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1 Abteilung Neurophysiologie, Max-Planck-Institut für Hirnforschung, 60528 Frankfurt; 2 Abt. Klinische Neurobiologie, Universität Heidelberg, 69120 Heidelberg; 3 Institut für Neurophysiologie und Pathophysiologie, Universitätsklinikum Hamburg-Eppendorf, 20246 Hamburg, Germany
Submitted 1 July 2002; accepted in final form 31 March 2003
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONDISCLOSURESACKNOWLEDGMENTSREFERENCES |
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-band. The
cellular mechanisms underlying these phenomena of synchronization and
oscillatory patterning have been studied mainly in vitro due to the difficulty
in designing a direct in vivo assay. With the prospect of using conditional
genetic manipulations of cortical network components, our objective was to
test whether the mouse would meet the criteria to provide a model system for
the study of -band synchrony. Multi-unit and local field potential
recordings were made from the primary visual cortex of anesthetized C57BL/6J
mice. Neuronal responses evoked by moving gratings, bars, and random dot
patterns were analyzed with respect to neuronal synchrony and temporal
patterning. Oscillations at -frequencies were readily evoked with all
types of stimuli used. Oscillation and synchronization strength were largest
for gratings and decreased when the noise level was increased in random-dot
patterns. The center peak width of cross-correlograms was smallest for bars
and increased with noise, yielding a significant difference between coherent
random dot patterns versus patterns with 70% noise. Field potential analysis
typically revealed increases of power in the -band during response
periods. Our findings are compatible with a role for neuronal synchrony in
mediating perceptual binding and suggest the usefulness of the mouse model for
testing hypotheses concerning both the mechanisms and the functional role of
temporal patterning. |
INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONDISCLOSURESACKNOWLEDGMENTSREFERENCES |
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-frequency range (>30 Hz)
(Gray et al. 1989
). The
coordination of spike timing is generated intracortically and dependent on
global stimulus properties (for review see
Engel and Singer 2001;
Engel et al. 2001;
Singer 1999;
Singer and Gray 1995).
Therefore it is not a mere reflection of network connectivity but, rather, a
result of context-dependent dynamic interactions involving a combination of
bottom-up and top-down influences (reviewed in
Engel et al. 2001). It has been
proposed to provide a general mechanism by which activity patterns in
spatially separate regions of the brain are temporally coordinated
(Gray et al. 1989;
Singer and Gray 1995). Dynamic
cell assemblies formed by synchronously firing neurons would signal to
subsequent processing stages the relatedness of their responses, thus
establishing relations among features of perceptual objects and favoring
further joint processing due to the increased saliency of the correlated
responses (for review see Singer
1999; Singer and Gray
1995).
Such precise stimulus-induced synchronization is not restricted to the
mammalian visual system. Rather, it represents a common phenomenon across
species and has, for instance, also been found in the visual system of
reptiles (Prechtl 1994) and
birds (Neuenschwander and Varela
1993). Moreover, these findings can be generalized across neural
systems. Thus synchronization is well known to occur in the olfactory system
of various vertebrate and invertebrate species, where these phenomena have
been related to the processing of odor information
(Freeman 1988;
Laurent 1996). Moreover, in
both the auditory (deCharms and Merzenich
1996; Eggermont
1992) and the somatosensory cortex
(Nicolelis et al. 1995;
Steriade et al. 1996) precise
neuronal synchronization has been observed. Furthermore, neuronal interactions
with a precision in the millisecond range have been described in the
hippocampus (Bragin et al.
1995; Buzsáki and
Chrobak 1995), in the frontal cortex
(Abeles et al. 1993;
Vaadia et al. 1995), and in
the motor system where neural synchronization has been discovered during both
preparation and execution of movements
(Murthy and Fetz 1992;
Riehle et al. 1997;
Sanes and Donoghue 1993).
Recent electroencephalographic (EEG) and magnetoencephalographic (MEG) studies
indicate that stimulus-induced synchronization at -frequencies is also
present in the human brain (for review see
Tallon-Baudry and Bertrand
1999) and occurs during auditory
(Galambos et al. 1981;
Pantev et al. 1991), visual
(Herrmann et al. 1999;
Müller et al. 1997;
Rodriguez et al. 1999;
Tallon-Baudry et al. 1997),
and language processing (Pulvermüller
et al. 1995) as well as during execution of motor tasks
(Kristeva-Feige et al.
1993).
Despite their widespread occurrence and hypothesized importance, the
cellular mechanisms underlying the phenomena of stimulus-induced
synchronization and oscillatory patterning of neural responses remain poorly
understood. Mice are especially well suited as a model organism for the
exploration of cellular mechanisms due to the possibility of targeted genetic
manipulation. The majority of data related to neuronal synchrony and
-oscillations has been obtained from the visual cortices of cat and
monkey. Our objective was to test whether the mouse could provide a model
system in this context. Ideally, neurons in the mouse visual cortex would
possess functional features similar to those found in cat and monkey (i.e., be
able to synchronize their activity in a stimulus-related fashion and to engage
in stimulus-induced -oscillations). Here we report the first in vivo
study of synchronization and -oscillations in the mouse and provide a
baseline suitable for comparison with data from mutant mice. Portions of this
work have previously appeared in abstract form
(Nase et al. 1999).
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METHODS |
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In total, 65 adult C57Bl/6J mice of either sex were used in the study. Animals were obtained from Charles River Deutschland GmbH, Sulzfeld, Germany. All experimental procedures were in accordance with the German Law for the Protection of Experimental Animals and conformed with the Guiding Principles for Research Involving Animals and Human Beings embodied in the Declaration of Helsinki.
Preparation, anesthesia, and surgical procedures
Experimental procedures for recording from mouse visual cortex were adapted
from Drager (1975) and Gordon
and Stryker (1996). After
induction of anesthesia with ketamine (Ketanest, Parke-Davis, Courbevoie,
France; 10 mg/kg, ip) and xylazine (Rompun, Bayer, Wuppertal, Germany; 2
mg/kg, ip), a tracheotomy was made for artificial ventilation, and the animal
was placed in a stereotaxic apparatus. Throughout surgery and during the
recordings, general anesthesia was maintained by ventilating the animal with a
mixture of 50% N2O and 50% O2 supplemented by
0.4–1.0% halothane or 0.6–1.0% isoflurane. Body temperature was
kept in the range of 36.8–37.2°C. A recording chamber was mounted on
the skull and a craniotomy was made exposing area 17. The dura was left intact
and covered with agar and silicon oil. Following surgery, paralysis was
induced and maintained by subcutaneous infusion of pancuronium bromide
(Pancuronium, Organon Teknika-Cappel, Malvern, PA; 1
mg·kg–1·h–1,
sc). Ventilation pressure and electrocardiogram were monitored continuously.
The cornea was protected from drying with silicone oil throughout the course
of the experiment. Slight mydriasis was present throughout the experiment,
presumably caused by the administered xylazine. In physiologically stable
animals, strong neuronal responses were readily obtained through a recording
period of 8–10 h. Only when ECG abnormalities or corneal cataracts
developed, responsiveness declined. Such experiments were then terminated.
Visual stimulation and experimental design
Stimulation was monocular. Visual stimuli were computer generated and
back-projected onto a tangent screen 28 cm in front of the animal at an angle
of 60° with respect to the animals' midline (frame rate, 60 Hz; background
luminance, 3.3 cd·m–2; frame size, 80 by
80°, duration 2.2 s). Stimuli comprised bars (width, 4°; length
60°, contrast 0.96; velocity, 20°/s), sinusoidal drifting gratings
(size, 70 by 70°; spatial frequency, 0.1 cycles/°; temporal frequency,
5 cycles/s; contrast, 0.66), and random dot patterns (area coverage, 10%; dot
size, 0.6 by 0.6°; velocity, 20°/s) with or without visual noise.
Directional tuning was tested for with different directions of motion of the
respective stimulus (in steps of 45°). The direction of movement of the
bars and gratings was always kept perpendicular to their orientation. Each
stimulus was presented 
10 times. Stimulus presentations were interleaved
in a block-wise randomized fashion. Individual sessions lasted approximately
1–1.5 h.
Multiple-electrode recordings
Multi-unit activity (MUA) was recorded with tungsten electrodes (impedance
0.4–0.8 M
) placed within the primary visual cortex. Four
electrodes were used simultaneously. The electrodes were arranged in a square
with an edge length of 200–250 µm. The separation, therefore, ranged
approximately between 200 and 350 µm, with electrodes connected by an
"edge" of the square closer together and the electrodes connected
by a "diagonal" slightly further apart. The electrode signal was
amplified and band-pass filtered between 1 and 3 kHz, fed through a Schmitt
trigger whose threshold was set higher than twice the noise level, and the
trigger pulses were sampled at 2 kHz. Simultaneously, local field potentials
(LFP) were recorded from the same electrodes by band-pass filtering of the raw
signals between 1 and 100 Hz. The signals were digitized at 2 kHz using a 1401
CED interface controlled by Spike2 software (Cambridge Electronic Design,
Cambridge, UK) and stored on computer disk for off-line analysis.
Data analysis
Off-line analysis was performed using LabVIEW (National Instruments,
Austin, TX) and IDL (Research Systems, Boulder, CO). Oscillatory response
modulation and synchronization were analyzed by computing and averaging auto-
and cross-correlograms for all trials per condition and recording site with a
bin size of 1 ms. To avoid inclusion of nonstationary, phasic response
components, the first 200 ms after visual stimulus onset were discarded before
analysis. In addition, shift-predictor correlograms were calculated. Since
these were consistently flat, quantification was carried out using raw
correlograms without subtraction of shift-predictors. For quantification of
the correlogram modulation, a damped cosine (Gabor) function was fitted to the
correlograms (König
1994). The fitted function had to account for 15% of the data
variance and the z-scores of significant peaks had to be >2. The
strength of oscillatory modulation was assessed from the ratio of the
amplitude of the first satellite peak over the offset of the function fitted
to the auto-correlograms, yielding the modulation amplitude of the first
satellite peak (MAS). Similarly, synchronization strength was evaluated using
the modulation amplitude (MA) of the central peak in the correlograms (i.e.,
the ratio between the peak amplitude and offset of the fitted function). In
addition, frequency of oscillation, phase shift of the modulation, and width
of center and first satellite peak, respectively, were determined using
parameters of the fitted function.
To visualize the development of oscillatory patterning and response synchronization over time, a sliding window analysis was performed by moving a short analysis window (200 ms) in successive steps (50 ms) over the responses. The correlograms obtained for each of those windows are plotted in a two-dimensional graph, where the y-axis denotes the time shift of the correlation and the x-axis denotes the time course of the responses. The amplitudes of the correlogram peaks are displayed with a color code (see Fig. 4).
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The LFPs were analyzed with respect to their spectral composition and their
correlation with the MUA. The relative power of the LFP during baseline
activity and during response epochs was assessed by calculating normalized
power spectra for the respective time intervals. Bins from 25–48 Hz were
summed up to obtain a measure for the relative power in the -frequency
band. As a measure of synchronization between MUA and LFP, the spike-field
coherence (SFC) (Fries et al.
1997) was computed. First, spike-triggered averages (STAs) were
computed by averaging the LFPs within an analysis window of ±128 ms
centered on each trigger spike. Second, spike-triggered power spectra (STPs)
were obtained by averaging of the power spectra of the LFP segments used for
the computation of the STAs. Finally, the SFC was obtained as the ratio of the
power spectrum of the STA over the STP. The SFC is unitless and ranges between
zero for lack of phase synchronization and one for total phase
synchronization. As a measure for SFC in the -frequency range, the
values from 25–48 Hz were summed up.
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RESULTS |
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Data from a total of 206 recording sites were analyzed for the presence of
oscillatory responses. At the majority of sites, oscillations were readily
evoked by visual stimuli and were easily detectable in both MUA and LFPs of
individual trials by visual inspection. As illustrated in
Fig. 1, the spikes of the MUA
are clustered in bursts that recur at -frequencies. A typical
observation is that the spikes are phase-locked to the negative peaks of the
LFP oscillations. Our data sample comprised a total of 159 pairs of MUA
recordings that were used for cross-correlation analysis. As shown in
Fig. 2, -oscillations at
individual recording sites could be highly synchronous, yielding a strongly
modulated cross-correlogram. Computation of shift-predictors revealed that
neither the oscillations nor the synchronized spikes were phase-locked to the
visual stimuli, as indicated by the absence of modulation in the
shift-predictor correlograms (Fig.
2b).
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Dynamic variability of correlation patterns
Both the strength and the frequency of oscillatory modulation and the degree of synchronization between different recording sites displayed noticeable inter-trial variability. Typically, 10 trials with identical stimuli were recorded in a time window of 60–90 min (randomly interleaved with trials where other stimuli were shown, cf. METHODS). In a number of cases, the modulation of cross-correlograms was investigated on a trial-by-trial basis. An example for such a single-trial analysis is illustrated in Fig. 3. In this case, most of the trials displayed significant synchronization, accompanied by oscillatory modulation with slightly varying frequency. Four of the trials showed either a flat correlogram or only a center peak, indicating nonoscillatory synchronization. The neuronal firing rates also showed some variation across trials but there was no obvious relationship between activation levels and the degree of temporal patterning in the responses (Fig. 3, c and d), as revealed by computing the correlation between the firing rate and the other parameters. These correlation coefficients were 0.04 for oscillation frequency, 0.01 for oscillation strength, 0.16 for synchronization strength, and 0.0002 for the absolute value of the phase lag, respectively.
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Sliding-window analysis
Using sliding-windows for computation of average cross-correlograms (cf.
METHODS), we investigated the oscillatory patterning in multi-unit
activity and the temporal correlation between pairs of MUAs along the
stimulation epochs (Fig. 4).
This analysis typically showed that oscillatory patterning and response
synchronization had a rapid onset and lasted throughout the response epochs.
In many cases, oscillation frequency decreased during sustained responses. The
highest oscillation frequency typically occurred immediately at response
onset, even if the maximum firing rates had not yet been achieved. Averaged
across our entire data sample, however, this decrease in frequency was not
significant, and oscillation frequencies were always within the
-band.
Analysis of local field potentials
LFPs consistently revealed the same temporal patterning as the unit
activity. For all stimulus conditions, power spectra exhibited an oscillatory
modulation at approximately 40 Hz and significant increases in power in the
-band during response periods as compared with baseline activity
(P < 0.0001, Wilcoxon signed-rank test)
(Fig. 5a). The
magnitude of the stimulus-induced increase in power at -frequencies was
maximal in response to bars and declined significantly with the coherence of
the random dot stimuli (P < 0.005 for random dot patterns without
vs. with 30% visual noise, and P < 0.0001 for random dot patterns
without vs. with 70% visual noise, Wilcoxon signed-rank test)
(Fig. 5b). We computed
STAs of the LFPs and their corresponding power spectra. For all tested
stimulus conditions, an oscillatory modulation in the -frequency range
was observed. However, no significant differences occurred in the SFCs for the
different stimulus conditions. SFC at -frequencies ranged between 0.01
and 0.20 and was 0.09 ± 0.01 for bars, 0.08 ± 0.01 for gratings,
0.08 ± 0.01 random dot patterns, and 0.08 ± 0.01 and 0.08
± 0.01 for random dot patterns with 30 and 70% noise, respectively
(mean ± SE).
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Quantitative analysis of temporal patterning in unit activity
Oscillations at -frequencies were readily induced with all types of
stimuli used, provided that the stimuli had yielded sufficient activation of
the neurons to permit correlation analysis. Oscillatory patterning was
comparable for randomly interleaved responses to bars, gratings, and random
dot patterns. Although synchrony and oscillations were readily observed if
random dot patterns contained visual noise, the temporal precision of spike
synchronization was reduced with this stimulus type.
The collective incidence of oscillations and synchrony for all stimuli used
is illustrated in Fig. 6. A
significant oscillatory modulation was observed at 126 recording sites (61%,
n = 206) (Fig.
6a). In none of the cases was the oscillatory modulation
phase-locked to visual stimulus onset. Cross-correlation analysis showed that
129 pairs of recording sites (81%, n = 159) exhibited significant
response synchronization (Fig.
6b). Shift-predictor controls did not show significant
center peaks. In all cases, synchrony was stimulus induced and not present
during spontaneous activity. The incidence of oscillations in
cross-correlograms was comparable to that found for auto-correlograms. An
oscillatory modulation in the -frequency range was observed in 67%
(n = 106) of the synchronized responses
(Fig. 6c). In most
cases (65%, n = 104) synchronization occurred together with
-oscillations; in 25 cases (16%) synchronization was not accompanied by
-oscillations and in two cases (1%) -oscillations occurred
without synchronization between sites (Fig.
6d).
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Figure 7 summarizes the
statistical analysis of the firing rates, oscillation frequencies, center peak
width, oscillation strength, and strength of synchrony for the different
stimulus types. The indices for oscillations and synchrony were extracted from
the Gabor functions fitted to the auto- and cross-correlograms. Firing rates
did not differ significantly for the various stimulus classes
(Fig. 7a). Oscillation
frequencies were most common in the range of 35–40 Hz and similar across
all stimuli (range 25–50 Hz) (Fig.
7b). These oscillations are of intrinsic origin since
there was no evidence for phase-locking to stimulus transients and since
oscillation frequencies were well below the frame rate (60Hz) of the stimulus
projector. Synchronization of responses to coherent stimuli consistently had
millisecond precision, as shown by the analysis of the center peak width in
the cross-correlograms. For single bars and gratings, peak width was typically
in the range of 7 to 14 ms (Fig.
7c). However, the width of the center peaks increased
with visual noise, yielding a significant difference between random dot
patterns without noise and patterns with 70% visual noise (P <
0.005, Wilcoxon signed-rank test). Both oscillation and synchronization
strength (Fig. 7, d and
e) were maximal for gratings. Across animals (mice with
10 different pairs of analyzed recording sites), no significant
differences were observed in oscillation strength, synchronization strength,
and center peak width.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONDISCLOSURESACKNOWLEDGMENTSREFERENCES |
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-oscillations and synchronize their firing in response to
visual stimulation. Furthermore, we demonstrate the feasibility of correlation
analysis of visually evoked unit activity recorded in vivo in the mouse and
provide a full characterization of the features of stimulus-induced
-oscillations and neuronal synchronization in this species. Oscillations and synchrony are robust phenomena in the mouse
Neurons in the visual cortex of mice display stimulus-induced
-oscillations and neuronal synchronization that can be readily evoked
using standard stimuli. In most cases light responsive neurons exhibited
stimulus-evoked oscillatory firing in the -frequency range, and the
majority of analyzed co-activated multi-unit pairs showed response
synchronization with millisecond precision. Similar parameters of
stimulus-induced oscillatory patterning and spike time coordination were
detected in all analyzed mice and were present throughout the duration of the
respective experiments. This demonstrates that oscillatory patterning and
synchronization of responses represent robust phenomena in the primary visual
cortex of mice. The relative ease of eliciting -frequency oscillations
and response synchronization might be related to the fact that neurons in the
primary visual cortex of the mouse have large receptive fields and are less
selective (Drager 1975;
Metin et al. 1988;
Wagor et al. 1980) than
neurons in cat or monkey primary visual cortex (Hubel and Wiesel
1962,
1968) regarding stimulus
properties such as orientation and direction of movement. Therefore it becomes
more likely to encounter neurons with similar preferences, yielding a
relatively high incidence of spike time coordination locally and between pairs
of electrodes. Nevertheless, correlation patterns in primary visual cortex of
the mouse are dependent on stimulus conditions.
Rapid and reliable assessment of correlation patterns
The little time and effort needed to complete the in vivo preparation in
the mouse as compared with similar experiments in cats allows for the
assessment of the respective parameters of oscillatory patterning and
synchrony at multiple sites in large numbers of animals, which is a basic
requirement when probing the effect of a gene mutation on these parameters. In
addition, it is important to apply standard methods for the independent
evaluation of synchronization and oscillatory patterning to obtain robust
quantitative values for statistical assessment. To this end, we employed a
method devised by König which involves fitting Gabor functions to the
correlograms, the details of which have been discussed elsewhere
(König 1994).
Furthermore, the collected data in the study compare well across individual
experimental animals, which constitutes an additional requirement for rapid
and reliable screening of changes of temporal patterning in genetically
modified animals. Taken together, it seems feasible to compare correlation
patterns of visually evoked activity among mutant and wild-type mice.
Similarities to cat and monkey
Numerous physiological studies have addressed the characterization of
patterns of coordination in spike timing among cortical neurons (reviewed in
Engel et al. 2001;
Singer 1999;
Singer and Gray 1995). The
present results of temporal firing behavior from mouse primary visual cortex
neurons are in good agreement with data obtained in related studies in awake
(Fries et al. 1997;
Gray and Viana Di Prisco 1997)
and anesthetized cats (Eckhorn et al.
1988; Gray and Singer
1989; Gray et al.
1989; Steriade et al.
1996) and monkeys (Frien et
al. 1994; Kreiter and Singer
1996; Maldonado et al.
2000). Correlation patterns in the mouse visual cortex are as
precise as in cats, and as in cats (Engel
et al. 2000), the precision of temporal coincidences reflects
stimulus coherence. With decreasing coherence of motion in random dot
patterns, the width of the center peak increased. The other parameters
obtained from the fitted Gabor functions such as the synchronization strength,
oscillation frequency, oscillation strength, and phase lag agree well with the
corresponding values in cat and monkey visual cortex
(Eckhorn et al. 1988;
Engel et al. 1990;
Friedman-Hill et al. 2000;
Frien et al. 1994;
Gray and Singer 1989;
Gray et al. 1989;
Kreiter and Singer 1996;
Maldonado et al. 2000). The
same was true for the time course of the correlation patterns. Oscillations
and synchronization had a rapid onset, were present throughout the response
period (Gray et al. 1992), and
showed a progressive decay in frequency
(Castelo-Branco et al. 1998).
The correlation patterns observed in mice also exhibit some degree of
variation from one trial to the next, which is a typical feature also in
anesthetized and awake preparations of higher mammals
(Engel et al. 1990).
However, we also found two noteworthy differences of the correlation
patterns in the mouse as compared with those in the cat. One difference
concerns the frequency of the evoked oscillations. In mouse visual cortex we
did not measure frequencies higher than 55 Hz and in most cases the
oscillation frequency was below 50 Hz. These values are lower than those
reported for oscillation frequencies in the anesthetized
(Gray and Singer 1989) and
awake cat (Fries et al. 1997;
Gray and Viana Di Prisco
1997), where values 
70 Hz are frequently observed. Moreover,
neurons in mouse primary visual cortex did not display the high-frequency
oscillations of retinal origin
(Castelo-Branco et al. 1998;
Neuenschwander and Singer
1996), suggesting either a lack of retinal oscillations or an
inability of the intra-cortical circuitry to follow such high frequencies. A
second difference concerns the incidence of -frequency oscillations and
of response synchronization. In mice, recording sites exhibiting oscillatory
firing and/or synchronized firing were encountered more frequently than in the
cat (Engel et al. 1990;
Gray and Singer 1989;
Gray et al. 1989). The higher
incidence of correlated firing probably relates to the lower degree of
functional segregation within mouse visual cortex, as evident from rather
uniform response characteristics and the apparent lack of columnar clustering,
consistent with findings in rat primary visual cortex
(Girman et al. 1999).
In conclusion, the results presented here confirm the notion that
stimulus-induced -oscillations and neuronal synchronization are a
general phenomenon. In mice, these correlation patterns exhibit the hallmarks
reported for other species. Therefore the mouse can serve as a model for
testing hypotheses concerning both the mechanisms and the functional role of
stimulus-induced -oscillations and neuronal synchronization.
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DISCLOSURES |
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ACKNOWLEDGMENTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONDISCLOSURESACKNOWLEDGMENTSREFERENCES |
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FOOTNOTES |
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Address for reprint requests: G. Nase, Abt. Neurophysiologie, Max-Planck-Institut für Hirnforschung, Deutschordenstr. 46, 60528 Frankfurt, Germany (E-mail: nase{at}mpih-frankfurt.mpg.de).
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