Dynamics of Striate Cortical Activity in the Alert Macaque: II. Fast Time Scale Synchronization

Pedro E. Maldonado1, Stacia Friedman-Hill2 and Charles M. Gray3

The Center for Neuroscience, University of California, 1544 Newton Ct, Davis, CA 95616, USA


    Abstract
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Notes
 References
 
Synchronous neuronal activity with millisecond precision has been postulated to contribute to the process of visual perceptual grouping. We have performed multineuron recordings in striate cortex of two alert macaque monkeys to determine if the occurrence and properties of this form of activity are consistent with the minimal requirements of this theory. We find that neuronal synchronization with millisecond precision is a prevalent and robust feature of stimulus-evoked activity in striate cortex. It occurs among adjacent cells recorded by the same electrode (<120 µm), among cells recorded at separate but nearby sites (300–400 µm) and between cells recorded at locations separated by 3–4 mm. The magnitude and probability of synchronous firing is inversely related to the spatial separation between the cells and it occurs within and between groups of cells that are both tuned and untuned for stimulus orientation and direction. Among those tuned for orientation, cell pairs separated by <400 µm showed no clear dependence of correlated firing on orientation preference. The occurrence of gamma-band (20–70 Hz) oscillations in the cellular firing patterns was a strong predictor of synchronous firing at each of the spatial scales. Nearly 90% of the cell pairs showing significant correlation also showed oscillatory firing in one or both cells of the pair. These results are consistent with some, but not all, of the previous reports of synchronous activity in striate cortex of both cat and primates. The similarities in the properties of synchronous oscillations in the monkey and cat suggest that this form of neuronal activity is a general property of mammalian striate cortex. The relation between correlation and oscillation suggests that neuronal rhythmicity is an important mechanism contributing to synchronization.


    Introduction
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Notes
 References
 
In the mammalian visual system, pattern recognition is thought to be preceded by a rapid preattentive process referred to as perceptual grouping. This process occurs in parallel across the visual field and enables the visual system to rapidly segment scenes into their constituent objects. For this to occur, some form of representation should be available to establish relationships among features and separate one set of grouped features from another. One theory has proposed that grouped features are represented dynamically by the formation of cell assemblies, which are themselves defined by the synchronization of distributed neuronal activities on a millisecond time scale (Milner, 1974Go; von der Malsburg, 1981Go, 1985Go; Singer and Gray, 1995Go; Singer, 1999Go; Gray, 1999Go). This mechanism is thought to enable the segregation of one representation from another, and provide highly salient neuronal signals for further processing. One minimal requirement of this theory is that synchronous firing within a range of a few milliseconds time lag (i.e. ±10 ms) should be a prevalent, robust and stimulus-dependent property of neuronal activity in the visual cortex of alert mammals. While these requirements have been established for cat striate cortex (Fries et al., 1997Go; Gray and Viana Di Prisco, 1997Go), there is disagreement regarding the properties of synchronous activity in the primate. The purpose of this study was therefore to determine if neuronal activity in the striate cortex of alert macaque monkeys is consistent with this minimal set of theoretical requirements.

Although studies of neuronal synchronization in monkey striate cortex are few in number, they have yielded a wide range of findings. In anesthetized macaques, correlated firing with millisecond precision has been reported to occur in ~50% of neuronal pairs separated by <1 mm (Krüger and Aiple, 1988Go; Ts'o and Gilbert, 1988Go). Paired recordings in V1 and V2 of anesthetized macaques have demonstrated that synchronization also occurs between these cortical areas (Roe and Ts'o, 1997Go; Nowak et al., 1999Go), but correlations having millisecond precision were rare. The dominant time scale of correlations in several of these studies was in the range of 20–100 ms (Krüger and Aiple, 1988Go; Roe and Ts'o, 1997Go; Nowak et al., 1999Go), suggesting some commonality with the cat (Nelson et al., 1992Go; Nowak et al., 1995Go). In contrast to these findings, one study in the anesthetized squirrel monkey has shown that correlated firing with millisecond precision is abundant, stimulus dependent and dominated by an oscillatory temporal structure in the gamma frequency band (Livingstone, 1996Go). These data are similar to some studies of cat striate cortex (Gray et al., 1989Go, 1990Go; Engel et al., 1990Go; Munk et al., 1996Go) and suggest a possible species difference in the temporal structure of striate cortical activity.

Disparate findings have also been reported in two studies of temporal correlation in striate cortex of alert macaques. Frien et al. (Frien et al., 1994Go) found robust stimulus-dependent synchronization between neurons in V1 and V2 that was dominated by oscillations in the gamma frequency band. Lamme and Spekreijse (Lamme and Spekreijse, 1998Go), on the other hand, found stimulus-evoked temporal correlations at a slower time scale (i.e. 20–50 ms) that showed no evidence of gamma-band oscillations and only weak variations across differing stimuli. While these data rule out a strict separation on the basis of species, they do suggest that the properties of correlated activity may depend on stimulus parameters and possibly on behavioral task as well. Thus, the picture that emerges from the few studies of anesthetized and alert monkeys is one of widespread variation in the incidence and properties of correlated activity in striate cortex. This fact is responsible in large part for the ongoing debate concerning the functional significance of synchronous activity in the visual system (Ghose and Maunsell, 1999Go; Gray, 1999Go; Shadlen and Movshon, 1999Go; Singer, 1999Go).

In an effort to resolve these disparate findings, we have performed multineuron recordings in area V1 of alert macaque monkeys to determine the incidence, stimulus-dependence and temporal properties of local and long range synchronous activity occurring on a millisecond time scale. Our goal was to employ stimuli that would lead to coactivation of pairs of recorded cells, and to compare the properties of synchronous activity occurring spontaneously with those evoked by visual stimulation. To accomplish this, we utilized single gratings to coactivate pairs of cells with overlapping receptive fields, and either large single gratings or pairs of independent gratings to coactivate cells with nonoverlapping receptive fields. We varied the direction, spatial frequency or drift speed of the gratings in order to evaluate responses to stimuli that produced the greatest coactivation of the cells. Using these stimuli, and cross-correlation analysis of the spike trains, we find that synchronized firing on a millisecond time scale occurs in a substantial fraction of cell pairs during the response to visual stimuli. Ninety percent of this synchronized firing occurs when one or both cells of a pair display gammaband oscillations in firing probability, and correlation strength is closely correlated with oscillation amplitude. These findings demonstrate that millisecond time scale synchronization is a robust and stimulus-dependent feature of striate cortical activity in the alert macaque, and suggest that neuronal rhythmicity is an important mechanism contributing to its generation (König et al., 1995aGo; Gray and McCormick, 1996Go).

The results of this study have been reported previously in abstract form (Friedman-Hill et al., 1995Go).


    Materials and Methods
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Notes
 References
 
Surgery, Behavioral Training and Recording

The methods for surgical preparation, behavioral training and electrophysiological recording are described in detail in the accompanying paper (Friedman-Hill et al., 2000Go). All surgical and experimental techniques were in accordance with NIH guidelines and were approved by the University of California at Davis animal care and use committee.

Visual Stimuli

Our goal was to evaluate the incidence and properties of temporally correlated activity under conditions where the cells were coactivated by visual stimuli. To accomplish this, we utilized drifting sine-wave or square-wave gratings presented on a dark background (Friedman-Hill et al., 2000Go). A small portion of the data from the initial phase of the experiments was collected using drifting bars. During each session, our recording configuration (Friedman-Hill et al., 2000Go) resulted in two nonoverlapping populations of cellular receptive fields in the lower visual hemifield that were typically separated from each other by 2–3°. We attempted to coactivate as many cells as possible in the two groups by presenting large grating stimuli that covered both sets of receptive fields and pairs of gratings positioned to activate each set of receptive fields simultaneously. We used different combinations of orientation, direction of movement and spatial frequency in an effort to activate as many cells as possible. Once this procedure was completed, we then presented grating patches over each set of receptive fields separately and varied the direction, spatial frequency and drift speed over a range of values to enable the calculation of tuning curves for these parameters. Because recording stability varied from session to session, only a fraction of the cell pairs in the study were subjected to the complete range of stimulus parameters.

Data Analysis

Spike separation, peristimulus–time histograms (PSTH), autoand crosscorrelation histograms (ACH, CCH), including trial-shuffled controls (shift-predictor), associated power spectra and statistical confidence limits, were computed as described elsewhere (Gray et al., 1995Go; Gray and Viana Di Prisco, 1997Go; Friedman-Hill et al., 2000Go). All correlation histograms (single trial and cumulative across trials) had time lags of ±128 ms and a bin width of 1 ms, and were computed over two userdefined epochs, one prior to (win1) and another during (win2) the response to the stimulus. The former ranged in duration from 0.2 to 1.0 s and the latter from 1.5 to 2.5 s. The occurrence and properties of oscillatory firing were evaluated according to the methods described in Friedman-Hill et al. (Friedman-Hill et al., 2000Go).

Our principal objective in quantifying response synchronization was to establish an unbiased measure of the magnitude and statistical significance of correlated firing that occurred within ±10 ms of zero time-lag. In particular, we sought to avoid assumptions about the underlying temporal structure of the spike trains, and to determine the magnitude of correlated firing above and beyond that which might have been introduced by the stimulus. We therefore explicitly avoided using spectral analysis or curve-fitting methods to detect correlation peaks, and utilized the variance in the shift-predictor control correlograms to establish the confidence limits for significance. Our measure, referred to as the significance ratio (SR), is the ratio of two integral values: A peak value (P), representing the magnitude of spike correlation, computed by taking the sum of the bins in the central 20 ms of the CCH that exceed the 95% confidence limits, and a variance value (V), representing the expected occurrence of coincident spikes, computed from the sum of the central 20 ms in each histogram lying between the 95% confidence limits and the mean value of the correlogram. This ratio measure is illustrated in Figure 1Go and was computed as follows:



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Figure 1. Graphic illustration of the method employed for calculation of the significance ratio (SR). (A) Cumulative (across-trials, n = 70 trials) CCH computed from the spike trains of two single units recorded on separate tetrodes spaced 300 µm apart. The calculation was limited to the visual response portion of each trial. The middle (thick) horizontal line represents the mean value of the correlogram. The two thinner horizontal lines, above and below the mean, represent the mean value plus and minus 2 SD of the corresponding shift-predictor correlogram (i.e. 95% confidence limits). The confidence limits for all correlation histograms are plotted in a similar manner in subsequent figures. (B) Cumulative shift-predictor correlogram computed from the same data set as shown in A. The middle (thick) horizontal line is the mean value of the shift-predictor correlogram and the two thinner horizontal lines are the same confidence limits as shown in (A). The calibration bars for the CCHs in this and all subsequent figures indicate the number of spikes. (C) The cumulative CCH obtained after subtracting the correlogram mean from each bin. The horizontal lines are the same values as shown in A. The darkly shaded regions represent coincident firing between the two cells that occurs more (or less) often than expected if the two spike trains were independent. The lightly shaded regions represent the coincident firing expected to occur by chance. (D) A plot of the central 20 ms of the cross-correlation histogram shown in C. The significance ratio is the area of the dark region divided by the area of the light region.

 

(1)

where


(2)

and


(3)


(4)

and where


(5)

and


(6)


(7)

and


(8)


(9)

where X is the mean value of all the bins in the CCH, {sigma} is the standard deviation of all the bins in the corresponding shift-predictor histogram, and i and j range from –10 to +10 ms. Consequently, this measure treats negative and positive deviations from the correlation mean with equal weight. By using the standard deviation of the shift-predictor histogram to establish the confidence limits for significance, we were not only able to estimate the correlated firing that occurred beyond that introduced by the stimulus, but also to estimate significance utilizing spike trains whose temporal structure was identical to that of the signals being evaluated. This measure increased the stringency of our significance test, but it may also have contributed to the occurrence of false negatives. However, we considered it preferrable to err in this direction rather than falsely assign correlograms as significant.

Because the SR measure yielded values over a wide range, it became necessary to establish a separate confidence limit for the statistical significance of each SR value. To do this, we utilized the Monte Carlo procedure described in the preceding report (Friedman-Hill et al., 2000Go). In this analysis, however, we employed two different simulations of the experimental data. For each experimental spike train, we computed an equivalent pseudorandom spike train (i.e. random samples taken from a uniform distribution, with no spike times having the same value) and an interval-shuffled spike train obtained by randomly shuffling the time of occurrence of the interspike intervals in the experimental data. Both simulations yielded control spike trains identical in spike count, mean firing rate and duration to the experimental data. The latter simulation, however, preserved the interspike interval distribution of the original data but resulted in a random shuffling of interval times. All of the results in this study are based on the confidence limits computed from the pseudorandom spike trains. The interval-shuffled spike trains are used to control for the possibility that the temporal structure of the firing patterns might yield statistically significant correlations of a spurious origin.

As with the autocorrelation analysis, both simulations were repeated 500 times for each window of data analyzed on each trial. The CCH was computed from each simulated spike train and corresponding cumulative session correlograms were calculated. The resulting correlograms were edge corrected as described earlier (Friedman-Hill et al., 2000Go), and the SR value was then computed for each CCH. This yielded two sets of 500 control SR values (pseudorandom and ISI-shuffled) for each experimental CCH [i.e. single trial (win1,win2), session (win1,win2) and shiftpredictor (win1,win2)]. We assigned a confidence limit for statistical significance by choosing the SR values in the control distributions that were greater than 99% of all the values. This resulted in two different cutoff values, one computed from the pseudorandom spike trains [cutoff(rnd)] and the other computed from the interval-shuffled spike trains [cutoff(isi)]. Any experimental SR value equal to or greater than either of these cutoff values was considered statistically significant at a P-value of <0.01. As with the autocorrelation analysis, we calculated the ratio of the SR/cutoff to provide a measure of correlation strength that was independent of the total spike count in the data. Values of this ratio >1.0 were considered significant. Our use of two different Monte Carlo procedures to estimate statistical significance further enabled us to compare the incidence and magnitude of significant correlation in our sample between pseudorandom and interval-shuffled controls.

For those correlograms classified as significant, we calculated the width and time lag of the corresponding peaks utilizing the full CCH (i.e. ±128 ms time lag). Full widths at half height (i.e. midpoint between peak value and correlogram mean) of CCH peaks were measured after subjecting each CCH to a low pass, boxcar filter with a 3 ms bin width. In order to measure the time-lag of each significant CCH, we fit a Gaussian function to the central peak using the maximum likelihood method (Maldonado and Gray, 1996Go). The time-lag was computed by taking the difference between the peak of the Gaussian function and the 0 ms bin. Power spectra and peak frequencies of the CCHs were computed and tested for significance as described in the preceding report (Friedman-Hill et al., 2000Go).


    Results
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Notes
 References
 
We recorded multiunit and single unit activity at a total of 245 sites in area V1. Because of the spatial arrangement of our recording probes, we divided our data into two categories, based on the tip separation between paired recordings. Long-range pairs are defined as cells recorded at sites separated by 3–4 mm and short-range pairs are defined as cells sampled from adjacent probes (200–400 µm separation), or from the same tetrode or microelectrode (<120 µm separation). All long-range pairs had nonoverlapping receptive fields, while all short-range pairs showed substantial receptive field overlap. Since paired recordings of both multiunit and single unit activity were considered for analysis, we further divided our data into three subcategories: multiunit–multiunit (MM), multiunit–single unit (MS) and single unit–single unit (SS) pairs.

Incidence of Response Synchronization

After separating the spike trains arising from different cells (Gray et al., 1995Go), our sample consisted of 608 paired recordings. For each pair, we used from one to 32 different stimuli varying in spatial frequency, direction and speed. We selected for analysis the stimulus condition that elicited the highest combined firing rate of the cells, computed by taking the square root of the product of the mean firing rates of the two spike trains. Using this selection criterion, and the confidence limits derived from the pseudorandom spike trains, the incidence of significant response synchronization is summarized in Table 1Go. Several notable findings are apparent from these results. Consistent with previous correlation measurements in the cat and monkey, the incidence of significant correlation decreased as a function of spatial separation between the cells: 38% of all shortrange pairs and 17% of all long-range pairs exhibited significant synchronized firing in response to the stimulus condition that evoked the highest combined firing rates of the cells. Similarly, the strength of correlation also decreased with spatial separation. The mean correlation magnitude was significantly greater (P < 0.01, U-test) for short-range as compared with long-range CCHs (Figure 2AGo).


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Table 1 Summary statistics of the incidence of significant correlation among multiunit and single unit recordings for all sessions
 


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Figure 2. Cumulative distributions of the correlation magnitude (SR/cutoff) obtained for statistically significant session (A) and single-trial (B) measurements.

 
Synchronous activity was largely restricted to periods of stimulus-evoked firing and was not phase-locked to the stimulus or the video refresh of the monitor. We calculated the CCHs from each pair of cells during the period of spontaneous activity immediately prior to the onset of the stimulus (Table 1CGo), and during the stimulus presentation after shuffling the trial sequence (Table 1DGo). In the absence of visual stimulation, spontaneous synchronous firing occurred in <7% (n = 42) of the cell pairs and its magnitude (mean = 2.3 ± 2.4) was lower than that observed during visual stimulation. The overall rate and magnitude of correlation was even lower for the shuffled correlograms. Less than 3% of the shift-predictor correlograms were statistically significant and their mean amplitude was 1.8 ± 0.9. Thus, in the large majority of cases, synchronized firing was evoked by, but not locked to, the presentation of visual stimuli. Figures 3 and 4GoGo show examples of significant short-range and long-range correlated firing, respectively, that are characteristic of much of the synchronized activity we observed. In both examples, the cells had similar orientation preferences and were coactivated by a single stimulus (A,D). The CCHs (B) and their trial-shuffled controls (E) revealed significant correlated firing that was induced by, but not time-locked to, the stimulus. The power spectra of the correlation histograms, shown in C and F, further revealed that the synchronized firing was rhythmic and that the phase of the oscillations with respect to the stimulus was independent across trials (E). Note also that the synchronous firing shown in Figure 4Go had an average time-lag of 4 ms.



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Figure 3. Example of short range, single unit–single unit correlation. (A,D) PSTHs of single unit activity recorded on two separate but adjacent tetrodes separated by 300 µm. The cells had overlapping receptive fields and preferred the same orientation. They were coactivated by a single drifting grating of optimal orientation and direction. (B) CCH computed from the two spike trains during the response to the stimulus. On average the cells tended to fire at a time lag of 0 ms. (C) Power spectrum of the CCH shown in B. The correlation magnitude (A = SR/cutoff) and the peak frequency (F) are shown to the right in this and all other figures when the magnitude values are significant. (E,F) CCH and power spectrum, respectively, computed from the same data after shuffling the spike trains by one stimulus period.

 


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Figure 4. Example of long range, multiunit–multiunit correlation. (A,D) PSTHs of multiunit activity recorded at two sites in V1 separated by 4 mm. The cells had nonoverlapping receptive fields and both preferred the same orientation. They were coactivated by a single drifting grating of optimal orientation and direction. (B) CCH computed from the two spike trains during the response to the stimulus. The time lag of the peak in the CCH is 4 ms. (C) Power spectrum of the correlation histogram shown in B. (E,F) The CCH and power spectrum computed from the same data, after shuffling the spike trains by one stimulus period.

 
During the course of this study, we found that synchronous firing was often not limited to the stimuli that evoked the highest combined firing rates of the cells. In fact, in some cases the strongest correlations occurred in response to stimuli yielding intermediate firing rates, with the correlation observed during the maximum combined firing rates occasionally being insignificant. For comparative purposes, we therefore repeated our analysis of the incidence of correlation, but selected the data based on the stimulus that yielded the highest significant SR value. When using this criterion, we found that the incidence of correlated firing was greater. Overall, 44% of the cell pairs showed significant correlated firing in response to at least one stimulus condition. The data selected on the basis of this criterion displayed similar trends to that shown in Table 1Go. Correlated firing was more prevalent for short-range pairs and occurred far more often during stimulus-evoked firing than during spontaneous activity. These findings indicate that the stimulus that elicits the highest combined firing rate of the cells is not always the one that leads to response synchronization. This might arise under conditions where one of the cells is firing at a low rate but the combined rate of the pair remains high. Thus, it is important to evaluate a range of stimulus conditions to determine if pairs of cells are capable of synchronizing their spike trains.

In addition to the cumulative measure of cross-correlation, we also found that synchronous activity was frequently of sufficient strength to be detected on single trials, and therefore did not require extensive averaging to be resolved. We applied our analysis to the data collected on single trials and found a pattern of results similar to that seen in the cumulative measurements (Figure 2BGo). Table 2Go shows summary statistics for this analysis on the basis of the maximum combined firing rate criterion. Overall, the incidence of single-trial correlations was approximately one-third of that observed in the session measurements, with short-range correlations being more prevalent and greater in magnitude than long-range correlations. These differences were due in part, however, to the increase in variance relative to the mean that is present in the single-trial CCHs. As a result, our measure of significance is likely to have led to an underestimate of significant correlation when the number of sampled spikes was small. This effect is illustrated in Figure 5Go, where the CCHs for 10 consecutive trials (B) are shown for a short-range pair that displayed a strong correlation in the cumulative correlogram (A). Each of the 10 single-trial CCHs displays a clear indication of response synchronization, but the central peaks in four of the correlograms (indicated by *) failed to reach statistical significance.


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Table 2 Summary statistics of the incidence of significant correlation among multiunit and single unit recordings for data sampled on single trials
 


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Figure 5. Significant synchronized activity is readily apparent in the CCHs computed from single trials. The neuronal pair was composed of a single unit and a multiunit recording sampled from different, but adjacent tetrodes. (A) Session CCH illustrating strong response synchronization. (B) CCHs computed from 10 consecutive trials of a 60 trial session. Trials 1, 2, 5 and 6 failed to pass the Montecarlo test and were classified as insignificant. The values to the right of each correlogram indicate the correlation magnitude and the peak frequency. The upper calibration bar is for trials 0–5 and the lower calibration bar is for trials 6–9.

 
Another prominent feature of the correlation histograms in our data set was the presence of a distinct periodic structure. We further evaluated the incidence of rythmicity in those CCHs found to exhibit significant central peaks and found a high percentage of oscillatory cross-correlograms (Table 1EGo). More than 70% of the cumulative CCHs classified as significant were found to be significantly oscillatory by the criterion established in the previous report (Friedman-Hill et al., 2000Go). This percentage dropped to ~50% in the single-trial correlograms (Table 2CGo), but we suspect, as before, that this reduction was due to the greater relative variance in these measurements. As with the autocorrelation measurements (Friedman-Hill et al., 2000Go), the frequency of oscillatory cross-correlation was largely limited to the gamma-band between 30 and 60 Hz (Figure 6Go). There was no significant difference in the distribution of frequencies between short- and long-range pairs or between single-trial and session measurements. Moreover, there was a sharp drop in the number of oscillatory CCHs calculated from the spontaneous activity. Only seven out of 176 pairs displayed significant oscillatory CCHs in the absence of visual stimulation (Table 1FGo).



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Figure 6. Distribution of the peak frequencies calculated from all the CCHs [sessions (A) and trials (B)] showing significant oscillatory modulation. None of the differences between short and long range or between sessions and trials were significant.

 
Our finding that significant spike train correlations often occurred in the presence of strong oscillatory firing suggests that spurious correlation might arise in the data due to common frequencies. To control for this possibility, we repeated our calculations of statistical significance using an estimate of the correlation variance based on the random shuffling of the interspike-interval distribution (see Materials and Methods). This estimate has the advantage that the interval structure of the shuffled data is identical to the actual data, but the time of occurrence of the intervals is randomized. Thus, the estimate of significance obtained by this method is likely to be more rigorous than a simple pseudorandom spike train. Applying this method to the entire sample, we found that the incidence of significant correlation, as measured in the cumulative correlograms, dropped to 84% of the measure based on pseudorandom spike trains. This finding indicates that the interval-shuffled control provides a more strict criterion for statistical significance. More importantly, it also demonstrates that the oscillatory synchronization we have observed is not due to the spurious correlation of neurons firing rhythmically at a common frequency.

Dependence of Temporal Correlations on Orientation and Direction Preference

In both the cat and the monkey, it has been reported that correlated firing occurs most often and with greatest magnitude among cells that are tuned to the same orientation and direction (Ts'o et al., 1986Go; Ts'o and Gilbert, 1988Go; Schwarz and Bolz, 1991Go). In our own experience, however, we have found that significant correlations can occur among cells with widely different orientation preferences as long as they can be coactivated, particularly with a single stimulus (Gray et al., 1989Go; Engel et al., 1990Go; Kreiter and Singer, 1996Go). To address this issue, we examined the dependence of short-range temporal correlation on orientation and direction preferences by presenting a single drifting grating over a range of 360° (22.5° steps) in pseudorandom order. For each cell in a pair, we fit a Gaussian function, utilizing the maximum likelihood method (Maldonado and Gray, 1996Go), to a 180° region of the tuning curve centered over the stimulus that elicited the maximum response and determined the difference in orientation preference for each pair of tuned cells. We then analyzed the responses to all 16 stimuli to determine if at least one of them evoked significant correlated firing (Table 3Go). Of 105 pairs examined, 72 (~69%) displayed significant peaks in their CCHs. The majority of these 72 cell pairs differed in their orientation preferences by <45°. This bias in our sample is likely to be due to the physical arrangement of our short-range measurements in which the recorded cells were separated by no more than 400 µm. When cells did differ widely in their preferred orientations, it was often difficult to coactivate them with a single drifting grating. The resulting low firing rates are likely to have further biased our estimate of correlation because of the sensitivity of our measure to spike count. In spite of these factors, we did find a number of significantly correlated cell pairs in which the preferred orientation of the cells differed by 45–90°. Interestingly, the percentage of significant correlations did not differ substantially as a function of the difference in orientation preference (Table 3Go).


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Table 3 Relation between short-range correlation and the difference in preferred orientation for pairs of cells
 
Examples of these results are illustrated for two different recording sessions in Figure 7Go. Both examples are taken from sessions in which the activity of three cells was monitored simultaneously. The upper (A,C) and lower (B,D) plots illustrate the direction tuning curves and cross-correlation histograms, respectively. The cells shown in the left column (A,B) were well tuned for orientation. Two of them were tuned to the same orientation (0 and 2), while the orientation preference of the remaining cell (1) differed from the other two by 67.5°. In spite of this large difference, it was possible to coactivate the cells by presenting a drifting grating at an orientation and direction that was intermediate to that preferred by all three cells (292.5°, arrow pointing up in A). In response to this stimulus, all three cells engaged in statistically significant synchronized firing. The correlation was greatest between the cells preferring the same orientation (0–2), but was also readily apparent between the cell pairs that differed in orientation preference by 67.5°. Interestingly, there were also systematic differences in the time-lag of the correlation peaks for these three cells. Pairs 1–2, 0–2 and 0–1 had time-lags of 0, 2 and 6 ms, respectively. The other example, shown in the right column (C,D), was taken from cells that were more broadly tuned for orientation. The preferred orientations of cells 1 and 2 differed by 90°, while the preferred stimulus for cell 0 was 45° intermediate. Because of their relatively broad tuning, it was possible to evaluate the correlated firing among each pair by choosing the appropriate stimulus along the tuning curve that evoked visual responses. The resulting correlation histograms are shown in D. Cell pairs 0–1 and 0–2, each differing in preferred orientation by 45°, displayed pronounced synchronization, while the remaining cell pair 1–2, having orthogonal orientation preferences, was not significantly correlated for any of the stimuli. These examples, and the summary data in Table 3Go, demonstrate that correlated firing occurs among cells with different as well as similar orientation preferences and that stimulus-evoked correlations are not limited to the stimulus orientations preferred by the cells.



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Figure 7. Response synchronization occurs in cell pairs with different as well as similar orientation preferences. The data shown in the left and right columns were taken from two different recording sessions. Direction tuning curves (A,C) and CCHs (B,D), computed from the responses to selected stimuli, are shown in the upper and lower plots, respectively. In both experiments, unit activity was recorded from three adjacent tetrodes and the cell pairs all had overlapping receptive fields. In the experiment shown in the left column, activity on channel 0 was sampled from a single unit, while that on channels 1 and 2 was of multiunit activity. All the CCHs shown in B were computed from the response to the same stimulus, indicated by the upward vertical arrow in A. The data in the right column were taken from three single units. Each CCH shown in D was computed from the response to a different stimulus. The correlation magnitude and peak frequency are shown in the upper right of each CCH. The CCH shown in the lower plot of D (1–2) was statistically insignificant. The arrows in A and C mark the tuning curves of each cell which are displayed with different line thickness to distinguish them.

 
Finally, we also found that response synchronization was not limited to cell pairs that were tuned for orientation and direction. We observed significant correlations among neuronal pairs that showed no clear preference for orientation and were therefore classified as unoriented. Eleven of the significant shortrange correlations listed in Table 3Go were from pairs in which both cells showed no orientation preference.

Stationary versus Drifting Stimuli

In the accompanying paper (Friedman-Hill et al., 2000Go), we reported that stationary stimuli elicited lower frequency oscillatory responses when compared with those evoked by drifting stimuli. This implies that if response synchronization is to be maintained for both stationary and drifting stimuli then oscillatory responses must covary in frequency among cells that are coactivated by the same stimulus. We addressed this issue by comparing the frequency of oscillatory synchronization evoked by stationary and moving gratings. Of 133 cell pairs investigated, 17 (12.8%) showed significant synchronization for both stationary and drifting stimuli. Another 21 (15.7%) pairs showed correlation only in response to a stationary grating, and 7 (5.2%) pairs showed significant correlation only during the presentation of a drifting grating. For those cell pairs that exhibited synchronous oscillations in response to both stimuli, the mean frequency was significantly higher when the stimulus was drifting (45 ± 7 Hz) than when stationary (33 ± 2 Hz; P << 0.001, paired t-test).

Correlation Peak Width and Time Lag

Previous studies, employing cross-correlation analysis in areas 17 and 18 of the cat and monkey, have emphasized the presence of at least three distinct temporal scales of neuronal synchronization (Krüger and Aiple, 1988Go; Nelson et al., 1992Go; Nowak et al., 1994Go, 1995Go, 1999Go). By computing the width of correlation peaks, these authors have demonstrated that correlated firing tends to occur on time scales of ~1–20, 20–100 and 100–1000 ms. To determine if our data could be categorized in a similar manner, we computed the width at half-height of all the significant correlation peaks in our sample. Figure 8AGo shows the resulting distribution of peak widths for longand short-range pairs. We found a clustering of values below 12 ms and little or no evidence for peak widths >15 ms (mean values were 6.2 ± 1.7 ms and 5.7 ± 1.6 ms for short- and long-range pairs, respectively). Because the maximum time-lag of our calculations did not exceed 128 ms, we were unable to assess the presence of correlation peaks broader than ~200 ms. The distribution of correlation widths in our data clearly reflects the characteristic temporal structure of the synchronous neuronal oscillations found in our sample.



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Figure 8. (A) Distribution of the correlation peak widths at half height calculated from the significant short- and long-range CCHs for session measurements. The mean values for short and long range pairs are 6.2 ± 1.7 and 5.7 ± 1.6 ms, respectively. (B) Distribution of time lags in milliseconds calculated from the peaks of the significant short- and long-range CCHs for session measurements. The mean values for short and long range pairs are 2.6 ± 2.1 and 3.0 ± 2.6 ms, resepctively.

 
Previous studies of temporal correlations have also demonstrated that synchronous firing often occurs with little or no average time-lag, even between cells that are separated by several millimeters within an area, between areas or across hemispheres (Gray et al., 1989Go; Engel et al., 1990Go, 1991aGo, Engel et al., bGo; König et al., 1995aGo; Roelfsema et al., 1997Go). We examined the distribution of time-lags in all significant short- and long-range CCHs, and the results of this analysis are shown in Figure 8BGo. Similar to earlier studies in both the cat and the monkey (Frien et al., 1994Go; Livingstone, 1996Go), we found that the majority of time-lags occurred within ±3 ms of time 0. However, when we computed the mean absolute time-lag of the correlation peaks, it became readily apparent that the vast majority of significant correlations did not occur with a time-lag of 0 ms (mean timelag: short-range = 2.6 ± 2.1 ms; long-range = 3.0 ± 2.6 ms; no significant difference).

In spite of the clustering of time-lags within 2–3 ms, we also found a substantial fraction of cell pairs with time-lags in the range of 4–9 ms. We suspected that these large delays may have resulted from the combined dependence on stimulus orientation and orientation preference reported in a recent study (König et al., 1995bGo). These authors demonstrated that optimally driven neurons tend to fire slightly before suboptimally activated cells, with the magnitude of the difference being dependent on both the differences in preferred orientation of the cells and the orientation of the stimulus. To examine this relation, we measured the time-lag as a function of stimulus orientation among significantly correlated short-range pairs of cells having overlapping receptive fields. The data were taken from paired recordings during the execution of direction tuning curves. For each CCH having a significant peak, we determined the time-lag of the peak and then computed the slope of the linear fit to the data points comparing time-lag with stimulus orientation. We found that all the slopes were positive and their values were significantly correlated with the difference in preferred orientation of the cell pairs. An example of this result is illustrated in Figure 9AGo. Here, the CCHs of a neuronal pair stimulated by a drifting grating presented at four different directions show a continuous shift in time-lag. This transition occurs as the stimulus changes from the nonpreferred direction of the reference cell (Figure 9AGo, top) to the preferred direction (Figure 9AGo, bottom). The magnitude of the shift for a given change in stimulus orientation differed among cell pairs. The rate of change of the time-lag with respect to stimulus orientation was greater for cell pairs with large differences in orientation preference. Plotting the slope of the change in time-lag as a function of the orientation preference difference for the entire sample revealed a positive correlation (Figure 9B, PGo < 0.05), indicating that the magnitude of the shift depends on both the stimulus orientation and the difference in orientation preference of the cell pair. These results are consistent with those reported by König et al. (König et al., 1995bGo), where a similar dependence on stimulus orientation was found among neuronal pairs recorded in cat striate cortex.



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Figure 9. The time-lag of synchronized activity changes systematically with stimulus orientation. (A) Four CCHs obtained from a short-range pair of single units stimulated with a single drifting grating at four different orientations. The cells differed in their preferred orientations by 45°. The stimulus orientation is indicated to the left of each histogram. Time-lag values in milliseconds were measured by fitting a Gaussian function over the central peak in the CCHs (continuous line). In the upper plot the time lag is negative when the cells are being stimulated with an orientation preferred by the nonreference cell. As the orientation is shifted towards that preferred by the reference cell, the time-lag decreases. (B) The change in time-lag that occurs with changes in stimulus orientation is proportional to the difference in the preferred orientation of the cells. The slope of the relationship between time-lag and stimulus orientation is plotted versus the magnitude of the orientation preference difference of each cell pair. Cells having a large difference in preferred orientation give rise to larger changes in time-lag for a given change in stimulus orientation. The straight line represents the linear regression fit to the data (P < 0.001, n = 22).

 
Correlation between Oscillations and Synchrony

In our sample of significant CCHs, we observed that nearly two-thirds of the long-range pairs and three-quarters of the short-range pairs exhibited oscillatory modulation (Table 1EGo). This suggested that synchronization might be related to the occurrence of oscillatory firing, as has been recently demonstrated in the cat (König et al., 1995aGo) and the squirrel monkey (Livingstone, 1996Go). To test this conjecture, we examined the ACHs of each pair of significantly correlated cells in our sample to determine if the occurrence of oscillatory firing was related to response synchronization. For each neuronal pair, one of three relationships could be true: both ACHs could be oscillatory, one cell of a pair could be oscillatory and the other nonoscillatory, or both could be nonoscillatory. If response synchronization occurred with equal probability among oscillatory and nonoscillatory cells, we would expect the relation between oscillatory firing and synchronization to simply reflect the incidence of rhythmic (56.3%) and nonrhythmic (43.7%) firing in our sample of single-unit and multiunit recordings (Friedman-Hill et al., 2000Go). Specifically, among those pairs of cells that exhibit synchronization, ~32% (0.563 x 0.563) would be derived from cells that are both oscillatory, ~49% [(0.563 x 0.437) x 2] would contain one site that is oscillatory and ~19% [1 – (0.32 + 0.49)] would come from cell pairs that are both nonoscillatory. On the other hand, if synchrony were more likely to occur when the cells exhibit oscillatory firing, we would expect the incidence of synchronization to be highest when the cells are firing rhythmically and lowest when they show no rhythmic modulation.

Our findings were consistent with the latter interpretation (Figure 10AGo). The incidence of significant synchronization (52%) was highest when both members of a pair were oscillatory, decreased to 38% when only one member of a pair was oscillatory and fell to 10% when neither site exhibited oscillatory firing (black-filled bars). Consistent with this finding, uncorrelated firing was least likely to occur when both members of a pair were oscillatory, and was most commonly observed when one or neither member of a pair was firing rhythmically (hatched bars). Using a Chi-square test, we found the correlation between oscillation and synchronization to be highly significant (P < 0.00001), and a similar result held when we restricted the analysis to pairs of single units only (P < 0.024). These data reveal that oscillatory firing, as detected in the ACH, was a strong predictor of the occurrence of significant peaks in the CCH. Significant crosscorrelation occurred more often than predicted by chance if both ACHs in a pair were oscillatory (i.e. 52% versus the predicted 32%).



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Figure 10. Response synchronization is correlated with the occurrence of oscillatory firing. (A) This plot shows the percentage of CCHs that are derived from pairs of cells that are both oscillatory (both), from pairs where only one cell is oscillatory (one) or from pairs where both cells are nonoscillatory (none). The black bars show the distribution for significant CCHs, the hatched bars show the distribution of insignificant CCHs and the unfilled bars show the distribution of significant CCHs expected if there were no relation between oscillatory firing and response synchronization. Note that correlated firing occurs more often than expected by chance when both cells in a pair are oscillating. (B) Scatter plot of oscillation strength (peak power/cutoff) versus the correlation magnitude (SR/cutoff) for all significant CCHs that were also classified as significantly oscillatory. The trend in the data demonstrates that correlation magnitude is proportional to oscillation magnitude.

 
A further indication of the close relation between oscillation and synchronization was obtained by plotting correlation magnitude (SR/cutoff) versus oscillation strength (peak power/cutoff) for each significantly oscillatory CCH (n = 125). This plot, shown in Figure 10BGo, revealed that correlation strength was directly related to oscillation strength.

Our finding that oscillatory firing was a strong predictor of synchronization raised the possibility that independent oscillators could, in principle, show significant correlation simply because they have similar frequencies. To address this issue, we modified the previous analysis by first selecting those pairs where both cells displayed oscillatory firing and then determined the incidence of significant correlation among these pairs. For the correlograms meeting this condition, 71% (64/90) of the short-range pairs and 30% (28/93) of the long-range pairs exhibited significant synchronous firing. This demonstrates that, although correlation probability is greatest when the participating cells fire rhythmically, oscillatory responses are by no means obligatorily coupled. They can occur simultaneously and remain temporally independent even when their frequencies are very similar. An example illustrating this result is shown in Figure 11Go. The data were taken from four recordings where the cells were simultaneously activated by two gratings drifting in the same direction (A). The cells recorded on channels 2, 3 and 4 were sampled from either the same or adjacent tetrodes and had overlapping receptive fields, while the activity recorded on channel 1 was sampled from a separate tetrode located 4 mm distant and its receptive field did not overlap with the others. All of the cells had preferred orientations that spanned a range of <30°. Strong oscillatory responses of similar mean frequency were evoked in all four spike trains during the presentation of the stimuli (B). However, while the activity on channels 2, 3 and 4 showed clear synchronization (D), the oscillatory firing between these cells and the activity on channel 1 was independent (C).



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Figure 11. Oscillatory firing is not a sufficient condition for the occurrence of response synchronization. This experiment illustrates the activity recorded simultaneously on four channels in response to the presentation of two gratings drifting in the same direction. One grating was positioned to activate the cells recorded on channel 1 (multiunit), while the other grating was positioned to activate the cells recorded on channels 2 (multiunit), 3 (single unit) and 4 (multiunit) whose receptive fields were overlapping with each other, but not with the cells on channel 1. Hand mapping of the receptive fields revealed that all the cells had similar orientation preferrences. The cells recorded on channel 1 were ~4 mm distant from those recorded on the other channels. (A) PSTHs computed from the responses to the stimuli. With the exception of channel 2, each of the cells displays a vigorous response to the stimulus. (B) Autocorrelation histograms of the activity sampled during the response to the stimulus. Each of the cells displayed strong oscillatory responses at a similar frequency range. (C,D) Crosscorrelation histograms of the long-range (C) and short-range (D) pairs. The CCHs of all the long-range pairs were flat, while the short-range pairs showed pronounced synchronization.

 
What can account for this temporal independence of oscillatory activity? Previous studies in both the cat (Engel et al., 1990Go; Gray et al., 1990Go, 1992Go) and monkey (Eckhorn et al., 1993Go; Livingstone, 1996Go; Friedman-Hill et al., 2000Go) have demonstrated that oscillatory responses to visual stimuli exhibit variations in frequency over time scales lasting tens to hundreds of milliseconds. This nonstationarity of the activity means that synchronization will only occur among those groups of cells whose frequency and phase variations are correlated over time. If these parameters do not covary, even cells with the same mean frequency of oscillation will be temporally independent. Although a detailed analysis of this issue is beyond the scope of this study, we closely inspected the raw data from a subset of recordings showing strong oscillations to search for evidence of covariation. As suggested by previous reports (Engel et al., 1990Go; Gray et al., 1992Go), we found that the lack of synchronization in some of the recordings was due to independent variations in the frequency and phase of their rhythmic firing. An example illustrating these findings is shown in Figure 12Go. These data, taken from cells 1, 2 and 3 shown in Figure 11Go, illustrate the raw spike trains measured on a single trial. Each of the three recordings exhibited strong oscillations at or near the same frequency, but only cells 2 and 3 showed clear evidence of synchronization. Close examination of the raw data revealed that the lack of temporal correlation between these cells and cell 1 was due to independent variations in the frequency and phase of the oscillatory activity (Figure 12GoB–E).



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Figure 12. Raw spike trains collected on a single trial from cells 1, 2 and 3 shown in Figure 11Go. The stimulus time course is indicated by the long horizontal line at the bottom of panel A. The short horizontal lines labeled B, C, D and E indicate the epochs of activity that are displayed at a faster time scale in panels B–E below. Notice in panels B–E that cells 2 and 3 display a consistent phase relation while the activity on channel 1 appears to be independent of the other two cells.

 

    Discussion
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Notes
 References
 
We and others have postulated that synchronous neuronal activity with millisecond precision may contribute to the processes underlying visual perceptual grouping (Milner, 1974Go; von der Malsburg, 1981Go, 1985Go; Singer and Gray, 1995Go; Gray, 1999Go; Singer, 1999Go). In this theory, response synchronization is thought to contribute to perceptual grouping by signaling relationships among common features in an image. A minimal requirement of this theory postulates that neuronal synchrony must be prevalent in the visual cortex of alert animals and dependent on visual stimulation for its occurrence. These properties have been demonstrated for the striate cortex of cats (Gray and Viana Di Prisco, 1997Go; Fries et al., 1997Go), and we show here that they are also a feature of striate cortical activity in alert macaque monkeys. While these data are consistent with the postulated role of neuronal synchrony in perceptual grouping, there are other possible functions activity of this sort could be involved in, and at least one study has failed to find evidence in support of this theory (Lamme and Spekreijse,1998) [but see also (Gray, 1999Go)]. Thus, although our findings demonstrate that response synchronization with millisecond precision is a robust feature of striate cortical activity in the alert macaque, these results are only an initial step in the ongoing evaluation of the functional role of neuronal synchrony in visual processing.

Methodological Considerations

At least two factors may have affected our ability to assess the properties of synchronous activity. First, several forms of uncontrolled sampling bias might have affected our data, as discussed in the comanion paper (Friedman-Hill et al., 2000Go). Secondly, our measure of synchronization may also have introduced errors in our analysis. In designing this measure (the significance ratio), we attempted to minimize this influence in several ways. First, we made no assumptions regarding the temporal strucuture of the underlying activity. We simply searched for joint firing within the central 20 ms of the CCH that was greater and/or less than that estimated from the confidence limits set by the trialshuffled CCH. This provided a way to estimate the joint firing exceeding that introduced by the stimulus and by any common temporal structure of the signals. We then determined the significance of the SR values by comparing them to distributions of surrogate SR values computed from equivalent pseudorandom and interval-shuffled spike trains. Consequently, two strict criteria had to be met for an SR value to be considered significant. When we applied these methods, the quantitative assignment of significant correlations closely matched the assignment that each of us made by visually inspecting the cross-correlograms. We are therefore reasonably confident that our measure of statistical significance is rigorous and accurate. By limiting our estimate of correlated firing to the central 20 ms of the CCH, we might have easily missed correlation peaks whose time lags were outside this window, and we would have incorrectly estimated the magnitude of correlation peaks whose widths exceeded this window. However, careful visual inspection of all the CCHs in our data set showed no evidence of the former, and our post hoc analysis of the peak widths revealed no evidence of the latter within the time lags (±128 ms) of our CCH calculation.

Comparison to Other Studies of Synchronization in Monkeys

In several respects, our findings are both similar to and different from previous studies of neuronal synchronization in primate visual cortex. We have summarized these studies in Table 4Go, which reveals a surprisingly broad spectrum of findings for such a small number of reports. Our results most closely resemble those of Frien et al. and Livingstone (Frien et al., 1994Go; Livingstone, 1996Go), who reported abundant neuronal synchronization on a fast time scale that was both stimulus dependent and dominated by rhythmic firing in the gamma frequency band. Our findings differ, however, from several of the other studies of V1. For example, we found no evidence for synchronization at the intermediate time scale (20–100 ms), while several studies have not reported evidence for synchronous gamma-band activity. Although it is difficult to fully account for these differences, there are several factors that are likely to contribute. The absence of intermediate time scale correlations in our data might simply reflect the abundance of gamma-band activity we observed (Friedman-Hill et al., 2000Go). Synchronous oscillatory firing at 30–60 Hz would naturally lead to correlation peak widths on the order of ~15 ms or less. If for some reason our data were biased in favor of gamma-band activity [see discussion in (Friedman-Hill et al., 2000Go)], we would expect to see less of the slower time scale correlations.


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Table 4 Summary of the results reported for 13 different studies using cross-correlation analysis of unit activity recorded in different areas of the visual cortex of primates
 
The use of anesthesia in some of the studies could be another factor (Krüger and Aiple, 1988Go; Ts'o and Gilbert, 1988Go; Nowak et al., 1999Go). Anesthetics are known to induce electroencephalogram (EEG) synchronization which could contribute to the slower time scale correlations observed by some. However, this explanation is not entirely satisfactory. Livingstone (Livingstone, 1996Go) found ample evidence of gamma-band activity in the anesthetized squirrel monkey, while Lamme and Spekreijse (Lamme and Spekreijse, 1998Go) found no evidence of gammaband oscillations in V1 of alert monkeys. Conversely, Krüger and Aiple, and Ts'o and Gilbert (Krüger and Aiple, 1988Go; Ts'o and Gilbert, 1988Go) reported fast time scale synchronization in the anesthetized macaque in the absence of rhythmic firing. How can we account for these differences? One explanation comes from a recent study demonstrating that synchronous gammaband activity in areas 17 and 18 of the cat is closely correlated with the state of EEG arousal (Herculano-Houzel et al., 1999Go). This suggests the possibility that synchrony on the fast and intermediate time scales may be present during states of EEG synchronization, while gamma-band synchrony would appear predominantly during states of EEG arousal. If this were the case, it raises the possibility that the bulk of the measurements made by Krüger and Aiple, Ts'o and Gilbert, and Nowak et al. (Krüger and Aiple, 1988Go; Ts'o and Gilbert, 1988Go; Nowak et al., 1999Go) might have been taken while the animals were in a state of EEG synchronization, while those made by Livingstone (Livingstone, 1996Go) were sampled more often during EEG arousal. This explanation, however, does not account for the absence of gamma-band synchrony in the study of Lamme and Spekreijse (Lamme and Spekreijse, 1998Go). In this case, it is possible that the small size of their sample, or the properties of their stimuli, may have precluded the measurement of gamma-band activity.

Differences in analysis techniques may provide another source of variation. In some studies, cross-correlograms were computed from long epochs that combined periods of spontaneous and evoked activity across different stimuli (Aiple and Krüger, 1988Go; Krüger and Aiple, 1988Go; Ts'o and Gilbert, 1988Go). Several studies indicate that pooling data in this way can lead to erroneous estimates of the properties of synchronization (Engel et al., 1990Go; Kreiter and Singer, 1996Go; Livingstone, 1996Go; Gray and Viana Di Prisco, 1997Go; Nowak et al., 1999Go). We attempted to reduce these factors by analyzing spontaneous and stimulus-evoked activity separately. Differences in the methods used to estimate the statistical significance of correlations are likely to provide a second source of analytic variation. Here the number of methods employed have been surprisingly diverse for such a small number of studies (Table 4Go). Some studies simply reported the incidence of peaked cross-correlograms without providing a measure of their statistical significance. Other studies applied statistical confidence limits to assess correlated firing, but the details of these methods vary widely across studies. Needless to say, these factors make direct comparisons difficult, and given the wide variety of both qualitative and quantitative techniques, it is perhaps not surprising that such a wide range of results would occur.

Two other factors may contribute to the differences between studies. First, cross-correlation measurements from a number of cortical areas indicate that the temporal dynamics of synchronous activity vary across cortical areas (Gochin et al., 1991Go; Frien et al., 1994Go; Freiwald et al., 1997Go; Roe and Ts'o, 1997Go; Nowak et al., 1999Go). Thus, it is not entirely unexpected that the properties of correlated firing would differ between V1 and IT, for example. Second, variation in behavioral state is also likely to influence the properties of synchronous activity. One striking example of this is apparent between two studies of area MT. In alert fixating macaques, Kreiter and Singer (Kreiter and Singer, 1996Go) reported that synchronization was stimulus dependent. They showed changes in synchronized firing that depended on whether the cells were stimulated by one or two drifiting bars. In contrast, de Oliveira et al. (de Oliveira et al., 1997Go) showed that neuronal synchronization was suppressed by visual stimulation during a motion discrimination task. This suggests that task demands may have a profound impact on synchronization dynamics in area MT.

When each of these factors are taken into consideration, it is not possible to reconcile all the reported differences in results between the studies listed in Table 4Go. There are common findings in some of the studies, but there are also widespread differences. The broad divergence of findings suggests that agreement between studies will only come when efforts are made to apply common experimental and analytical methods to the investigation of specific cortical areas.

Comparison of Response Synchronization in Cat and Monkey

In spite of the complexity of correlation dynamics in the monkey, as well as in the cat, a number of our findings are comparable to those of previous studies. As has been reported numerous times, we find that the incidence and magnitude of correlated firing decreases with the spatial separation between the cells (Toyama et al., 1981aGo,bGo; Michalski et al., 1983Go; Ts'o et al., 1986Go; Ts'o and Gilbert, 1988Go; Gray et al., 1989Go; Engel et al., 1990Go; Schwarz and Bolz, 1991Go; Hata et al., 1993Go; Livingstone, 1996Go), and that short-range synchronization occurs with roughly equal probability between cells of all orientation preferences (Engel et al., 1990Go, 1991cGo; Das and Gilbert, 1999Go). These data are roughly consistent with the patterns of intracortical connections in striate cortex, where the density of horizontal connections falls off sharply with distance, and where local connections tend to lack the bias for connecting regions of similar orientation preference that is present in the long range connections (Gilbert and Wiesel, 1983Go, 1989Go; Rockland and Lund, 1983Go; Kisvarday and Eysel, 1992Go; Lund et al., 1993Go; Malach et al., 1993Go; Yoshioka et al., 1996Go; Levitt et al., 1996Go; Kisvarday et al., 1997Go).

We also find that the properties of synchronous gamma-band activity in monkey striate cortex are very similar to those observed in the same area of the cat. In both species, rhythmically firing cells tend to fire in high-frequency bursts (Gray et al., 1990Go; Friedman-Hill et al., 1996Go; Livingstone, 1996Go). The probability of occurrence, the distributions of frequency and phase, and the stimulus dependence of synchronous oscillations are similar in the two species (Engel et al., 1990Go; Fries et al., 1997Go; Gray and Viana Di Prisco, 1997Go). Synchronous gamma-band activity rarely occurs in the absence of visual stimulation, can occur over a wide range of stimulus orientations, directions and speeds (Engel et al., 1990Go; Freiwald et al., 1995Go; Gray and Viana Di Prisco, 1997Go; Maldonado and Gray, 1997Go), and exhibits specific changes in phase related to stimulus orientation and the orientation preferences of the cells (König et al., 1995bGo). Together, these data suggest a close correspondence in the mechanisms generating synchronous gamma-band activity in the cat and monkey (Gray and McCormick, 1996Go), and imply that this form of activity may be a general feature of mammalian striate cortex, including the human (Sem-Jacobsen et al., 1956Go; Chatrian et al., 1960Go; Tallon-Baudry et al., 1996Go, 1997aGo, Tallon-Baudry et al., bGo).

Correlation between Synchrony and Oscillations

Finally, as has been reported in both the cat (König et al., 1995aGo) and the squirrel monkey (Livingstone, 1996Go), we have found that the probability and strength of response synchronization is directly correlated with rhythmic firing in the gamma frequency band. These data indicate that, although oscillatory firing is neither necessary nor sufficient for synchronization, it is a good predictor of its occurrence. This raises the possibility that oscillatory firing may contribute to the generation of synchronous activity. How this might be mediated is largely unknown, but the recent discovery of superficial pyramidal neurons in striate cortex that are capable of intrinisically generating oscillatory activity suggests one possible mechanism (Gray and McCormick, 1996Go). When depolarized above firing threshold, these neurons, referred to as ‘chattering cells’, fire brief bursts of 2–5 action potentials that are followed by a powerful afterhyperpolarization (Wang, 1999Go). Both events may be particularly effective in coordinating the activity of other neurons. Bursts of spikes can lead to temporal summation postsynaptically (Miles and Wong, 1986Go) and an increase in presynaptic release probability (Thomson et al., 1993Go; Stevens and Wang, 1995Go), each of which is likely to produce an enhanced postsynaptic response. The afterhyperpolarization following each burst may act synergistically with these effects by reducing the effectiveness of nonsynchronous inputs. The additional finding that chattering cells have horizontal axon collaterals that project within superficial cortical layers (Gray and McCormick, 1996Go) places these neurons in a key position for generating synchronized activity. Evidence for chattering cells in the striate cortex of monkeys is limited and indirect (Friedman-Hill et al., 1996Go, 2000Go), thus their role in synchronization remains speculative until further evidence can be obtained.


    Notes
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Notes
 References
 
We thank Raul Aguilar for software support and Lee Rognlie-Howes for her excellent care of the animals and technical assistance. We also thank Mike Wehr, Rony Azouz, Jean-Philippe Lachaux and Shih-Cheng Yen for their helpful comments on an earlier version of the manuscript. This work was supported by a grant from the National Eye Institute (C.M.G.) and Fellowships from the Klingenstein Foundation (C.M.G.), NIMH (S.F.H.) and McDonnell-Pew Foundation (S.F.H. and P.E.M.).

Address correspondence to Charles M. Gray, Ph.D., Center for Computational Biology, Lewis Hall, Montana State University, Bozeman, MT 59717-3505, USA. Email: cmgray{at}nervana.montana.edu.


    Footnotes
 
1 Present address: Instituto de Ciencias Biomedicas, Facultad de Medicina, Universidad de Chile, Santiago, Chile Back

2 Present address: Laboratory of Neuropsychology, NIMH, NIH, Bldg 49, Room 1B80, 49 Convent Drive, MSC 4415, Bethesda MD 20892–4415, USA Back

3 Present address: Center for Computational Biology, Lewis Hall, Montana State University, Bozeman, MT 59717-3505, USA Back


    References
 Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Notes
 References
 
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