Laboratoire de Neurophysiologie, Département de Physiologie, Faculté de Médecine, Université Laval, Quebec, Quebec G1K 7P4, Canada
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Collins, Dawn R.,
J. Guillaume Pelletier, and
Denis Paré.
Slow and Fast (Gamma) Neuronal Oscillations in the Perirhinal
Cortex and Lateral Amygdala.
J. Neurophysiol. 85: 1661-1672, 2001.
Most lesion studies emphasize the
distinct contributions of the amygdala and perirhinal cortex to memory.
Yet, the presence of strong reciprocal excitatory projections between
these two structures suggests that they are functionally coupled. To
gain some insight into this issue, the present study examined whether the close anatomical ties existing between perirhinal and lateral amygdala (LA) neurons are expressed in their spontaneous activity. To
this end, multiple simultaneous recordings of single unit discharges and local field potentials were performed in the LA and perirhinal cortex in ketamine-xylazine anesthetized cats. The perirhinal cortex
and LA exhibited a similar pattern of spontaneous activity. Recordings
at both sites were dominated by a slow focal oscillation at 1 Hz onto
which was superimposed a faster rhythm (30 Hz) whose amplitude
fluctuated cyclically. Computing crosscorrelograms between focal waves
recorded simultaneously in the perirhinal cortex and LA revealed a
close relationship between their spontaneous activity. Even when
recording sites were separated by as much as 8 mm, the slow focal
oscillation remained highly correlated (r
0.7). In contrast, the correlation between fast oscillations was usually lower
(r
0.3). Perievent histograms of neuronal
discharges revealed that the firing probability of most LA and
perirhinal neurons increased during the depth-negative component of the
slow oscillation. In addition, respectively, 47 and 64% of LA and
perirhinal neurons exhibited a significant modulation of firing
probability in relation to the fast oscillations. Finally,
crosscorrelating unit discharges simultaneously recorded in the LA and
perirhinal cortex confirmed the presence of phase-related oscillatory
events in both structures. In summary, our results suggest that the
interconnections existing between the perirhinal cortex and LA can
support the genesis of coherent neuronal activities at various
frequencies. These results imply that cooperative interactions must be
taking place between these structures.
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
In general, lesion studies
emphasize the distinct role of the perirhinal (PRH) cortex and amygdala
in memory. Indeed, PRH lesions were reported to interfere with
recognition and associative memory in all sensory modalities tested so
far (Buckley and Gaffan 1998; Buffalo et al.
1998
; Eacott et al. 1994
; Herzog and Otto 1997
; Higuchi and Miyashita 1996
; Meunier
et al. 1993
; Mumby and Pinel 1994
; Suzuki
et al. 1993
; Zola-Morgan et al. 1989
, 1993
). In
contrast, it was found that amygdala lesions have little impact on
performance in such memory tasks (Parker and Gaffan
1998
; Zola-Morgan et al. 1989
, 1991
). Instead,
these lesions were reported to interfere with classically conditioned
fear responses (Davis et al. 1994
; Kapp et al.
1992
; LeDoux 2000
), an issue that remains
controversial (Cahill et al. 1999
).
However, other findings suggest that the dichotomy between the role of
the amygdala and PRH cortex is not absolute. For example, PRH lesions
performed after classical fear conditioning reduce or abolish
conditioned fear responses (Campeau and Davis 1995; Corodimas and LeDoux 1995
; Rosen et al.
1992
). In addition, much data suggest that the amygdala
modulates the neural processes involved in the formation of declarative
memory (Cahill 2000
; Cahill and McGaugh
1998
). For instance, long-term explicit memory of emotionally
arousing stories is enhanced compared with neutral ones, and this
effect is abolished by amygdala lesions (Adolphs et al.
1997
; Cahill et al. 1995
). Moreover,
brain-imaging studies have found a high correlation between long-term
recall of emotionally arousing or neutral material and the amount of
amygdala activation observed when these stimuli were first presented
(Cahill et al. 1996
; Canli et al. 1998
;
Hamann et al. 1999
).
At present, it is unclear as to how the amygdala might modulate
declarative memory. The reciprocal amygdalo-PRH connections (Krettek and Price 1977a,b
; Russchen
1982
) constitute an obvious possibility, but little is known
about their physiology. Most amygdalocortical efferents stem from the
basolateral (BL) amygdaloid complex (Krettek and Price
1977a
,b
), a group of nuclei including the lateral (LA),
basolateral, and basomedial nuclei. Although each of these nuclei
contributes a different set of cortical projections, amygdalocortical
pathways always originate from spiny projection neurons
(McDonald 1992a
,b
). These neurons have axon terminals that are glutamate-immunoreactive and form asymmetric (presumably excitatory) synaptic contacts, generally with spiny cortical cells (Paré et al. 1995
; Smith and Paré
1994
).
The cerebral cortex often reciprocates these excitatory projections
(Russchen 1982). The prevalent amygdaloid targets of
cortical axons are the BL spiny projection cells themselves
(Brinley-Reed et al. 1995
; Hall 1972
;
Smith et al. 2000
). In contrast, BL inhibitory interneurons, at least those that display parvalbumin-immunoreactivity, receive virtually no cortical inputs (Smith et al.
2000
). However, cortical axons can influence them indirectly,
through the intranuclear axon collaterals of projection cells
(Smith et al. 2000
).
Thus the available ultrastructural evidence suggests that amygdalocortical projections are mainly excitatory, leading us to predict that reciprocally connected amygdala and cortical territories, such as the LA and PRH cortex, should display correlated neuronal activity. To test this hypothesis, we examined the activity of multiple simultaneously recorded neurons of the LA and PRH cortex. Consistent with our hypothesis, similar patterns of spontaneous neuronal activity were seen in the PRH cortex and LA, with neurons at both sites exhibiting phase-related oscillations in firing probability.
![]() |
METHODS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Surgery
Experiments were conducted in agreement with ethical guidelines of the Canadian Council on Animal Care. Five cats (2.5-3.5 kg) were anesthetized with a mixture of ketamine and xylazine (11 and 2 mg/kg im). This species was chosen because the large size of the cat brain facilitates the placement of multiple microelectrodes within the amygdala and PRH cortex. The level of anesthesia was determined by continuously monitoring the electroencephalogram (EEG). Supplemental doses of ketamine-xylazine (2 and 0.3 mg/kg iv, respectively) were given to maintain a synchronized EEG pattern.
In three cats, the bone overlying the left amygdala and PRH cortex was
removed on one side, and the dura mater was opened. Then, an array of
25 tungsten microelectrodes (2-6 M at 1 kHz; outer diameter of 80 µm; FHC, Brunswick, ME), arranged in the configuration shown in Fig.
1A, was lowered
stereotaxically until the electrodes reached the LA nucleus and the
dorsal aspect of the PRH cortex (for details, see Collins and
Paré 1999
). In two other cats, only LA electrodes were
implanted.
|
To construct the array, small holes were drilled in a circular Teflon block, and the electrodes were inserted into them. The length and position of LA and PRH electrodes was adjusted so that simultaneous recordings could be obtained from both structures. The Teflon block was inserted in a tightly fitting Delrin sleeve, which was cemented to the bone. The microelectrodes could be lowered as a group by means of a micrometric screw.
Recording methods
To ensure that we did not record the same cells twice, the
electrode array was lowered 100 µm between each recording. All electrodes were examined for units with a high signal-to-noise ratio
(
3). Then the spontaneous activity of neurons with the highest
signal-to-noise ratio (
8 at a time) was recorded. The spontaneous
activity of selected neurons was observed on a digital oscilloscope,
printed on a chart recorder, digitized, and stored on tape. Data from
neurons that were not held for the duration of the recording was discarded.
Identification of recording sites
At the end of the experiments, selected recording sites were marked with electrolytic lesions (0.5 mA for 5 s). Following this, the animals were given an overdose of barbiturate (pentobarbital sodium, 40 mg/kg iv) and perfused with 500 ml of a cold saline solution (0.9%) followed by 1 l of fixative, containing 2% paraformaldehyde and 1% glutaraldehyde in 0.1 M phosphate buffer saline (pH 7.4). The brains were later sectioned on a vibrating microtome (at 80 µm) and stained with thionin to verify the position of the recording electrodes. The microelectrode tracks were reconstructed by combining micrometer readings with the histological controls. Knowledge of the electrodes' relative position allowed reliable determination of recording sites.
Analysis
Analyses were performed off-line with the software IGOR (Wavemetrics, Oregon) and home-made software running on Macintosh microcomputers. Spikes were detected using a window discriminator after digital filtering (0.3-10 kHz) of the raw waves. Focal waves were extracted by digital filtering (0-100 Hz) and analyzed by means of fast Fourier transforms (FFT), auto- and crosscorrelograms. In addition, we computed cross-correlation matrices for all sets of simultaneously recorded neurons. Perievent histograms of unit discharges were also computed with respect to particular focal events. All histograms were normalized so that the average bin value was 1. This measure ensured that the individual histograms had an equal weight when population histograms were constructed. It also facilitated comparisons between individual histograms.
![]() |
RESULTS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Database
Overall, 56 groups of four to eight neurons were recorded in this
study, a total of 348 cells. Histological controls (Fig. 1,
B-G) revealed that 99 of these cells were recorded in the
LA and 160 in the PRH cortex. These neurons constitute the basis of the
present report. The spontaneous activity (0-10 kHz) of each cell group
was recorded for 3 min. Neuronal discharges (0.3-10 kHz) and local
field potentials (0-100 Hz) that were simultaneously picked up by the
microelectrodes were dissociated off-line by digital filtering.
Hereafter, the local field potentials will be referred to as focal
waves or focal activity. Below, we will first analyze the temporal
relation between the focal activity of the LA and PRH cortex and then
examine coincident fluctuations in firing probability.
Relation between the focal activity of the LA and PRH cortex
Under ketamine-xylazine anesthesia, LA and PRH focal waves
were dominated by a slow oscillation (0.8-1.7 Hz; Fig.
2A) as previously observed in
the neocortex and thalamus (Steriade 1997). This
oscillation usually occurred in trains of three to eight cycles that
began and ended more or less synchronously at all sites. For example, in Fig. 2A note that epochs of high-amplitude slow
oscillations seemed to occur simultaneously at all LA and PRH sites
even though the distance between the most rostral
(LA1) and caudal recording sites
(PRH3) was 8 mm (Fig. 2B). This is in
sharp contrast with the low correlation previously found between the
PRH cortex and adjacent neocortical sites (see Fig. 4 in Collins
et al. 1999
).
|
FFTs of focal waves recorded in the LA (Fig. 2C1) and PRH
cortex (Fig. 2C2) revealed a bimodal spectral composition: a
peak at ~1 Hz, which reflects the slow oscillation and a second one, in the 20- to 35-Hz range. To determine whether there was a temporal relation between this faster activity and the slow oscillation, focal
waves were filtered digitally between 20 and 35 Hz and compared with
the full signal (Fig. 2D). In keeping with previous findings in the neocortex (Steriade et al. 1996), the amplitude
of fast focal waves fluctuated cyclically. Epochs of high-amplitude
fast activity coincided with the decaying negative component of the slow oscillation. Although this was more obvious in the PRH cortex than
in the LA (Fig. 2D, compare PRH1-3
and LA1-2), statistical comparison of the 20- to
35-Hz power in the 0.4 s preceding versus following the negative
peak of the slow oscillation (3 cats, 10 LA and 10 PRH sites each),
revealed that the difference was statistically significant in both
structures [Student's paired t-test, P < 0.05; 76 ± 11.2 and 35 ± 6.3% (means ± SD)
power increase in the PRH and LA, respectively].
Since visual inspection of the data suggested that there was a close temporal association between focal waves in the PRH cortex and LA, the focal activity of simultaneously recorded sites was crosscorrelated in the 0- to 2- and 20- to 35-Hz bands. This analysis revealed that correlation coefficients were significantly higher for the slow (S) oscillation than the fast (F) activity (Student's paired t-test, P < 0.05) whether the signals were recorded simultaneously within the LA (S, 0.75 ± 0.053; F, 0.62 ± 0.036; n = 22), within the PRH cortex (S, 0.81 ± 0.023; F, 0.35 ± 0.016; n = 84), or in the LA and PRH cortex (S, 0.70 ± 0.023; F, 0.33 ± 0.049; n = 101). Note that the difference was less marked for correlations among LA sites presumably because the distance between the electrodes was shorter than in the PRH cortex (Fig. 1A).
In keeping with this, the correlation of the fast oscillation decreased as the distance between PRH recording sites increased. This point is illustrated in Fig. 3A, which plots the correlation coefficient as a function of distance between the recording sites for the slow (dark circles, upper curve) and the fast (empty circles, lower curve) focal waves. Moreover, representative examples of correlograms are shown for the slow (Fig. 3B) and fast oscillations (Fig. 3C), each with superimposed short (1 mm; thick line) and long (6 mm; thin line) inter-site distances. Note that distance had a negligible effect on the correlation of the slow oscillation but significantly reduced the correlation of fast activities (Student's t-test, P < 0.05).
|
In contrast, correlation of fast oscillatory activity between LA and PRH sites did not show the same trend. Correlations varied highly, irrespective of the distance (overall average, 0.33 ± 0.049).
Fluctuations in firing probability related to the slow oscillation
Since the oscillation induced by ketamine-xylazine has been
observed throughout the neocortex (reviewed in Steriade
1997), as well as in several subcortical structures
(Mariño et al. 2000
; Wilson and Kawaguchi
1996
), it is conceivable that the focal activity described
above is only an artifact due to volume conduction. However, this
explanation would appear unlikely if the focal oscillations were
related to rhythmic fluctuations in the firing probability of LA and
PRH neurons.
As shown in Figs. 4 and 5, most LA and PRH neurons exhibited robust variations in discharge rate, phase-related to the slow oscillation. Two examples of this are shown for LA (Fig. 4A) and PRH (Fig. 4B) cells, including raw data (Fig. 4, A, 1 and 2, and B1) and perievent histograms (PEH) of neuronal discharges with respect to the negative peak of the focal slow oscillation (Fig. 4, A3 and B2). In both cells, note that the firing probability peaked during the negative phase of the focal slow oscillation, reaching 2.7 (Fig. 4A3) and 2.4 (Fig. 4B2) times the average firing probability, and was lowest during the preceding positivity (0.1 and 0.5 times the average firing probability, respectively).
|
|
This trend was not only observed in neurons with high firing
rates (Fig. 4, A and B, 20.2 and 5.1 Hz,
respectively) but also in slow firing cells (Fig. 5A,
LA1-2, PRH1,3-4). Indeed as was previously reported in naturally sleeping animals, most amygdala
(Bordi et al. 1993; Gaudreau and
Paré 1996
; Jacobs and McGinty 1971
;
Paré and Gaudreau 1996
) and perirhinal
(Collins et al. 1999
) neurons fired at low rates under
ketamine-xylazine anesthesia. Nevertheless, even in such neurons,
visual inspection of the data revealed a clear increase in firing
probability during the negative phase of the slow oscillation (Fig.
5A, LA1-2, PRH1,3-4). However, in contrast with more active
cells, they did not fire with every cycle and often remained silent for several seconds. Overall, respectively, 82 and 76% of LA and PRH neurons displayed such clear modulations of firing probability with
respect to the slow oscillation.
To further examine the temporal relation between LA and PRH unit
activity and the slow focal oscillations, PEHs of neuronal discharges
were computed, normalized for firing rate and averaged (LA, Fig.
5B1, n = 15; PRH, Fig. 52,
n = 20). Because it seemed evident that most LA and PRH
neurons behaved similarly with respect to the slow oscillation, only
cells having a signal to noise ratio >6 and a firing rate of 1 Hz
were selected for this analysis. The difference between the firing
probability associated to the peak positive and negative focal waves
averaged 3.7 (LA, Fig. 5B1) and 3.9 (PRH, Fig.
5B2) times the respective standard deviation (SD) of bin values.
Fluctuations in firing probability related to the fast oscillation
As the relation between the fast oscillations and unit discharges seemed more variable, we computed PEHs of neuronal discharges for all available cells. To ensure that we limited our observations to epochs of high-amplitude fast activity, we considered only negative gamma peaks with an amplitude higher than 1.5 times the SD of the entire epoch after digital filtering of the data (20-35 Hz).
To determine whether the firing modulation evidenced in these histograms was statistically significant, the histograms were smoothed with a moving average of 3. The maximal peak to trough difference found within half a gamma cycle next to the origin was divided by the SD of the entire histogram. The modulation was considered significant when the peak to trough difference was >2.5 SD.
Overall, respectively 47 and 64% of LA and PRH neurons exhibited a significant modulation of firing probability with respect to the fast oscillations. Figure 6 shows examples of LA (Fig. 6A) and PRH (Fig. 6, B and C) neurons that exhibited significant firing modulations, including raw data (Fig. 6, left) and corresponding PEHs (Fig. 6, right).
|
Of the cells exhibiting a significant firing modulation, most increased their firing probability in relation to the negative phase of focal gamma waves (Fig. 6, A and B; histogram peak within ±45° of the origin; LA, 100%; PRH, 87%). However, in the PRH cortex, we identified a subgroup of cells (13%) whose firing probability was lowest in relation to the negative phase of focal gamma waves (Fig. 6C). This difference did not reflect dissimilarities in the depths at which these cells were recorded as neurons with the typical phase relation were recorded subsequently along the same electrode tracks.
Temporal relation between the activity of LA and PRH neurons
As the preceding analyses suggested that the PRH cortex and LA exhibit a similar pattern of spontaneous activity, we next examined temporal relations between the discharges of simultaneously recorded neurons. To this end, cross-correlation histograms were computed for all simultaneously recorded cell pairs, and the statistical significance of the firing modulation was assessed using the approach described above for the PEHs (peak to through difference >2.5 SD). In addition, because we had evidence that neuronal activity in the LA and PRH cortex was modulated by slow and fast rhythms, cross-correlations were computed with bins of 50 and 2 ms.
With a low temporal resolution (50-ms bins), respectively 61 and 29%
of cell pairs simultaneously recorded in the LA (n = 141) or PRH cortex (n = 236) exhibited significantly
correlated activity. As was seen with the focal waves, note that the
lower proportion of significant relations observed in the PRH cortex probably reflects the longer distance between recorded neurons. Consistent with this, the proportion of significant PRH
cross-correlations climbed to 47% when we considered only cells
recorded within 2 mm of each other (n = 132).
Representative examples of significant cross-correlations are shown in Fig. 7, including ones where the peak was close to the origin (LA, Fig. 7A, 1 and 3; PRH, Fig. 7B, 1 and 2) and others that were clearly offset (LA, Fig. 7A2; PRH, Fig. 7B3). Also, note that we illustrated pairs of neurons whose firing rates varied tremendously (see details in figure legend).
|
Overall, 29 and 32% of significant crosscorrelograms had peaks
centered on the origin (interval 2 bins) in the LA and PRH cortex,
respectively. Nevertheless, the average interval between the origin and
the peak was less than the binwidth (
35 ± 44.7 and
42 ± 52.3 ms for pairs of LA or PRH cells, respectively). As shown in Fig.
7, many of the significant crosscorrelograms exhibited regularly spaced
peaks and troughs with a period of
1 s (LA, 78%; PRH, 73%), as
expected for neurons exhibiting a slow phase-locked oscillation in
firing probability.
Cross-correlations between pairs of simultaneously recorded LA and PRH
cells (n = 335; Fig. 7C) yielded results
similar to those obtained with pairs of PRH cells. Thirty-eight percent
of crosscorrelograms reached significance, but the proportion climbed to 49% when we considered only PRH cells located within 2 mm of the
amygdala in the rostrocaudal axis (n = 101). Overall,
23% of significant correlograms had a peak centered on the origin (distance to origin 2 bins). Nevertheless the absolute interval between the origin and the peak averaged
32 ± 56.2 ms. Thus
although there were significant variations in spike timing among LA,
PRH, and LA-PRH cell couples, as a group they fired roughly in phase.
Since LA and PRH neurons have low discharge rates (LA, 0.9 ± 0.22 Hz; PRH, 1.1 ± 0.17 Hz), repeating the preceding analysis with 2-ms bins yielded histograms with very few counts. Thus cross-correlations were carried out for all cell pairs, and normalized averages were computed for each animal and as a group. In all the cases discussed below (with the exception of Fig. 8A3), the amplitude of the central peak was >2.5 times the corresponding SD (see figure legend for exact values). To quantify the importance of the firing modulation evidenced in these correlograms, the amplitude of the three central peak to trough differences was averaged. Because spike counts for each histogram had been normalized so that the bin values averaged one, the resulting modulation index (MI) allowed comparison between histograms.
|
In the PRH cortex, crosscorrelograms varied with the distance between
recorded cells (compare Fig. 8A, 1-3). With short distances (Fig. 8A1, 1 mm), the population crosscorrelogram exhibited
a peak centered on the origin flanked by regularly spaced peaks and
troughs (interval 28-34 ms), indicating a synchronized modulation of firing probability in the gamma range (MI = 0.48). However, the
amplitude of this firing modulation diminished quickly as the distance
between recorded cells increased to 2 (Fig. 8A2; MI = 0.25)
and 3 mm (Fig. 8A3; MI = 0.18).
Because LA neurons were recorded within 1.5 mm of each other (Fig.
1A), all crosscorrelograms were averaged (Fig.
8B). However, within each pair, the most lateral and/or
rostral neuron was always used as the reference cell. Note that the
peak of the population crosscorrelogram is offset to the right by 8 ms
(Fig. 8B), indicating that the reference cells tended to
fire before the test cells. This is consistent with the lateromedial
trajectory of intra-amygdaloid pathways in the cat (Krettek and
Price 1978; Smith and Paré 1994
). However,
the rhythmicity apparent in this crosscorrelogram (Fig. 8B)
was weaker (MI = 0.44) and less regular (interval between peaks
ranged between 16 and 30 ms) than that observed between closely spaced
PRH cells (Fig. 8A1).
No effect of distance between recording sites was noted when LA and PRH unit activities were crosscorrelated (data not shown). However, this analysis revealed inter-cat variability in the amount and frequency of the gamma rhythmicity. In one animal, crosscorrelating LA and PRH unit activity yielded a population histogram with prominent gamma rhythmicity (Fig. 8C1; MI = 1.31). In the other two cats, the rhythmicity was weaker (MI < 0.3) and irregular (interval between peaks ranged between 12 and 24 ms). Figure 8C2 illustrates the average of all histograms for the three cats (MI = 0.28).
![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
The present study was undertaken to determine whether the close
anatomical ties existing between the LA and PRH cortex (Krettek and Price 1977b; Russchen 1982
) are expressed in
their spontaneous activity. The main findings can be summarized as
follows. First, the PRH cortex and LA exhibit a similar pattern of
spontaneous focal activity: a slow oscillation onto which a fast
(20-35 Hz) rhythm is superimposed. Second, the slow focal oscillations
are highly correlated even when the recording sites are separated by as
much as 8 mm. In contrast, the correlation of fast oscillations is
generally lower and, in the PRH cortex, decreases steeply with distance. Fourth, LA and PRH neurons display a significant modulation of firing probability in relation to the slow and fast focal
oscillations. Last, they exhibit rhythmic phase-related changes in
discharge probability that are strongest for the slow rhythm.
Thus our analysis suggests that there are close functional links in this amygdalocortical circuit, consistent with the strong reciprocal connections evidenced in tract tracing studies.
Origin of the oscillatory activity recorded in the LA and PRH cortex
SLOW OSCILLATION.
The slow oscillation was first described in the neocortex of
anesthetized cats (Steriade et al. 1993b) and, later,
during slow-wave sleep in cats (Steriade et al. 1996
)
and humans (Acherman and Borbély 1997
).
Consistent with our findings, the slow neocortical oscillation is
comprised of a depth-negative phase coinciding with an increased firing
rate in all classes of cortical cells, and a depth positive one
associated with neuronal silence (Steriade 1997
).
Moreover, this oscillation also occurs in subcortical structures such
as the thalamus and striatum (Mariño et al.
2000
; Steriade et al. 1993a
; Wilson and
Kawaguchi 1996
), but it is dependent on cortical inputs
(Timofeev and Steriade 1996
).
FAST (GAMMA) OSCILLATIONS.
Fast oscillations have been observed in numerous cortical and
subcortical sites, behavioral states, and species (Steriade 1997; Traub et al. 1998
). While the fast
oscillations occur spontaneously under anesthesia and during slow-wave
sleep (Steriade et al. 1996
), they have also been
implicated in cognitive, perceptual and attentional processes
(Gray 1999
; Koch and Crick 1991
;
Llinás and Ribary 1992
; Singer
and Gray 1995
).
![]() |
ACKNOWLEDGMENTS |
---|
We thank E. J. Lang for comments on an earlier version of the manuscript as well as P. Giguère and D. Drolet for technical support.
This work was supported by the Medical Research Council of Canada and the National Sciences and Engineering Research Council.
![]() |
FOOTNOTES |
---|
Address for reprint requests: D. Paré (E-mail: Denis.Pare{at}phs.Ulaval.CA).
Received 19 September 2000; accepted in final form 22 December 2000.
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|