Shimaoka et al. Nonlinear Biomedical Physics 2010, 4(Suppl 1):S7
http://www.nonlinearbiomedphys.com/content/4/S1/S7

PROCEEDINGSOpen AccessTransient process of cortical activity during
Necker cube perception: from local clusters to
global synchrony
Daisuke Shimaoka1,2*, Keiichi Kitajo2,3, Kunihiko Kaneko1,4, Yoko Yamaguchi2
From Consciousness and its Measures: Joint Workshop for COST Actions NeuroMath and Consciousness
Limassol, Cyprus. 29 November – 1 December 2009
Abstract
Background: It has been discussed that neural phase-synchrony across distant cortical areas (or global phasesynchrony) was correlated with various aspects of consciousness. The generating process of the synchrony,
however, remains largely unknown. As a first step, we investigate transient process of global phase-synchrony,
focusing on phase-synchronized clusters. We hypothesize that the phase-synchronized clusters are dynamicallyorganized before global synchrony and clustering patterns depend on perceptual conditions.
Methods: In an EEG study, Kitajo reported that phase-synchrony across distant cortical areas was selectively
enhanced by top-down attention around 4 Hz in Necker cube perception. Here, we further analyzed the phasesynchronized clusters using hierarchical clustering which sequentially binds up the nearest electrodes based on
similarity of phase locking between the cortical signals. First, we classified dominant components of the phasesynchronized clusters over time. We then investigated how the phase-synchronized clusters change with time,
focusing on their size and spatial structure.
Results: Phase-locked clusters organized a stable spatial pattern common to the perceptual conditions. In addition,
the phase-locked clusters were modulated transiently depending on the perceptual conditions and the time fromthe perceptual switch. When top-down attention succeeded in switching perception as subjects intended,independent clusters at frontal and occipital areas grew to connect with each other around the time of theperceptual switch. However, the clusters in the occipital and left parietal areas remained divided when top-down
attention failed in switching perception. When no primary biases exist, the cluster in the occipital area grew to its
maximum at the time of the perceptual switch within the occipital area.
Conclusions: Our study confirmed the existence of stable phase-synchronized clusters. Furthermore, these clusters
were transiently connected with each other. The connecting pattern depended on subjects’ internal states. These
results suggest that subjects’ attentional states are associated with distinct spatio-temporal patterns of the phaselocked clusters.

Background
It has been discussed that phase-synchrony across distant cortical areas (or global phase-synchrony) was correlated with various aspects of consciousness [1-3].
However, it remains largely unknown what function
such synchronous activities play in conscious processing.
* Correspondence: shimaoka@complex.c.u-tokyo.ac.jp
1
Department of Basic Science, Graduate School of Arts and Sciences, The
University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan

Recently, some researchers postulate that synchronous
activity is not sufficient for consciousness, but assists
enhancing a nascent assembly of neurons in its competition with other assemblies [1,4-6]. To clarify the role oftransient global-phase-synchrony, it is crucial to reveal
not only synchronous phenomena, but also the dynamical mechanism underlying the phenomena.
As a first step in this clarification, we focus on characterizing the transient process of formation of the global© 2010 Shimaoka et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.

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phase-synchrony. One possibility to form a global synchrony is growth of one small “core”. Another possibility
could be the merging of coexisting clusters. These processes may depend on perceptual states such as consciousness and attention that accompany the global
synchrony. As an example to investigate the transient
process towards the global synchrony, we choose to analyze the global phase-synchrony around 4 Hz in Necker
cube perception. In this system, phase-synchronyof electroencephalography (EEG) signals across distantcortical areas was selectively enhanced by top-down
attention [7].
In this report, we analyze the spatio-temporal
dynamics of phase-synchronized (or phase-locked) clusters using a hierarchical clustering algorithm. Hierarchical clustering binds cortical areas by linking pairs ofcortical areas that are close in phase. With this algorithm, we first extract the dominant component of theclusters over time. We subsequently investigate temporal modulation of the clusters, focusing on their sizeand spatial structure. We thereby characterize the transient process of the attention-enhanced global phasesynchrony.

Methods
Subjects

Sixteen healthy human subjects with normal or corrected-to-normal vision (mean age: 24.7 years; SD: 4.7
years) participated in the study. The study was approvedby the RIKEN research ethics committee, and theexperiment was undertaken with written informed
consent.
Experimental task

The subjects were presented with the Necker cube
(width: 4.22°) on a black background at the centre ofa 19 inch CRT monitor (Figure 1(a)). They were
instructed to maintain fixation at a centre fixation crosson the monitor throughout each 180 s block and to
avoid making eye movements and eye blinks. When one
looks at the Necker cube, his/her perception switches
between percept A and B (Figure 1(a)). During the 180 s
period, the subjects reported switches in their perception by pressing different keys. In addition, they were
instructed to follow one of the four perceptual
conditions:(1) Passive view condition (Neutral condition), in
which they looked at the cube passively.
(2) Percept A biasing condition (Top down biasing
condition 1), in which they tried to maintain percept A
as long as possible.
(3) Percept B biasing condition (Top down biasing
condition 2), in which they tried to maintain percept B
as long as possible.

Figure 1 Visual stimulus and EEG recording sites(4) Self-paced key pressing condition (Control condition), in which the cube was not presented and subjects
pressed the keys at their own paces.
In addition to the above conditions, we set the baseline condition (referred to as the Resting state condition) in which subjects looked at the centre cross on theblank screen without pressing keys.
In this paper, we will show the results for the switchesfrom Percept B to A. These perceptual switches areShimaoka et al. Nonlinear Biomedical Physics 2010, 4(Suppl 1):S7
http://www.nonlinearbiomedphys.com/content/4/S1/S7classified into the following 3 cases. In the “desired”
switch, they try to perceive A, and their percept switches
to A. In the “undesired” switch, their percept switches toA, even though they try to continue perceiving B. In theneutral switch, they have no primary biases before theswitch.
Effects of top-down attention assessed withbehavioural data

Behavioural effects of top-down attention were assessedwith selective modulation of dominance durations. In
condition (2), the dominance duration of percept A(desired percept) was significantly longer (Mann-Whitney U test, p < 5.00×10-199) than that of percept B (undesired percept). Likewise in condition (3), the dominanceduration of percept B (desired perception) was significantly longer (Mann-Whitney U test, p < 5.00×10-125 )than that of percept A (undesired perception). These
results were consistent with a previous study [8]. We
thereby confirmed that the subjects actually viewed the
cube with top-down attention in condition (2) and (3).
Electroencephalographic recordings

Cortical activity was recorded using a 62 ch EEG (Figure1(b)). Electrooculogram (EOG) was simultaneously
recorded at the position 1 cm from the outer canthi ofboth eyes and above and below the left eye. EEG andEOG were amplified with a gain of 500 (Neuroscan, EL
Paso, TX), band-pass filtered between 0.1 and 100 Hz,
and digitalized at 500 Hz. Scalp voltages were referenced
to a linked earlobe reference. Electrode impedanceswere kept 5 kΩ. One EEG epoch was defined as a period between 1.5 s before and 1.5 s after a key press thatreported a perceptual switch. Epochs with artefacts
caused by blinks or eye movements or amplifier saturation were detected using an amplitude criterion (±100μV) and excluded from further analysis. EEG epochs
were extracted in which perception of the rivalling views
persisted for 1 s or longer both before and after the perceptual switching. A total of 398 epochs for each perceptual condition were obtained from 16 subjects. To
reduce the effect of volume conduction, EEG scalp current density [9] was calculated from the raw signals. For
a visualization of topographic maps, EEGLAB Matlab
toolbox was used [10].
Data analysis
Detection of phase-locking

Scalp current density data were band-pass filteredaround 4 Hz (3.6 – 4.4 Hz). This frequency was chosenbecause average phase-locking across recording electrodes was higher at this frequency than at other frequencies [7]. The instantaneous phase was extracted fromthe analytic signal calculated with the Hilbert transform.

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Degrees of phase locking between signals recorded fromdifferent electrodes were measured as Phase Locking
Value (PLV) [11]. PLV quantifies how consistent thephase relationships between the two signals are acrossepochs at a given time. PLV between signals from electrode i and j at time t is given by:1
PLVij (t ) =NN∑ exp i ( (t , n) −  (t , n)) ,
i

j

n =1

where N (= 398 for each condition) is the number of
total epochs and j(t, n) represents the instantaneous
phase at time t in the n-th epoch. PLV ranges from 0(random phase difference or no phase locking) to 1
(constant phase difference or perfect phase-locking).
Relative change of phase-locking

To extract the relative change of phase-locking from thebaseline condition, PLV was normalized with the mean
and the standard deviation (over time) of PLV of thebaseline condition [4,12] as:(

PLVzij (t ) = PLVij (t ) − PLVijrest ,mean) PLVijrest ,SD .

Since PLVz measures the difference from baseline
PLV, it is less sensitive to synchrony arising via volume
conduction from sources.
Classification of phase-locked clusters

Using PLV or PLVz, we classified cortical areas into
phase-locked clusters. To obtain such clusters, weapplied the hierarchical clustering to the PLV and PLVz
data. The hierarchical clustering sequentially binds up
the nearest electrodes based on the “distance” or similarity between cortical signals recorded with electrode iand j at time t defined below. For clustering with PLV,
distance was defined as
1 − PLVij (t ),so that all distance scores are larger than 0. For theclustering with PLVz, negative PLVz scores were reset
to 0 before applying the clustering algorithm, because
our interest is in significantly enhanced phase-locking.
Distance between cortical signals recorded with electrode i and j was defined as
max

i , j ,cond ,t( PLVzij,cond(t)) − PLVzij,cond(t),where PLVz for a given electrode pair and condition
at time t was subtracted from the maximum score of
PLVz across electrode pairs, conditions and time. Notethat for determining the distance, the geometry of electrodes in the topographical map did not factor into theShimaoka et al. Nonlinear Biomedical Physics 2010, 4(Suppl 1):S7
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Figure 2 Sample dendrogram and topographical maps. Left: an example of a dendrogram as a result of the hierarchical clustering. The xaxis indicates EEG recording electrode sites. The y-axis represents the “distance” between cortical areas. The distance indicates how strongly
signals from distant cortical areas are phase-locked (see Methods for details). Note that the geometry of electrodes in the topographical map did
not factor into the calculation of the distance. Right: each topographical map is obtained from a corresponding clustering threshold (designatedas a red dotted line) in the left dendrogram. Note that the colour itself does not have any meaning. Electrodes marked with “x” indicate clusters
that have only one node.

calculation in either PLV or PLVz clustering. Additionally, the distance between groups was defined as theminimum distance of all the pair of electrodes between
the groups (nearest neighbour). The result of the binding process is expressed as a hierarchical tree or dendrogram (Figure 2, left). Each clustering threshold gives
corresponding phase-locked clusters (Figure 2, right) bypruning branches off the bottom of the dendrogram,and assigning all the objects below each cut to a single
cluster. Clustering obtained by a higher threshold
regime gives weakly phase-locked clusters.

Results
The stable component of phase-locked clusters
throughout the test time

We investigated phase-locked clusters from time-averaged PLV in order to elucidate the spatial structure ofphase-locked clusters. The clustering results are shownin Figure 3 (left) as dendrograms. In the resting state
condition, rigid clusters were organized in occipital andfrontal areas. These features were also observed in thedendrograms of the other perceptual conditions. Thissimilarity suggests the existence of phase-locked clusters
conserved across the perceptual conditions.
To estimate the similarity of the dendrograms, we calculated the Euclid distance between the PLV matrix ofthe resting state and those of the other perceptual conditions. Smaller Euclid distance indicates the similarityof the matrices and therefore the dendrograms obtained

from them. In Figure 4, these distances are compared tothe distance between the PLV matrix of the resting state
and the surrogate matrix of the resting state. The surrogate PLV matrices were generated by randomly shuffling
the EEG signals across epochs and calculating PLVs forthis shuffled data as for the original data. As the figure
shows, the former distances were far shorter than thelatter distances. This result confirms the existence of
intrinsic phase locking pattern between signals at distantcortical areas.
To evaluate the significance of phase-locking as awhole pattern of the cortical network, we calculated the
Euclid distance between the surrogate matrix and thecorresponding original PLV matrix. The resulting distances were: 0.1039±0.7858×10 -3 (desired), 0.1010±0.8643×10-3 (undesired), 0.1027±0.7363×10-3 (neutral),
0.0995±0.7852×10 -3 (self-paced key press), 0.0936±0.7561×10 -3 (rest) (mean ± 3 SD), all of which wereclearly larger than 0. These results indicate that the
intrinsic phase-locking pattern is significant comparedto the background fluctuation.
In Figure 3 (right), we showed the topographical mapsof the phase-locked clusters obtained with the clustering
threshold of 0.48. Note that different colours in thesefigures indicate different clusters but the identity of thecolours does not have any meaning. A common featureacross the conditions is that the clusters are divided into
occipital, parietal and frontal areas. A similar clustering
pattern was observed for the range of the thresholdShimaoka et al. Nonlinear Biomedical Physics 2010, 4(Suppl 1):S7
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Figure 3 Time-averaged dendrograms and clusters on a topographical map when the clustering threshold is 0.48 Clustering results withthe time-averaged PLV matrix from 750 ms before key press to 750 ms afterwards. In the dendrograms on the left, the y-axis indicates “distance”
between electrodes defined as 1-PLV. The topographical maps on the right are obtained when the clustering threshold is set at 0.48 in thedendrograms on the left. PLV links that are greater than 0.52 are superimposed as gray lines for reference. In (a), the colour of the electrodes inthe dendrograms on the left corresponds to the colour of the phase-locked cluster on the right. For comparison among conditions, the same
cortical regions are kept roughly the same colours.

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Figure 4 Matrix distances between the resting state andperceptual conditions The y-axis indicates the Euclid distance of
the PLV matrix of the perceptual conditions to that of the restingstate condition. The horizontal black line is the distance of thesurrogate resting state condition to the original resting state
condition. To obtain the surrogate PLV of the resting state, epochs
were randomly chosen for each recording electrode. For each of
the 100 surrogate PLVs, the distance to the original PLV of theresting state was obtained. Gray lines represent mean ± 3 SDfrom 0.44 to 0.52 (Figure 3(a), right). This indicates thatthe clustering pattern is not sensitive to the clustering
threshold. We also confirmed that the clustering pattern
was stable over time. These observations suggest that
this pattern is stable across time and conditions. The
clustering pattern is comparable to structural connectivity obtained from the diffusion spectrum imaging (Figure 6a in [13]). Although the clustering algorithm
adopted in this study is not exactly the same as ours,some features are common to the modules of structuralconnectivity and the clusters of phase-locking (see Discussion for details).
Transient change of phase-locked cluster

In the previous section, we observed that phase-locked
clusters have a common stable structure across the conditions. In this section, we investigate the transientchange of phase-locking from the stable structure. For
this purpose, we normalize PLV relative to the timeaveraged PLV in the resting state condition (called
PLVz. See Methods for details.) By applying the hierarchical clustering to the PLVz, we can extract the transient component of the phase-locked activity that runson the condition-independent clusters observed in theprevious section.
Temporal evolutions of the cluster size are displayedin Figure 5, in which clustering thresholds are 7.0, 8.0,
and 9.0 (expressed in units of PLVz). In the desired
switch at each threshold, the cluster gradually grew to
its maximum around the key press time (0 ms). The

Figure 5 Time course of maximum cluster size for different
clustering thresholds Time courses of phase-locked clusters
obtained from the PLVz matrix. Red, green and blue lines represent
the clustering result when the clustering threshold = 7.0, 8.0, and9.0, respectively in PLVz units. The y-axis indicates the maximum
cluster size at each time. Here, the cluster size is defined as thenumber of recording electrodes that belong to the identical cluster.
Horizontal lines above each figure represent the periods during
which the maximum cluster size is significantly larger than that in
the self-paced key press condition.

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same tendency was seen in the neutral switch. In theundesired switch, cluster size was maximized at 400 ms.
Thus, strength and timing of the modulation of thephase-locked clusters depended on perceptual conditions.
To check whether these changes in cluster size were
due to the evoked activity by the key press, we compared
the cluster size between a perceptual condition (desired,
undesired, neutral) and the self-paced key press condition
by applying a permutation test to the time series dataof maximal cluster size. We first set time windows of
100 ms and did a random partition to the combined data
set of the two conditions within the window (we confirmed that the result of the test was not sensitive to thewindow size). On this random partition, the average ofthe maximum cluster size was calculated as a test static.
We repeated these procedures for 50000 times andobtained a histogram of the test static. From the histogram and the test static that was actually observed, ap-value was obtained. The total significance level(p < 0.05) was controlled by the number of time windows(i.e. 15 windows). The significant periods (p < 0.05 in
total, p < 0.003 for each time window) are displayed in
Figure 5 as horizontal lines. Within these periods, it isconsidered that the changes in the maximum cluster size
were not due to the key press, but to perceptual changes.
We alternatively performed the permutation test between

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a perceptual condition and the self-paced key press condition, confirming that the null hypothesis was rejected
for even wider periods.
We subsequently investigated how the modulation of
the cluster depends on perceptual conditions from thetopographic aspects. Figure 6 shows the spatio-temporal
patterns of the clusters of different conditions when theclustering threshold is set at 7.0. In the desired switch,
two initially separated clusters at the frontal and occipitalareas connected with each other at 0 ms. This integrationof the clusters accounts for the maximization of cluster
size at the key press time observed in Figure 5(a). In theundesired switch, however, the clusters localized in theoccipital and left parietal areas remained divided, evenwhen the cluster size is maximized (at 400 ms). This
separation pattern is comparable to the time-averaged
clustering pattern (Figure 3, right). Likewise, in the neutral switch, the cluster in the occipital area remained
separated from clusters in the other areas. Although
there existed a small cluster connecting the occipital andparietal areas during -400 to 0 ms, this cluster and theother cluster in the occipital area did not merge. Instead,the cluster in the occipital area grew to its maximum at
0 ms within the occipital area. We observed this difference of the spatial clustering pattern across the perceptual conditions for the range of the threshold from 6.6 toFigure 6 Time course of phase-locked clusters on topographical maps Spatio-temporal patterns of clustering results with PLVz at each time
section. Topographical maps are obtained when the clustering threshold = 7.0 in units of PLVz. Gray lines represent PLVz links that are greaterthan 7.0. Note that the same colour in the same condition indicates the identical cluster.

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Figure 7 Phase-locked clusters at different clustering
thresholds Spatial patterns of clustering results with PLVz at the
key-press time. Results from different clustering thresholds are laidout in line. Note that colour itself does not have any meaning. The
same colour in different threshold conditions does not imply thesame cluster.

7.7 (Figure 7). We thereby conclude that the difference ofcluster size among the conditions as observed in Figure 5
is explained by the difference in spatial organization of
the clusters shown in Figure 6.
Integrating the temporal and spatial characteristics
described above leads to a dynamical nature toward global synchrony, especially in the desired switch. Small
clusters that coexist at the beginning gradually merge
one after another to organize a large cluster around the
key press time. It can be postulated that there would beseveral possible ways to achieve global phase synchrony.
One possibility is growth of only one small core cluster.
Another possibility is merging of coexisting clusters.
The dynamics observed in the desired switch are moreconsistent with the latter view than the former.

Discussion
Previous studies have suggested that frontal-occipitalsynchrony network is enhanced when top-down selective attention is involved in perceptual switch. Our
result on transient modulation of phase-locked clusters
is consistent with this notion. In addition, our result
suggests that processes towards synchrony are also different depending on desired, undesired and neutral
switch. In the top-down attentional conditions (desired
and undesired switch), phase-synchrony in occipital andfrontal areas was independently enhanced before thePage 8 of 10

perceptual switch. In contrast, in the neutral switch,
only occipital areas were enhanced. Subsequently, occipital and frontal areas were transiently connected morestrongly when top-down attention succeeded in switching perception (desired switch) than when top-down
attention did not succeed (undesired switch).
The relationship between functional network and anatomical structure has been investigated in detail [14-17]
while transient changes of this relationship have notbeen yet clarified. However, brain functions can beadaptive faster than anatomical structures so that thenon-stationarity or metastability of functional networks
should be critical in the processing of perception, as
Bullmore and Sporns pointed out in [18]. Our results
suggest that stable functional clusters (Figure 3, right)
are transiently connected more strongly for the desired
switch than for the other conditions. More theoreticaland modelling studies are needed to reveal the dynamical mechanism of the transient change of phase-locking,especially the dynamical clustering.
The dynamical clustering observed in this report may
be related to functional clustering observed in globallycoupled nonlinear systems [19-26]. In these systems, when
multiple identical elements with nonlinear dynamics interact through a mean field or through a random network,
the elements differentiate into some clusters. In each cluster, elements oscillate synchronously although elements indifferent clusters oscillate with different amplitude, frequency, or phase. The idea of this clustering has been
extended to cell differentiation [27,28] and pattern
dynamics in gas-discharge systems [29] as well as inexperiments of electro-chemical oscillations [30-32]. One
feature that may be useful in the context of neuroscienceis its flexibility in making clusters.
In investigating the stable component of the phaselocked clusters around the time of perceptual switch, we
found that the clusters were divided into occipital, parietal, and frontal areas (Figure 3, right). This clustering
pattern is comparable to the modules of structural connectivity reported in [13]. In this study, structural connectivity was estimated with the use of the diffusion
spectrum imaging in which nerve fiber architecture ismapped from the 3D spectra of tissue water diffusion
with magnetic resonance. From the resulting corticalconnection matrix, structural modules were identified
employing spectral graph partitioning [33]. Hagmann etal. reported that optimal modularity was achieved withsix clusters. In particular, four contralaterally matched
modules were localized to the frontal and temporo-parietal areas of a single hemisphere. The two remaining
modules comprised regions of the bilateral medial cortex, one centred on the posterior cingulate cortex andanother centred on the precuneus and pericalcarine cortex. We also applied the same clustering algorithm [33]Shimaoka et al. Nonlinear Biomedical Physics 2010, 4(Suppl 1):S7
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to our data and confirmed that the resulting phaselocked clusters were closer to the structural connectivity
reported in [13] than the cluster obtained from the hierarchical clustering (Figure 3, right). Nevertheless, theseclusters had similar structure to the structural connectivity in that the clusters were divided into occipital,parietal and frontal areas. This result is thereby consistent with the previous knowledge obtained from modelling [34,35] and physiological studies [13-16], which
reported that functional clusters coincide with the
topological community structures. Thus it is natural toexpect that via cortico-cortical synaptic connections[36], phase-locking around 4Hz is mediated.
Other than the hierarchical clustering that we used,
there have been some clustering algorithms, for example, based on network modularity [14,33] and based on
an information-theoretic perspective [37]. In this report,
however, we chose a rather simple algorithm, hierarchical clustering, because we were interested in the degreeof phase-locking rather than network structure. In addition, our method is suitable for observing clusters thathave the same degree of phase-locking over time. In
contrast, the criteria for clustering are not fixed in theother methods.
In this report, we characterized the transient process towards attention-enhanced global synchrony as
dynamical clustering. It would be interesting to investigate whether the dynamical clustering is observed in
the other global synchrony phenomena that are selectively enhanced by perceptual states such as consciousness. We hope our findings will help to reveal
the unique process of global synchrony and unveil the
dynamical mechanism that constitutes conscious
processing.

Conclusions
Through hierarchical clustering, we investigated functional phase-locked clusters during Necker cube perception. The spatial structure of the clusters was stable
across time and conserved across the perceptual conditions. We also observed that these stable clusters weretransiently connected around the time of perceptual
switch. This transient modulation was stronger in thedesired switch than in the other conditions. Theseobservations suggest the characteristic dynamics towards
the attention-modulated global synchrony in whichsmall clusters that coexisted at the beginning graduallymerge one after another to organize a large clusteraround the time of perceptual switch.
List of abbreviations
EEG: electroencephalogram; EOG: electrooculogram; PLV: phase locking
value.

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Acknowledgements
We thank J. Ito for valuable discussion and critical reading of the manuscript,
and F. Kleberg for careful reading of the manuscript.
This article has been published as part of Nonlinear Biomedical Physics
Volume 4 Supplement 1, 2010: Consciousness and its Measures: Joint
Workshop for COST Actions Neuromath and Consciousness. The fullcontents of the supplement are available online at
http://www.nonlinearbiomedphys.com/supplements/4/S1.
Author details
1
Department of Basic Science, Graduate School of Arts and Sciences, The
University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan.
2
RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama 351-0198 Japan.
3
PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi,
Saitama 332-0012, Japan. 4ERATO Complex Systems Biology Project, Japan
Science and Technology Agency, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902,
Japan.
Authors’ contributions
DS performed analysis and drafted the manuscript. KKitajo and YY conceived
and designed the experiments. KKitajo ran the experiments. KKitajo, KKaneko,
and YY helped to draft the manuscript.
Competing interests
The authors declare that they have no competing interests.
Published: 3 June 2010
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doi:10.1186/1753-4631-4-S1-S7
Cite this article as: Shimaoka et al.: Transient process of cortical activity
during Necker cube perception: from local clusters to global synchrony.
Nonlinear Biomedical Physics 2010 4(Suppl 1):S7.

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