Synchrony Dynamics in Monkey V1 PredictSuccess in Visual Detection

Chris van der Togt1, Stiliyan Kalitzin2, Henk Spekreijse1,

Victor A.F. Lamme1,3 and Hans Super1,3

1Vision and Cognition II, The Netherlands Ophthalmic

Research Institute, Meibergdreef 47, 1105BA Amsterdam, The

Netherlands; 2Dutch Epilepsy Clinics Foundation, Medical

Physics Department, Achterweg 5, 2103 SW Heemstede,

The Netherlands and 3Cognitive Neuroscience Group,

Department of Psychology University of Amsterdam

Roeterstraat 15, 1018 WB Amsterdam, The Netherlands

Behavioral measures such as expectancy and attention have beenassociated with the strength of synchronous neural activity. On thisbasis, it is hypothesized that synchronous activity affects our abilityto detect and recognize visual objects. To investigate the role ofsynchronous activity in visual perception, we studied the magnitudeand precision of correlated activity, before and after stimuluspresentation within the visual cortex (V1), in relation to a monkey’sperformance in a figure--ground discrimination task. We show thatduring the period of stimulus presentation a transition in synchro-nized activity occurs that is characterized by a reduction of thecorrelation peak height and width. Before stimulus onset, broadpeak correlations are observed that change towards thin peakcorrelations after stimulus onset, due to a specific decrease of low-frequency components. The magnitude of the transition in corre-lated activity is larger, i.e. a stronger desynchronization occurs,when the animal perceives the stimulus correctly than when theanimal fails to detect the stimulus. These results therefore showthat a transition in synchronous firing is important for the detectionof sensory stimuli. We hypothesize that the transition in synchronyreflects a change from loose and global neuronal interactionstowards a finer temporal and spatial scale of neuronal interactions,and that such a change in neuronal interactions is required forfigure--ground discrimination.

Keywords: attention, correlation, cortex, figure--ground, macaque,wavelet

Introduction

A prominent feature of cortical processing is that neurons may

engage in synchronous firing. Synchronous activity has been

proposed to play a role in many high level processes such as

perceptual organization, sensory-motor binding, attention,

arousal and even consciousness (Eckhorn et al., 1988; Gray

et al., 1989; Riehle et al., 1997; Tallon-Baudry et al., 1999; Gail

et al., 2000, 2004; Steinmetz et al., 2000; Von Stein et al., 2000;

Engel and Singer, 2001; Engel et al., 2001; Fries et al., 2001,

2002; Mima et al., 2001; Varela et al., 2001; Woelbern et al.,

2002). However, the role of neural synchrony remains contro-

versial. In particular, the function of synchrony in perceptual

grouping and scene segmentation has been challenged (Lamme

and Spekreijse, 1998; Shadlen and Movshon, 1999; Bair et al.,

2001; Thiele and Stoner, 2003). An alternative view, not

necessarily incompatible with a role in binding (Singer and

Gray, 1995; Singer, 1999; Varela et al., 2001), is that fast

dynamical changes in synchronous activity occur in relation

to changes in attention or expectancy (Lee, 2003; Super et al.,

2003), which may subsequently affect the manner in which

sensory input is processed. That fast shifts in cortical mode

occur is suggested by human EEG studies that have demon-

strated fast desynchronisation of neural activity in the visual

cortex in response to visual input (Morrell, 1967; Vijn et al.,

1991). Dynamical changes in synchrony are also observed at the

level of spike activity and local field potentials in animal studies

(Eckhorn et al., 1993; Vaadia et al., 1995; Bressler, 1996; Cardoso

de Oliveira et al., 1997). However, the relation between

dynamical changes in correlated spike activity and visual per-

ception has received limited attention (Woelbern et al., 2002).

We studied dynamical changes in correlated activity in the

visual cortex of monkeys that were engaged in a figure--ground

discrimination task. The animals were trained to report the

presence or absence of a textured figure within a homogenous

textured background. Previously, we have shown that the

late responses of V1 neurons segregate figure from back-

ground (Lamme, 1995; Zipser et al., 1996) and that these late

modulated responses predict the behavioral report of the

animal in a figure--ground discrimination task (Super et al.,

2001). Here we show that in such a task correlated activity in

V1 changes over time. Before stimulus onset, broad peak

correlations are observed that change towards thin peak

correlations after stimulus onset. This change in synchronous

activity is not a direct result of visual stimulation or small

eye movements. Instead, it correlates with the behavioral

report of the animal where the transition in correlated activity

is stronger when the animal reports the stimulus correctly than

when it fails to detect the stimulus. We propose that the

dynamical switch in synchronized activity in V1 represent

a change in neuronal interactions, and that this change is

important for the segregation of figure from background and

thus for visual perception.

Materials and Methods

Behavioral TaskMonkeys (Macaca mulatta) were seated in a primate chair 75 cm from

a monitor screen, in a dark room. Stimuli were presented on a 21-inch

monitor driven by a No. 9 GxiTC TIGA graphics board (resolution:

1024 3 768 pixels, refresh: 72.34 Hz, visual angle: 28 3 21�). In each trial,

a red fixation dot (0.2�) popped up within a texture of randomly

oriented line segments filling the whole screen. After the monkey had

fixated for 300 ms (i.e. eye position remained within a 131� fixation

window surrounding the fixation point) the stimulus texture appeared.

Stimulus textures consisted of randomly positioned line segments of

1631 pixels (0.44�) with an average density of five line segments per

square degree, and an orientation of either 45 or 135�. In figure-present

trials, the stimulus could be perceived as a square containing line

segments in one orientation on a background of orthogonal line

segments (Fig. 1). Both orientations were used for both figure and

background, resulting in complementary stimulus pairs (Lamme, 1995).

The position of the ‘figure’ square was randomly chosen out of one of

three possible locations with an eccentricity of 2.7--4.4� from the

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fixation point and had a dimension of 3.033.0�. On catch trials, all line

segments had the same orientation, so no figure was visible.

To obtain a juice reward in figure-present trials, the monkeys had to

report thepresenceof the stimulus (80%of all trials) bymaking a saccadic

eye movement toward the figure. Trials were categorized as ‘Seen’ only

when saccades ended within the region of the figure and within 500 ms

after stimulus onset, otherwise they were considered incorrect. When

themonkeymaintained fixation for 500ms after stimulus onsetwhen the

figurewas nonetheless present (7% of all figure present trials) ormade an

incorrect eye movement (9% of all figure present trials), the trial was

categorized as ‘Not-Seen’ (16% of all figure present trials). To obtain

reward in catch trials (no figure; 20% of all trials), the monkey had to

maintain fixation for 500 ms after stimulus onset. In this manner the

monkey could intentionally report the non-presence of a figure.

Data Recording and AnalysisNeural activity was recorded simultaneously from 16 chronically

implanted platinum-iridium micro wires (Trimel coated, diameter

25 lm, tips exposed between 50 and 150 lm, impedances 100--350 kX,at 1000 Hz). Multiple unit activity (MUA) was obtained through a four-

step filtering process: Amplified 40 000 times, band pass (750--5000 Hz)

filtered, full wave rectified and then low pass ( <200 Hz) filtered. The

resultant signal represents the envelope of the high-frequency (i.e.

spiking) neural activity also known as MUA (Legatt et al., 1980) and

gives comparable results as single-unit activity (Super and Roelfsema,

2004). This signal was then digitized at 400 Hz, stored on disk and

analyzed off-line with routines developed in MATLAB�. Electrodes for

recording were selected from a larger set (~40) that had previously been

implanted in mainly the superficial layers of the opercular region of area

V1 of the monkey visual cortex, within an area <2 cm2 and with an inter-

electrode spacing of ~1 mm. Selection was based on the quality of the

signal and position of the receptive field (for exact receptive fields

position and size of the selected electrodes, see Super et al., 2001).

When the figure covered the receptive fields, ‘figure’ responses were

obtained. In the other two locations, ‘ground’ responses were recorded.

Aggregate receptive field size and position at each electrode was

determined using moving bars. Receptive field size ranged from 0.55

to 1.7� (median 1.0�), and eccentricity from 1.3 to 2.8� in one monkey

and from 3.4 to 5.7� in the other. Direction and orientation selectivity

was moderately expressed by these electrodes (mean ratio ~2.0; seeSuper and Roelfsema, 2004) and the recording sites could typically be

driven from either of the two eyes. Strong ocular dominance, as has been

reported for layer 4C cells, was usually absent (Lamme et al., 1998).

Taking the RF sizes, tuning ratio and ocular dominance into account, we

concluded that these electrodes sampled neural activity over a distance

of ~200 lm. Data was obtained during ~30 recording sessions and

analyzed for an interval extending from 250 ms before stimulus onset to

250 ms after stimulus onset. Responses were sorted and averaged

according to stimulus property and behavioral response.

The activity from an electrode is represented as Sjr(t) for the jth

electrode and rth trial. The averaged response or peri-stimulus time

histogram (PSTH) can be represented as:

Pj ðt Þ[ÆSr

jðt Þær with Ææ representing averaging over all trials r :

The SEMwas calculated from the average variance of the samples within

each window. To analyze the dynamics of the correlations we calculated

a matrix of covariance’s for all combinations of electrodes averaged over

all trials. Shuffle-corrected covariance matrices are represented as:

Jj ;kðt1; t2Þ = ÆSr

jðt1ÞSr

kðt2Þær –Pj ðt1ÞPkðt2Þ

This denotes the averaged (over all trials r) cross product of the

responses from electrode j and k, minus the cross product of the

averaged responses. The cross product of the averaged responses has

been termed the shuffle predictor and is used to reduce common input

due to the stimulus. This equation is also known as the un-normalized

joint peristimulus time histogram (JPSTH) (Aertsen et al., 1989).

The (time-dependent) standard error of the electrode response j is

derived from the auto-covariance matrix of j:

rj ðt Þ =ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiJj ;j ðt1; t2Þ

qfor t1 = t2 = t

This is the square root of the values on the main diagonal of the auto-

covariance matrix. Normalized covariance matrices or normalized

JPSTHs can then be defined as:

Rj ;kðt1; t2Þ =Jj ;kðt1; t2Þ

rj ðt1Þrkðt2Þ

In this equation, division with the cross product of the standard

deviations of the jth and kth electrode is used to normalize the

covariance matrix and obtain a two dimensional cross-correlogram.

To estimate correlation peak area at time t, sample values between –25

and +25 ms lag were summed. Lag is the offset in time from the central

diagonal of the correlation matrix. We define lag as (t1 – t2). Lag = 0 ms

for t1 = t2 = t. Peak width and peak area are further estimated for two

epochs of 100 ms, corresponding to what we define as the pre-stimulus

and late period (see Results). This was done by first averaging the

covariance functions in these epochs, followed by normalization with

the corresponding auto-covariances resulting in a correlation function

for each combination of electrodes. Peak width was then estimated as

the number of samples with a value greater or equal to one-third peak

height. Peak height is defined as the maximum of the correlation

function for these averaged epochs and therefore corresponds in nearly

all cases to the correlation coefficient at lag = 0 ms. To compare peak

heights in different epochs and conditions, the Fisher z-transform was

applied to these correlation coefficients, whereby z-values are obtained

with a normal distribution. Peak area for these two periods was also

estimated between –25 and +25 ms lag. Because the range of values

differed for the two monkeys, we define normalized area. This is simply

obtained by dividing area with the largest value within all combinations

Figure 1. Behavioral task and figure--ground stimulus. (A) Animals fixated a small dot(FP) at the center of a screen on which a texture was presented with randomlyoriented line segments. After 300 ms fixation figure--ground texture was presented. Areward was given when the monkey made a saccade towards the location of the figurewithin 500 ms after stimulus onset (arrow). The earliest saccades appeared at 250 msafter stimulus onset. Data acquisition was done from 250 ms before to 250 ms afterstimulus onset. In 20% of the trials no figure was presented and the monkey wasrewarded if fixation was maintained. (B) The figure--ground stimulus consisting ofa square with line segments in one orientation on a background of line segments with90� difference. Orientations were randomly exchanged between figure and ground, toensure that receptive field stimulation was identical irrespective of stimulus context.

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of electrodes, separately for both monkeys. For example, when

comparing pre-stimulus and late correlation peak area for all combina-

tions of electrodes, the largest value was in the pre-stimulus period. We

then divided all values in both pre-stimulus and late period with this

value. In this manner relative differences are retained and the values for

both monkeys may be plotted over each other and statistically

combined.

Wavelet AnalysisAdditionally we explored the dynamics of the frequency components

between 5 and 150 Hz by estimating the time- and frequency-dependent

phase clustering index (PCI) (Kalitzin et al., 2002) associated with any

pair of electrodes. We define the time--frequency complex amplitudes

F rj ðt ;xÞ by using a set of normalized Gabor filters:

Fr

jðt ;xÞ =

Zdt 9g ðt ; t 9; kÞSr

jðt 9Þ

gðt ; t 9;kÞ = 1ffiffiffiffiffiffi2p

pke

–ðt – t 9Þ2

k2– i

ðt – t 9Þk

where k = 2p/x is the aperture of the corresponding Gabor-filter, which

is directly connected to the filter’s frequency. The univariate PCI

associated with electrode j can then be defined as:

jj ðt ;xÞ =ÆF r

jðt ;xÞær

ÆjF r

jðt ;xÞjær

with Æær, denoting averaging over all trials r. The absolute value

(amplitude) of this complex number is always smaller than 1 and

indicates the degree of consistency between the phases of frequency

components over consecutive trials, for a given frequency and time

within an epoch. The phase of the PCI represents the average phase

among the trials. Similarly, the mutual phase consistency between traces

j and k can be quantified by the complex number:

jjkðt ;xÞ =ÆF r

jðt ;xÞF r

kðt ;xÞær

ÆjF r

jðt ;xÞF r

kðt ;xÞjær

:

Where F rj ðt ;xÞ indicates the complex conjugate of the time--frequency

complex amplitude. High mutual phase consistency can result from

common input, e.g. due to visual stimulation, or it can represent

a ‘genuinely’ independent phase locking between the signals. To

separate these two options, we define the partialized mutual PCI as:

jpart

jkðt ;xÞ = jjkðt ;xÞ –jj ðt ;xÞjkðt ;xÞ:

Partialized PCI amplitudes close to one would indicate phase locking

between the two signals that cannot be explained by the univariate PCIs,

and therefore not evoked by the visual responses on individual electro-

des. Further in this paper we present only the absolute values

(amplitudes) of the PCI quantities without explicit notation.

To evaluate the statistical significance of themeasured PCI amplitudes

we simulated sequences of complex numbers with phases uniformly

distributed from 0 to 360�. Since our PCI amplitudes were obtained by

averaging over different numbers of trials for the Seen and the Not-Seen

condition, we generated distributions of PCI amplitudes for two

sequence lengths. For each sequence length (n = number of trials

within a condition), we generated >10 000 sequences and determined

the corresponding distributions of PCI. We simulated sequences

with constant amplitudes, with normally distributed amplitudes, and se-

quences with ‘flat’ amplitude distributions. As a result, we define the

PCI critical (PCIcr), as the PCI value for a given number of trials (n),

such that the probability of obtaining a PCI value > PCIcr is smaller than

5% in these random sequences. In other words, the PCIcr determines

a confidence level of 5% (P0.05), with significant measurements

corresponding to PCI amplitudes greater than PCIcr. We found

that for n = 400 the PCIcr = 0.08 and for n = 1200 the PCIcr = 0.05

(n = number of trials per animal).

To quantify the difference between the Seen and Not-Seen condition,

we subtracted the absolute PCI amplitudes in the Not-Seen condition

from the PCI amplitudes of the Seen condition. For each point in the

time--frequency plane we thus obtained a distribution of PCI differences

within a population embracing all combinations of electrodes (n = 120).

From these we derived a mean and SEM for each point in the time--

frequency plane and calculated a t-score (=mean/SEM), which indicated

how significant the mean difference was within the population of

channel combinations at each point in the time--frequency plane.

Finally we note that our method differs essentially from that of Lee

(2003), where a measure similar to our PCI was introduced. In the latter

work the complex cross-spectrum is normalized across all electrode

pairs. A low value of their PCI, in such an approach, can be due to

different relative phases between the different electrode pairs. In our

approach, we normalize PCI for each individual pair separately, only

averaging over trials.

Analysis of Eye MovementsEye movements were monitored using scleral search coils with the

double magnetic induction method (Bour et al., 1984). Eye monitor

signals for the x and y directions were digitized at 400 Hz and stored on

disk with the simultaneously recorded neural data. The eye position

signals in the x and y directions were differentiated to obtain vectors

representing instantaneous velocity and direction of eye motion. To

control for fixational eye movements and to estimate their effect on the

correlations we calculated the standard error of the eye position and

measured the incidence of fixational saccades within successive epochs,

before and after stimulus onset. Fixational saccades were detected using

a velocity threshold of 10�/s. To investigate the effects of fixational eye

motion on synchrony, we split the neural data into two groups

containing an equal number of trials. In the first group (High) the stan-

dard error of the eye position of each trial was larger than the median

standard error of all trials and in the second group (Low) the standard

error of each trial was lower than the median standard error of all trials.

We also calculated the strength of correlations between eye velocity and

neural response strength over time. For this purpose, two-dimensional

cross-correlograms with time versus lag on the x-axis and y-axis and

correlation strength on the vertical axis were calculated.

Results

Figure--ground evoked multi-unit responses from the primary

visual cortex are characterized by an initial transient followed

by a late modulated response, commencing 70--100 ms after

stimulus onset (Fig. 2A). Previously, we have reported that the

modulated response is a neural correlate of figure--ground

segregation, where the responses to figure elements are

stronger than the responses to identical ground elements

(also termed contextual modulation and is indicated by the

blue shading in Fig. 2A; see Lamme, 1995; Lamme et al., 1998;

Zipser et al., 1996).

Here we investigated the time course of correlation co-

efficients within two-dimensional (time versus lag) cross-

correlograms (2D-CC) and compared this with the time course

of averaged multi-unit activity responses. This was done for two

monkeys (denoted as T and U), and based on trials within the

‘Seen’ condition. The ‘Seen’ condition represented those trials in

which the monkeys correctly identified the target position. In

the first part of this analysis 2D-CCs were averaged over figure

and ground responses grouped together.

Dynamics of Correlated Activity

Characteristically, a correlation peak centered at 0 ms lag can be

observed during the total period of analysis, i.e. 250 ms before

stimulus until 250 ms after stimulus onset. We define this type of

correlated activity, due to the occurrence of peaks with

a maximum at zero time lag, as synchrony. Correlated activity

was found to reach a maximum before stimulus onset, which

then decreases in height and sinks to a minimum well after

the initial response transient within the late response period

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(Fig. 2B). As a parameter of the magnitude of synchrony we

measured area under the peak between –25 to +25 ms lag for

both monkeys. Consistent with the changes in peak height,

a maximum in peak area was found before stimulus onset in

both animals (T: –55 ± 16 ms; U: –43 ± 17 ms; time relative to

stimulus onset). Following this maximum, the correlations de-

crease and a minimum in average peak area occurs at 164 ± 20

and 124 ± 30 ms after stimulus onset for T and U, respectively.

In addition, the 2D-CCs (Fig. 2B lower panels) indicate that

narrowing of the correlations contribute to the decrease of

area in the late period.

To quantify the differences between the pre-stimulus and late

activity period we averaged auto- and cross-covariance matrices

for all electrode combinations (120 per animal) in two 100 ms

periods. The first period ending at stimulus onset and in-

corporating the pre-stimulus maximum and a second period

centered on the peak minimum after stimulus onset. Due to

differences in the time course of the synchrony the second

period is thus different for both monkeys (T: 115--215 ms; U:

75--175 ms after stimulus onset). Both monkeys display a de-

crease in peak height and peak width in the late (post-) stimulus

period in comparison with the pre-stimulus period (Fig. 3A).

Correlated activity therefore desynchronizes within most elec-

trode combinations. To capture this transition in correlated

activity, the correlation functions were analyzed with respect to

three parameters: peak width, peak height and peak area.

As Figure 3A shows, the correlation functions in both

monkeys indicate the superposition of a thin correlation peak

on a broad correlation peak and it seems that only the broad part

of the correlation functions is modulated. Therefore we used

peak width at one-third peak height as a measure of peak width.

This measure is more appropriate than half peak height to

detect the changes in the lower half of the correlation peaks.

Using this measure, a significant decrease in peak width was

found for both animals (sign test, MATLAB�: T: P < 2.5 3 10–11,

U: P < 2.0 3 10–7). Within a total of 240 correlation peaks, 153

became thinner, 15 became wider, 22 became non-significant

and 50 showed no difference in the late period. Since the

change was restricted to the lower part of the correlation peaks

this suggests a loss of synchrony (desynchronisation) for low-

frequency components only.

We then estimated peak height for all combinations of

electrodes (Fig. 3B), and found that peak height was signifi-

cantly smaller in the late period compared to the pre-stimulus

period (Sign test: T: P < 3.2 3 10–14; U P < 2.0 3 10

–5). To

estimate the significance of the difference between pre-

stimulus and late peak height, we used a z-score obtained by

(Zfpre -- Zfpost)/O(2/n -- 3), where n = number of trials, for each

combination of channels. Zf-values of correlation peak height

were obtained after Fisher z-transform of the correlation

coefficients. These results show that a large proportion of the

channel combinations have a significant (P < 0.05) decrease in

Figure 2. Multiunit activity responses and cross-correlations. (A) Averaged responses for trials in which monkeys T and U correctly detected the location of the figure. Time isrelative to stimulus onset. Response onset in both monkeys occurs at ~40 ms and is followed by figure--ground modulation (blue shading). Thick black lines represent responses tofigure and thin gray lines to ground responses. Dashed line indicates error of the mean. (B) Two-dimensional (time versus lag) cross-correlogram, averaged over all trials andcombinations of electrodes. Color corresponds (see color bar) with height of the correlation coefficient at each (time, lag) point. Note that the scale of the color bar lumps all values[0.045 to emphasize the changes in width. The upper panels show the time course of the correlation coefficient at 0 ms lag (central peak) over the same period (--0.25 to 0.25 swith respect to stimulus onset). Vertical lines indicate time points where maximal (red) and minimal (green) correlation peak heights were estimated. Gray shaded bars with Pre andLate show the periods of data analysis.

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peak height (T: 60/120; U: 34/120). In contrast, only few

channel combinations were found with a significant (P < 0.05)

increase in peak height (T: 4/120; U: 0/120).

Finally, as a measure of the difference between pre-stimulus

and late activity, we estimated area under the peak (Fig. 3C). To

estimate area we summed the averaged correlation functions

between –25 and +25 ms lag and normalized these values to

obtain similar ranges of values in both animals. Normalization

was done by division with the maximum area found within all

combinations of electrodes in the pre-stimulus period (this was

done for both monkeys separately). Relative differences be-

tween periods remain unaffected in this way. A significant

decrease of area from pre-stimulus to late activity can be ob-

served (Signed Rank Test: T: P < 3.5 3 10–20; U: P < 1.3 3 10

–19).

This measure gave the most robust difference between pre-

stimulus and late activity, which is not surprising because this

measure incorporates both height and width changes. This is

corroborated by our spectral analysis, showing that the transi-

tion in correlated activity involves mainly a decrease of power

for frequency components below 40 Hz (Fig. 3D).

Previously we have reported a relation between spatial scale

and synchrony (van der Togt et al., 1998). In that study, we

provide evidence that broad correlation peaks (low-frequency

synchrony) represent common input to a large number of

neurons whereas thin peaks are generated within small assem-

blies of neurons. In the present study, we implanted all the

electrodes within an area of <2 cm2 of V1 and we selected

electrodes that have adjacent or overlapping receptive fields,

which fall within a square of 3� of visual angle. Based on

a cortical magnification factor of 2.5--5.0 mm/degree (Tootell

et al., 1988), we estimate that the majority of the electrode pairs

have an inter electrode distance of <1 cm, approximately half of

the pairs <0.5 cm and the smallest inter electrode distances

~1 mm. The largest transitions generally occurred within

electrode combinations with the largest pre-stimulus peaks.

Assuming that stronger synchrony is observed when distances

between electrodes become smaller (Das and Gilbert, 1999)

then even for nearest electrode pairs a transition in their neural

interactions occurs.

Figure--Ground Responses

Next we analyzed figure and ground responses separately and

compared the 2D-CCs of figure and ground responses within

the pre-stimulus and late period, when figure--ground segrega-

tion is expressed in the firing rate of the recorded neurons

(Fig. 2A). For both the figure and ground conditions a strong

transition in synchrony was found in both animals (Fig. 4A;

shown only for monkey T). Thus, a large decrease in synchrony

from pre-stimulus to late post-stimulus activity occurs irrespec-

tive of the location of the figure.

Figure 3. Differences between pre-stimulus and late correlated activity. (A) Averagedcross-correlation functions from the pre-stimulus and late period. Note that thedifference is mainly due to change within the lower part of the correlation functions.(B) Scatter plots showing the difference between the heights of the central peak in thepre-stimulus period versus the late period. Black dots show combinations of electrodeswith significant changes in peak height, i.e. correlation strength (R). (C) Scatter plot ofpeak area in pre-stimulus period versus late period. Peak area was calculated between�25 and 25 ms lag and normalized by division with the largest area (pre-stimulus)within all combinations of electrodes. (D) Power of averaged and normalized cross-spectral density functions between 6 and 200 Hz. Black lines denote the pre-stimulusspectral power and gray lines the spectral power within the late activity.

Figure 4. Cross-correlations of figure and ground. (A) Time course of the correlationcoefficient of peak area (calculated from �25 and 25 ms lag) for figure and groundtrials separately. (B) Strength of figure--ground responses versus strength of thesynchrony transition (peak area in pre-stimulus period minus peak area in post stimulusperiod, see Fig. 3C). Line is linear regression line.

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Furthermore, there appears to be a correspondence between

the onset of figure--ground modulation (T: 94 ms; U: 62 ms) and

the time of the minimum in the late post-stimulus synchrony

(T: 164 ms; U: 124 ms). To explore the possible relation be-

tween synchrony transition and figure--ground signal, we

measured the correlation strength between the amount of

figure--ground activity and the extent of desynchronization

(change of area under the peak from pre-stimulus to late post-

stimulus period). This correlation was weakly positive for both

animals within all electrode pairs (Fig. 4B; T: r = 0.62, df = 14,

P < 0.005, n = 120; U: r = 0.42, df = 14, P < 0.05, n = 120,

Spearman Rank). This finding suggests that the strength of

contextual modulation depends on the magnitude of desynch-

ronization. To test this further we calculated synchrony in

separate figure and ground trials. In monkey U a significant

difference in correlation peak area between figure and ground

responses was not found (not shown). For monkey T only

a small difference in correlation peak area was found in the late

period (115--215 ms: t-test, P < 4.0 3 10–5) (Fig. 4A). However,

this difference is short lasting and therefore did not reflect the

difference in figure and ground responses found earlier

(Lamme, 1995; Zipser et al., 1996; Lamme et al., 1998).

Eye Movements

A potential concern is that differences in eye position or eye

movements during the pre-stimulus and late post-stimulus

periods can cause synchrony changes. For example, eye motion

may induce common input to neurons leading to an increase in

synchrony even without changes in their level of activity. To

control for differences in fixation behavior we first determined

the distribution of fixational saccades from 200 ms before

stimulus onset to 200 ms after stimulus onset. We divided the

data into four groups (200--100 and 100--0 ms before stimulus

onset, and 0--100 and 100--200 ms after stimulus onset). Micro-

saccades mainly occurred at the start of fixation (200--100 ms

before stimulus onset) and declined to a constant level (Fig. 5B).

This finding was confirmed by comparing the averaged standard

deviations of the eye positions during four periods. Note that

a trial starts after the monkey’s eyes enter the fixation window

and that the stimuli are presented 300 ms after correct fixation.

We also determined the strength of neural synchrony during

these four intervals (Fig. 5C). The neural correlation strength

shows a different distribution where the strongest correlations

are observed 100 ms before stimulus onset and gradually

decrease thereafter (Fig. 5C).

We then divided trials in two groups based on the quality of

fixation; one group with relatively good fixation (Low) and one

with poor fixation (High; see Materials and Methods). Both

saccades and drifting fixational eye movements have been

shown to induce neuronal firing in V1 neurons (Snodderly

et al., 2001). The standard error of the eye position signal

captures both types of eye motion, whereas the velocity

threshold method only captures saccade onset and frequency.

We present statistics on both measures and it can be seen that

the standard error of eye motion is largely reflected in saccade

frequency. In fact in the low motion group the majority of the

trials have no saccades at all, whereas in the high velocity group

the majority of trials contain one or more saccades. On average,

saccade frequency in the low motion group is 0.9 in the high

motion group 2.7, which shows that the number of saccades in

the two groups differed by at least a factor 3. If eye motion is

the main cause for the synchrony transition we should see

a difference in synchrony between these two groups. In both

groups however we found a clear transition in synchrony, i.e.

both groups show a significant difference between the pre-

stimulus period (100--0 ms) and the late period [100--200 ms

after stimulus onset; analysis of variance (ANOVA), P < 0.05]. In

addition, the transitions in correlated activity were not signif-

icantly different between the two groups (Fig. 5C). We also

calculated the correlation between eye velocity and neural

activity. These results show that eye velocity from pre-stimulus

up to 200 ms following stimulus onset was minimally correlated

with neural activity. In fact, no transition is noticeable at any

time lag up to the time the monkey makes a saccade (Fig. 6A,B).

Thus, these results indicate that the desynchronization is not an

effect of small eye movements during fixation.

To investigate whether the synchrony transition is related to

the target saccade towards the figure location, we analyzed the

responses from a delayed figure--ground task where visual

responses are separated from saccade related responses (Super

et al., 2004). Here we analyzed the data from the start of fixation

(= 300 ms before stimulus onset) until the end of the trial. This

analysis shows that also in such a task desynchronization occurs

at the time of stimulus presentation (Fig. 6C, lower panel). The

fact that desynchronization starts around stimulus onset in-

dicates that the transition is not directly related to the target

saccade, which occurs much later (after 1 s). Desynchronized

activity continues at a constant level during the entire delay

period. In this period a large variation in spike rate occurs. This

shows that the transition is not an artifact of the partialization

procedure to remove stimulus induced synchrony, since then

we would expect a decrease of synchrony only to occur just

after response onset, where the highest spike rate levels are

reached. We also correlated the eye velocity with neural

responses during this task (Fig. 6C, upper panel). Note that

the synchrony increase at the end of the trial compares

favorably with the synchrony increase between eye motion

and neural activity, suggesting that here the neural correlations

are related to eye motion. In contrast, although saccades to the

fixation point were aligned with trial onset (300 ms before

stimulus onset) the correlation between eye motion and neural

activity is not comparable to neural--neural correlated activity in

the pre-stimulus period (lower panel). These results corrobo-

rate earlier findings suggesting that the transition in synchrony

is not an effect of small fixational eye movements, and that

saccades to the fixation window also cannot explain our results.

Wavelet Analysis

Overall, during the trial, the correlations become reduced in

size, with significantly thinner peaks at the end of the trial. The

power spectra in Figure 3D, indicate that this change is caused

mainly by a decrease of frequency components below 40 Hz. As

described above, the pre-stimulus correlation functions in both

monkeys seem to be a superposition of a narrow on a broad

correlation peak (Gochin et al., 1991; Nowak et al., 1999). This

suggests different modes of neural interaction in the pre-

stimulus period, one of which (associated with broad peaks,

and low-frequency components) is strongly reduced after the

appearance of the stimulus, while the other (narrow peaks)

seemingly remains constant. To investigate whether differences

in the modulation of frequency components between 5 and

150 Hz occur, we applied wavelet analysis. In this study we use,

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as a measure of coherency of any frequency component, the

phase clustering index (PCI; Kalitzin et al., 2002; see also

Materials and Methods). Without further measures this index

would be greatly dominated by the visual response. Therefore,

to investigate the mutual (between electrodes) PCIs, indepen-

dent of the visual response, the effect of the visual response was

removed by partialization (see methods). This method is

comparable to subtracting the shuffle predictor as was applied

to the two-dimensional cross--correlograms (2D-CC).

A good correspondence with the synchrony transition in the

2D-CCs is observed after applying this method (S in Fig. 7). Pre-

stimulus PCI amplitudes are large over a wide range of

frequency components (5--40 Hz). In one monkey (U) there is

also a relatively strong frequency component around 70 Hz, but

this neural activity is induced by the monitor frequency.

Following stimulus onset a decrease is observed to non-

significant values over the whole frequency range with an

incomplete rebound at intermediate frequencies (20--50 Hz)

before the monkeys start to make eye movements. Thus, similar

to the 2D-CC results, a desynchronization of activity occurs

after the stimulus. Furthermore, it can be observed that

frequencies below 20 Hz show the greatest difference between

pre-stimulus and late activity. These findings agree with the

disappearance of the broad part of the correlation functions in

the late activity period.

Seen versus Not-Seen

On most figure present trials, the monkeys were able to de-

tect the figure (84%, ‘Seen’ trials), but on some instances (16%,

Figure 5. Fixational eye movements. (A) Example of eye traces during fixation. Left panel shows traces of the group (High) with relatively many fixational eye movements and rightof the group (Low) with few fixational eye movements. Numbers 1--4 represent time period of data analysis (see B--E). Time is relative to stimulus onset. (B,C) Distribution ofsaccade frequency (B) and peak area (C) during the four periods indicated in (A). Black bars represent data from the group with many fixational eye movements and gray bars thegroup with few eye movements. Note extraordinarily high frequency of saccades (7/s) in the first bin in (B). This high value is due to the many cases in which one or two smallcorrectional eye shifts follow the main saccade to the fixation window. Overall we find a saccade frequency of 1--3/s in accordance with other studies. This indicates that ourvelocity threshold of 10�/s is sufficiently above the noise of the eye position data (D,E). Distribution of fixational saccades in the Seen (black) and Not-Seen (gray) condition (D) andof the standard deviation of the eye position (E). NS and * signify no significant and a significant (P\ 0.05) difference between conditions within one period, respectively.

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‘Not-Seen’ trials) the figure was not detected (Super et al.,

2001). Since the stimulus was identical in both conditions,

differences in behavioral responses must have been due to some

difference in cortical state, possibly reflecting changes in

attention or expectancy (Fries et al., 2001). We were therefore

interested in whether the synchrony transition varies with the

ability of the monkey to detect the stimulus. This was done by

comparing two dimensional cross-correlation functions (2D-

CCs) for Seen versus Not-Seen trials and, as will be described

below, by differences in the magnitude of the spectral compo-

nents (PCI) for these two-conditions.

Figure 8A,B shows an example of the difference in this

transition for averaged Seen and Not-Seen trials. In the Not-Seen

condition a clear transition is lacking. Since area under the peak

was by far the most robust parameter of the difference between

pre-stimulus and late activity and a good estimate of the total

correlated activity, we analyzed the development of peak area

between –25 and 25 ms delay for Seen and Not-Seen trials. Seen

trials start out with a higher area under the peak than Not-Seen

trials, yet end up with a smaller area under the peak during the

late period (Fig. 9A). For the Not-Seen condition there is

a significant reduction in synchrony in the pre-stimulus period

(Fig. 9B; Student’s t-test; T: P < 5.0 3 10–5; U: P < 1.0 3 10

–14,

n = 120) and a significantly stronger synchrony in the late

period (Fig. 9C; Student’s t-test; T: P < 1.5 3 10–15, U: P = 0.019,

n = 120). The distribution of differences for Seen versusNot-Seenwas skewed in several instances. Although tests for normality did

not show that these distributions significantly differed from

normal distributions, we nevertheless applied a nonparametric

Sign test. A non-significant difference was only found monkey U

in the late period between Seen and Not-Seen. When values

from both animals were combined significant differences were

found for both pre-stimulus (Sign test;P <8.0310–6,n = 240) and

late period (Sign test; P < 3.0 3 10–13, n = 240). These results

indicate significantly higher synchrony in the pre-stimulus

period and lower synchrony in the late poststimulus period for

Seen versus Not-Seen.

Finally, subtracting late peak area from pre-stimulus peak area

yields the transition difference for each combination of electro-

des (Fig. 9D). The transition was stronger for Seen trials

compared with Not-Seen trials. The difference in transition

was highly significant (Sign test: T: P < 3.0 3 10–14, n = 120; U:

P < 3.0 3 10–13, n = 120). Even if we assume that the synchrony

transition is evoked by the stimulus, these results suggest that it

is modulated by attention or expectancy. Note also that our

statistics may underestimate this proposed effect of attention. A

number of electrode combinations that were included in the

analysis have small insignificant correlation peaks in the pre-

stimulus period and show indeterminate differences in the late

period. We have not excluded these cases and they possibly

reflect combinations of electrodes with the largest separations

since correlations between neurons have been shown to

decrease with cortical distance (Das and Gilbert, 1999).

Similar differences are also found in the time--frequency

plane between Seen and Not-Seen trials using the PCI (Fig. 7).

Analysis of the differences between Seen and Not-Seen trials

was done by subtracting Not-Seen from Seen PCI amplitudes for

all channel combinations and all components in the time--

frequency plane (see Materials and Methods). For each compo-

nent in the time--frequency plane a distribution of differences

was thus obtained for all electrode combinations. Figure 7D

shows t-scores for the mean of each distribution in the time--

frequencyplane. In thefigureswe showareas of valueswhere the

most significant differences were found (absolute value >3,

Figure 6. Correlations of fixational eye movements and neural activity. (A) Correlation between eye velocity and neural activity. Note that correlated activity only appears duringperiod when the animal makes a target saccade. Color denotes correlation strength. (B) Time course of the correlation values at a delay (~40 ms lag) when eye motion has thegreatest effect on neural activity. (C) Correlation strengths between eye velocity and neural activity (upper panel), and an average 2D-CC from all combinations recording sites(lower panel) during a delayed figure--ground response task. Color corresponds (see color bar) with height of the correlation coefficient at each (time, lag) point. Time is relative tostimulus onset.

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corresponding to a confidence level with a = 0.005%, for

n = 120). The results of this analysis show that in monkey U

the greatest difference between Seen and Not-Seen trials

occurred immediately after stimulus onset, and is expressed

as a larger decrease in phase consistency around 10 Hz for

Seen trials. In the other monkey (T) the predominant

difference is an enhancement of synchrony for Seen trials

in the pre-stimulus period, for frequency components be-

tween 10 and 20 Hz. Thus, the results show that the main

effects of the difference in synchrony transition between

Seen and Not-Seen condition is found in the low-frequency

components. In both monkeys these differences support

Figure 7. Time/frequency distribution of mutual phase consistency (wavelet analysis). The upper two rows show representative examples, obtained from the same combination ofelectrodes, of the distribution of mutual phase consistency in the Seen condition (S) and Not-Seen (NS) condition. PCI amplitudes are significant above the estimated PCIcr value(see methods). In the Seen condition the PCIcr5 0.05 and for the Not-Seen condition the PCIcr5 0.08. The bottom row (D) presents statistical results for the whole population ofelectrode combinations in both monkey T and U. Average differences between the Seen and Not-Seen condition are displayed as t-scores. The t-scores are positive when Seen is onaverage greater than Not-Seen and negative otherwise. The encircled regions indicate areas in the time--frequency plane where the most significant differences between Seen andNot-Seen were found. Frequency is logarithmically scaled on the y-axis. Time is relative to stimulus onset.

Figure 8. Correlated activity: seen versus not-seen condition. Example for one combination of electrodes, averaged over all trials, showing the difference in the 2D-CC for the Seen(A) and Not-Seen (B) condition. A side and a top view are shown of these correlations. Color indicate correlation strength.

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a synchrony transition with greater magnitude for the Seen

case, although time of occurrence and sign are seemingly

different. Note also that the differences do not seem to be

well associated with stimulus or response onset, which

suggests a role for attention as a cause for these differences.

The differences in correlated responses between Seen and

Not-Seen condition are not likely caused by differences in eye

movements. We measured both the probability of fixational

saccades and standard error of eye position (Fig. 5D,E) and

compared the differences between Seen and Not-Seen trials.

The findings show that fixational eye movements do not

significantly differ between the Seen and the Not-Seen condi-

tion during the pre-stimulus and late periods (ANOVA,

P > 0.05).

Interestingly during the correlation transition, a short en-

hancement of broad peak synchrony (80--100 ms after stimulus

onset) can be observed for Seen trials and not for Not-Seen trials

(Figs 2B, 8 and 9A). To analyze this enhancement, the difference

in area was estimated between this and the preceding window

of 20 ms for all combinations of electrodes. Comparison

between Seen and Not-Seen trials demonstrated a significant

enhancement of broad peak synchrony for Seen trials (Sign test;

T: P < 5.0 3 10–4; U: P < 3.2 3 10

–12). Thus during the longer

decrease of synchrony around stimulus onset, a transient in-

crease in synchrony is observed which may be related to the

detection of the figure.

Discussion

We have studied the dynamical changes in synchronous activity

that occur in the primary visual cortex during figure--ground

discrimination, within 2D-CC and within the time--frequency

plane based on the distribution of mutual phase consistency

(PCI). Our correlation functions are comparable to the JPSTH

developed by Aertsen et al. (1989) to study dynamical changes

in correlated activity. We prefer the term two-dimensional

cross-correlogram because our sampled signals represent a con-

tinuous waveform of the neural firing rate (see Materials and

Methods) and not single spikes, binned into discrete epochs.

With both methods (2D-CC and PCI) we find an increase in

synchronous activity before stimulus onset and a desynchroni-

zation of activity following stimulus onset. Nevertheless, overall

changes in synchrony are better reflected by the 2D-CCs than

the PCI distribution. The latter gives an indication of the

dominant frequency components within dynamical changes of

synchronous activity. Since both increases and decreases of

coherency for different frequency components may occur

during a perceptual task, the impact of changes in synchrony

is better reflected in terms of change in correlation peak area.

The partialization procedure we applied removes stimulus

locked synchrony. Howmuch synchrony is removed depends on

the spike level in the PSTH of both MUA traces. The desynch-

ronization we observe could therefore be an artifact of this

procedure since the spike rate increases greatly after stimulus

onset. A large peak of spike firing occurs in the average PSTH of

monkey T between 50 and 100 ms after stimulus onset, and

around 100ms in the PSTH of monkey U. If partialization has this

affect it should lead to a particularly strong, albeit short

desynchronization at these same time periods. Within the 2D-

CCs this does not seem to be the case. On average maximal

desynchronization occurs later as spike rate decreases. The same

logic can be applied to the PCI. Indeed, here a short, sudden and

complete desynchronization can be seen at these moments in

time. However, beyond these periods the spike rate can even be

lower than in the pre-stimulus period (Fig. 2A), nevertheless PCI

amplitudes remain lower ( <20 Hz) in the late period than in the

pre-stimulus period. This is even more striking in a delayed

response task where synchrony remains at a low level until the

monkey makes a saccade to a visual target. This shows that the

synchrony transient is not an artifact due to partialization.

Figure 9. Pre-stimulus and late activity in the seen and not-seen condition. A. Average changes in peak area over time for Seen (black lines) and Not-Seen (gray lines) conditionwithin all electrode combinations of monkey T and U. Dashed line indicates the 95% confidence limit for the Seen condition. Gray shaded areas indicate the periods of analysis (seeFig. 2). (B,C) Peak area in Seen condition versus peak area in Not-Seen condition for the pre-stimulus (B) and late period (C). (D) Difference between pre-stimulus and late peak area.All values were normalized by division with the largest value in each dataset for T and U separately, so that the relative differences remain unchanged. T and U denote the animal.

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Figure--Ground and Synchrony Transition

Our results are in agreement with previous reports which show

a desynchronization at stimulus onset within area MT/MST

(Cardoso de Oliveira et al., 1997) and in visual areas (EEG, MEG)

in association with a perceptual response (Rodriguez et al.,

1999). Our results show that the transition mainly involves

frequency components below 20 Hz. As a consequence, syn-

chrony evolves from broad correlation peaks before stimulus

onset to small, thin peaks during the late response period.

We did not find an increase in high-frequency synchrony for

figure versus ground responses nor changes in high-frequency

coherency as a perceptual correlate of stimulus detection. This

seems to conflict with a substantial amount of literature that

shows modulation of gamma oscillations induced by visual

stimulation (Eckhorn et al., 1988, 1993; Gray et al., 1989; Engel

et al., 1991), related to changes in attention (Steinmetz et al.,

2000; Fries et al., 2001) or associated with a perceptual task

(Tallon-Baudry et al., 1997, 1998; Rodriguez et al., 1999; Gail

et al., 2000; Fries et al., 2002; Woelburn et al., 2002; but see Gail

et al., 2004; Gross et al., 2004). We note, however, that the type

of stimulus used in the present study is fundamentally different

from those in the cited literature. The texture elements that

make up the figure evoke a strong activation of V1 neurons.

However, particularly for the receptive fields that were

recorded from (either in the center of the figure or in the

background), local feature interactions are completely irrele-

vant for identifying the figure or making a perceptual choice.

Contextual information well beyond the receptive fields (see

Fig. 1) and well beyond possible intra-cortical lateral interac-

tions (Das and Gilbert, 1999) determine the presence of a figure.

Whether gamma oscillations develop representing feature

combinations at the contours of the stimulus, or in higher order

visual regions, where large figure features are bound, cannot be

tested nor refuted with the present study. Feedforward activa-

tion and local feature combinations within the stimuli used

here are identical irrespective of all conditions in the present

study (figure versus ground, Seen versus Not-Seen). This under-

scores that the effects we observe are mediated by feedback

connections.

Evidence shows that figure--ground discrimination depends

on the presence of late modulated activity (Super et al., 2001),

which is probably mediated by feedback projections (Lamme

et al., 1998). The synchrony transition is not spatially selective

and occurs irrespective of the figure location. This indicates that

the transition in synchronous activity does not represent figure--

ground segregation, which is consistent with psychophysical

(Kiper et al., 1996; Farid and Adelson, 2001) and earlier

neurophysiological studies (Lamme and Spekreijse, 1998;

Shadlen and Movshon, 1999; Bair et al., 2001; Thiele and Stoner,

2003). However, a transition in synchronous activity may be

essential for detecting the stimulus. We showed a significant

correlation between the strength of the synchrony transition and

the strength of the figure--ground activity, and if no or weak

desynchronization occurs the figure will not be perceived

(present study). Therefore, the role of desynchronization in

figure--ground discrimination may be to facilitate its occurrence.

Fixational Eye Movements and Feedforward Signals

Fixational eye movements have been shown to evoke bursts of

activity in single neurons when an optimally oriented stimulus is

within their receptive field (Martinez-Conde et al., 2000, 2002;

Snodderly et al., 2001). Since fixational eye movements affect

the whole visual field, they could induce wide scale synchro-

nous activity in the visual cortex. However, the single unit data

from these studies may not be directly applicable to our results.

Visual stimulation in our multi-unit recordings was on average

non-optimal, which ameliorates the effects of fixational eye

movements on neural activity (Martinez-Conde et al., 2002).

This may explain why we did not observe any correlation

between eye motion and neural activity, which is in agreement

with an earlier report (Super et al., 2004). Moreover, during the

period of fixation we did not find any difference in fixational eye

motion for the Seen and Not-Seen conditions, whereas we did

find a difference in synchrony transition between these two

conditions. In contrast, if we compare trials with a relatively

high amount of eye movements to trials with a low amount of

eye movements we find a transition in neural synchrony in both

conditions that is equally strong.

Could the transition in synchrony be due to the stimulus? Not

as a by-product of the partialization but as an actual decrease of

correlated activity evoked by the stimulus. It has long been

known that visual stimulation evokes a desynchronization of the

EEG (Moruzzi and Magoun, 1949; Morell, 1967; Vijn et al., 1991),

and this is confirmed by recent findings showing that an

appropriate stimulus has a very strong desynchronizing effect

on the cortex (Miller and Schreiner, 2000). The change in

synchrony we observe may thus well be partly due to the

stimulus. However, several observations indicate that the

stimulus alone cannot explain the changes we observe. For

example, whereas the visual response onset is at ~40 ms in both

monkeys, the time course of the transition not only completely

differs from the time course of the response, but is also different

for both monkeys (see Fig. 2). In addition, we observed

a difference in the strength of the synchrony transition between

Seen and Not-Seen cases whereas the visual stimuli are identical

and the visual evoked responses are not significantly different

for these two conditions (see also Super et al., 2003). Finally, the

enhancement of synchrony before the presentation of the

stimulus and its precocious decline cannot be induced by the

change in stimulus configuration (= appearance of the figure--

ground texture).

Altogether, this strongly suggests that visual stimulation and

small eye movements cannot explain our results.

Low-frequency Components

Correlated activity has been interpreted as a global state reflecting

task engagement (Rougeul et al., 1979; Donoghue et al., 1989) or

attentional state (Murthy and Fetz, 1996) in the motor cortex and

expectation (CardosodeOliveira et al., 1997;Worden et al., 2000)

or attention (Fries et al., 2001) in the visual cortex. Such

interpretations are consistent with a modulation of synchrony

before stimulus onset. Previous findings show that synchronous

activity in the median (8--20 Hz) frequency range occurs in alert

subjects in various cortical areas (Mima et al., 2001), which may

reflect top-down feedback (Vanni et al., 1997; Watanabe et al.,

1998; Siegel et al., 2000; Pessoa et al., 2003) and affect the

discrimination and detection of stimuli (Von Stein et al., 2000;

Fanselow et al., 2001; Sherman, 2001; Weyand et al., 2001; Gross

et al., 2004). In our study, frequency components below 20 Hz

also play a prominent role in the synchrony transition. Since low-

frequency activity is generally associated with less attentive

states, a larger decrease in low-frequency synchrony after

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stimulus onset is consistent with the notion that the monkeys

have a higher level of attention in the Seen condition. However,

the increase of low-frequency synchrony before stimulus onset

for the Seen condition is surprising in this context. Note also that

due to the difficulty of the task, the monkeys had to maintain

a high level of attention. This suggests that the state of the visual

cortex does not reflect general attentiveness but rather momen-

tary demands of a perceptual task. Since the visual task was

repeatedly done with an identical fixation period, the pre-

stimulus increase of synchrony may therefore reflect the

expectancy of the monkey, in the sense that the stimulus is not

expectedwithin that period of time. In this period, visual cortical

activity may reflect a state of idleness or some kind of resetting

(Rodriguez et al., 1999; Gross et al., 2004).

Hypothesis

In order to explain the results, we propose that the transition in

synchrony reflects a state change of the cortex. This process

might be envisioned in the following manner. Pre-stimulus

synchronization of low-frequency activity reflects anticipatory

feedback from higher visual areas (Von Stein et al., 2000). This

feedback suppresses local (high-frequency) dependencies be-

tween visual neurons (a cleaning of the slate, reset), that are

associated with local feature combinations in former sensory

input. When new sensory information arrives this low-fre-

quency activity disappears (desynchronization) and new, local

and possibly even inter-areal high-frequency dependencies may

develop (Frien et al., 1994; Roelfsema et al., 1997; Tallon-Baudry

et al., 1997). This type of activity may be more optimal for

selective modes of information transfer (Azouz and Gray, 2000),

and facilitates figure--ground perception (Super et al., 2001;

present study). With new incoming visual information this

process is then repeated iteratively.

Conclusion

In conclusion, our results show that during figure--ground

discrimination a transition in synchronized activity occurs.

This transition is characterized by a change from broad

correlation peaks before stimulus onset towards thin correla-

tion peaks after stimulus onset, suggesting a break down in the

spatial scale of neural interactions (Silberstein, 1995; Steriade

et al., 1996; Van der Togt et al., 1998). The strength of this

transition predicts whether the monkey will detect the stimulus

correctly or not. We propose that the change in synchronized

activity reflects a change in visual cortical state.

Notes

We would like to thank Dr Pieter Roelfsema for helpful comments on

earlier versions of this manuscript. We also thank Kor Brandsma and

Jacques de Feiter for biotechnical support, and Peter Brassinga and

Hans Meester for technical assistance. This study was supported by

a Medical Science (MW) grant from the Netherlands Organization for

Scientific Research (NWO).

Address correspondence to Chris van der Togt, Department of

Vision and Cognition, The Netherlands Ophthalmic Research Insti-

tute, Meibergdreef 47, 1105BA Amsterdam, The Netherlands. Email:

c.vandertogt@ioi.knaw.nl.

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