Visual processing relies on an integration of information over space, and an understanding of spatial relationships. This integration takes place in part in a feedforward manner through convergence of neurons with small receptive fields (RFs) onto neurons with larger RFs in higher areas (Felleman and Van Essen, 1991; Serre et al., 2005; Lamme and Roelfsema, 2000; DiCarlo et al., 2012). However, already in early visual cortex, neuronal responses to sensory inputs into the RFs are strongly modulated by the spatio-temporal context in which they are embedded. For instance, the firing rates of V1 neurons to stimuli in their classical RF (CRF), that is the region in space where stimuli have a strong driving effect, can be increased or decreased by stimuli presented in their surround (Vinje and Gallant, 2000; Rao and Ballard, 1999; Angelucci et al., 2017; Gilbert, 1992). In terms of anatomy, surround modulation represents a departure from feedforward processing. It is mediated by recurrent lateral and feedback connections, through which a given V1 neuron can be informed about a larger region of space than covered by its CRF (Lund et al., 1993; Gilbert, 1992; Angelucci et al., 2017). One view on surround modulation is that it merely represents a form of normalization, such that neuronal firing rates are essentially scaled by the amount of drive in the surround (Carandini and Heeger, 2011). Additionally, it has been suggested that surround modulation may play an important role in various related functions like contour integration (Liang et al., 2017), perceptual filling-in (Zweig et al., 2015; Land, 1959; Wachtler et al., 2003), figure-ground separation (Lamme, 1995), computation of a saliency map (Coen-Cagli et al., 2012; Li, 2002), as well as efficient and predictive coding operations (Rao and Ballard, 1999; Vinje and Gallant, 2000).

Theories of efficient coding postulate that surround suppression of neuronal firing contributes to remove image redundancies across space from neuronal representations (Schwartz and Simoncelli, 2001; Simoncelli and Olshausen, 2001; Coen-Cagli et al., 2012; Coen-Cagli et al., 2015; Rao and Ballard, 1999; Barlow, 2001; Vinje and Gallant, 2000; Zhu and Rozell, 2013). Predictive coding theories hold that neuronal responses result from a comparison between predictions from the surround and the inputs into the CRF (Friston, 2005; Rao and Ballard, 1999; Spratling, 2010). Most studies on surround modulation have focused on the modulation of neuronal firing rates. However, if the modulation of neuronal firing rates arises from specific relationships between stimuli across space, then it might also have important consequences for temporal correlations among neuronal responses (Singer and Gray, 1995).

Recent work has extended the frameworks of efficient and predictive coding beyond firing rate modulations to include neuronal synchronization (Bastos et al., 2012; Jadi and Sejnowski, 2014; Chalk et al., 2016; Vinck and Bosman, 2016). Neuronal synchronization plays a functional role for the encoding and transmission of information, as well as for synaptic plasticity, and may therefore play an important role in contextual integration processes (Buzsáki et al., 2012; Vinck et al., 2010a; Havenith et al., 2011; Salinas and Sejnowski, 2000; Simoncelli and Olshausen, 2001; Sejnowski and Paulsen, 2006; Fries, 2005; Fries et al., 2007; Fries, 2009; Börgers and Kopell, 2008; Kopell et al., 2000; Varela et al., 2001; Wang, 2010; Buzsáki, 2006; Cardin et al., 2009; Singer, 1999; Singer and Gray, 1995; Bressler et al., 1993; O'Keefe and Recce, 1993; Bernander et al., 1994; Abeles, 1982; Kempter et al., 1998; Azouz and Gray, 2000; Sohal et al., 2009; Ballard and Jehee, 2011; Ballard and Zhang, 2018; Akam and Kullmann, 2010; Akam and Kullmann, 2014; Palmigiano et al., 2017). A distinguishing feature of V1 activity, induced by many stimulus conditions, is synchronization of neuronal activity in the gamma-frequency band ($\approx$30–80 Hz) (Vinck and Bosman, 2016; Fries, 2009; Gray et al., 1989; Ray and Maunsell, 2010; Gieselmann and Thiele, 2008; Jia et al., 2013b). A link between contextual modulation processes and V1 gamma-band synchronization is suggested by the finding that the amplitude of V1 gamma oscillations increases with stimulus size and therefore surround stimulation (Gieselmann and Thiele, 2008; Ray and Maunsell, 2011; Jia et al., 2013b; Chalk et al., 2010; Perry et al., 2013; Jia et al., 2011; Gray et al., 1990, for a detailed discussion, see Vinck and Bosman, 2016). There are different views on the way in which gamma oscillations might relate to predictive and efficient coding operations and therefore center-surround relationships (Jadi and Sejnowski, 2014; Bastos et al., 2012; Arnal and Giraud, 2012; Vinck and Bosman, 2016; Chalk et al., 2016; Korndörfer et al., 2017). Bastos et al. (2012) and Arnal and Giraud (2012) hypothesized that gamma-band synchronization subserves the encoding and transmission of prediction error signals in the feedforward direction, and that lower frequency bands carry feedback predictions from higher areas (Bastos et al., 2012; Bastos et al., 2014; Arnal and Giraud, 2012). Consistent with this hypothesis, bottom-up and top-down Granger-causal influences are strongest in the gamma and alpha/beta ($\approx$10–20 Hz) band, respectively (Bastos et al., 2014; Richter et al., 2018; van Kerkoerle et al., 2014 Bressler et al., 2006; Bosman et al., 2012; Michalareas et al., 2016). According to this hypothesis, a mismatch between center and surround stimuli should lead to an increase in both firing rates and gamma-band synchronization, conveying prediction error signals. In contrast, Vinck and Bosman (2016) recently hypothesized that (1) the amplitude of gamma oscillations in a given column reflects the extent to which classical RF inputs are predictable from the surround and (2) that gamma-band synchronization among columns with non-overlapping RFs reflects predictability among their visual inputs. This could in turn provide a mechanism for orchestrating interactions between distributed neuronal columns, and for integrating efficiently encoded signals in higher visual areas (Vinck and Bosman, 2016) (see Discussion). According to this hypothesis, redundancy between center and surround stimuli should lead to a decrease in firing rates (reflecting efficient coding) yet an increase in gamma-band synchronization. To distinguish between these conflicting views, precise manipulations of center-surround predictability are required.

As a starting point to test the interdependence between center-surround relationships, synchronization and firing rates, we considered the case of uniform surfaces and varied their size, center-surround relationships and color. The latter is an important feature for object recognition and visual search, and plays a role in social interactions and foraging (Santos et al., 2001; Waitt et al., 2006; Gerald et al., 2007; Bichot et al., 2005; D'Zmura, 1991; Corso et al., 2016). Despite the importance of the color domain in vision, colored stimuli have rarely been studiedwith respect to gamma synchronization (Rols et al., 2001; Shirhatti and Ray, 2018; Brunet et al., 2015). Furthermore, uniform surfaces are of particular interest because they contain highly redundant information across a relatively large image region. The predictability hypothesis (Vinck and Bosman, 2016) suggests that large, uniform surfaces should reliably induce gamma-band synchronization but be accompanied by low neuronal discharge rates. It further suggests that a modulation of center-surround predictability due to a chromatic mismatch (e.g. red center and green surround stimulus) should strongly reduce gamma-band synchronization but increase firing rates.

Uniform surfaces are characterized by hue, luminance and saturation and can be broadly divided into chromatic and achromatic (black and white) surfaces. Although uniform chromatic and achromatic surfaces both have a high degree of predictability at the (physical) image level, there are likely substantial differences in the way these surfaces are processed by area V1. Correspondingly, their predictability on the neuronal level likely differs. There are two different ways through which V1 surface representations may arise (Zweig et al., 2015). First, neurons with RFs at the uniform region of a surface stimulus (e.g. the center) may be directly activated. These may generate redundant (predictable) signals locally. Specifically for chromatic surfaces, single-opponent, hue-selective neurons (in LGN or V1) with RFs at the uniform surface region may directly encode color and luminance information. These are neurons with L+/M-, M+/L-, or blue (S) and yellow (L and M) color opponencies (Shapley and Hawken, 2011; Livingstone and Hubel, 1984). Second, surface information may be encoded by neurons with RFs at the edge of the surface, and then propagate toward neurons with RFs at the uniform region of the surface (Zweig et al., 2015; Land, 1959; Wachtler et al., 2003). The relative contributions of these two mechanisms (local vs. edge-derived) remain largely unknown and likely differ between chromatic and achromatic surfaces (Zweig et al., 2015; Zurawel et al., 2014). Zweig et al. (2015) have addressed this question by making voltage-sensitive dye recordings of V1 populations and comparing the responses at the surface’s center to the edge (Zweig et al., 2015). Activity patterns for achromatic surfaces were consistent with an edge-derived ‘fill-in’ process (Zweig et al., 2015). However, this was not observed for chromatic surfaces (Zweig et al., 2015), which could be due to the availability of another, surface rather than edge-based, information source provided by single-opponent cells. Because contextual interactions likely have a different nature for achromatic than chromatic stimuli, we asked whether there are differences in the contextual modulation of firing activity and gamma-band synchronization between these two classes of stimuli.

In this study, we investigated the contextual modulation of V1 firing activity and gamma-band synchronization using chromatic and achromatic surfaces of different sizes, and a center-surround mismatch paradigm. Additionally, we examined differences among color hues, adaptation over time, and the influence of the full-screen background (FSB) on which surfaces were displayed.

We recorded multi-unit (MU) activity and local field potentials (LFP) from the primary visual cortex (area V1) in two macaque monkeys, while they performed a fixation task. These recordings were made using a 64-channel chronic microelectrode array in monkey H and a 32-channel semichronic microelectrode array in monkey A (see Materials and methods). Classical receptive fields (RFs, referring to classical RFs unless otherwise mentioned) of the MU activity were estimated using moving bar stimuli (see Materials and methods; monkey H: median RF eccentricity 6.2 deg, range 5.2–7.1 deg, median RF diameter 0.48 deg, range 0.26–1.88 deg; monkey A: median eccentricity 5.4 deg, range 3.2–8.5 deg, median RF diameter 0.91 deg, range 0.46–2.3 deg). Compared to a surface stimulus of 6 deg diameter, receptive fields had a median proportional diameter of 0.08 (0.04–0.31, monkey H) or 0.15 (0.08–0.38, monkey A). We first studied LFP and MU responses to the presentation of stationary surface stimuli, namely large uniform disks covering the cluster formed by all RFs (6 deg diameter, flashed on and then maintained on screen; Figure 1A–B; Dataset 1, see Materials and methods). The stimuli did not overlap with the fixation spot. Note that the stimuli were much larger than the RFs of the multi-units, such that they covered a large portion of the multi-units’ respective surround regions.

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Initially, we analyzed differences between chromatic and achromatic surface stimuli (Figures 1 and 2; see Materials and methods). We then considered the specific differences among responses to distinct color hues and achromatic stimuli (Figures 4–7). The LFP power spectra had similar frequency profiles in the two monkeys (i.e. the peaks were well aligned; Figure 1—figure supplement 2), and the MUs showed similar temporal profiles (Figure 1—figure supplement 2). Therefore, we pooled the data from the two animals. Note that statistical parameters are largely described in the figure captions.

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Modulation of firing activity and LFP signals by center-surround predictability

A potential explanation for the results shown in Figures 1–2 may be the center-surround predictability hypothesis outlined in the Introduction (Vinck and Bosman, 2016). Yet, the employed paradigm used stimuli of different sizes, which may have recruited different neuronal circuits and may also have changed stimulus salience. We therefore used an additional stimulus paradigm in which surround influences were modified, while stimulus size was held constant. Specifically, we created three sets of equally sized stimuli. In one set, the surround was fully predictive of the RF stimulation, because it used a uniform surface (called ‘uniform’ stimulus). In the second set (called ‘blob mismatch’), the surround was not predictive of the RF stimulation, because the surround stimulus and the 1 deg RF stimulus had different colors (which were physically equiluminant). In the third set (called ‘annulus mismatch’), the surround had the same color as the RF stimulation, but the two were separated by an annulus ring of a different, physically equiluminant color. This annulus ring had 0.25 deg thickness and an inner diameter of 1 deg.

We found that compared to the uniform surfaces, stimuli with a chromatic (blob or annulus) mismatch had higher MU firing activity (Figure 3C). This held true both for the initial transient period and the subsequent sustained response period (Figure 3C). At the same time, we observed a marked decrease in the amplitude of LFP gamma oscillations for the chromatic mismatch compared to the uniform surface stimuli (Figure 3B–C). This result was consistent across animals (Figure 3—figure supplement 1A).

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We further investigated whether this pattern of changes was specific to the sites having RFs near the center stimulus. To this end, we examined sites with RFs on the outer uniform regions of the stimulus (with RF centers 1.5–2 deg from the stimulus center; Figure 3D). For these sites the MU firing responses did not differ significantly between conditions during the initial transient period (Figure 3D). During the later sustained response phase, however, MU firing activity was reduced for the chromatic mismatch stimuli compared to the uniform surface stimulus (Figure 3D). Note that whenever the RF center covered a large uniform surface region, either in the uniform stimulus condition or when it covered the surround region of the mismatch stimuli, sustained firing levels were below baseline. This confirmed the respective finding reported in Figure 1.

These results suggest that a mismatch between stimuli at the RF center and the surround can dramatically change the surround influence on responses to the center. We wondered whether the surround influence on gamma oscillations originates from the uniform surface region or rather from the edge of the surface. To this end, we analyzed sessions in which we compared two sets of trials: First, trials with a full surface stimulus centered on a site’s RF (‘RF-on-center’ condition; Figure 3—figure supplement 1B). Second, trials with a full surface stimulus positioned such that its edge fell into the RF center, that is with the surface shifted by 3 deg horizontally (‘RF-on-edge’ condition; Figure 3—figure supplement 1B). We found that the amplitude of gamma oscillations was significantly higher at the center (‘RF-on-center’) than at the edge of the surface stimulus (‘RF-on-edge’), whereas the opposite was observed for MU firing activity (Figure 3—figure supplement 1B). In one session (monkey H), we also showed disk stimuli that had their edge blurred with a Gaussian (2.5 deg size, 1 deg standard deviation). There were clear gamma-responses also in this case (Figure 3—figure supplement 1C).

Together, these results indicate that for colored surfaces the amplitude of gamma-band oscillations is commensurate with the ‘chromatic’ predictability among visual inputs in space, and that gamma-band oscillations are not a mere consequence of input drive to a larger cortical region. Furthermore, these results suggest that gamma strength can be dissociated from stimulus salience, because the chromatic mismatch condition provided a highly salient stimulus in the RF, but resulted in weaker gamma.

Differences in firing activity and LFP signals between color hues

The results above show prominent differences between chromatic and achromatic surfaces in terms of gamma-band synchronization. The respective analyses pooled different chromatic conditions (equiluminant red, green and blue) together. However, there may exist further differences in gamma-band synchronization within the chromatic conditions, that is between different hues. To investigate this we used two types of stimulus sets, which were presented in separate sessions. In the first stimulus set (Figure 4A), we presented each surface color at its maximum possible luminance level (given the limits of the employed monitor), and sampled from the entire spectrum of hues available with the monitor (see Materials and methods). In the second stimulus set, we presented surface stimuli with different color hues at equated luminance levels (Figure 4B–C).

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Using the first stimulus set, we found that gamma-band LFP oscillations were reliably induced across the entire spectrum of hues (Figure 4A, Figure 4—figure supplement 1, see also Supplementary file 1 for all luminance and CIE values). In addition, we found that gamma-band synchronization was reliably induced by surfaces with ‘extra-spectral’ colors, that is colors resulting from a mixture of blue and red primaries (Figure 4A), as well as brownish hues. We further replicated our finding that gamma oscillations were relatively weak for both black and white surface stimuli as compared to all colored surfaces (Figure 4A). In one monkey (A), we found that gamma-band activity was stronger for black than for white stimuli, consistent with previous results showing stronger firing rate responses to black than white stimuli (Xing et al., 2010; Yeh et al., 2009). However, a trend in the opposite direction was observed for monkey H (Figure 4—figure supplement 2).

For the first stimulus set (Figure 4A), the different colors were presented at their maximum possible luminance levels, which might confound the effects of hue and luminance. We therefore used a second stimulus set in which we presented surface stimuli with different color hues at three levels of equal physical luminance,that is different color values (Figure 4B–C). For all three hues, gamma amplitudes were greater for the highest compared to the lowest luminance condition (p$<$0.05, bootstrap test, see Materials and methods; Figure 4B–C). The dependence of gamma amplitude on stimulus luminance was greater for green than for red or blue surface stimuli (difference between high versus low, Figure 4B–C). Gamma oscillations had a higher amplitude for red than for blue or green surface stimuli across all three luminance conditions (Figure 4B–C), whereas gamma amplitude was higher for blue than green surface stimuli for low and intermediate luminance conditions (Figure 4(B–C)). Another difference between the hues was that the gamma peak had a significantly lower frequency for green compared to red or blue surface stimuli (p$<$0.05, bootstrap test; Figure 4B–C and Figure 4—figure supplement 2). The results of these analyses were consistent across both monkeys (Figure 4—figure supplement 2).

Given the relationships between MU firing activity and LFP gamma-band oscillations shown in Figures 1–3, we asked how these differences in LFP gamma oscillations were related to changes in firing activity. During the initial transient, MU firing activity was higher for red and blue rather than for green surface stimuli, with slightly stronger responses for red than blue surface stimuli (Figure 4D). Yet, we found that the post-transient decrease in MU firing activity over time was particularly pronounced for red and particularly weak for green stimuli (Figure 4D). In agreement with the data shown in Figure 1, we observed that MU firing activity fell below baseline levels for red and blue surface stimuli (Figure 4D).

Together, these results indicate that surfaces of all color hues tend to induce gamma-band oscillations with a higher amplitude compared to achromatic surfaces, and that the amplitude of gamma oscillations is relatively high for red surfaces.

Temporal evolution of gamma-band responses

The observed differences in gamma oscillations between the color hues (Figure 4) might reflect a static and context-independent property of visual cortex to respond differently to distinct hues. Yet, the continuous presentation of a uniform surface stimulus for the duration of an entire trial likely induces substantial adaptation at many levels of the nervous system. We thus wondered whether different hues might adapt at different rates. To address this, we examined the temporal evolution of LFP power spectra over a longer time period, that is up to 3 s after stimulus onset. Time-frequency representations showed that qualitative differences between hues and between luminance levels tended to be relatively stable over time (Figure 5A). However, we found that the amplitude of gamma-oscillations decreased more rapidly over time for green than for blue or red surface stimuli (Figure 5A–B). This result held also for both animals individually (monkey H/A red change over time −0.07$\pm$0.05/–0.12$\pm$0.04, green −0.51$\pm$0.06/–0.36$\pm$0.04, blue −0.23$\pm$0.05/–0.10$\pm$0.04). The main effect of decrease with time, as well as stronger decreases for green compared to both red and blue, were signicant in both animals individually (all p < 0.05 corrected for multiple comparisons).

Figure 5

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This suggests that there may be differences in the time course and strength of adaptation between color hues, specifically stronger adaptation for green surface stimuli.

Dependence on full-screen background hue

One potential source of adaptation, other than the surface stimulus of a given trial, is the color composition of the continuously presented background. In the experiments described above, all surface stimuli were displayed on a gray FSB. Gamma-band responses to achromatic and chromatic surface stimuli may have been affected by the use of this gray FSB, given that the FSB itself may induce adaptation at many levels of the nervous system. We therefore asked how gamma-band responses to surface stimuli depend on the color of the FSB. To answer this question, we performed experiments in which we used different FSBs in separate, adjacent sessions (gray, white, black, blue, green, yellow and red) (Figure 6A and Figure 6—figure supplement 1; see Materials and methods). The FSB was continuously presented during the entire session, that is remained on both during the pre-stimulus period, post-stimulus period and the period during which the surface stimuli were displayed (Figure 6A). In Figure 6, we analyze LFP responses to the presentation of chromatic surface stimuli of different hues, which were presented at the maximum possible luminance level (see Figure 6—figure supplement 2 for equiluminant red, green and blue as well as achromatic surface stimuli).

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We first examined how the responses to surface stimuli with specific hues (e.g. green) were altered by using an FSB with the same hue (e.g. green), comparing them to the sessions with a gray FSB (Figure 6B). Surprisingly, we found that when the FSB had the same hue as the surface stimulus, there was a nearly complete abolishment of gamma oscillations for blue, green and yellow stimuli (Figure 6B and Figure 6—figure supplement 2). This was observed both when the surface stimulus had the same luminance as the background (Figure 6) and when the surface stimulus had a lower luminance (Figure 6—figure supplement 2). Interestingly, red stimuli could still induce detectable gamma oscillations when presented on a red FSB, although the gamma amplitude was strongly reduced compared to the gray FSB condition (Figure 6B and Figure 6—figure supplement 2).

The reduction in gamma-band oscillations for the same-hue FSB condition may have been an effect of stimulus size, because the background is effectively a very large surface. Alternatively, it may have been an effect of stimulus history. To investigate these possibilities, we analyzed the post-stimulus period immediately following the offset of a gray surface stimulus that was displayed on a colored FSB. We found that the reappearance of the FSB after the offset of the gray surface induced prominent gamma-band oscillations (Figure 6—figure supplement 3). This indicates that the decrease in gamma-band oscillations with the same hue FSB condition was not due to the large size of the background color stimulation, but that it was due to the continuous presence of the same-hue background. This is also consistent with a previous report showing strong gamma with full-screen color stimuli that change color across trials (Shirhatti and Ray, 2018), and with the positive relation between gamma and stimulus size shown in Figure 2C.

Next, we considered interactions between distinct hues. We wondered whether gamma oscillations can not only be reduced by same-hue FSBs, but also enhanced by FSB hues that are different from the stimulus hue, in particular when FSB and stimulus assume opponent colors. The organization of color vision around color-opponency axes, namely the red-green and the blue-yellow axes, is a key principle found both at the neurophysiological and psychophysical level (Wachtler et al., 2003; Livingstone and Hubel, 1984; Solomon and Lennie, 2007; Tailby et al., 2008a). These color opponencies are thought to result from the computation of differences among signals deriving from L and M cones (red-green), and S cones versus L and M cones (blue-yellow). We found that for all surface hues, gamma oscillations were amplified when stimuli and FSBs were of opponent color hues (Figure 6C). This suggests that gamma oscillations are dependent on opponency signals along the red-green and the blue-yellow axes (Figure 6C). Given the strong dependence of gamma-band oscillations on the FSB, we asked whether the use of a gray FSB may have induced differences in gamma-band amplitude among distinct hues. To examine this possibility, we used a black FSB, which should induce minimal adaptation for all cones. Quite surprisingly, the difference among red, green and blue hues that we had observed with a gray FSB could not be replicated when we presented the stimuli on a black FSB (Figure 6D). Compared to the gray FSB condition, gamma-band amplitudes were significantly lower for red and blue surface stimuli and significantly higher for yellow and green surface stimuli (Figure 6D). As a consequence, for the black FSB condition, gamma-band power was no longer highest in response to red stimuli, but showed a different dependence on hue (Figure 6D; Figure 6—figure supplement 2). Specifically, gamma-band power was higher for green and yellow than red and blue surface stimuli (Figure 6D). The resulting pattern could not be explained by luminance contrast differences, because contrast increased for all hues on the black compared to the gray FSB, whereas gamma increased for some hues and decreased for others (Figure 6D).

In Figure 6B–C, we compared stimulus responses in the same-hue and opponent-hue background conditions with stimulus responses in the gray background condition. However, because of the evidence that the gray FSB may not have affected all stimulus hues equally, we also directly compared the same-hue and opponent-hue FSB conditions with the black FSB condition. This analysis revealed a marked difference between red and the other hues (Figure 6E). First, when an FSB of the same hue as the stimulus was compared to a black FSB, gamma was almost abolished for blue, green and yellow, but not for red stimuli. Second, when an FSB of the opponent hue was compared to a black FSB, gamma was enhanced for all colors, but particularly strongly for red (Figure 6E). The full matrix of gamma responses for different FSB conditions in Figure 6—figure supplement 2 shows that for all non-red chromatic FSBs, gamma oscillations were strongly amplified for red surface stimuli.

We also analyzed the gamma-responses to achromatic stimuli on colored backgrounds, and asked in particular whether responses of achromatic stimuli on colored surfaces were as strong as responses of colored stimuli on achromatic surfaces (Figure 6F ). Achromatic responses on colored backgrounds were substantially weaker than the reverse (see Section 'Controls for luminance-contrast and cone-contrast'). These data demonstrate that gamma oscillations depend strongly on the FSB, in a way that follows the color-opponency axes. Furthermore, a commonly used ‘default’ of the display, namely gray, introduces adaptation effects that are color-specific.

A quantitive model relating hue dependence of gamma-band oscillations to adaptation

To explain how gamma-band responses to surface stimuli depend on the FSB, we constructed a quantitative model by estimating the degree to which each FSB differentially adapts the S-, M- and L-cone pathways. Note that this model is agnostic to the neuronal locus at which adaptation of the distinct cone pathways occurs, for example it might occur in the retina, LGN or visual cortex. We hypothesize that gamma-band oscillations for colored surface stimuli are mediated by the activation of single color-opponent cells in a large spatial region by the same color input. The combination of bottom-up drive at each point of the surface and strong surround modulation may then lead to gamma oscillations. The result that gamma oscillations are particularly strong in the opponent-hue background condition (Figure 6) further suggests that when this circuit is more strongly activated (leading to stronger input drive as well as stronger surround modulation), gamma oscillations increase.

Following this reasoning, we further hypothesized that the dependence of gamma-band oscillations on the FSB can be explained by adaptation of specific cone pathways (see Discussion for further argumentation). As an example, a green FSB should lead to stronger adaptation of the M-cone compared to the L-cone pathway. This should increase the degree to which single-opponent cells with L+/M- color-opponencies are activated by red surface stimuli, which may in turn increase the amplitude of gamma-band oscillations.

To capture this intuition in a quantitative manner, we constructed a model in which we aimed to predict the difference in gamma-band amplitudes between red and green surface stimuli (for blue and yellow surface stimuli see further below). The variable to be predicted was the red-green gamma ratio, defined as ${\gamma }_{ratio}={\log }_{10}({\gamma }_{red}/{\gamma }_{green})$, where ${\gamma }_{red}$ and ${\gamma }_{green}$ are the respective gamma-amplitudes for red and green surface stimuli. This ${\gamma }_{ratio}$ was computed separately for all the different FSBs. We estimated the degree to which each FSB adapts the M- and L-cones, using the known response curves of the three cones as a function of wavelength from macaque monkeys (Hárosi, 1987) (see Materials and methods, Figure 7A). We then measured the physical wavelength spectrum for each FSB as realized on our monitor. We multiplied the FSB spectra of the different color primaries with the response functions of each cone and summed over wavelengths. This yielded for each FSB stimulus two parameter values, $M_{adapt}$ and $L_{adapt}$. We then fitted a multiple regression model predicting ${\gamma }_{ratio}$ from $M_{adapt}$ and $L_{adapt}$ plus a constant regression intercept (Figure 7B). For this model, we used response data for both green and red surfaces presented at maximum possible luminance, as well asequiluminant red and green surfaces, across the different FSBs.

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The regression analysis reveals that ${\gamma }_{ratio}$ can be highly accurately predictedbythe way in which each FSB adapts the L and the M cones (Figure 7B; R${}^2$ = 0.91, p < 0.05, F-Test). The regression coefficients for $M_{adapt}$ and $L_{adapt}$ were positive and negative, respectively. This indicates that adaptation of the M-cone increases ${\gamma }_{ratio}$, whereas adaptation of the L-cone decreases ${\gamma }_{ratio}$ (Figure 7B). We also found that the${\gamma }_{ratio}$ could not be significantly predicted when using $S_{adapt}$ and $L_{adapt}$ as predictors (p=0.23), consistent with the idea that the neuronal mechanisms underlying the red-green opponency are dependent on the M versus L cone contrast. The regression intercept of the model (on the ${\gamma }_{ratio}$ axis) was not significantly different from zero. This indicates that green and red tend to generate gamma oscillations of similar amplitude when the FSB does not adapt the cones, consistent with the findings shown for the black FSB (Figure 6). Strikingly, we found that the $M_{adapt}$ coefficient had an absolute magnitude approximately twice as large as the $L_{adapt}$ coefficient (Figure 7B). This suggests that uniform surfaces tend to adapt the M-cone pathway more strongly than the L-cone pathway, or that adaptation of the M-cone pathway has a stronger effect on gamma-band oscillations than adaptation of the L-cone pathway.

The model further explains some non-trivial findings that would have been unexpected if FSBs had affected the M- and L-cone pathway in a similar way: We observed that the yellow FSB strongly amplified ${\gamma }_{ratio}$ (Figure 7B). Given its wavelength spectrum, the yellow FSB is expected to adapt the L-cones more strongly than the M-cones, which would predict a reduced ${\gamma }_{ratio}$,that is red responses being weaker than green responses. By contrast, we found that ${\gamma }_{ratio}$ was enhanced. The models explain this by the fact that the stronger L-cone than M-cone activation by the yellow background is more than compensated by the much greater $M_{adapt}$ than $L_{adapt}$ coefficient. Similarly, the ${\gamma }_{ratio}$ increased for a gray compared to a black FSBs, even though gray FSBs should in principle adapt the M- and L-cone pathways to a similar degree (Figure 6). This was again compensated by the much greater $M_{adap}$ than $L_{adap}$ coefficient.

We performed a similar analysis for the yellow-blue (L + M S) opponency axis, aiming to predict the gamma ratio of blue over yellow (Figure 7C). We first fitted a model with the S, L and the M cone parameters, and ${\gamma }_{ratio}$ was now defined as ${\gamma }_{ratio}={\log }_{10}({\gamma }_{blue}/{\gamma }_{yellow})$. This regression model explained a large degree of variance ($R^2=0.99$), with almost equal magnitude of S (negative, −1.23) and L (positive, 1.24) coefficients, but a much smaller and non-significant coefficient for the M cone (−0.29). The finding that the model fit included a highly positive L-cone coefficient and non-significant (and negative) M-cone coefficient seems prima facie to contradict the canonical idea that the perceptual blue-yellow opponency axis is mediated by an S versus (L + M) opponency. However, neurophysiological data has shown that the main opponency for LGN cells on the yellow-blue axis is the L versus S cone (Tailby et al., 2008b). We further simplified our model using two predictive parameters, equating the blue axis to the S cone and the yellow axis to the L cone (Figure 7C). Again, we found a highly predictive relationship with negative weight for the S cone and a positive weight for the L cone (Figure 7C), with only a small and non-significant difference in the magnitude of the adaptation coefficients. The results were qualitatively highly similar between animals and individually significant (Figure 7—figure supplement 1). These findings indicate that gamma oscillations are mediated not only by opponency signals along the red-green axis, but also along the blue-yellow axes, consistent with the data shown in Figure 4—figure supplement 4.

All procedures complied with the German and European regulations for the protection of animals and were approved by the regional authority (Regierungspräsidium Darmstadt).

As described in the Methods section, several datasets were acquired in this study. Datasets have been uploaded onto Dryad (https://doi.org/10.5061/dryad.4809qj4). These include individual sessions with each session preprocessed (downsampled, see Methods) with MUA and single-trial spectra as described on Dryad.

1. 1. Alina Peter
   2. Cem Uran
   3. Johanna Klon-Lipok
   4. Rasmus Roese
   5. Sylvia van Stijn

   (2019) Dryad Digital Repository

   Data from: Surface color and predictability determine contextual modulation of V1 firing and gamma oscillations.

   https://doi.org/10.5061/dryad.4809qj4

1. 1. Abeles M

   (1982)

   Role of the cortical neuron: integrator or coincidence detector?

   The Israel Medical Association Journal  18:83–92.

   • Google Scholar
2. 1. Akam T
   2. Kullmann DM

   (2010) Oscillations and filtering networks support flexible routing of information

   Neuron 67:308–320.

   https://doi.org/10.1016/j.neuron.2010.06.019

   • PubMed
   • Google Scholar
3. 1. Akam T
   2. Kullmann DM

   (2014) Oscillatory multiplexing of population codes for selective communication in the mammalian brain

   Nature Reviews Neuroscience 15:111–122.

   https://doi.org/10.1038/nrn3668

   • PubMed
   • Google Scholar
4. 1. Angelucci A
   2. Bijanzadeh M
   3. Nurminen L
   4. Federer F
   5. Merlin S
   6. Bressloff PC

   (2017) Circuits and mechanisms for surround modulation in visual cortex

   Annual Review of Neuroscience 40:425–451.

   https://doi.org/10.1146/annurev-neuro-072116-031418

   • PubMed
   • Google Scholar
5. 1. Arnal LH
   2. Wyart V
   3. Giraud AL

   (2011) Transitions in neural oscillations reflect prediction errors generated in audiovisual speech

   Nature Neuroscience 14:797–801.

   https://doi.org/10.1038/nn.2810

   • PubMed
   • Google Scholar
6. 1. Arnal LH
   2. Giraud AL

   (2012) Cortical oscillations and sensory predictions

   Trends in Cognitive Sciences 16:390–398.

   https://doi.org/10.1016/j.tics.2012.05.003

   • PubMed
   • Google Scholar
7. 1. Azouz R
   2. Gray CM

   (2000) Dynamic spike threshold reveals a mechanism for synaptic coincidence detection in cortical neurons in vivo

   Proceedings of the National Academy of Sciences 97:8110–8115.

   https://doi.org/10.1073/pnas.130200797

   • PubMed
   • Google Scholar
8. 1. Ballard DH
   2. Jehee JF

   (2011) Dual roles for spike signaling in cortical neural populations

   Frontiers in Computational Neuroscience 5:22.

   https://doi.org/10.3389/fncom.2011.00022

   • PubMed
   • Google Scholar
9. Preprint

   1. Ballard D
   2. Zhang R

   (2018) Cortical spike multiplexing using gamma frequency latencies

   bioRxiv.

   https://doi.org/10.1101/313320

   • Google Scholar
10. 1. Barlow H

    (2001) Redundancy reduction revisited

    Network: Computation in Neural Systems 12:241–253.

    https://doi.org/10.1080/net.12.3.241.253

    • PubMed
    • Google Scholar
11. 1. Barone P
    2. Batardiere A
    3. Knoblauch K
    4. Kennedy H

    (2000) Laminar distribution of neurons in extrastriate areas projecting to visual areas V1 and V4 correlates with the hierarchical rank and indicates the operation of a distance rule

    The Journal of Neuroscience 20:3263–3281.

    https://doi.org/10.1523/JNEUROSCI.20-09-03263.2000

    • PubMed
    • Google Scholar
12. 1. Bartolo MJ
    2. Gieselmann MA
    3. Vuksanovic V
    4. Hunter D
    5. Sun L
    6. Chen X
    7. Delicato LS
    8. Thiele A

    (2011) Stimulus-induced dissociation of neuronal firing rates and local field potential gamma power and its relationship to the resonance blood oxygen level-dependent signal in macaque primary visual cortex

    European Journal of Neuroscience 34:1857–1870.

    https://doi.org/10.1111/j.1460-9568.2011.07877.x

    • PubMed
    • Google Scholar
13. 1. Bartos M
    2. Vida I
    3. Jonas P

    (2007) Synaptic mechanisms of synchronized gamma oscillations in inhibitory interneuron networks

    Nature Reviews Neuroscience 8:45–56.

    https://doi.org/10.1038/nrn2044

    • Google Scholar
14. 1. Bastos AM
    2. Usrey WM
    3. Adams RA
    4. Mangun GR
    5. Fries P
    6. Friston KJ

    (2012) Canonical microcircuits for predictive coding

    Neuron 76:695–711.

    https://doi.org/10.1016/j.neuron.2012.10.038

    • PubMed
    • Google Scholar
15. 1. Bastos AM
    2. Briggs F
    3. Alitto HJ
    4. Mangun GR
    5. Usrey WM

    (2014) Simultaneous recordings from the primary visual cortex and lateral geniculate nucleus reveal rhythmic interactions and a cortical source for γ-band oscillations

    Journal of Neuroscience 34:7639–7644.

    https://doi.org/10.1523/JNEUROSCI.4216-13.2014

    • PubMed
    • Google Scholar
16. 1. Bastos AM
    2. Vezoli J
    3. Bosman CA
    4. Schoffelen JM
    5. Oostenveld R
    6. Dowdall JR
    7. De Weerd P
    8. Kennedy H
    9. Fries P

    (2015) Visual areas exert feedforward and feedback influences through distinct frequency channels

    Neuron 85:390–401.

    https://doi.org/10.1016/j.neuron.2014.12.018

    • PubMed
    • Google Scholar
17. 1. Bernander Ö
    2. Douglas RJ
    3. Martin KA
    4. Koch C

    (1991) Synaptic background activity influences spatiotemporal integration in single pyramidal cells

    PNAS 88:11569–11573.

    https://doi.org/10.1073/pnas.88.24.11569

    • PubMed
    • Google Scholar
18. 1. Bernander Ö
    2. Koch C
    3. Usher M

    (1994) The effect of synchronized inputs at the single neuron level

    Neural Computation 6:622–641.

    https://doi.org/10.1162/neco.1994.6.4.622

    • Google Scholar
19. 1. Bichot NP
    2. Rossi AF
    3. Desimone R

    (2005) Parallel and serial neural mechanisms for visual search in macaque area V4

    Science 308:529–534.

    https://doi.org/10.1126/science.1109676

    • PubMed
    • Google Scholar
20. 1. Börgers C
    2. Kopell NJ

    (2008) Gamma oscillations and stimulus selection

    Neural Computation 20:383–414.

    https://doi.org/10.1162/neco.2007.07-06-289

    • PubMed
    • Google Scholar
21. 1. Bosman CA
    2. Schoffelen JM
    3. Brunet N
    4. Oostenveld R
    5. Bastos AM
    6. Womelsdorf T
    7. Rubehn B
    8. Stieglitz T
    9. De Weerd P
    10. Fries P

    (2012) Attentional stimulus selection through selective synchronization between monkey visual areas

    Neuron 75:875–888.

    https://doi.org/10.1016/j.neuron.2012.06.037

    • PubMed
    • Google Scholar
22. 1. Brainard D

    (1996)

    Human Color Vision

    Cone contrast and opponent modulation color spaces, Human Color Vision, Springer.

    • Google Scholar
23. 1. Bressler SL
    2. Coppola R
    3. Nakamura R

    (1993) Episodic multiregional cortical coherence at multiple frequencies during visual task performance

    Nature 366:153–156.

    https://doi.org/10.1038/366153a0

    • PubMed
    • Google Scholar
24. Conference

    1. Bressler SL
    2. Richter CG
    3. Chen Y
    4. Ding M

    (2006) Top-Down cortical influences in visual expectation

    IEEE International Joint Conference on Neural Network Proceedings.

    https://doi.org/10.1109/IJCNN.2006.246678

    • Google Scholar
25. 1. Brunet N
    2. Vinck M
    3. Bosman CA
    4. Singer W
    5. Fries P

    (2014) Gamma or no gamma, that is the question

    Trends in Cognitive Sciences 18:507–509.

    https://doi.org/10.1016/j.tics.2014.08.006

    • PubMed
    • Google Scholar
26. 1. Brunet N
    2. Bosman CA
    3. Roberts M
    4. Oostenveld R
    5. Womelsdorf T
    6. De Weerd P
    7. Fries P

    (2015) Visual cortical gamma-band activity during free viewing of natural images

    Cerebral Cortex 25:918–926.

    https://doi.org/10.1093/cercor/bht280

    • PubMed
    • Google Scholar
27. 1. Buffalo EA
    2. Fries P
    3. Landman R
    4. Buschman TJ
    5. Desimone R

    (2011) Laminar differences in gamma and alpha coherence in the ventral stream

    Proceedings of the National Academy of Sciences 108:11262–11267.

    https://doi.org/10.1073/pnas.1011284108

    • PubMed
    • Google Scholar
28. 1. Buschman TJ
    2. Miller EK

    (2007) Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices

    Science 315:1860–1862.

    https://doi.org/10.1126/science.1138071

    • PubMed
    • Google Scholar
29. Book

    1. Buzsáki G

    (2006) Rhythms of the Brain

    USA: Oxford University Press.

    https://doi.org/10.1093/acprof:oso/9780195301069.001.0001

    • Google Scholar
30. 1. Buzsáki G
    2. Anastassiou CA
    3. Koch C

    (2012) The origin of extracellular fields and currents--EEG, ECoG, LFP and spikes

    Nature Reviews Neuroscience 13:407–420.

    https://doi.org/10.1038/nrn3241

    • PubMed
    • Google Scholar
31. 1. Buzsáki G
    2. Wang XJ

    (2012) Mechanisms of gamma oscillations

    Annual Review of Neuroscience 35:203–225.

    https://doi.org/10.1146/annurev-neuro-062111-150444

    • PubMed
    • Google Scholar
32. 1. Carandini M
    2. Heeger DJ

    (2011) Normalization as a canonical neural computation

    Nature Reviews Neuroscience 13:.

    https://doi.org/10.1038/nrn3136

    • PubMed
    • Google Scholar
33. 1. Cardin JA
    2. Palmer LA
    3. Contreras D

    (2005) Stimulus-dependent gamma (30-50 Hz) oscillations in simple and complex fast rhythmic bursting cells in primary visual cortex

    Journal of Neuroscience 25:5339–5350.

    https://doi.org/10.1523/JNEUROSCI.0374-05.2005

    • PubMed
    • Google Scholar
34. 1. Cardin JA
    2. Carlén M
    3. Meletis K
    4. Knoblich U
    5. Zhang F
    6. Deisseroth K
    7. Tsai LH
    8. Moore CI

    (2009) Driving fast-spiking cells induces gamma rhythm and controls sensory responses

    Nature 459:663–667.

    https://doi.org/10.1038/nature08002

    • PubMed
    • Google Scholar
35. 1. Chalk M
    2. Herrero JL
    3. Gieselmann MA
    4. Delicato LS
    5. Gotthardt S
    6. Thiele A

    (2010) Attention reduces stimulus-driven gamma frequency oscillations and spike field coherence in V1

    Neuron 66:114–125.

    https://doi.org/10.1016/j.neuron.2010.03.013

    • PubMed
    • Google Scholar
36. 1. Chalk M
    2. Gutkin B
    3. Denève S

    (2016) Neural oscillations as a signature of efficient coding in the presence of synaptic delays

    eLife 5:e13824.

    https://doi.org/10.7554/eLife.13824

    • PubMed
    • Google Scholar
37. 1. Coen-Cagli R
    2. Dayan P
    3. Schwartz O

    (2012) Cortical surround interactions and perceptual salience via natural scene statistics

    PLOS Computational Biology 8:e1002405.

    https://doi.org/10.1371/journal.pcbi.1002405

    • PubMed
    • Google Scholar
38. 1. Coen-Cagli R
    2. Kohn A
    3. Schwartz O

    (2015) Flexible gating of contextual influences in natural vision

    Nature Neuroscience 18:1648–1655.

    https://doi.org/10.1038/nn.4128

    • PubMed
    • Google Scholar
39. 1. Colgin LL
    2. Denninger T
    3. Fyhn M
    4. Hafting T
    5. Bonnevie T
    6. Jensen O
    7. Moser MB
    8. Moser EI

    (2009) Frequency of gamma oscillations routes flow of information in the hippocampus

    Nature 462:353–357.

    https://doi.org/10.1038/nature08573

    • PubMed
    • Google Scholar
40. 1. Corso J
    2. Bowler M
    3. Heymann EW
    4. Roos C
    5. Mundy NI

    (2016) Highly polymorphic colour vision in a New World monkey with red facial skin, the bald uakari (Cacajao calvus)

    Proceedings of the Royal Society B: Biological Sciences 283:20160067.

    https://doi.org/10.1098/rspb.2016.0067

    • Google Scholar
41. 1. D'Zmura M

    (1991) Color in visual search

    Vision Research 31:951–966.

    https://doi.org/10.1016/0042-6989(91)90203-H

    • PubMed
    • Google Scholar
42. 1. de Lange FP
    2. Heilbron M
    3. Kok P

    (2018) How do expectations shape perception?

    Trends in Cognitive Sciences 22:764–779.

    https://doi.org/10.1016/j.tics.2018.06.002

    • PubMed
    • Google Scholar
43. 1. De Valois RL
    2. De Valois KK
    3. Mahon LE

    (2000) Contribution of S opponent cells to color appearance

    PNAS 97:512–517.

    https://doi.org/10.1073/pnas.97.1.512

    • PubMed
    • Google Scholar
44. 1. Derrington AM
    2. Krauskopf J
    3. Lennie P

    (1984) Chromatic mechanisms in lateral geniculate nucleus of macaque

    The Journal of Physiology 357:241–265.

    https://doi.org/10.1113/jphysiol.1984.sp015499

    • PubMed
    • Google Scholar
45. 1. DiCarlo JJ
    2. Zoccolan D
    3. Rust NC

    (2012) How does the brain solve visual object recognition?

    Neuron 73:415–434.

    https://doi.org/10.1016/j.neuron.2012.01.010

    • PubMed
    • Google Scholar
46. Conference

    1. Dowdall JR
    2. Schmiedt JT
    3. Stephan M
    4. Fries P

    (2018)

    Arcade: a modular multithreaded stimulus presentation software for the Real-Time control of stimuli, actions and reward during behavioral experiments

    SfN 2018. 254.18. 2018.

    • Google Scholar
47. 1. Ekstrom A

    (2010) How and when the fMRI BOLD signal relates to underlying neural activity: the danger in dissociation

    Brain Research Reviews 62:233–244.

    https://doi.org/10.1016/j.brainresrev.2009.12.004

    • PubMed
    • Google Scholar
48. 1. Engbert R
    2. Kliegl R

    (2003) Microsaccades uncover the orientation of covert attention

    Vision Research 43:1035–1045.

    https://doi.org/10.1016/S0042-6989(03)00084-1

    • PubMed
    • Google Scholar
49. 1. Engel AK
    2. König P
    3. Kreiter AK
    4. Schillen TB
    5. Singer W

    (1992) Temporal coding in the visual cortex: new vistas on integration in the nervous system

    Trends in Neurosciences 15:218–226.

    https://doi.org/10.1016/0166-2236(92)90039-B

    • PubMed
    • Google Scholar
50. 1. Felleman DJ
    2. Van Essen DC

    (1991) Distributed hierarchical processing in the primate cerebral cortex

    Cerebral Cortex 1:1–47.

    https://doi.org/10.1093/cercor/1.1.1

    • PubMed
    • Google Scholar
51. 1. Friedman HS
    2. Zhou H
    3. vonder Heydt R

    (2003) The coding of uniform colour figures in monkey visual cortex

    The Journal of Physiology 548:593–613.

    https://doi.org/10.1113/jphysiol.2002.033555

    • Google Scholar
52. 1. Fries P

    (2005) A mechanism for cognitive dynamics: neuronal communication through neuronal coherence

    Trends in Cognitive Sciences 9:474–480.

    https://doi.org/10.1016/j.tics.2005.08.011

    • PubMed
    • Google Scholar
53. 1. Fries P
    2. Nikolić D
    3. Singer W

    (2007) The gamma cycle

    Trends in Neurosciences 30:309–316.

    https://doi.org/10.1016/j.tins.2007.05.005

    • PubMed
    • Google Scholar
54. 1. Fries P

    (2009) Neuronal gamma-band synchronization as a fundamental process in cortical computation

    Annual Review of Neuroscience 32:209–224.

    https://doi.org/10.1146/annurev.neuro.051508.135603

    • PubMed
    • Google Scholar
55. 1. Fries P

    (2015) Rhythms for cognition: communication through coherence

    Neuron 88:220–235.

    https://doi.org/10.1016/j.neuron.2015.09.034

    • PubMed
    • Google Scholar
56. 1. Friston K

    (2005) A theory of cortical responses

    Philosophical Transactions of the Royal Society B: Biological Sciences 360:815–836.

    https://doi.org/10.1098/rstb.2005.1622

    • Google Scholar
57. 1. Gail A

    (2000) Contour decouples gamma activity across texture representation in monkey striate cortex

    Cerebral Cortex 10:840–850.

    https://doi.org/10.1093/cercor/10.9.840

    • Google Scholar
58. 1. Gerald MS
    2. Waitt C
    3. Little AC
    4. Kraiselburd E

    (2007) Females pay attention to female secondary sexual color: an experimental study in macaca mulatta

    International Journal of Primatology 28:1–7.

    https://doi.org/10.1007/s10764-006-9110-8

    • Google Scholar
59. 1. Ghodrati M
    2. Morris AP
    3. Price NSC

    (2015) The (un)suitability of modern liquid crystal displays (LCDs) for vision research

    Frontiers in Psychology 6:1–11.

    https://doi.org/10.3389/fpsyg.2015.00303

    • Google Scholar
60. 1. Gieselmann MA
    2. Thiele A

    (2008) Comparison of spatial integration and surround suppression characteristics in spiking activity and the local field potential in macaque V1

    European Journal of Neuroscience 28:447–459.

    https://doi.org/10.1111/j.1460-9568.2008.06358.x

    • PubMed
    • Google Scholar
61. 1. Gilbert CD

    (1992) Horizontal integration and cortical dynamics

    Neuron 9:1–13.

    https://doi.org/10.1016/0896-6273(92)90215-Y

    • PubMed
    • Google Scholar
62. 1. Gray CM
    2. König P
    3. Engel AK
    4. Singer W

    (1989) Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties

    Nature 338:334–337.

    https://doi.org/10.1038/338334a0

    • PubMed
    • Google Scholar
63. 1. Gray CM
    2. Engel AK
    3. König P
    4. Singer W

    (1990) Stimulus-Dependent neuronal oscillations in cat visual cortex: receptive field properties and feature dependence

    European Journal of Neuroscience 2:607–619.

    https://doi.org/10.1111/j.1460-9568.1990.tb00450.x

    • PubMed
    • Google Scholar
64. 1. Gray CM
    2. McCormick DA

    (1996) Chattering cells: superficial pyramidal neurons contributing to the generation of synchronous oscillations in the visual cortex

    Science 274:109–113.

    https://doi.org/10.1126/science.274.5284.109

    • PubMed
    • Google Scholar
65. 1. Gregoriou GG
    2. Gotts SJ
    3. Zhou H
    4. Desimone R

    (2009) High-frequency, long-range coupling between prefrontal and visual cortex during attention

    Science 324:1207–1210.

    https://doi.org/10.1126/science.1171402

    • PubMed
    • Google Scholar
66. 1. Grosof DH
    2. Shapley RM
    3. Hawken MJ

    (1993) Macaque V1 neurons can signal 'illusory' contours

    Nature 365:550–552.

    https://doi.org/10.1038/365550a0

    • PubMed
    • Google Scholar
67. 1. Grothe I
    2. Neitzel SD
    3. Mandon S
    4. Kreiter AK

    (2012) Switching neuronal inputs by differential modulations of gamma-band phase-coherence

    Journal of Neuroscience 32:16172–16180.

    https://doi.org/10.1523/JNEUROSCI.0890-12.2012

    • PubMed
    • Google Scholar
68. 1. Hadjipapas A
    2. Lowet E
    3. Roberts MJ
    4. Peter A
    5. De Weerd P

    (2015) Parametric variation of gamma frequency and power with luminance contrast: a comparative study of human MEG and monkey LFP and spike responses

    NeuroImage 112:327–340.

    https://doi.org/10.1016/j.neuroimage.2015.02.062

    • PubMed
    • Google Scholar
69. Preprint

    1. Haller M
    2. Donoghue T
    3. Peterson E
    4. Varma P
    5. Sebastian P
    6. Gao R
    7. Noto T
    8. Knight RT
    9. Shestyuk A
    10. Voytek B

    (2018) Parameterizingneural power spectra

    bioRxiv.

    https://doi.org/10.1101/299859

    • Google Scholar
70. 1. Hansen T
    2. Gegenfurtner KR

    (2013) Higher order color mechanisms: evidence from noise-masking experiments in cone contrast space

    Journal of Vision 13:26.

    https://doi.org/10.1167/13.1.26

    • PubMed
    • Google Scholar
71. 1. Hárosi FI

    (1987) Cynomolgus and rhesus monkey visual pigments. Application of Fourier transform smoothing and statistical techniques to the determination of spectral parameters

    The Journal of General Physiology 89:717–743.

    https://doi.org/10.1085/jgp.89.5.717

    • PubMed
    • Google Scholar
72. 1. Hasenstaub A
    2. Shu Y
    3. Haider B
    4. Kraushaar U
    5. Duque A
    6. McCormick DA

    (2005) Inhibitory postsynaptic potentials carry synchronized frequency information in active cortical networks

    Neuron 47:423–435.

    https://doi.org/10.1016/j.neuron.2005.06.016

    • PubMed
    • Google Scholar
73. 1. Havenith MN
    2. Yu S
    3. Biederlack J
    4. Chen NH
    5. Singer W
    6. Nikolić D

    (2011) Synchrony makes neurons fire in sequence, and stimulus properties determine who is ahead

    Journal of Neuroscience 31:8570–8584.

    https://doi.org/10.1523/JNEUROSCI.2817-10.2011

    • PubMed
    • Google Scholar
74. 1. Henrie JA
    2. Shapley R

    (2005) LFP power spectra in V1 cortex: the graded effect of stimulus contrast

    Journal of Neurophysiology 94:479–490.

    https://doi.org/10.1152/jn.00919.2004

    • PubMed
    • Google Scholar
75. 1. Herculano-Houzel S
    2. Munk MH
    3. Neuenschwander S
    4. Singer W

    (1999) Precisely synchronized oscillatory firing patterns require electroencephalographic activation

    The Journal of Neuroscience 19:3992–4010.

    https://doi.org/10.1523/JNEUROSCI.19-10-03992.1999

    • PubMed
    • Google Scholar
76. 1. Jadi MP
    2. Sejnowski TJ

    (2014) Cortical oscillations arise from contextual interactions that regulate sparse coding

    PNAS 111:6780–6785.

    https://doi.org/10.1073/pnas.1405300111

    • PubMed
    • Google Scholar
77. 1. Jia X
    2. Smith MA
    3. Kohn A

    (2011) Stimulus selectivity and spatial coherence of gamma components of the local field potential

    Journal of Neuroscience 31:9390–9403.

    https://doi.org/10.1523/JNEUROSCI.0645-11.2011

    • PubMed
    • Google Scholar
78. 1. Jia X
    2. Tanabe S
    3. Kohn A

    (2013a) Gamma and the coordination of spiking activity in early visual cortex

    Neuron 77:762–774.

    https://doi.org/10.1016/j.neuron.2012.12.036

    • PubMed
    • Google Scholar
79. 1. Jia X
    2. Xing D
    3. Kohn A

    (2013b) No consistent relationship between gamma power and peak frequency in macaque primary visual cortex

    Journal of Neuroscience 33:17–25.

    https://doi.org/10.1523/JNEUROSCI.1687-12.2013

    • PubMed
    • Google Scholar
80. 1. Johnson EN
    2. Hawken MJ
    3. Shapley R

    (2008) The orientation selectivity of Color-Responsive neurons in macaque V1

    Journal of Neuroscience 28:8096–8106.

    https://doi.org/10.1523/JNEUROSCI.1404-08.2008

    • Google Scholar
81. 1. Kapadia MK
    2. Ito M
    3. Gilbert CD
    4. Westheimer G

    (1995) Improvement in visual sensitivity by changes in local context: parallel studies in human observers and in V1 of alert monkeys

    Neuron 15:843–856.

    https://doi.org/10.1016/0896-6273(95)90175-2

    • PubMed
    • Google Scholar
82. 1. Kapadia MK
    2. Westheimer G
    3. Gilbert CD

    (1999) Dynamics of spatial summation in primary visual cortex of alert monkeys

    PNAS 96:12073–12078.

    https://doi.org/10.1073/pnas.96.21.12073

    • PubMed
    • Google Scholar
83. 1. Kayser C
    2. Salazar RF
    3. Konig P

    (2003) Responses to natural scenes in cat V1

    Journal of Neurophysiology 90:1910–1920.

    https://doi.org/10.1152/jn.00195.2003

    • PubMed
    • Google Scholar
84. 1. Kempter R
    2. Gerstner W
    3. van Hemmen JL
    4. Wagner H

    (1998) Extracting oscillations. neuronal coincidence detection with noisy periodic spike input

    Neural Computation 10:1987–2017.

    https://doi.org/10.1162/089976698300016945

    • PubMed
    • Google Scholar
85. 1. Knoblich U
    2. Siegle JH
    3. Pritchett DL
    4. Moore CI

    (2010) What do we gain from gamma? Local dynamic gain modulation drives enhanced efficacy and efficiency of signal transmission

    Frontiers in Human Neuroscience 4:185.

    https://doi.org/10.3389/fnhum.2010.00185

    • PubMed
    • Google Scholar
86. 1. König P
    2. Engel AK
    3. Singer W

    (1996) Integrator or coincidence detector? The role of the cortical neuron revisited

    Trends in Neurosciences 19:130–137.

    https://doi.org/10.1016/S0166-2236(96)80019-1

    • PubMed
    • Google Scholar
87. 1. Kopell N
    2. Ermentrout GB
    3. Whittington MA
    4. Traub RD

    (2000) Gamma rhythms and beta rhythms have different synchronization properties

    PNAS 97:1867–1872.

    https://doi.org/10.1073/pnas.97.4.1867

    • PubMed
    • Google Scholar
88. 1. Korn EL
    2. Troendle JF
    3. McShane LM
    4. Simon R

    (2004) Controlling the number of false discoveries: application to high-dimensional genomic data

    Journal of Statistical Planning and Inference 124:379–398.

    https://doi.org/10.1016/S0378-3758(03)00211-8

    • Google Scholar
89. 1. Korndörfer C
    2. Ullner E
    3. García-Ojalvo J
    4. Pipa G

    (2017) Cortical spike synchrony as a measure of input familiarity

    Neural Computation 29:2491–2510.

    https://doi.org/10.1162/neco_a_00987

    • PubMed
    • Google Scholar
90. 1. Krauskopf J
    2. Williams DR
    3. Heeley DW

    (1982) Cardinal directions of color space

    Vision Research 22:1123–1131.

    https://doi.org/10.1016/0042-6989(82)90077-3

    • PubMed
    • Google Scholar
91. 1. Kruse W
    2. Eckhorn R

    (1996) Inhibition of sustained gamma oscillations (35-80 hz) by fast transient responses in cat visual cortex

    PNAS 93:6112–6117.

    https://doi.org/10.1073/pnas.93.12.6112

    • PubMed
    • Google Scholar
92. 1. Lamme VA

    (1995) The neurophysiology of figure-ground segregation in primary visual cortex

    The Journal of Neuroscience 15:1605–1615.

    https://doi.org/10.1523/JNEUROSCI.15-02-01605.1995

    • PubMed
    • Google Scholar
93. 1. Lamme VA
    2. Roelfsema PR

    (2000) The distinct modes of vision offered by feedforward and recurrent processing

    Trends in Neurosciences 23:571–579.

    https://doi.org/10.1016/S0166-2236(00)01657-X

    • PubMed
    • Google Scholar
94. 1. Land EH

    (1959) Color vision and the natural image. part i

    Proceedings of the National Academy of Sciences 45:115–129.

    https://doi.org/10.1073/pnas.45.1.115

    • Google Scholar
95. 1. Legatt AD
    2. Arezzo J
    3. Vaughan HG

    (1980) Averaged multiple unit activity as an estimate of phasic changes in local neuronal activity: effects of volume-conducted potentials

    Journal of Neuroscience Methods 2:203–217.

    https://doi.org/10.1016/0165-0270(80)90061-8

    • PubMed
    • Google Scholar
96. 1. Li Z

    (2002) A saliency map in primary visual cortex

    Trends in Cognitive Sciences 6:9–16.

    https://doi.org/10.1016/S1364-6613(00)01817-9

    • PubMed
    • Google Scholar
97. 1. Li X
    2. Chen Y
    3. Lashgari R
    4. Bereshpolova Y
    5. Swadlow HA
    6. Lee BB
    7. Alonso JM

    (2015) Mixing of chromatic and luminance retinal signals in primate area V1

    Cerebral Cortex 25:1920–1937.

    https://doi.org/10.1093/cercor/bhu002

    • PubMed
    • Google Scholar
98. 1. Liang H
    2. Gong X
    3. Chen M
    4. Yan Y
    5. Li W
    6. Gilbert CD

    (2017) Interactions between feedback and lateral connections in the primary visual cortex

    PNAS 114:8637–8642.

    https://doi.org/10.1073/pnas.1706183114

    • Google Scholar
99. 1. Livingstone MS

    (1996) Oscillatory firing and interneuronal correlations in squirrel monkey striate cortex

    Journal of Neurophysiology 75:2467–2485.

    https://doi.org/10.1152/jn.1996.75.6.2467

    • PubMed
    • Google Scholar
100. 1. Livingstone MS
     2. Hubel DH

     (1984) Anatomy and physiology of a color system in the primate visual cortex

     The Journal of Neuroscience 4:309–356.

     https://doi.org/10.1523/JNEUROSCI.04-01-00309.1984

     • PubMed
     • Google Scholar
101. 1. Logothetis NK
     2. Wandell BA

     (2004) Interpreting the BOLD signal

     Annual Review of Physiology 66:735–769.

     https://doi.org/10.1146/annurev.physiol.66.082602.092845

     • PubMed
     • Google Scholar
102. 1. Lowet E
     2. Roberts MJ
     3. Bosman CA
     4. Fries P
     5. De Weerd P

     (2016) Areas V1 and V2 show microsaccade-related 3-4-Hz covariation in gamma power and frequency

     European Journal of Neuroscience 43:1286–1296.

     https://doi.org/10.1111/ejn.13126

     • PubMed
     • Google Scholar
103. 1. Lund JS
     2. Yoshioka T
     3. Levitt JB

     (1993) Comparison of intrinsic connectivity in different areas of macaque monkey cerebral cortex

     Cerebral Cortex 3:148–162.

     https://doi.org/10.1093/cercor/3.2.148

     • PubMed
     • Google Scholar
104. 1. Lund JS
     2. Angelucci A
     3. Bressloff PC

     (2003) Anatomical substrates for functional columns in macaque monkey primary visual cortex

     Cerebral Cortex 13:15–24.

     https://doi.org/10.1093/cercor/13.1.15

     • PubMed
     • Google Scholar
105. 1. Maier A
     2. Wilke M
     3. Aura C
     4. Zhu C
     5. Ye FQ
     6. Leopold DA

     (2008) Divergence of fMRI and neural signals in V1 during perceptual suppression in the awake monkey

     Nature neuroscience 11:1193–1200.

     https://doi.org/10.1038/nn.2173

     • PubMed
     • Google Scholar
106. 1. Markov NT
     2. Vezoli J
     3. Chameau P
     4. Falchier A
     5. Quilodran R
     6. Huissoud C
     7. Lamy C
     8. Misery P
     9. Giroud P
     10. Ullman S
     11. Barone P
     12. Dehay C
     13. Knoblauch K
     14. Kennedy H

     (2014) Anatomy of hierarchy: feedforward and feedback pathways in macaque visual cortex

     Journal of Comparative Neurology 522:225–259.

     https://doi.org/10.1002/cne.23458

     • PubMed
     • Google Scholar
107. 1. Melin AD
     2. Chiou KL
     3. Walco ER
     4. Bergstrom ML
     5. Kawamura S
     6. Fedigan LM

     (2017) Trichromacy increases fruit intake rates of wild capuchins (cebus capucinus imitator)

     PNAS 114:10402–10407.

     https://doi.org/10.1073/pnas.1705957114

     • PubMed
     • Google Scholar
108. 1. Michalareas G
     2. Vezoli J
     3. van Pelt S
     4. Schoffelen JM
     5. Kennedy H
     6. Fries P

     (2016) Alpha-Beta and Gamma Rhythms Subserve Feedback and Feedforward Influences among Human Visual Cortical Areas

     Neuron 89:384–397.

     https://doi.org/10.1016/j.neuron.2015.12.018

     • PubMed
     • Google Scholar
109. 1. Miller KJ
     2. Zanos S
     3. Fetz EE
     4. den Nijs M
     5. Ojemann JG

     (2009) Decoupling the cortical power spectrum reveals real-time representation of individual finger movements in humans

     Journal of Neuroscience 29:3132–3137.

     https://doi.org/10.1523/JNEUROSCI.5506-08.2009

     • PubMed
     • Google Scholar
110. 1. Milner PM

     (1974) A model for visual shape recognition

     Psychological Review 81:521–535.

     https://doi.org/10.1037/h0037149

     • PubMed
     • Google Scholar
111. 1. Niessing J
     2. Ebisch B
     3. Schmidt KE
     4. Niessing M
     5. Singer W
     6. Galuske RA

     (2005) Hemodynamic signals correlate tightly with synchronized gamma oscillations

     Science 309:948–951.

     https://doi.org/10.1126/science.1110948

     • PubMed
     • Google Scholar
112. 1. Nir Y
     2. Fisch L
     3. Mukamel R
     4. Gelbard-Sagiv H
     5. Arieli A
     6. Fried I
     7. Malach R

     (2007) Coupling between neuronal firing rate, gamma LFP, and BOLD fMRI is related to interneuronal correlations

     Current Biology 17:1275–1285.

     https://doi.org/10.1016/j.cub.2007.06.066

     • PubMed
     • Google Scholar
113. 1. Nowak LG
     2. Azouz R
     3. Sanchez-Vives MV
     4. Gray CM
     5. McCormick DA

     (2003) Electrophysiological classes of cat primary visual cortical neurons in vivo as revealed by quantitative analyses

     Journal of Neurophysiology 89:1541–1566.

     https://doi.org/10.1152/jn.00580.2002

     • PubMed
     • Google Scholar
114. 1. O'Keefe J
     2. Recce ML

     (1993) Phase relationship between hippocampal place units and the EEG theta rhythm

     Hippocampus 3:317–330.

     https://doi.org/10.1002/hipo.450030307

     • PubMed
     • Google Scholar
115. 1. Oostenveld R
     2. Fries P
     3. Maris E
     4. Schoffelen JM

     (2011) FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

     Computational Intelligence and Neuroscience 2011:1–9.

     https://doi.org/10.1155/2011/156869

     • PubMed
     • Google Scholar
116. 1. Palmigiano A
     2. Geisel T
     3. Wolf F
     4. Battaglia D

     (2017) Flexible information routing by transient synchrony

     Nature Neuroscience 20:1014–1022.

     https://doi.org/10.1038/nn.4569

     • PubMed
     • Google Scholar
117. 1. Perrenoud Q
     2. Pennartz CM
     3. Gentet LJ

     (2016) Membrane potential dynamics of spontaneous and visually evoked gamma activity in V1 of awake mice

     PLOS Biology 14:e1002383.

     https://doi.org/10.1371/journal.pbio.1002383

     • PubMed
     • Google Scholar
118. 1. Perry G
     2. Hamandi K
     3. Brindley LM
     4. Muthukumaraswamy SD
     5. Singh KD

     (2013) The properties of induced gamma oscillations in human visual cortex show individual variability in their dependence on stimulus size

     NeuroImage 68:83–92.

     https://doi.org/10.1016/j.neuroimage.2012.11.043

     • PubMed
     • Google Scholar
119. 1. Pesaran B
     2. Vinck M
     3. Einevoll GT
     4. Sirota A
     5. Fries P
     6. Siegel M
     7. Truccolo W
     8. Schroeder CE
     9. Srinivasan R

     (2018) Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation

     Nature Neuroscience 21:903–919.

     https://doi.org/10.1038/s41593-018-0171-8

     • PubMed
     • Google Scholar
120. 1. Rao RP
     2. Ballard DH

     (1999) Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects

     Nature Neuroscience 2:79–87.

     https://doi.org/10.1038/4580

     • PubMed
     • Google Scholar
121. 1. Ray S
     2. Maunsell JH

     (2010) Differences in gamma frequencies across visual cortex restrict their possible use in computation

     Neuron 67:885–896.

     https://doi.org/10.1016/j.neuron.2010.08.004

     • PubMed
     • Google Scholar
122. 1. Ray S
     2. Maunsell JH

     (2011) Different origins of gamma rhythm and high-gamma activity in macaque visual cortex

     PLOS Biology 9:e1000610.

     https://doi.org/10.1371/journal.pbio.1000610

     • PubMed
     • Google Scholar
123. 1. Richter CG
     2. Coppola R
     3. Bressler SL

     (2018) Top-down beta oscillatory signaling conveys behavioral context in early visual cortex

     Scientific Reports 8:6991.

     https://doi.org/10.1038/s41598-018-25267-1

     • PubMed
     • Google Scholar
124. 1. Roberts MJ
     2. Lowet E
     3. Brunet NM
     4. Ter Wal M
     5. Tiesinga P
     6. Fries P
     7. De Weerd P

     (2013) Robust gamma coherence between macaque V1 and V2 by dynamic frequency matching

     Neuron 78:523–536.

     https://doi.org/10.1016/j.neuron.2013.03.003

     • PubMed
     • Google Scholar
125. 1. Rohenkohl G
     2. Bosman CA
     3. Fries P

     (2018) Gamma synchronization between V1 and V4 improves behavioral performance

     Neuron 100:953-963.

     https://doi.org/10.1016/j.neuron.2018.09.019

     • Google Scholar
126. 1. Rols G
     2. Tallon-Baudry C
     3. Girard P
     4. Bertrand O
     5. Bullier J

     (2001) Cortical mapping of gamma oscillations in areas V1 and V4 of the macaque monkey

     Visual Neuroscience 18:527–540.

     https://doi.org/10.1017/S0952523801184038

     • PubMed
     • Google Scholar
127. 1. Salinas E
     2. Sejnowski TJ

     (2000) Impact of correlated synaptic input on output firing rate and variability in simple neuronal models

     The Journal of Neuroscience 20:6193–6209.

     https://doi.org/10.1523/JNEUROSCI.20-16-06193.2000

     • PubMed
     • Google Scholar
128. 1. Salinas E
     2. Sejnowski TJ

     (2001) Correlated neuronal activity and the flow of neural information

     Nature Reviews Neuroscience 2:539–550.

     https://doi.org/10.1038/35086012

     • PubMed
     • Google Scholar
129. 1. Santos LR
     2. Hauser MD
     3. Spelke ES

     (2001) Recognition and categorization of biologically significant objects by rhesus monkeys (Macaca mulatta): the domain of food

     Cognition 82:127–155.

     https://doi.org/10.1016/S0010-0277(01)00149-4

     • PubMed
     • Google Scholar
130. 1. Scheeringa R
     2. Koopmans PJ
     3. van Mourik T
     4. Jensen O
     5. Norris DG

     (2016) The relationship between oscillatory EEG activity and the laminar-specific BOLD signal

     PNAS 113:6761–6766.

     https://doi.org/10.1073/pnas.1522577113

     • PubMed
     • Google Scholar
131. 1. Schluppeck D
     2. Engel SA

     (2002) Color opponent neurons in V1: a review and model reconciling results from imaging and single-unit recording

     Journal of vision 2:5.

     https://doi.org/10.1167/2.6.5

     • PubMed
     • Google Scholar
132. 1. Schmid MC
     2. Schmiedt JT
     3. Peters AJ
     4. Saunders RC
     5. Maier A
     6. Leopold DA

     (2013) Motion-sensitive responses in visual area V4 in the absence of primary visual cortex

     Journal of Neuroscience 33:18740–18745.

     https://doi.org/10.1523/JNEUROSCI.3923-13.2013

     • PubMed
     • Google Scholar
133. 1. Schwartz O
     2. Simoncelli EP

     (2001) Natural signal statistics and sensory gain control

     Nature neuroscience 4:819–825.

     https://doi.org/10.1038/90526

     • PubMed
     • Google Scholar
134. 1. Sejnowski TJ
     2. Paulsen O

     (2006) Network oscillations: emerging computational principles

     Journal of Neuroscience 26:1673–1676.

     https://doi.org/10.1523/JNEUROSCI.3737-05d.2006

     • PubMed
     • Google Scholar
135. 1. Self MW
     2. van Kerkoerle T
     3. Supèr H
     4. Roelfsema PR

     (2013) Distinct roles of the cortical layers of area V1 in figure-ground segregation

     Current biology : CB 23:2121–2129.

     https://doi.org/10.1016/j.cub.2013.09.013

     • PubMed
     • Google Scholar
136. Report

     1. Serre T
     2. Kouh M
     3. Cadieu C
     4. Knoblich U
     5. Kreiman G
     6. Poggio T

     (2005)

     A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex

      CSAIL Technical Reports.

     • Google Scholar
137. 1. Shapley R
     2. Hawken M

     (2002) Neural mechanisms for color perception in the primary visual cortex

     Current Opinion in Neurobiology 12:426–432.

     https://doi.org/10.1016/S0959-4388(02)00349-5

     • PubMed
     • Google Scholar
138. 1. Shapley R
     2. Hawken MJ

     (2011) Color in the cortex: single- and double-opponent cells

     Vision Research 51:701–717.

     https://doi.org/10.1016/j.visres.2011.02.012

     • PubMed
     • Google Scholar
139. 1. Shirhatti V
     2. Ray S

     (2018) Long-wavelength (reddish) hues induce unusually large gamma oscillations in the primate primary visual cortex

     Proceedings of the National Academy of Sciences 115:4489–4494.

     https://doi.org/10.1073/pnas.1717334115

     • PubMed
     • Google Scholar
140. 1. Simoncelli EP
     2. Olshausen BA

     (2001) Natural image statistics and neural representation

     Annual Review of Neuroscience 24:1193–1216.

     https://doi.org/10.1146/annurev.neuro.24.1.1193

     • PubMed
     • Google Scholar
141. 1. Singer W

     (1999) Neuronal synchrony: a versatile code for the definition of relations?

     Neuron 24:49–65.

     https://doi.org/10.1016/S0896-6273(00)80821-1

     • PubMed
     • Google Scholar
142. 1. Singer W

     (2018) Neuronal oscillations: unavoidable and useful?

     European Journal of Neuroscience 48:2389–2398.

     https://doi.org/10.1111/ejn.13796

     • Google Scholar
143. 1. Singer W
     2. Gray CM

     (1995) Visual feature integration and the temporal correlation hypothesis

     Annual Review of Neuroscience 18:555–586.

     https://doi.org/10.1146/annurev.ne.18.030195.003011

     • PubMed
     • Google Scholar
144. 1. Softky W

     (1994) Sub-millisecond coincidence detection in active dendritic trees

     Neuroscience 58:13–41.

     https://doi.org/10.1016/0306-4522(94)90154-6

     • PubMed
     • Google Scholar
145. 1. Sohal VS
     2. Zhang F
     3. Yizhar O
     4. Deisseroth K

     (2009) Parvalbumin neurons and gamma rhythms enhance cortical circuit performance

     Nature 459:698–702.

     https://doi.org/10.1038/nature07991

     • PubMed
     • Google Scholar
146. 1. Solomon SG
     2. Lennie P

     (2007) The machinery of colour vision

     Nature Reviews Neuroscience 8:276–286.

     https://doi.org/10.1038/nrn2094

     • PubMed
     • Google Scholar
147. 1. Spratling MW

     (2010) Predictive coding as a model of response properties in cortical area V1

     Journal of Neuroscience 30:3531–3543.

     https://doi.org/10.1523/JNEUROSCI.4911-09.2010

     • PubMed
     • Google Scholar
148. 1. Tailby C
     2. Solomon SG
     3. Dhruv NT
     4. Lennie P

     (2008a) Habituation reveals fundamental chromatic mechanisms in striate cortex of macaque

     Journal of Neuroscience 28:1131–1139.

     https://doi.org/10.1523/JNEUROSCI.4682-07.2008

     • PubMed
     • Google Scholar
149. 1. Tailby C
     2. Solomon SG
     3. Lennie P

     (2008b) Functional asymmetries in visual pathways carrying S-cone signals in macaque

     Journal of Neuroscience 28:4078–4087.

     https://doi.org/10.1523/JNEUROSCI.5338-07.2008

     • PubMed
     • Google Scholar
150. 1. Thomsen K
     2. Offenhauser N
     3. Lauritzen M

     (2004) Principal neuron spiking: neither necessary nor sufficient for cerebral blood flow in rat cerebellum

     The Journal of Physiology 560:181–189.

     https://doi.org/10.1113/jphysiol.2004.068072

     • PubMed
     • Google Scholar
151. 1. Tiesinga P
     2. Sejnowski TJ

     (2009) Cortical enlightenment: are attentional gamma oscillations driven by ING or PING?

     Neuron 63:727–732.

     https://doi.org/10.1016/j.neuron.2009.09.009

     • PubMed
     • Google Scholar
152. 1. Todorovic A
     2. van Ede F
     3. Maris E
     4. de Lange FP

     (2011) Prior expectation mediates neural adaptation to repeated sounds in the auditory cortex: an MEGstudy

     Journal of Neuroscience 31:9118–9123.

     https://doi.org/10.1523/JNEUROSCI.1425-11.2011

     • PubMed
     • Google Scholar
153. 1. Ts'o DY
     2. Gilbert CD

     (1988) The organization of chromatic and spatial interactions in the primate striate cortex

     The Journal of Neuroscience 8:1712–1727.

     https://doi.org/10.1523/JNEUROSCI.08-05-01712.1988

     • Google Scholar
154. 1. van Kerkoerle T
     2. Self MW
     3. Dagnino B
     4. Gariel-Mathis MA
     5. Poort J
     6. van der Togt C
     7. Roelfsema PR

     (2014) Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex

     Proceedings of the National Academy of Sciences 111:14332–14341.

     https://doi.org/10.1073/pnas.1402773111

     • PubMed
     • Google Scholar
155. 1. Varela F
     2. Lachaux JP
     3. Rodriguez E
     4. Martinerie J

     (2001) The brainweb: phase synchronization and large-scale integration

     Nature Reviews Neuroscience 2:229–239.

     https://doi.org/10.1038/35067550

     • PubMed
     • Google Scholar
156. 1. Veit J
     2. Hakim R
     3. Jadi MP
     4. Sejnowski TJ
     5. Adesnik H

     (2017) Cortical gamma band synchronization through somatostatin interneurons

     Nature neuroscience 20:951.

     https://doi.org/10.1038/nn.4562

     • PubMed
     • Google Scholar
157. 1. Vinck M
     2. Lima B
     3. Womelsdorf T
     4. Oostenveld R
     5. Singer W
     6. Neuenschwander S
     7. Fries P

     (2010a) Gamma-phase shifting in awake monkey visual cortex

     Journal of Neuroscience 30:1250–1257.

     https://doi.org/10.1523/JNEUROSCI.1623-09.2010

     • PubMed
     • Google Scholar
158. 1. Vinck M
     2. van Wingerden M
     3. Womelsdorf T
     4. Fries P
     5. Pennartz CM

     (2010b) The pairwise phase consistency: a bias-free measure of rhythmic neuronal synchronization

     NeuroImage 51:112–122.

     https://doi.org/10.1016/j.neuroimage.2010.01.073

     • PubMed
     • Google Scholar
159. 1. Vinck M
     2. Womelsdorf T
     3. Buffalo EA
     4. Desimone R
     5. Fries P

     (2013a) Attentional modulation of cell-class-specific gamma-band synchronization in awake monkey area v4

     Neuron 80:1077–1089.

     https://doi.org/10.1016/j.neuron.2013.08.019

     • PubMed
     • Google Scholar
160. 1. Vinck M
     2. Womelsdorf T
     3. Fries P

     (2013)

     Principles of Neural Coding

     Gamma-band synchronization and information transmission, Principles of Neural Coding, CRC Press, 10.1201/b14756-26.

     • Google Scholar
161. 1. Vinck M
     2. Bosman CA

     (2016) More Gamma more predictions: gamma-synchronization as a key mechanism for efficient integration of classical receptive field inputs with surround predictions

     Frontiers in Systems Neuroscience 10:35.

     https://doi.org/10.3389/fnsys.2016.00035

     • Google Scholar
162. 1. Vinje WE
     2. Gallant JL

     (2000) Sparse coding and decorrelation in primary visual cortex during natural vision

     Science 287:1273–1276.

     https://doi.org/10.1126/science.287.5456.1273

     • PubMed
     • Google Scholar
163. 1. Viswanathan A
     2. Freeman RD

     (2007) Neurometabolic coupling in cerebral cortex reflects synaptic more than spiking activity

     Nature Neuroscience 10:1308–1312.

     https://doi.org/10.1038/nn1977

     • PubMed
     • Google Scholar
164. Book

     1. Von Der Malsburg C

     (1994) The Correlation Theory of Brain Function

     Springer.

     https://doi.org/10.1007/978-1-4612-4320-5_2

     • Google Scholar
165. 1. vonder Heydt R
     2. Peterhans E
     3. Baumgartner G

     (1984) Illusory contours and cortical neuron responses

     Science 224:1260–1262.

     https://doi.org/10.1126/science.6539501

     • PubMed
     • Google Scholar
166. 1. Wachtler T
     2. Sejnowski TJ
     3. Albright TD

     (2003) Representation of color stimuli in awake macaque primary visual cortex

     Neuron 37:681–691.

     https://doi.org/10.1016/S0896-6273(03)00035-7

     • PubMed
     • Google Scholar
167. 1. Waitt C
     2. Gerald MS
     3. Little AC
     4. Kraiselburd E

     (2006) Selective attention toward female secondary sexual color in male rhesus macaques

     American Journal of Primatology 68:738–744.

     https://doi.org/10.1002/ajp.20264

     • PubMed
     • Google Scholar
168. 1. Wang XJ

     (2010) Neurophysiological and computational principles of cortical rhythms in cognition

     Physiological Reviews 90:1195–1268.

     https://doi.org/10.1152/physrev.00035.2008

     • PubMed
     • Google Scholar
169. 1. Wang P
     2. Nikolić D

     (2011) An LCD monitor with sufficiently precise timing for research in vision

     Frontiers in Human Neuroscience 5:.

     https://doi.org/10.3389/fnhum.2011.00085

     • PubMed
     • Google Scholar
170. 1. Webster MA
     2. Mizokami Y
     3. Webster SM

     (2007) Seasonal variations in the color statistics of natural images

     Network: Computation in Neural Systems 18:213–233.

     https://doi.org/10.1080/09548980701654405

     • PubMed
     • Google Scholar
171. 1. Werner A
     2. Sharpe LT
     3. Zrenner E

     (2000) Asymmetries in the time-course of chromatic adaptation and the significance of contrast

     Vision Research 40:1101–1113.

     https://doi.org/10.1016/S0042-6989(00)00012-2

     • PubMed
     • Google Scholar
172. 1. Whittington MA
     2. Traub RD
     3. Jefferys JG

     (1995) Synchronized oscillations in interneuron networks driven by metabotropic glutamate receptor activation

     Nature 373:612–615.

     https://doi.org/10.1038/373612a0

     • PubMed
     • Google Scholar
173. 1. Womelsdorf T
     2. Valiante TA
     3. Sahin NT
     4. Miller KJ
     5. Tiesinga P

     (2014) Dynamic circuit motifs underlying rhythmic gain control, gating and integration

     Nature Neuroscience 17:1031–1039.

     https://doi.org/10.1038/nn.3764

     • PubMed
     • Google Scholar
174. 1. Xing D
     2. Yeh CI
     3. Shapley RM

     (2010) Generation of black-dominant responses in V1 cortex

     Journal of Neuroscience 30:13504–13512.

     https://doi.org/10.1523/JNEUROSCI.2473-10.2010

     • PubMed
     • Google Scholar
175. 1. Xing D
     2. Yeh CI
     3. Burns S
     4. Shapley RM

     (2012) Laminar analysis of visually evoked activity in the primary visual cortex

     PNAS 109:13871–13876.

     https://doi.org/10.1073/pnas.1201478109

     • PubMed
     • Google Scholar
176. 1. Yeh CI
     2. Xing D
     3. Shapley RM

     (2009) "Black" responses dominate macaque primary visual cortex v1

     Journal of Neuroscience 29:11753–11760.

     https://doi.org/10.1523/JNEUROSCI.1991-09.2009

     • PubMed
     • Google Scholar
177. 1. Zhu M
     2. Rozell CJ

     (2013) Visual nonclassical receptive field effects emerge from sparse coding in a dynamical system

     PLOS Computational Biology 9:e1003191.

     https://doi.org/10.1371/journal.pcbi.1003191

     • PubMed
     • Google Scholar
178. 1. Zurawel G
     2. Ayzenshtat I
     3. Zweig S
     4. Shapley R
     5. Slovin H

     (2014) A contrast and surface code explains complex responses to black and white stimuli in V1

     Journal of Neuroscience 34:14388–14402.

     https://doi.org/10.1523/JNEUROSCI.0848-14.2014

     • PubMed
     • Google Scholar
179. 1. Zweig S
     2. Zurawel G
     3. Shapley R
     4. Slovin H

     (2015) Representation of Color Surfaces in V1: Edge Enhancement and Unfilled Holes

     Journal of Neuroscience 35:12103–12115.

     https://doi.org/10.1523/JNEUROSCI.1334-15.2015

     • PubMed
     • Google Scholar

Author details

1. Alina Peter

   1. Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
   2. International Max Planck Research School for Neural Circuits, Frankfurt, Germany

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Cem Uran

   For correspondence

   alina.peter@esi-frankfurt.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8497-6235

2. Cem Uran

   Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Alina Peter

   Competing interests

   No competing interests declared

3. Johanna Klon-Lipok

   1. Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
   2. Max Planck Institute for Brain Research, Frankfurt, Germany

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

4. Rasmus Roese

   Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany

   Contribution

   Investigation

   Competing interests

   No competing interests declared

5. Sylvia van Stijn

   1. Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
   2. Max Planck Institute for Brain Research, Frankfurt, Germany

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

6. William Barnes

   Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany

   Contribution

   Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3833-1214

7. Jarrod R Dowdall

   Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany

   Contribution

   Resources, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

8. Wolf Singer

   1. Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
   2. Frankfurt Institute for Advanced Studies, Frankfurt, Germany

   Contribution

   Resources, Supervision, Funding acquisition, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

9. Pascal Fries

   1. Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
   2. Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Writing—original draft, Project administration, Writing—review and editing

   Contributed equally with

   Martin Vinck

   For correspondence

   pascal.fries@esi-frankfurt.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4270-1468

10. Martin Vinck

    Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany

    Contribution

    Conceptualization, Resources, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

    Contributed equally with

    Pascal Fries

    For correspondence

    martin.vinck@esi-frankfurt.de

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4044-0970

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