may be retrieved only sporadically during
sleep occurring 24 hr or more after the last instantiation of the experience.
An interesting and somewhat unexpected finding was that, at least after
highly familiar experiences, the reactivation phenomenon was present
during SWS and quiet wakefulness but not during REM sleep.
Unfortunately, there were insufficient REM sleep data available in
experiment 2 to assess whether this pattern may change after a novel
experience, and further study of this question is needed. It is
possible that some reactivation does occur in REM and that the failure
to detect it in terms of significant EV is a consequence of different
memory retrieval dynamics in the two states (see McNaughton, 1999;
Skaggs and McNaughton, 1998). For example, if the speed of playback of
event sequences was higher in SWS than in REM sleep, then more of the
previous states could be represented in a given period of time,
possibly making the correlation structure of the ensemble during a
relatively brief SWS episode more similar to that of the preceding
waking epoch. There is some evidence suggesting that sequence
reactivation occurs during SWS at an accelerated rate (Skaggs and
McNaughton, 1996). Alternatively, the extent of memory trace retrieval
during REM sleep may be affected by the relative familiarity of the
experience. Recently Poe et al. (1997) have shown that the firing
patterns of CA1 pyramidal cells during REM sleep are different
depending on whether the cells participated in the encoding of a novel
experience or only a familiar one in the previous waking period. In the
latter case, the firing was both less robust and occurred
preferentially at a phase of the local EEG theta rhythm 180° reversed
from the peak firing phase during waking behavior. They speculated that
the firing of cells related to familiar events may be actively
suppressed during REM, which would explain the failure, in the present
study, to detect during REM any significant EV for familiar
experiences. Finally, the data were not sufficient to rule out the
possibility that the lack of significant effects during REM sleep is
merely a consequence of the decay of the EV during the 15-30 min of
SWS that typically precedes any REM bout.
Although the EV in REM caused by the preceding waking episode was not
statistically significant, there was a significant EV in REM
attributable to the immediately preceding episode of SWS. We do not
think that either the data or the current analytical techniques warrant
any strong conclusions about the possible functional significance of
this phenomenon at present.
Pavlides and Winson (1989) reported that CA1 complex spike cells
exhibited increased firing rates and more bursting activity in SWS
after selective exposure of the rat to their respective place fields.
The current data show a rather modest (and not statistically significant) increase (14%) in firing rates between the first and
second sleep epochs of cells that were spatially active during the
intervening behavior on the track. The larger effects seen in the
Pavlides and Winson study could be caused by the fact that their
animals were confined to a small region containing the field of a cell
for 10-15 min before being allowed to sleep. Thus, the reactivation
during subsequent sleep was biased to cell populations that were active
in the region of the environment that the rats visited. This would lead
to an apparent elevation in the mean firing rates of those cells
encoding this region relative to that of those cells encoding other,
unvisited regions. This would be completely compatible with the present
results. In general, if recent, spatially specific patterns of activity
are reexpressed in sleep, then the larger the space explored during
waking, the smaller will be the expected increase in mean firing rate.
Overall, our results indicate a persistent similarity in the pattern of
neuronal ensemble discharge in the rat hippocampus during SWS compared
with the previous waking experience, suggesting a reactivation of
recent memory traces. It is possible that the hippocampal trace
reactivation may contribute to a slowly developing synaptic
reorganization in the neocortex, leading to a long-lasting memory. This
remains to be shown empirically [see, however, recent results of
Higuchi and Miyashita (1996)]. There was an unexpected lack of such
reactivation during REM sleep, at least for previously familiar
experiences (insufficient data were available for the novel
experiences). This was surprising in view of the emphasis that has been
placed on REM sleep per se in the memory consolidation literature.
Future studies should also address the possible role of REM sleep in
the replay and consolidation of novel experiences as well as the
possibility that later REM cycles in the same sleep session may be
devoted to the more recent experience (Smith and Butler, 1982; Smith
and MacNeill, 1993).
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FOOTNOTES |
Received Nov. 30, 1998; revised Feb. 23, 1999; accepted Feb. 24, 1999.
This work was supported by National Institute of Mental Health Grants
MH46823 and MH01227 and National Institute on Aging Grant AG12609 and
was conducted in partial fulfillment of the requirements for the degree
of Doctor in Philosophy (H.S.K.). We thank M. A. Wilson for
software support, W. E. Skaggs, S. L. Cowen, M. Tsodyks,
T. J. Sejnowski, and A. Treves for useful comments on this
manuscript, Jie Wang for assistance with cluster cutting, Casey Stengel
and Michael Williams for technical support, and Matt Suster for help
with recording.
Correspondence should be addressed to Dr. B. L. McNaughton,
ARL-Neural Systems Memory and Aging, 384 Life Sciences North
Building, University of Arizona, Tucson, AZ 85724.
| |
REFERENCES |
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TOP
ABSTRACT
INTRODUCTION
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