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The Journal of Neuroscience, April 1, 1999, 19(7):2728-2739
Patterns of Spontaneous Purkinje Cell Complex Spike Activity in
the Awake Rat
Eric J.
Lang1,
Izumi
Sugihara2,
John P.
Welsh1, and
Rodolfo
Llinás1
1 Department of Physiology and Neuroscience, New York
University Medical Center, New York, New York 10016, and
2 Department of Physiology, Tokyo Medical and Dental
University School of Medicine, 1-5-45 Yushima, Bunkyo-ku, Tokyo
113-8519, Japan
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ABSTRACT |
The olivocerebellar system is known to generate periodic
synchronous discharges that result in synchronous (to within 1 msec) climbing fiber activation of Purkinje cells (complex spikes) organized in parasagittally oriented strips. These results have been obtained primarily in anesthetized animals, and so the question remains whether
the olivocerebellar system generates such patterns in the awake animal.
To this end, multiple electrode recordings of crus 2a complex spike
activity were obtained in awake rats conditioned to execute tongue
movements in response to a tone. After removal of all movement- and
tone-related activity, the remaining data were examined to characterize
spontaneous complex spike activity in the alert animal. Spontaneous
complex spikes occurred at an average firing rate of 1 Hz and a clear
 10 Hz rhythmicity. Analysis of the autocorrelograms using a rhythm
index indicated that the large majority of Purkinje cells displayed
rhythmicity, similar to that in the anesthetized preparation. In
addition, the patterns of synchronous complex spike activity were also
similar to those observed in the anesthetized preparation (i.e.,
simultaneous activity was found predominantly among Purkinje cells
located within the same parasagittally oriented strip of cortex). The
results provide unequivocal evidence that the olivocerebellar system is
capable of generating periodic patterns of synchronous activity in the awake animal. These findings support the extrapolation of previous results obtained in the anesthetized preparation to the waking state
and are consistent with the timing hypothesis concerning the role of
the olivocerebellar system in motor coordination.
Key words:
olivocerebellar; synchrony; oscillation; rhythmicity; inferior olive; climbing fiber
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INTRODUCTION |
The olivocerebellar system
constitutes one of the two major afferent systems to the cerebellum,
and its activity is central to motor coordination because lesions of
the inferior olive are followed by severe motor disturbances, similar
to those resulting from cerebellar damage (Soechting et al., 1976 ). Yet
the exact contribution of olivocerebellar activity to cerebellar
functioning continues to be much debated (for review, see Simpson et
al., 1996). The enigmatic status of the olivocerebellar system
is probably in large measure caused by the rather unusual
characteristics of its activity. Indeed it has been argued that because
of its low average single-cell firing rate of 1 Hz and maximum rate of 10 Hz, the olivocerebellar system cannot produce significant changes in cerebellar output on its own (Keating and Thach, 1995). However, an alternative, suggested by its physiological and anatomical organization, is that the olivocerebellar system may alter cerebellar output by the temporal binding property generated by the synchronous activation of sets of neuronal ensembles (Llinás and Sasaki, 1989).
The ability of the olivocerebellar system to generate ensemble
synchronous activity has been extensively documented using multiple
electrode recording of complex spikes (CSs) from Purkinje cells in
anesthetized animals (Bell and Kawasaki, 1972; Llinás and Sasaki,
1989; Sasaki et al., 1989; Sugihara et al., 1993; Wylie et al., 1995;
Lang et al., 1996, 1997). At the level of the cerebellar cortex, these
patterns most often take the form of simultaneous (to within 1 msec)
CSs in parasagittally oriented strips of Purkinje cells (Llinás
and Sasaki, 1989; Sasaki et al., 1989; Sugihara et al., 1993). The
simultaneity of CS activity has been ascribed to the electrotonic
coupling of inferior olivary neurons via gap junctions located mainly
in structures known as glomeruli (Llinás et al., 1974; Sotelo et
al., 1974; Llinás and Yarom, 1981a) and a matching of axonal
conduction velocity to climbing fiber length (Sugihara et al., 1993).
However, these patterns are not unchangeable entities fixed by the
anatomical connectivity of the system. Rather, the specific spatial
distribution of synchronous CS activity is determined primarily by the
ongoing activity of the GABAergic cerebellar nucleo-olivary pathway,
which provides a major synaptic input to the olivary glomeruli (de
Zeeuw et al., 1989) and can produce momentary functional decoupling of
inferior olive (IO) neurons (Lang et al., 1996). Thus, this parasagittal banding pattern reflects a particular functional state of
the olivocerebellar system, whereas other functional states would be
represented by other such patterns. Indeed, distinct patterns of
synchronous CS activity occur in relation to given movements (Welsh et
al., 1995a).
Olivocerebellar activity is also characterized by an 8-12 Hz
rhythmicity. Using in vitro techniques or in vivo
anesthetized or decerebrated preparations, this rhythmicity has been
amply documented in a number of species (Belcari et al., 1977;
Llinás and Yarom, 1981a,b, 1986; Bloedel and Ebner, 1984; Benardo
and Foster, 1986; Llinás and Sasaki, 1989; Sasaki et al., 1989;
Lampl and Yarom, 1993, 1997; Sugihara et al., 1995; Wylie et al., 1995; Lang et al., 1996, 1997) and may become particularly pronounced and
lead to phase-locked motor activity with tremorigenic drugs such as
harmaline (de Montigny and Lamarre, 1973; Llinás and Volkind,
1973).
Taken together these results provide significant evidence that the
olivocerebellar system generates periodic synchronous discharges and
support for the hypothesis that it functions as an internal clock for
organizing motor sequences (Llinás, 1991). Nevertheless, because
these studies were performed in anesthetized animals or in
vitro preparations, their relevance to the normal physiological activity of the olivocerebellar system may be questioned, and so the
extent to which the activity observed under the different anesthetic
states reflects the normal spontaneous activity patterns in the awake
animal must be determined.
Spontaneous CS activity has in fact been reported to display an 10
rhythmicity in awake frogs (Rushmer and Woodward, 1971) and guinea pigs
(Bell and Kawasaki, 1972); however, these initial reports did not
quantify its strength or prevalence. Moreover, whereas studies in
behaving rodents have demonstrated CS rhythmicity (Welsh et al.,
1995a,b), those in primates have produced mixed results, with some
investigators finding rhythmic CS activity (Pellerin et al., 1997) and
others not (Keating and Thach, 1995). Thus, the presence of rhythmic CS
activity in the awake animal remains somewhat controversial.
Although CS synchrony in awake animals has been previously reported
(Bell and Kawasaki, 1972), recordings were obtained from only a limited
number (n = 5) of closely spaced cell pairs, preventing any conclusions about the spatial extent of synchronous activity from
being drawn. More recently, multiple electrode recordings have been
used to determine the movement-related patterns of synchronous CS
activity (Welsh et al., 1995a). In this study, the patterns of
synchronous CS activity differed significantly from the parasagittal banding patterns observed in anesthetized preparations (Welsh et al.,
1995a). In particular, movement-related synchronization of CS activity
was found between Purkinje cells located in different parasagittal
planes. This difference may have stemmed from the fact that the
functional states of the olivocerebellar and cerebellar nucleo-olivary
systems are very different in the anesthetized and awake animals, thus
limiting the conclusions to be drawn from studies in anesthetized
preparations concerning the spatial distribution of spontaneous
synchronous activity. Alternatively, this difference may have been
caused by the generation of specific movement-related patterns. To test
these alternatives, a quantitative description of the spontaneous
patterns of synchronous CS activity in the awake animal is needed. And
so, to determine the rhythmicity and spatial patterns of synchrony
inherent in spontaneous CS activity, we have obtained multiple
electrode recordings of spontaneous (i.e., non-movement related) CS
activity in awake rats. The results indicate that in the waking state,
the olivocerebellar system generates spontaneous patterns of rhythmic
and synchronous CS activity similar to what has been described in the
anesthetized animal.
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MATERIALS AND METHODS |
Surgical and recording procedures
Adult Sprague Dawley rats (225-300 gm) were used in all
experiments. These animals had been previously trained to perform a
conditioned tongue movement task in which a tone elicited a series of
licks (Welsh et al., 1995a). On the day of the recording, they were
anesthetized with an injection of ketamine (100 mg/kg, i.p.) and
implanted with an array of microelectrodes for recording CS activity
from crus 2a of the cerebellar cortex. The implantation technique has
been described previously (Sasaki et al., 1989; Sugihara et al., 1993).
In brief, a craniotomy was performed to expose the crus 2 region of the
left cerebellar hemisphere. After removal of the dura mater, a silicon
rubber-coated titanium electron microscope grid was cemented in place
over crus 2a. Glass microelectrodes filled with a 1:1 solution of
glycerol and saline were individually inserted to a depth of 100
µm where CS activity from individual Purkinje cells could be recorded
extracellularly. Interelectrode spacing was 250 µm. After
implantation of the electrode array, the animals were allowed to
recover from the anesthesia.
Spontaneous CS activity described in the present report was obtained
during two types of conditions. First, recordings were obtained from a
10-20 min period just before the beginning of conditioned movement
sessions during which the animals were alert but relatively inactive.
Second, CS activity was analyzed from the nonmovement periods during
the conditioned movement sessions. In these sessions, the animal's
head was immobilized, and movements of the tongue and jaw were
monitored with infrared photoemitter-detector devices. The conditioned
movement sessions typically lasted 4 hr and contained 500 trials
that were initiated by a tone that occurred once every 30 sec on
average. The tone was followed by a series of 6-7 Hz licking movements
that typically lasted 0.5 sec. To exclude movement-related and
tone-related CS activity, the data from 1 sec before until 5 sec after
the tone onset were eliminated, and only the activity from the
remaining 24 sec between trials was analyzed. In addition, because the
animals made occasional tongue and mouth movements during the
intertrial periods, CS activity from ±200 msec surrounding such
movements was eliminated.
For comparative purposes, data from multiple electrode experiments in
anesthetized rats were analyzed. In these experiments, the animals were
anesthetized with an initial injection of ketamine (100 mg/kg, i.p.),
xylazine (8 mg/kg, i.p.), and atropine (0.4 mg/kg, i.p.). The heart
rate was monitored, and supplemental doses of anesthetic were given to
maintain a deep anesthetic level. The surgical procedures were similar
to those described above for the awake animals and have been previously
described in detail (Sasaki et al., 1989; Lang et al., 1996). Recording
sessions typically lasted 15-30 min.
Data analysis
Analysis of CS synchrony. Here, as previously, we
define CSs that occur in different Purkinje cells as being synchronous
when their onsets occur within 1 msec of each other. The degree of synchronous activity displayed by different cell pairs was quantified by calculating C(0), the zero-time cross-correlation coefficient, using
a time bin of 1 msec, as described previously (Sasaki et al., 1989;
Lang et al., 1996). The term "synchronicity" is used to refer to
the spatial distribution of synchronous activity and was quantified by
calculating C(0) for all possible cell pairs and then plotting the
average value of C(0) as a function of the mediolateral separation
distance between the cells within a pair.
Analysis of CS rhythmicity. Autocorrelation histograms were
constructed from the CS activity of individual Purkinje cells using
time bins of 5 or 10 msec. The oscillation frequency was taken as the
reciprocal of the latency of the first peak in the autocorrelogram. The
strength of the oscillation was quantified with a rhythm index (RI)
calculated in a similar manner to that described previously (Sugihara
et al., 1995; Lang et al., 1997). In brief, the autocorrelation
coefficients of all peaks and valleys in the autocorrelogram that (1)
were significantly different from the baseline and (2) occurred at
specific latencies with regard to the initial peak were summed. Peaks
and valleys were considered significant if they were >1 SD above or
below the average level, respectively. The SD was determined using bins
at time lags of 1.5-2 sec where there was no evident periodic
activity. The average level was measured from bins of time lags of 50 msec to 1 sec. In the autocorrelograms that had no significant peaks
and valleys, a value of zero was given to the RI, and the activity was
considered nonoscillatory. In these cases, or when the RI was less than
an empirically determined value of 0.01, the oscillation frequency was
not determined. The latter cases were excluded because the autocorrelograms of such cells (0 < RI < 0.01) displayed
only weak rhythmicity with peaks that were very close to the baseline activity (see Fig. 5A), making unambiguous determination of
the primary peak, and therefore the oscillation frequency, difficult.
Runs test to detect systematic variation over time
Runs tests were used to determine any trend over time in the
values of the parameters used to measure CS rhythmicity and synchrony. First, the recording sessions were divided into 25 min segments, and
the values of the parameters were calculated for each segment. A runs
test was then performed on the sequence of values obtained for each
parameter. The runs test determines whether the successive values in
the time sequence are correlated or whether they vary about the median
as expected by chance. First, the number of times that the time
sequence crosses its median value is calculated. The number of runs is
then equal to the number of crossings plus one. The observed number of
runs (Ro) can then be compared with the expected number
(Re) and a z value determined as
(Ro  Re)/(SD) where Re (n/2) + 1, the SD [(n 1)/4]1/2, and n is the number of observations
in the time sequence (Wonnacott and Wonnacott, 1977). Thus, a
significant trend over time in the data is indicated by z
values more negative than 1.65, which corresponds to a p
value of 0.05 that Ro would be observed by chance.
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RESULTS |
Database
The present results were obtained from animals (n = 11) that were operantly conditioned to perform tongue movements in
response to a tone. The movement-related CS activity recorded from
these animals has been previously reported (Welsh et al., 1995a). Here we describe the patterns of spontaneous CS activity recorded while the
animals were not making any overt movements. This activity was recorded
either from an 15-20 min period before the actual conditioning
session started, during which the animal was sitting quietly
(n = 2), or from the intertrial periods of the
conditioning session when no tongue or mouth movements were occurring
(n = 9). In total, CS activity was recorded from 282 crus 2a Purkinje cells (range, 16-33 cells per animal). The average CS
firing rate for these cells was 1.01 ± 0.70 Hz (mean ± SD),
similar to previous reports in awake animals (Mano, 1970; Hobson and
McCarley, 1972; Armstrong and Rawson, 1979).
For comparative purposes, the data from seven multiple electrode
experiments (n = 257 Purkinje cells) in which the
animals were anesthetized with ketamine-xylazine anesthesia is
described. A similar, but somewhat higher average CS firing rate
(1.52 ± 0.89 Hz) was observed in these experiments.
CS synchrony in the awake rodent
Spontaneous CS activity in the awake animal showed a similar
spatial organization to that observed in the anesthetized preparation. There were generally much higher levels of synchronous CS activity among neighboring Purkinje cells than among widely separated Purkinje cells. In particular, the degree of synchronous CS activity dropped off
rapidly as the mediolateral separation between the Purkinje cells
increased, similar to what was observed in the anesthetized preparation
(Llinás and Sasaki, 1989; Sasaki et al., 1989). Thus, the spatial
organization of CS synchrony displayed a rostrocaudal preference, with
Purkinje cells within the same parasagittal strip of cortex having the
highest levels of synchrony. Examples of the spatial organization of CS
synchrony from an experiment in which 29 Purkinje cells were
simultaneously recorded are shown in Figure
1A. The relative
positions of the Purkinje cells are represented by the positions of the
circles in the plots and correspond to the electrodes of the array
shown in Figure 1B. The degree of synchronous CS
activity between a master cell (M) and each of the 28 other Purkinje cells was determined by calculating C(0) for each cell
pair and is indicated by the areas of the corresponding circles. In
these and subsequent plots, synchronous is defined as two CSs whose
onsets are within 1 msec of each other. Note how the pattern of
synchronization shifts according to which cell is chosen as the master
cell, such that Purkinje cells showing the highest levels of
synchronization occur within the same parasagittal strip of cortex as
the master cell (Fig. 1, compare A1, A2).
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Figure 1.
CS synchrony in a nonanesthetized rat.
A, Plot of synchrony distribution with respect to CS
activity of master cell (M). Synchrony
distribution shown for two master cells located in different
parasagittal planes, one lateral (A1) and one
more medial (A2). Circles and the
letter M represent the relative positions of recorded
Purkinje cells on the left crus 2a. The area of each circle is
proportional to the degree of synchronous CS activity with cell
M. Note how the highest levels of synchronization occur
in cells located in the same parasagittal plane as cell
M. B, Schematic of dorsocaudal view of
cerebellum and medulla showing the position of the recording array.
C, Cross-correlograms of CS activity in cell
M in plot of A2 and a cell 500 µm rostral to it within the same parasagittal plane
(b) and a cell located 500 µm lateral
(a). Time bin is 1 msec.
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The temporal precision of this synchrony is shown by the
cross-correlograms of Figure 1C. The cross-correlogram on
the right shows the relationship of activity between cell M of Figure
1A2 and the cell located 500 µm rostral
(b). Although there was a general increase in the
correlation for ±20 msec, by far the strongest correlation occurred at
a time lag of 0 msec. Thus, the CS discharges of these cells had a
strong tendency to be synchronized to within 1 msec. In contrast, the
cross-correlogram on the left shows the relationship of activity
between cell M and the cell located 500 µm lateral (a).
There is almost no synchronization of the CS activity of these two
Purkinje cells, as indicated by the flatness of the correlogram.
The spatial distribution of CS synchrony relative to the master cell
was similar regardless of the choice of the master cell. This was
demonstrated by calculating C(0) for all possible cell pairs and then
plotting the average C(0) value as a function of the mediolateral
separation distance between the cells (Fig.
2). The plot of Figure
2Ab corresponds to the experiment shown in Figure 1.
As was suggested by the examples shown in Figure 1, the highest level
of synchronization on average occurred among Purkinje cells located
within the same parasagittal plane (0 µm mediolateral separation
distance). At a separation distance of 250 µm there was still a
significant degree of synchronization, but at distances of 500 µm and
greater virtually no synchronization occurred.
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Figure 2.
Spatial distribution of CS synchrony.
A, Plots of average level of CS synchrony as a function
of mediolateral separation distance between Purkinje cells. Plots from
each of the 11 experiments show that CS synchrony decreases with
increasing mediolateral separation distance. Plots were obtained by
determining C(0) for all possible cell pairs, sorting cell pairs
according to separation distance between cells, and then calculating
the mean C(0) for each separation distance. B, Average
normalized synchrony as a function of mediolateral separation distance
for the nine experiments in which the highest average C(0) occurred at
 250 µm. All curves are normalized to the C(0) value at 0 µm.
C, Average of curves in B. Error bars in
A and C indicate SEM.
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A similar distribution of CS synchrony was observed in most of the 11 experiments performed in nonanesthetized animals. Plots of average CS
synchrony as a function of mediolateral separation distance are shown
for each of the experiments in Figure 2A. In all
cases, the average CS synchrony was significantly
(p < 0.02; two-sided t test) higher
for smaller separation distances (500 µm; 0.018 ± 0.030;
n = 33) than for larger separation distances (> 500 µm; 0.0045 ± 0.0075; n = 67). In the large of
majority experiments (9 of 11), the highest degree of synchronization
was found among Purkinje cells within the same parasagittal plane (Fig.
2Aa-e,g) or within 250 µm of
each other (Fig. 2Af,h,j). The
synchrony plots from these experiments were normalized to the synchrony
value at 0 µm separation distance (Fig. 2B), and the average normalized distribution of synchrony was determined (Fig.
2C). The shape of this curve demonstrates that the CS
activity of Purkinje cells separated by <500 µm in the mediolateral
direction was significantly more synchronized than that of Purkinje
cells that were more greatly separated. In sum, the spatial
distribution of synchrony of spontaneous CS activity in nonanesthetized
animals displayed a parasagittal banding pattern.
Temporal stability of banding structure
The above results demonstrated the presence of a parasagittal
banding organization to CS synchrony in the waking state. We next
investigated the stability of this banding pattern over time by
dividing the 4 hr recording sessions into successive 25 min periods
and determining the spatial distribution of CS synchrony for each
period. In each experiment (n = 8), it was found that while the average level of CS synchrony did vary between the successive periods, the overall spatial pattern remained constant. The results from one experiment are plotted in Figure
3A, in which the distribution of CS synchrony for nine successive 25 min periods is shown. Note that
despite some fluctuation in the average level of CS synchrony at each
separation distance, the shapes of the curves are quite similar. Even
in experiments where there was some variation from the typical
synchrony distribution (for example, plot 2Ac), the particular distribution was present throughout the recording
session.
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Figure 3.
Stability of CS synchronicity over time.
A, Plots of average synchrony as a function of
mediolateral separation distance for successive 25 min periods during
an 4 hr recording session. Note the constancy of the shape of the
curves, despite some fluctuation in absolute levels of synchrony.
B, Distribution of z values from runs
tests of time sequences of C(0). z values less than
1.65 indicate a statistically significant
(p < 0.05) trend in the data.
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To determine whether the variation in the average level of synchrony
was caused by a systematic shift over time, as might occur if the
changes were because of residual effects of the anesthesia, or simply
random fluctuations, a runs test was performed for the sequence of
synchrony values at each mediolateral separation distance. The
occurrence of significantly less (z < 1.65;
p < 0.05) runs than expected by chance was taken as
evidence of a trend in the data over time. In the experiment shown in
Figure 3A for all separation distances, the observed number
of runs was not significantly less than the expected number due to
chance. A total of 71 sequences were analyzed from the eight
experiments, and only six (8.45%) were found to contain significantly
fewer runs than expected, approximately the percentage of false
positives (5%) that should occur given the 0.05 significance level.
Moreover, the six sequences that were found to have a statistically
significant trend occurred across four experiments, and each occurred
at a different mediolateral separation distance (0, 500, 750, 1000, 1750, and 2000 µm). The complete distribution of z values
obtained from analysis of the 71 sequences is shown in Figure
3B. The distribution was symmetrical with many sequences
having greater than or the same number of runs than expected by chance
(z  0). Thus, the rostrocaudal banding patterns of CS
synchrony appear to be relatively stable structures, despite random
fluctuations in overall levels of synchrony.
If the parasagittally oriented bands represent functional units of the
cerebellar cortex, the level of synchronization within these bands
should be relatively stable over time compared with that observed
between the CS activity of Purkinje cells located in different
parasagittal planes. This was verified by calculating the coefficient
of variation (CV = SD/mean) as a measure of the relative variation
in the average synchrony. For each experiment (n = 8),
the CV was determined for each temporal sequence of synchrony values
and plotted as a function of the mediolateral separation distance of
the sequence (Fig. 4). In all
experiments, the CV increased with increasing separation distance,
resulting in a positive correlation (r = 0.80 ± .09; range, 0.66 to 0.96). In six of eight experiments the correlation
was statistically significant (p < 0.05).
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Figure 4.
Relative stability of intraband synchrony. Plots
of CV as a function of mediolateral separation distance. CV was
positively correlated with mediolateral separation distance.
A-C show results from the three
experiments in which the strongest, the average, and the weakest
relationship was found, respectively. The correlation
(r) was significant (p < 0.05) for A and B.
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CS rhythmicity in the awake animal
The extent to which spontaneous CS activity is rhythmic in awake
animals was studied by constructing autocorrelograms (n = 282). Visual inspection of these correlograms showed that rhythmic activity was present in the large majority of cells. Typically, the
autocorrelograms displayed a prominent primary peak followed by one or
more smaller higher order peaks at regularly spaced intervals, such as
those shown in Figure 5, B and
C. The RIs for the autocorrelograms of Figure 5,
B and C, were 0.0207 and 0.0260, respectively. In
some cells, the CS activity displayed a more pronounced rhythmicity,
such as the example shown in Figure 5D (RI = 0.0986).
In contrast, only 4% of the cells lacked any evidence of rhythmic
activity as defined by the absence of any significant peaks (see Fig.
8A), and an additional 18% had relatively weak rhythmicity (0 < RI < 0.01). Thus, in total only 22% of
the cells could be characterized as having weak or absent rhythmicity
(see Fig. 8B). The autocorrelogram of one such cell,
which had an RI of 0.0088, is shown in Figure 5A. Analysis
of the entire population (n = 282 cells) showed that
the autocorrelograms had 2.91 ± 1.84 peaks and an RI of
0.0332 ± 0.0377 on average, both of which were significantly
(p < 0.001) greater than zero. The
distributions of the peak number and RI are shown in Figure 8
(histogram bars). Note that although 77% of the
autocorrelograms displayed two or more peaks, indicating a sustained
oscillation for at least several cycles, 19% showed only a single
significant peak. However, even these latter autocorrelograms indicate
the presence, however transient, of an 10 Hz rhythmicity and not
simply a modal interval caused by the average firing rates of the
cells.
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Figure 5.
Autocorrelograms of CS activity. Autocorrelograms
of CS activity from four Purkinje cells showing the range of
rhythmicity present. A, Example of CS activity
displaying little rhythmicity (RI = 0.0088). B, C,
Cells showing more typical rhythmic activity (RIs = 0.0207, 0.0260). D, Example of strongly rhythmic CS activity
(RI = 0.0986). Bin width of histogram is 10 msec.
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The variation in the strength of rhythmic activity over time was
calculated for each of the eight experiments in which continuous recordings were obtained for 4 hr by dividing each experiment into
successive 25 min periods. Analysis of the autocorrelograms from the
successive periods showed that there was some variation in the average
number of peaks and in the RI. An example of this variation is shown in
Figure 6, where the values of these
parameters in one experiment are plotted for successive 25 min periods.
Similar variability was observed in each of the experiments; however, this variation was random in nature, because runs tests for peak number
and RI gave p values that were not significant in all but 1 of 16 cases (6.25%). Runs tests for the mean number of peaks yielded z values of 0.7548 ± 0.7169 (n = 8) on average, whereas for the RI the z
values averaged only 0.0418 ± 0.8691 (n = 8).
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Figure 6.
Variation of rhythmicity over time. Plot of
average number of peaks (A) and average value of
RI (B) for successive 25 min periods from one
experiment in which 29 cells were simultaneously recorded.
Horizontal lines indicate median values. Points of
crossing of median indicated by vertical dashed
lines.
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Most of the present recordings were obtained from long (4 hr)
sessions in which each cell had on the order of 10,000-20,000 CSs. The
relatively long recordings probably facilitated our observing the CS
rhythmicity and may, in part, explain the differences in the present
results from those of Keating and Thach (1995). In particular, the
variations in rhythmicity during the long recording sessions suggested
the possibility that CS rhythmicity could be missed with shorter
duration recordings. Thus, we asked the following question: for a cell
that shows significant rhythmicity during the entire recording session
(i.e., R 0.01), what is the probability of failing to detect
any rhythmicity (i.e., no significant peaks or RI = 0) in a
shorter duration recording? To answer this question, a long recording
session (250 min) was divided into shorter duration segments (25, 10, 5, and 2.5 min segments). The RI was then calculated for each cell
for every segment. Next, for each cell that displayed significant
rhythmicity (RI 0.01) during the entire recording session, the
probability that it displayed no rhythmicity (RI = 0 or
equivalently no significant peaks in the autocorrelogram) was
calculated for each segment duration. For example, the recording session was divided into ten 25 min segments. If a cell had an RI = 0 for one of those segments, the probability of failing to detect its
rhythmicity in 25 min duration recordings was 10%. The results of this
analysis for an experiment in which 24 cells displayed rhythmicity
(RI 0.01) for the entire recording session are shown in Figure
7. Each histogram plots the probability
of failing to detect rhythmicity (i.e., number of segments with RI = 0) for recording segments of a given duration. For the shorter duration segments (2.5 and 5 min), only 8 and 30% of the cells had
little to no chance (0-5%) of failing to detect the rhythmicity, as
indicated by the leftmost bins of the corresponding histograms in
Figure 7. Nevertheless, the distributions of these histograms indicate
that there is a relatively low chance of missing the rhythmicity. For
the 2.5 min segments there was a 21.7 ± 14.9% chance of missing
the rhythmicity on average, and for the 5 min segments a 17.9 ± 14.7% chance. When the duration of the recording segment was
increased, these percentages fell further to 12.7 ± 13.4% for
the 10 min segments and 7.5 ± 9.9% for the 25 min segments. Note
that for the 25 min segments there was virtually no chance of failing
to detect the rhythmicity in over half of the population (leftmost bin
in this histogram corresponds to 0% only, because there were only ten
such segments and one failure would give a value of 10%). The few
(n = 5) cells with more than one segment in which
rhythmicity was not detected for the 25 min segments had very low
firing rates (range, 0.1-0.34 Hz), which correspond to relatively
short spike trains of 150-510 spikes for 25 min recordings.
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Figure 7.
Probability of failing to detect CS rhythmicity in
short duration recordings. Each histogram plots the percent of cells as
a function of the percent of recording segments in which CS rhythmicity
was not detected for segments of a particular duration. Duration of
recording segments indicated above each histogram. Histogram bin width
is 5%. Centers of bins are indicated by x-axis label.
The analysis was performed on n = 24 cells. The
segments were obtained by dividing an 4 hr recording session into
nonoverlapping pieces. The number of segments used was 88, 49, 24, and
10 for the 2.5, 5, 10, and 25 min durations, respectively.
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The results indicate that recordings of 10-25 min are needed to be
confident that CS rhythmicity, when present, will be detected. The
average firing rate for this experiment was 0.60 Hz, which corresponds
to an average spike train of 360-900 spikes for recordings of 10 and
25 min, respectively. This suggests that CS rhythmicity may be missed
if autocorrelograms are generated from spike trains containing fewer
spikes. It should also be mentioned that in two of our awake animals,
as well as in the seven anesthetized animals, the entire recording
sessions were of short duration (15-30 min), yet the rhythmicity of
the CS activity was evident in the autocorrelograms and RIs of cells in
these experiments. Consistent with these values, we generally find that
to observe the rhythmicity in the autocorrelograms clearly by eye,
spike trains must contain at least 300-500 spikes, although in cells
displaying a strong rhythmicity less spikes are needed.
Given that most Purkinje cells displayed rhythmic CS activity, it
became of interest to investigate the preferred oscillation frequencies
displayed by spontaneous CS activity in the waking animal. The
oscillation frequency for each cell was calculated as the reciprocal of
the interval from t = 0 to the first peak in its
autocorrelogram. The oscillation frequency was only determined for
cells displaying significant rhythmicity (RIs of 0.01). Thus, cells
with autocorrelograms such as shown in Figure 5A were
excluded, whereas cells with correlograms similar to those in Figure
5B-D were included. Frequency analysis was
performed on eight experiments in which the majority of cells in each
case showed RIs 0.01. Overall, 177 of 215 cells (82.3%) in these
experiments met the criteria for oscillation frequency determination.
The average oscillation frequency for these cells was 10.27 ± 2.89 Hz. A large amount of the variation was caused by differences
between experiments. That is, the average oscillation frequency in the
different animals ranged from 7.03-13.31 Hz, and the average of the
individual animal SDs was 1.68 compared with the overall SD of
2.89.
Comparison of CS activity in anesthetized and awake animals
The similarity or difference of the functional state of the
olivocerebellar system under ketamine-xylazine anesthesia as compared with the unanesthetized condition was determined by characterizing the
rhythmicity and synchronicity of CS activity in the two states. The
10 Hz oscillation frequency observed in the waking state is very
similar to what has been reported previously under anesthesia (Sasaki
et al., 1989; Lang et al., 1997). Moreover, as shown by the peak number
and RI distributions (Fig. 8), the
strength of the rhythmicity was similar in the two states. Although the
RI distribution from the activity in awake animals appears somewhat shifted to the left relative to that from anesthetized animals, the
means of the RI distributions for anesthetized (0.0358 ± 0.0268; n = 257) and unanesthetized (0.0332 ± 0.0377;
n = 282) animals were similar. In addition, the mean of
the peak distribution was higher for cells recorded in unanesthetized
animals (2.91 ± 1.84; n = 282) than for those
recorded in anesthetized animals (1.99 ± 1.17; n = 257).
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Figure 8.
Comparison of CS rhythmicity in awake and
anesthetized states. A, Distribution of number of
significant peaks shown by autocorrelograms of CS activity in awake
(histogram bars; n = 282 cells) and
anesthetized (filled circles;
n = 257 cells) animals. B,
Distribution of RI in awake (histogram bars) and
anesthetized (filled circles) animals.
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With regard to CS synchronicity, the parasagittal banding pattern
observed in the waking state was similar to what has been observed
under ketamine-xylazine anesthesia. A direct comparison of the
anesthetized and nonanesthetized synchrony distributions was performed
by determining the normalized synchrony distribution from seven
experiments performed under anesthesia (Fig.
9A) and comparing the average
normalized synchrony curve from these experiments to that found for the
nonanesthetized animals (Fig. 9B). The similarity of the
curves in Figure 9B indicates that the predominant patterns of CS synchrony that have been previously described under
ketamine-xylazine anesthesia are the same as those observed in the
waking animal.
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Figure 9.
Comparison of CS synchronicity in awake and
anesthetized states. A, Average normalized synchrony
plotted as a function of mediolateral separation distance for seven
experiments in ketamine-xylazine-anesthetized rats. B,
Plot of average curve (open circles) from the seven
experiments shown in A. For comparison, the average
curve of Figure 2C obtained from the awake animals is
replotted (filled circles). Error bars indicate
SEM.
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DISCUSSION |
The ability of the olivocerebellar system to generate rhythmic
synchronous discharges has been proposed to play a central role in
motor coordination (Llinás, 1974, 1991). Yet although the
rhythmic and synchronous nature of spontaneous olivocerebellar activity
has been documented in anesthetized animals and in in vitro
preparations, there has been no quantitative description of these
characteristics of spontaneous olivocerebellar activity in the waking
state. Thus, we performed multiple electrode recordings of spontaneous
CS activity from lobule crus 2a in awake animals. Our results
demonstrate that in the awake animal the olivocerebellar system
generates synchronous CS activity and that the predominant spatial
distribution is similar to what has been described in anesthetized
animals (i.e., synchronous CS activity occurs mainly among Purkinje
cells located within parasagittally oriented strips of cortex).
Furthermore, we found that the large majority of Purkinje cells within
crus 2a display rhythmic CS activity with an 10 Hz periodicity.
Parasagittal organization of CS synchrony
The neurons of the inferior olive are characterized not only by
their intrinsic oscillatory electrical activity, which results from the
interplay between different voltage-dependent calcium and potassium
conductances (Llinás and Yarom, 1981a,b), but in addition, are
electrotonically coupled via gap junctions located within glomeruli
(Llinás et al., 1974; Sotelo et al., 1974). Indeed, the inferior
olive has the highest known density of neuronal gap junctions within
the mammalian CNS (de Zeeuw et al., 1995) and thus contains the
anatomical substrate for generating simultaneous electrical discharges,
which in turn could result in synchronous CS activity throughout the
cerebellar cortex. The potential for such global synchronization is
shown by the fact that synchronous CS activity has been observed
between Purkinje cells located in widely separated regions of the
cerebellar cortex (Wylie et al., 1995; de Zeeuw et al., 1996). However,
despite this potential, in anesthetized animals synchronous CS activity
is typically observed primarily among Purkinje cells located within
parasagittally oriented strips of cortex (Bell and Kawasaki, 1972;
Llinás and Sasaki, 1989; Sasaki et al., 1989). Although these
strips may be quite long, extending not just across the apex of a
folium, but also down its walls (Sugihara et al., 1993) and across
successive folia (E. Lang, I. Sugihara, and R. Llinás,
unpublished observations), they nevertheless are typically narrow
structures with a width of only 250-500 µm. The localization of the
gap junctions within glomeruli has suggested that the functional
coupling of IO neurons is modulated at those sites by inhibitory input
and could therefore underlie the limited spatial distribution of CS
synchrony that is normally observed (Llinás, 1974). Recent
experiments have provided support for this idea by demonstrating that
blocking the activity of the GABAergic cerebellar nucleo-olivary
pathway produces widespread synchronization of CS activity (Lang et
al., 1996).
The results of the present investigation indicate that the spatial
distribution of spontaneous CS synchrony is similarly restricted in the
awake animal. In all animals, CS synchrony was greater on average for
Purkinje cells with smaller mediolateral separation distances than for
those separated by large distances. In fact, in most animals high
levels of synchrony were limited to Purkinje cells separated by 250 µm or less, with a rapid reduction of synchronous activity occurring
between 250 and 500 µm. Thus, in awake animals in crus 2a,
synchronous CS activity occurs primarily among Purkinje cells located
within narrow parasagittally oriented strips of cortex, suggesting the
existence of functional corticonuclear domains in accordance with the
rostrocaudal organization of Purkinje cell innervation of the
cerebellar nuclei (Cicirata et al., 1992).
It is theoretically possible that the similarity of the awake patterns
to the anesthetized ones, rather than reflecting the normal
organization of CS synchrony, could have been caused by the residual
effect of the ketamine anesthesia used during electrode implantation.
We consider this possibility unlikely for the following reasons. First,
ketamine is a relatively short (30 min)-lasting anesthetic
(Flecknell, 1996), and the recordings always began several hours after
anesthetization and lasted for 4 hr after that in most cases.
Second, during recovery from the anesthesia the animals typically pass
through a light anesthetic state during which highly synchronous and
rhythmic CS activity and tremor-like movements occur (Lang, 1995).
During this period, the spatial organization of CS synchrony and
rhythmicity is different from that observed under the usual anesthetic
conditions and also different from that in the fully awake animal
capable of performing behavioral tasks. Third, analyses of the spatial
distribution of synchrony and rhythmicity from successive time periods
during the 4 hr recording sessions failed to disclose any systematic
trends that would be indicative of the wearing off of the anesthetic.
Finally, in most animals the data were obtained during a recording
session in which the animal was capable of accurately performing
complex movements, which would not have been possible if there were a continuing significant action of the anesthetic on cerebellar activity.
Moreover, significant changes in the patterns of synchronous CS
activity were observed in relation to the movements that were performed
during the recording session (Welsh et al., 1995a). Thus, the
olivocerebellar system was capable of generating alternative functional patterns.
Questions then arise concerning the functional significance of these
parasagittal bands, particularly in relation to movement-related patterns of activity. That these bands are functional entities is
supported by the constancy of the banding structure over time and by
the fact that the mean synchrony levels among Purkinje cells located
within the same parasagittal strip demonstrated less time variation
than did those of Purkinje cells separated in the mediolateral axis.
That is, the CS activity of Purkinje cells within a band appeared to be
strongly coherent, as would be expected if they formed a functional
unit. It seems plausible then, particularly given the somatotopic
organization of the motor output of the cerebellar nuclei (Cicirata et
al., 1992), to postulate that the activity of each band of cells would
function to control a small group of related muscles or a well defined
motor synergy (Lang, 1995). The CS synchrony patterns observed during
movements would then result from the coupling of activity from the
various parasagittal bands that control the muscle groups needed to
perform particular movements.
Spontaneous 10 Hz rhythmicity in olivocerebellar activity
It is clear that under anesthetized conditions or in in
vitro preparations, the olivocerebellar system can generate
rhythmic activity spontaneously (de Montigny and Lamarre, 1973;
Llinás and Volkind, 1973; Belcari et al., 1977;
Llinás and Yarom, 1981a,b, 1986; Benardo and Foster, 1986;
Llinás and Sasaki, 1989; Sasaki et al., 1989; Lampl and Yarom,
1993, 1997; Sugihara et al., 1995; Wylie et al., 1995; Lang et al.,
1996, 1997). Moreover, this rhythmic activity has been recorded from
both vermal and hemispheric regions (Sugihara et al., 1995) and from
several of the olivary subnuclei (Llinás and Yarom, 1981a,b),
suggesting that it is typical of olivocerebellar activity generally and
not characteristic of the activity of a particular division of this system.
However, there has been some debate concerning the presence of rhythmic
CS activity in the alert animal (Keating and Thach, 1995). The present
results should help to resolve this debate because they provide clear
evidence that the large majority of crus 2a Purkinje cells display
spontaneous 10 Hz rhythmic CS activity in the awake animal. The
present findings are consistent with early investigations of CS
activity in awake animals, which briefly reported the presence of
rhythmic CS activity (Rushmer and Woodward, 1971; Bell and Kawasaki,
1972), as well as with our previous work demonstrating rhythmic
movement-related CS activity (Welsh et al., 1995a).
Our data are also in general agreement with those of Pellerin et al.
(1997), who described the occurrence of rhythmic CS activity in
behaving primates. These authors, however, reported that only ~45%
of Purkinje cells displayed evidence of rhythmic CS activity, whereas
in our population >90% showed at least some evidence of such
activity. Part of this difference may be ascribable to analytic methodology (we detected peaks in autocorrelograms, whereas they analyzed the Fourier transforms of the averaged autocorrelograms from
repeated trials in their task). More likely this difference reflects
the fact that the olivocerebellar system must be in a distinct
functional state when the animal performs a movement as compared with
when it is remaining still, such that there is a differential
modulation of the rhythmicity of distinct subgroups of IO neurons
during the movement. That is, generation of a movement results from
pulsatile activation of a specific set of muscles (Vallbo and Wessberg,
1993). If, as we hypothesize, the periodic synchronous
discharges of the olivocerebellar system help organize these pulsatile
motor commands, during a movement there should be an upregulation of
the rhythmicity of IO neurons that help control the muscles that need
to be contracted and a downregulation of the rhythmicity of other IO
neurons that control muscles that must relax.
Our results, and those of Pellerin et al. (1997), differ significantly
from those of Keating and Thach (1995), who were unable to detect
rhythmicity in the CS activity recorded from primates performing motor
tasks. These differences are not likely to be caused by species
differences, because the recordings of Pellerin et al. (1997) were also
from primates. An explanation probably resides, in part, in the
analytical methodology used by Keating and Thach (1995). In particular,
their concatenation method for generating CS autocorrelograms
introduced false random interspike intervals into their data sets,
which would obscure a 10 Hz signal (as they themselves admit in a
subsequent paper, see Methods section of Keating and Thach, 1997). The
effect of this error can be observed in their Figure 3 (Keating and
Thach, 1995) in which rhythmic movement-related CS activity is readily
observed in the raster display of the raw data but barely visible in
the corresponding autocorrelogram. An additional factor in their
failure to detect CS rhythmicity may have been the relatively small
data sets used by Keating and Thach (1995). Their recordings ranged
between 102 and 3643 intervals. Most of our recordings contained spike
trains consisting of 5000-25,000 spikes. Analysis of short segments of these spike trains (Fig. 7) indicated that the rhythmicity might not be
detected if the number of spikes in a train dropped below 400-900.
Thus, for at least some percentage of their cells, the ability of
Keating and Thach (1995) to detect CS rhythmicity may also have been
compromised by their limited data sets.
Given that the olivocerebellar system generates a periodic activation
of the cerebellar cortex, the issue arises as to whether there is also
periodic modulation of cerebellar output at the nuclear level, as would
be required for the proposed timing role of the olivocerebellar system
in motor coordination. It is clear that when pharmacologically
enhanced, the activity of the olivocerebellar system does produce a
rhythmic activation of the cerebellar nuclei, one which leads to
phase-locked motor activity (de Montigny and Lamarre, 1973;
Llinás and Volkind, 1973). However, under more physiological
conditions, Keating and Thach (1997) have failed to observe any
periodicity indicative of olivocerebellar discharge in the
movement-related activity of cerebellar nuclear neurons. This failure
may have been caused by the limitations of the single-unit recording
technique used, because during normal movements the rhythmic activation
of a single cell is likely to be subtle, because pronounced rhythmic
activation of cerebellar nuclear neurons results in a large amplitude
tremor. This subtle modulation may not be detectable in the single-unit
recordings because activity generated by the olivocerebellar system is
likely to be buried within activity caused by other cerebellar nuclear
afferents. Nevertheless, there could still be a significant alteration
of activity across the population as a whole because of the synchronous
nature of olivocerebellar discharges. Indeed, as we have argued, the
olivocerebellar system is not designed to produce its effects by
generating large changes in single cell firing rates, which would be
readily detectable with single-unit recordings, but rather by changes
in the pattern of activity across populations of cells. Thus, testing
whether the olivocerebellar system, which produces rhythmic and
synchronous activation of the cerebellar cortex, also produces a
periodic activation of the cerebellar nuclei may require the use of
techniques, such as multiple electrode recordings, that measure the
population response of the cerebellar nuclei.
Functional significance of the olivocerebellar synchrony
and rhythmicity
The demonstration of synchronous and rhythmic CS activity in the
awake animal provides further support that these characteristics of
olivocerebellar activity have functional significance. The present
results are also consistent with the hypothesis that the olivocerebellar system may subserve a timing function for organizing the activation of muscle synergies needed to generate movements. Although at present we can only speculate on the specific functional relationship of the parasagittal bands of synchronous CS activity to
motor output, their presence and the unquestionable anatomical and functional organization of the olivary complex provides strong evidence that the olivocerebellar system has evolved to contribute significantly and directly to the output of the cerebellar cortex and nuclei.
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FOOTNOTES |
Received Sept. 22, 1998; revised Jan. 12, 1999; accepted Jan. 17, 1999.
This work was supported by the National Institutes of Health/National
Institute of Neurological Diseases and Stroke (NS13742, NS37028,
NS31224), the Office of Naval Research (N00014-93-1-0225), and the
National Science Foundation (IBN-9808353).
Correspondence should be addressed to Dr. E. J. Lang or Dr. R. Llinás, Department of Physiology and Neuroscience, New York University Medical Center, 550 First Avenue, New York, NY 10016.
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8005 - 8015.
[Abstract]
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D. Z. Wetmore, E. A. Mukamel, and M. J. Schnitzer
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Time and Frequency Characteristics of Purkinje Cell Complex Spikes in the Awake Monkey Performing a Nonperiodic Task
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I. Ozden, H. M. Lee, M. R. Sullivan, and S. S.-H. Wang
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R. Sanchez-Campusano, A. Gruart, and J. M. Delgado-Garcia
The Cerebellar Interpositus Nucleus and the Dynamic Control of Learned Motor Responses
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E. Chorev, Y. Yarom, and I. Lampl
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F. J. Urbano, J. I. Simpson, and R. R. Llinas
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[Abstract]
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T. Holtzman, T. Rajapaksa, A. Mostofi, and S. A. Edgley
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E. A. Rancz and M. Hausser
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May 17, 2006;
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E. J. Lang, R. Llinas, and I. Sugihara
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T. A. Blenkinsop and E. J. Lang
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T. Kimura, M. Sugimori, and R. R. Llinas
Purkinje cell long-term depression is prevented by T-588, a neuroprotective compound that reduces cytosolic calcium release from intracellular stores
PNAS,
November 22, 2005;
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[Abstract]
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B. E. McKay, M. L. Molineux, W. H. Mehaffey, and R. W. Turner
Kv1 K+ Channels Control Purkinje Cell Output to Facilitate Postsynaptic Rebound Discharge in Deep Cerebellar Neurons
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Z. M. Khaliq and I. M. Raman
Axonal Propagation of Simple and Complex Spikes in Cerebellar Purkinje Neurons
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V. B. Kazantsev, V. I. Nekorkin, V. I. Makarenko, and R. Llinas
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[Abstract]
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S. P. Marshall and E. J. Lang
Inferior Olive Oscillations Gate Transmission of Motor Cortical Activity to the Cerebellum
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K. Matsui and C. E. Jahr
Differential Control of Synaptic and Ectopic Vesicular Release of Glutamate
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I. Sugihara and Y. Shinoda
Molecular, Topographic, and Functional Organization of the Cerebellar Cortex: A Study with Combined Aldolase C and Olivocerebellar Labeling
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T. Yoshida, A. Katoh, G. Ohtsuki, M. Mishina, and T. Hirano
Oscillating Purkinje Neuron Activity Causing Involuntary Eye Movement in a Mutant Mouse Deficient in the Glutamate Receptor {delta}2 Subunit
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Synchronous, Focally Modulated {beta}-Band Oscillations Characterize Local Field Potential Activity in the Striatum of Awake Behaving Monkeys
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V. B. Kazantsev, V. I. Nekorkin, V. I. Makarenko, and R. Llinas
Olivo-cerebellar cluster-based universal control system
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[Abstract]
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The Contribution of NMDA and AMPA Conductances to the Control of Spiking in Neurons of the Deep Cerebellar Nuclei
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J. Csicsvari, D. A. Henze, B. Jamieson, K. D. Harris, A. Sirota, P. Bartho, K. D. Wise, and G. Buzsaki
Massively Parallel Recording of Unit and Local Field Potentials With Silicon-Based Electrodes
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E. J. Lang and J. Rosenbluth
Role of Myelination in the Development of a Uniform Olivocerebellar Conduction Time
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J. R Edgerton and P. H Reinhart
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M. A. Long, M. R. Deans, D. L. Paul, and B. W. Connors
Rhythmicity without Synchrony in the Electrically Uncoupled Inferior Olive
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P. Telgkamp and I. M. Raman
Depression of Inhibitory Synaptic Transmission between Purkinje Cells and Neurons of the Cerebellar Nuclei
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D. B. Katz and J. E. Steinmetz
Psychological functions of the cerebellum.
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R. Courtemanche, J.-P. Pellerin, and Y. Lamarre
Local Field Potential Oscillations in Primate Cerebellar Cortex: Modulation During Active and Passive Expectancy
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August 1, 2002;
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A. Devor and Y. Yarom
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E. J. Lang
GABAergic and Glutamatergic Modulation of Spontaneous and Motor-Cortex-Evoked Complex Spike Activity
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E. Leznik, V. Makarenko, and R. Llinas
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S. R Williams, S. R Christensen, G. J Stuart, and M. Hausser
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C. Schwarz and J. P. Welsh
Dynamic Modulation of Mossy Fiber System Throughput by Inferior Olive Synchrony: A Multielectrode Study of Cerebellar Cortex Activated by Motor Cortex
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Y. Loewenstein, Y. Yarom, and H. Sompolinsky
The generation of oscillations in networks of electrically coupled cells
PNAS,
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[Abstract]
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A. Devor, J.-M. Fritschy, and Y. Yarom
Spatial Distribution and Subunit Composition of GABAA Receptors in the Inferior Olivary Nucleus
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[Abstract]
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E. J. Lang
Organization of Olivocerebellar Activity in the Absence of Excitatory Glutamatergic Input
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D. A. Nicholson and J. H. Freeman Jr
Developmental Changes in Eye-Blink Conditioning and Neuronal Activity in the Inferior Olive
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S. Restituito, R. M. Thompson, J. Eliet, R. S. Raike, M. Riedl, P. Charnet, and C. M. Gomez
The Polyglutamine Expansion in Spinocerebellar Ataxia Type 6 Causes a beta Subunit-Specific Enhanced Activation of P/Q-Type Calcium Channels in Xenopus Oocytes
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September 1, 2000;
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[Abstract]
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D. G Placantonakis, C. Schwarz, and J. P Welsh
Serotonin suppresses subthreshold and suprathreshold oscillatory activity of rat inferior olivary neurones in vitro
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May 1, 2000;
524(3):
833 - 851.
[Abstract]
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V. Gauck and D. Jaeger
The Control of Rate and Timing of Spikes in the Deep Cerebellar Nuclei by Inhibition
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April 15, 2000;
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[Abstract]
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Y. Loewenstein, Y. Yarom, and H. Sompolinsky
The generation of oscillations in networks of electrically coupled cells
PNAS,
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[Abstract]
[Full Text]
[PDF]
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S. R Williams, S. R Christensen, G. J Stuart, and M. Hausser
Membrane potential bistability is controlled by the hyperpolarization-activated current IH in rat cerebellar Purkinje neurons in vitro
J. Physiol.,
March 1, 2002;
539(2):
469 - 483.
[Abstract]
[Full Text]
[PDF]
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TOP
ABSTRACT
INTRODUCTION
