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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Neurosci. Author manuscript; available in PMC 2010 July 2.
Published in final edited form as:
PMCID: PMC2896204
NIHMSID: NIHMS64707

The Orientation-Selectivity of Color-Responsive Neurons in Macaque V1

Abstract

Form has a strong influence on color perception. We investigated the neural basis of the form-color link in macaque primary visual cortex (V1) by studying orientation selectivity of single V1 cells for pure color patterns. Neurons that responded to color were classified, based on cone inputs and spatial selectivity, into chromatically single-opponent and double-opponent groups. Single-opponent cells responded well to color but weakly to luminance contrast; they were not orientation-selective for color patterns. Most double-opponent cells were orientation-selective to pure color stimuli as well as to achromatic patterns. We also found non-opponent cells that responded weakly or not at all to pure color; most were orientation-selective for luminance patterns. Double- and non-opponent cells’ orientation selectivities were not contrast-invariant; selectivity usually increased with contrast. Double-opponent cells were approximately equally orientation-selective for luminance and equiluminant color stimuli when stimuli were matched in average cone contrast. V1 double-opponent cells could be the neural basis of the influence of form on color perception. The combined activities of single- and double-opponent cells in V1 are needed for the full repertoire of color perception.

Keywords: visual cortex, color vision, spatial vision, single-opponent, double-opponent, contrast

Introduction

The latest view of philosophers is that color is an objective material property (Hyman, 2006) not a subjective experience. However, those who study visual perception know that surrounding colors have a great influence on color perception (Katz, 1935; Brainard, 2002). The neural mechanisms of color perception make computations that take into account the spatial layout of the scene as well as the spectral reflectances of the target surface (Brainard, 2002). It is not known how the visual system integrates form and color but it is now widely believed that the primary visual cortex, V1, plays an important role (Johnson et al., 2001; Friedman et al., 2003; Wachtler et al., 2003; Hurlbert and Wolf, 2004; Engel, 2005).

Opponent color signals travel from retina to V1 through the parvocellular neurons of the lateral geniculate nucleus (LGN). Parvocellular neurons and their retinal ganglion cell inputs (the P-cells) are chromatically single-opponent because they have inputs from two cone types (i.e., long (L-) and middle wavelength (M-) sensitive cones) that are of opposite sign (De Valois, 1965) but each cone input is of one sign across visual space (Reid and Shapley, 2002). These properties make single-opponent cells ideal for signaling the color of region covering the receptive field.

Perceptual color boundary effects were thought previously to depend upon circularly-symmetric V1 double-opponent neurons (Daw, 1967; Livingstone and Hubel, 1984) with concentric center and surround mechanisms that are each color-opponent but opposite in sign. For instance, the center might be +M−L, and then the surround would be +L−M. Such neurons were hypothesized and reported (Livingstone and Hubel, 1984; Michael, 1985), but others found few cells answering this description (Thorell et al., 1984; Lennie et al., 1990).

From the responses of the V1 neuronal population to color and luminance patterns (Johnson et al., 2001), we found a sub-population of neurons (48/167) that had approximately equal responses for color and luminance stimuli, and called them color-luminance cells. We used spatial frequency response functions to test for single-opponency vs. double-opponency. A single-opponent cell (for example, an LGN parvocellular cell) responds optimally at the lowest spatial frequencies to an equiluminant colored grating pattern. Conversely, color-luminance cells were tuned for the spatial frequency of a pure color pattern, with a suppressed response at the lowest spatial frequencies. Many were also orientation-selective for luminance patterns. When color-luminance neurons were stimulated with drifting gratings that isolated a single cone type by silent substitution (Forbes et al., 1955; Estevez and Spekreijse, 1982; Reid and Shapley, 1992, 2002), the spatial-frequency-tuned responses implied that each cone input had spatially-segregated excitatory and inhibitory zones. This means color-luminance cells are double-opponent and especially suited for signaling color boundaries. We also found a smaller group (19/167) of color-preferring V1 cells that were mostly single-opponent cells, resembling LGN parvocellular neurons in responding best to color surfaces.

In this paper, we studied the orientation-selectivity of V1 neurons for pure color (equiluminant) patterns. Our main results are: 1) V1 single-opponent cells are not orientation-selective for color patterns; 2) V1 double-opponent cells are as orientation-selective for color patterns as they are for patterns of cone-contrast-matched luminance contrast. These new results reinforce the hypothesis that single-opponent cells signal color regions, while double-opponent cells are designed to signal color boundaries (Johnson et al., 2001; Shapley and Hawken, 2002; Friedman et al., 2003). Both kinds of color-responsive neuron will contribute to the linkage of form and color.

Materials and Methods

We recorded extracellular responses from 147 neurons in the parafoveal primary visual cortex of anesthetized (sufentanil citrate, 6–18 μ-g/kg/h) and paralyzed (vecuronium bromide, 0.1 mg/kg/h) adult Old-World monkeys (Macaca fascicularis). Full experimental details were given in Johnson et al., (2004). All procedures conformed to the guidelines approved by the New York University Animal Welfare Committee. We recorded single units as described previously (Johnson et al., 2001, 2004). At the conclusion of the experiment, small electrolytic marking lesions (2–3 μA for 3 s, electrode tip negative) were made through each penetration in order to reconstruct the recording sites with respect to the laminar boundaries of the cortex (Hawken et al., 1988). We were able to reconstruct the location of 112/147 of the cells in our sample.

Visual stimuli were generated on a Silicon Graphics O2 computer and displayed on a Sony Multiscan 17seII color monitor measuring 31.4 cm wide and 23.5 cm high. The refresh rate of the monitor was 100 Hz, with a mean luminance of 53 cd/m2. The chromaticity of the background was x = 0.288, y = 0.294. The stimuli were viewed at a distance of 115 cm.

Each cell was characterized to determine the receptive field’s optimal parameters for orientation, temporal frequency, area, and contrast using sinusoidal luminance gratings. Luminance contrast was defined as {luminance modulation amplitude/mean luminance}. Cells were classified as simple or complex (Hubel and Wiesel, 1962) on the basis of the modulation ratio to optimal drifting gratings (Skottun et al., 1991). The same optimal values of orientation, temporal frequency, and area were used in the determination of the spatial frequency tuning for luminance, red-green equiluminance, and the three cone-isolating directions. The red-green equiluminant gratings were produced by modulating the red and green guns of the CRT in antiphase with modulation depths calibrated to be equal and opposite in luminance. The monitor calibrations for luminance were based on the human spectral sensitivity function (Vλ), and were determined photometrically with a Photo-Research spectroradiometer. All stimuli used in these experiments were of the same mean luminance as the surround. Stimuli for the three cone-isolating directions (L-, M- and S-cone), were produced by appropriately adjusting the modulation of the three CRT guns to null out the responses of two of the three cone types (Reid and Shapley, 2002; Johnson et al., 2001, 2004).

For this paper we devised a new classification scheme to partition the V1 population into three parts: single-, double- and non-opponent cells. Previously, we had partitioned the V1 population into three groups: color-preferring, color-luminance, and luminance-preferring (Johnson et al., 2001) on the basis of a color sensitivity index, which we defined as I=response_max(equilum)/response_max(luminance). But when we studied the cone inputs to these different classes (Johnson et al., 2004) we found that some cells classified as color-luminance received the same sign of input from L- and M-cones and therefore could be color-blind. They were “poorly calibrated photometers” (Gegenfurtner and Kiper, 2003) and should be grouped with non-opponent cells. We also found some color-preferring cells that were spatially-tuned for equiluminant patterns. Such cells, we thought, belonged more naturally with the spatial-frequency-tuned color-luminance cells that were cone-opponent. So we devised a new partition of the V1 population into non-opponent, double-opponent, and single-opponent cells. The basis for this new partition is inferred receptive field organization based on: 1) the color sensitivity index and also 2) on the spatial tuning for color and luminance stimuli. We have shown that these measures are highly correlated with proofs of cone-opponency derived from the temporal phase of responses to cone-isolating stimuli and/or from response patterns in color exchange experiments (Johnson et al., 2004). We first computed the color sensitivity index I (defined above). The responses used to calculate the ratio were the peak responses from spatial-frequency-tuning curves measured with drifting gratings (Johnson et al., 2001). Cells with I < 0.5 were classified as non-opponent cells. Cells where I > 0.5, but which behaved like miscalibrated photometers in color-exchange experiments (Shapley and Hawken, 1999, 2002; Johnson et al., 2004) were re-assigned to the non-opponent group.

For cells that had color sensitivity index I ≥ 0.5, we verified that these cells were “color cells” using a range of red-green color-exchange responses, as described previously (Johnson et al., 2004). Then these color cells were grouped as “single-opponent” or “double-opponent” from their spatial frequency tuning responses to equiluminant color and luminance in the following manner. Band-pass or low-pass spatial frequency tuning was determined by a least-squares fit to a Difference-of-Gaussians (DOG) function. If and only if the spatial frequency bandwidth of the best-fit DOG was undefined, the cell was classified as low-pass. If cells had I ≥ 2 and low-pass spatial frequency responses to equiluminant color, they were classified as “single-opponent.” If a cell had a color sensitivity index 0.5< I< 2, and produced low-pass spatial frequency responses to both color and luminance, it was classified as “single-opponent.”

If a cell had I ≥ 2 and its spatial frequency response to color gratings was tuned in spatial frequency (that is spatially “bandpass”), it was classified as “double-opponent.” If a cell had a color index 0.5< I<2, and its spatial frequency response to color and/or luminance was a bandpass response, such a cell was classified as “double-opponent.” Most neurons (59/62) classified here as double-opponent are color-luminance cells by the classification scheme of Johnson et al. (2001), but some (3/59) would have been classified as color-preferring.

Chromatic stimuli and contrast

In the spatial frequency tuning experiments, each type of grating was approximately equated for cone contrast, as follows. Cone excitations were calculated as the dot product of the cone absorption fundamentals and the spectral energy distribution of the CRT gun primaries measured with a Photo-Research spectroradiometer. Cone contrast was calculated as the modulation of each cone’s response divided by the mean excitation for each cone. For the equiluminant stimuli, L-cone contrast = 0.04 and M-cone contrast = −0.096. A chromatically opponent mechanism would respond to the difference between these contrasts, so the effective equiluminant cone contrast would be approximately 0.14. Red-green equiluminant stimuli in some later orientation experiments had an effective cone contrast of 0.17. Although the maximum luminance modulation attainable is 1.0, we attempted to equate the luminance and chromatic equiluminance stimuli in terms of cone contrast in the orientation experiments, using a luminance contrast of 0.15. We also employed high contrast stimuli, using 0.8 as our “high” contrast. Orientation tuning responses with both 0.15 and 0.8 luminance contrast were recorded from 46/62 double-opponent and 55/67 non-opponent cells. For cone-isolating stimuli (shown in Figures 1, ,22 and and3),3), the cone contrasts used were as follows: L-cone, 0.13; M-cone, 0.15; S-cone, 0.24.

Figure 1
Double-opponent simple cell from layer 2/3
Figure 2
Two double-opponent cell examples: a complex cell from layer 2/3 (A–D) and a simple cell from an unknown layer (E–H)
Figure 3
An example of a V1 single-opponent neuron from layer 6

Stimulus procedure

Spatial tuning was measured in all color directions with drifting sinusoidal gratings. Each stimulus was presented for 4 seconds on a background of mean luminance (53 cd/m2) followed by a blank of mean luminance of the same duration to determine the spontaneous firing rate and to avoid response adaptation. Spatial frequencies from full-field modulation to approximately 10 cycles per degree (c/deg) were presented in equal logarithmic intervals. In order to try to avoid chromatic aberration, we recorded in the parafovea (roughly 2–5° eccentric), where the spatial frequency tuning is limited to intermediate to low spatial frequencies. We believe the effects of chromatic aberration on our classification system are negligible because of the spatial frequency range and the low contrast of the stimuli.

The responses were compiled and averaged relative to the temporal period of the grating to form post-stimulus time histograms. These histograms were Fourier analyzed to calculate the mean response rate (DC) as well as the amplitude and phase of the fundamental stimulus frequency (F1). The cells were classified as simple or complex according to the ratio of the mean to first harmonic response. Cells that did not give a response of at least 10 spikes/sec above the mean spontaneous rate to either luminance or equiluminant chromatic gratings were excluded from the analysis.

In color-exchange experiments, the red gun contrast was held fixed at 1.0, and the green gun contrast varied from 0 to −1.0. The green and red modulation was 180° out of phase. The stimuli were drifting at the optimal orientation, spatial frequency and temporal frequency as determined by the initial receptive field characterization. The methods for color-exchange are described in detail elsewhere (Shapley and Hawken, 1999; Johnson et al., 2004).

Orientation-tuning

Orientation-tuning was determined for each cell with drifting grating stimuli of the optimal spatial and temporal frequency. Orientation was varied in 15° or 20° steps through a full 360°. Orientation responses for the two directions of drift were combined, and circular variance was determined from these response measurements. Circular variance measures the orientation-selectivity based on all the orientations measured, and it is defined (Mardia, 1972; Ringach et al., 2002) as V=1−|R|, where R is the resultant,

R=krke{i2πθk/180}krk

Here, θk represents equally spaced orientation angles spanning 0° to 360°, and rk represents the spike rate at each orientation. For complex cells, the spontaneous rate was subtracted from the mean spike rate, and for simple cells the spike rate was measured as the amplitude of the first harmonic response. Cells with very sharp orientation tuning are mapped to values of V close to 0, and those with broad orientation tuning are mapped to values close to V=1.

Cone maps and reverse correlation

The cone spatial maps in Figures 1, ,33 and Supplementary Figure 1 were measured using subspace reverse correlation (Ringach et al., 1997). In this experiment, images were drawn randomly from a low-pass subset of the two-dimensional Hartley functions. The Hartley stimuli consist of an orthogonal set of sinusoids of evenly spaced orientations, spatial frequencies, and spatial phases. Spatial frequencies ranged from 1 cycle per stimulus width up to a maximum that was chosen for each cell to be higher than its high-frequency cutoff. Orientations were evenly spaced around the full 360 degrees. Each stimulus in the set was matched by another stimulus, offset by 90 degrees in spatial phase. Stimuli were bounded by a square window, the width of which was at least as large as four cycles of the optimal spatial frequency, determined using drifting gratings. Each Hartley stimulus was presented for two consecutive video frames (20ms) as part of a continuous 15 minute stream. The color contrast of the Hartley stimuli was cone-isolating as described above.

Results

Single-, double- and non-opponent neurons

We classified cells as single-opponent, double-opponent, and non-opponent for chromatic stimuli, as specified in detail in Materials and Methods. Single-opponent cells are color-responsive cells that receive opponent cone input, meaning excitation from one cone and inhibition from another. Single-opponent cells respond best to large areas of color because there is no spatial antagonism within their cone-specific inputs. Non-opponent cells receive the same sign of input from different cones, and therefore are color-blind. Double-opponent cells are color-responsive, with cone-opponent inputs, but they prefer spatial patterns of color rather than full field, because there is spatial antagonism within their cone-specific inputs. This classification scheme is different from the one introduced in Johnson et al. (2001, 2004) that was based purely on a color-sensitivity-index (see Materials and Methods).

The 147 neurons in this study are a distinct population of neurons from those we studied earlier (Johnson et al., 2001, 2004). We sought to study orientation-selectivity for color and luminance patterns in roughly equal numbers of color-responsive and color-blind neurons, consequently the relative numbers of each type of cell in the study population in this paper do not reflect their relative frequency in V1. From our previous work on V1 color cells we estimate that the proportions of cells in the V1 population are roughly 60% non-opponent, 30% double-opponent, 10% single-opponent.

In addition to analyzing the electrophysiological responses of the neurons, we studied their anatomical location. Cells were assigned a cortical depth and layer by histological reconstruction of the electrode track (see Materials and Methods). The laminar assignment for each class of cells is shown in Table 1. Single-opponent cells were most often in layers 2/3 and 5; double-opponent cells were most often in layers 2/3 and 6; and non-opponent cells were most often in layers 4B and 6. Cells that could not be assigned a cortical depth are not reported in the table (n = 35).

Table 1
The laminar distribution of single-opponent, double-opponent, and non-opponent neurons in each layer of primary visual cortex expressed as the fraction and percentage of each type.

Double-opponent neurons

An example of a double-opponent simple cell that was spatial-frequency-selective for color but gave only a weak luminance response is shown in Figure 1. Two-dimensional maps (from subspace reverse correlation--see Materials and Methods; Ringach et al., 1997) of this cell’s sensitivity for L- and M-cone-isolating patterns are shown in Figure 1A and B: 1A is the L-cone map, and 1B is the M-cone map. These pseudo-color maps show excitation to contrast increments in red, and excitation to contrast decrements in blue. At corresponding points, marked in the figure, cone-isolating responses are of opposite sign for the two cones. Thus, Figure 1A and B is clear direct evidence for double-opponency in this neuron. In addition each spatial subregion is elongated, indicative of an orientation-selective receptive field. To explore color-opponency and spatial properties parametrically, we measured responses to spatial frequency and orientation, using both color and luminance stimuli. The spatial frequency tuning curves (Figure 1C and D) show that this double-opponent cell was spatial-frequency-tuned for red-green equiluminant patterns, and also for L- and M- and S-cone isolating patterns, consistent with the spatial opponency of the cone inputs to this neuron (Figure 1A and B).

The temporal phases of the response to cone-isolating gratings demonstrate that the cone inputs to this cell are of opposite sign (opponent) (cf. Johnson et al., 2001, 2004). The PSTH’s to M-cones, shown in Figure 1F, and L-cones, shown in Figure 1E, are precisely out-of-phase—a result consistent with the spatial cone maps in Figure 1A and B. Another example of a double-opponent simple cell that responded well to both color and luminance is shown in Supplementary Figure 1; it shows the same spatial opponent structure as the neuron described in Figure 1. Conway and Livingstone (2006) posited that the double-opponent cells we had identified (Johnson et al., 2004) lacked explicit signs of cone-opponency such as opposite-signed responses to different cone-isolating stimuli. However, the results in Figure 1E and F (and Supplementary Figure 1E and F) show that some double-opponent cells indeed give an ON response to one cone-isolating stimulus and the opposite-sign response to a different cone-isolating stimulus. Approximately one third of the double-opponent cells we found were simple cells that produced opposite-phase responses to M- and L- cone-isolating stimuli like the responses shown in Figure 1E and F (cf. Johnson et al., 2001, 2004).

Data like those in Figure 1G are the focus of this paper because they show that this double-opponent cell was orientation-selective to a purely chromatic grating. Responses to orientations orthogonal to the preferred orientation were close to zero. However, not all double-opponent cells were as orientation-selective as the cell in Figure 1. Orientation-tuning (as well as spatial-frequency-tuning) data from other double-opponent V1 neurons are shown in Figures 2 and and4.4. Figure 5 is a population analysis of orientation-selectivity in V1.

Figure 4
Orientation-tuning curves of representative single-opponent, double-opponent, and non-opponent neurons. Orientation-tuning was measured with red-green equiluminant patterns (red), and with luminance patterns (black; contrast, 0.15). Three measures of ...
Figure 5
Population analysis of orientation-selectivity in V1 neurons. Distributions of the O/P ratio (the ratio of the responses to orthogonal-to-preferred and preferred orientations) across the V1 subpopulations studied.

Spatial-frequency and orientation-tuning in double-opponent cells

Most double-opponent neurons had bandpass spatial-frequency tuning to both equiluminant-color and luminance stimuli with peak response rates that were within 50% of each other (Supplementary Figure 1; Figure 2A, E; Johnson et al., 2001, 2004). Some double-opponent V1 neurons gave responses to all three types of cones (Figure 2C), like some “color-luminance” cells described previously (Johnson et al., 2004). There were also double-opponent cells that gave responses only to L- and M-cone isolating stimuli but not to the S-cone stimulus (Figure 2E). The spatial frequency responses to cone-isolating stimuli (Figure 2C, G) were bandpass, meaning the best response was to a spatial pattern of color not to full-field color modulation. The orientation-tuning was very similar for luminance and equiluminant-color stimuli (Figure 2B, F). We will show population data on this point and compare the double-opponent with other types of neurons below.

Both complex (Figure 2A–D) and simple cells (Figure 2E–H) can be double-opponent. Complex cells do not show a modulated response to drifting gratings so that the temporal phase of the response cannot be used to determine opponency. Figure 2D graphs the results of a color-exchange experiment that indicates, by its lack of a steep local minimum, that the neuron was not adding L- and M-cone inputs it received but subtracting them, i.e. it was a color-opponent cell (cf. Shapley and Hawken, 1999; Johnson et al., 2004). Both complex (Figure 2A–D) and simple double-opponent cells (Figure 2E–H) can be orientation-tuned. This finding is consistent with results on simple and complex cells studied by Horwitz et al. (2007) in awake monkey V1.

Single-opponent V1 neurons

Contrast the double-opponent cells with an example of a single-opponent cell from layer 6 that responded well to color but only weakly to luminance contrast (Figure 3). Two-dimensional maps of this cell’s sensitivity for L- and M-cone-isolating patterns (Figure 3A and B respectively) are evidence for single-opponency in this neuron. There was only one receptive field subregion for each cone type. The spatial maps were consistent with the spatial frequency tuning curves for L- and M-cone input that were low-pass (Figure 3D) meaning that this cell preferred full-field modulation of cone-isolating stimuli more than spatially patterned stimuli. The spatial frequency response for equiluminant patterns was also low-pass (Figure 3C). The temporal phases of the response to cone-isolating gratings demonstrate that the cone inputs to this cell are of opposite sign (opponent) (cf. Johnson et al., 2001, 2004) because the PSTH’s to L-cones, shown in Figure 3E, and M-cones, shown in Figure 3F, are precisely out-of-phase. The cone-isolated subregions were roughly circular in shape consistent with the poor orientation selectivity of the neuron for color patterns (Figure 3G).

Orientation-tuning in single-, double-, and non-opponent V1 cells

Next we present orientation-tuning curves of neuron examples, picked to show the range of least and most selective cells of each type. For double-opponent (Figure 4C–E) and non-opponent (Figure 4F–H) classes, an example roughly in the middle (Figure 4D, G) of the distribution of selectivity is shown also. Only two single-opponent neurons were chosen (Figure 4A, B) because the whole population of single-opponent cells is not very orientation-selective (see Figure 5); these two non-selective single-opponent cells are representative of the range of selectivity. Orientation-tuning measured with red-green equiluminant patterns is drawn in red, while the tuning measured with luminance patterns is black. In the experiments used for Figure 4, the luminance contrast was 0.15, to match the cone contrast of equiluminant stimuli (Johnson et al., 2001).

Single-opponent cells responded vigorously to red-green gratings and little or not at all to luminance gratings of matched cone-contrast (Figure 4A, B), while non-opponent cells gave stronger responses to luminance patterns (Figure 4F–H). The double-opponent cells (Figure 4C–E) responded to both color and luminance as indicated by the equiluminant and luminance orientation-tuning curves. Three different quantitative measures of orientation-selectivity are written on Figure 4 as insets to the graphs: O/P, the ratio of the responses to orthogonal-to-preferred and preferred orientations; BW, the orientation bandwidth in units of degrees of orientation; CV, the circular variance (Mardia, 1972, Ringach et al., 2002) of the tuning curve. For these three measures, smaller values means more selective. O/P, BW, and CV are nearly the same for equiluminant and luminance stimuli for the double-opponent cell examples in Figure 4C–E.

Population analyses of orientation-selectivity

The O/P response ratio has been used previously as a measure of the degree of orientation-tuning (Gegenfurtner et al., 1996; Ringach et al., 2002). Ratios near zero indicate high selectivity because then the response at the orthogonal orientation is weak compared to the response at the preferred orientation (Figure 4C, F). An analysis of the V1 population shows that single-opponent neurons have the largest O/P ratios when examined with equiluminant red-green stimuli (<O/P> = 0.66 ± 0.04 (SE); Figure 5A). This means single-opponent cells are unselective or weakly-selective for the orientation of color patterns, which are the only patterns they respond to robustly. Double-opponent neurons have lower O/P ratios on average than single-opponent cells when tested with equiluminant gratings (<O/P> = 0.36 ± 0.03 (SE); Figure 5C), meaning they are more orientation-selective for color patterns. A similar distribution of O/P ratios is seen when double-opponent neurons are tested with luminance gratings that had the same cone-contrast as the equiluminant gratings (<O/P> = 0.32 ± 0.04 (SE); Figure 5D). Non-opponent cells were the most orientation-selective (<O/P> = 0.17 ± 0.03 (SE); Figure 5B). The bin representing 0–0.1 in the O/P histogram contains a larger fraction of non-opponent (Figure 5B) than of double-opponent cells (Figure 5D), but there is a great deal of overlap in the distribution of O/P ratios for the non-opponent and double-opponent cells (Figure 5B and 5D, respectively). There was sparse S-cone input to the cells we found; the cells that received significant S-cone input are plotted as shaded in the histograms in Figure 5. A very small number of double-opponent cells in our sample gave no response to any luminance contrast, responding only to red-green equiluminance (3/62). For a few other cells (13/62) in our double-opponent sample, we recorded orientation responses only with 0.8 luminance contrast stimuli. These 16 neurons are absent from the histogram in Figure 5D, but included in 5C.

In addition to studying the population distributions we asked whether orientation-tuning in individual double-opponent neurons was correlated for luminance and chromatic stimuli. Both the orthogonal/preferred (O/P) ratios and orientation bandwidths obtained from tuning with luminance and chromatic gratings are strongly correlated for the double-opponent population (Figure 6). When the O/P ratios for luminance contrast of 15% (labeled “low contrast”) are compared to the O/P ratios for equiluminant red-green stimuli (Figure 6A), the correlation is r = 0.81 with most points clustering around the diagonal line, the line of equality. Examining the orientation bandwidths provides a similar result (Figure 6C), with a somewhat lower correlation r = 0.58. These results suggest that the same underlying mechanisms are generating orientation-selectivity for color and luminance stimuli.

Figure 6
Comparison of the orientation-selectivity of double-opponent cells for color and luminance patterns

Orientation-tuning and contrast: contrast invariance?

Comparison of equiluminant with black-white gratings led to an interesting test of the contrast-invariance of orientation-selectivity. If there were contrast-invariant orientation-tuning, the equiluminant versus luminance tuning would be the same at all contrasts as for the cone-contrast matched stimuli (Figure 6A, C). The double-opponent population’s orientation-tuning is not contrast-invariant. Most neurons have a lower O/P ratio when measured with luminance gratings of high contrast (<O/P_high> = 0.25 ± 0.03 (SE), while <O/P_low > = 0.32 ± 0.04 (SE); Figure 6B and 6A). In the scatter plot (Figure 6B) for the O/P ratios for high luminance contrast vs. equiluminant gratings, most points fall below the unity line, and the correlation falls (r = 0.70 in Figure 6B) compared to that for matched luminance and equiluminant contrasts (r=0.81 in Figure 6A). Additionally, the color and high-luminance-contrast bandwidths (Figure 6D) are not correlated (r = 0.044).

This led us to investigate the contrast-invariance of orientation-selectivity for achromatic patterns. We compared orientation-tuning for neurons using 0.15 contrast gratings and high contrast (0.8 contrast) gratings. Contrast non-invariance was the rule. The O/P ratio for high luminance contrast is most often lower than for 0.15 luminance contrast (Figure 7A). This is the case for both double-opponent and non-opponent populations (Figure 7A filled and open symbols respectively). This explains why the equiluminant color O/P ratio agreed with the low luminance contrast O/P but was systematically higher than the high contrast O/P. The greatest difference in O/P ratio between high and low contrast was for cells in the middle range of O/P. Therefore we chose to analyze further those cells in the range 0.1<O/P<0.8. The histograms in Figure 7B plot the frequency distributions of the fractional change in O/P ratio with contrast for non-opponent and double-opponent cells in the selected range of O/P ratio. The mean of the fractional change in O/P ratio for double-opponent cells was −0.26 (i.e., a drop of 26% going from low to high contrast) while for non-opponent cells it was −0.21. The variation of O/P ratio with contrast is statistically significant across the double-opponent and non-opponent populations selected. If the O/P ratio were contrast invariant, then the distribution of fractional change in O/P ratio with contrast (in Figure 7B) would have a mean of zero. Based on a t-test, the probability that the mean is zero, either for non-opponent or double-opponent cells, is very small: for non-opponent cells, p<0.02, and for double-opponent cells p<0.006. These results on the absence of contrast invariance are consistent with the observations of Alitto and Usrey (2004). Note however that the change in bandwidth between low and high contrast conditions for both cell groups (Figure 7C and 7D) is not statistically significant, meaning that orientation bandwidth is contrast-invariant for these cells (Sclar and Freeman, 1982). The non-invariance with contrast of the O/P ratio has theoretical and practical consequences taken up in the Discussion.

Figure 7
Orientation-selectivity’s dependence on contrast. Comparisons of O/P ratio and bandwidth at high (0.8) and low (0.15) luminance contrast in double-opponent cells and in non-opponent cells

Circular variance

Another measure that captures the global characteristics of orientation-selectivity is circular variance, CV; we and our colleagues used CV previously to characterize orientation-selectivity (Ringach et al., 2002;Xing et al., 2004). To connect the present study with earlier papers, Figure 8 shows population histograms of circular variance measures of orientation-tuning curves for single-, double- and non-opponent neurons. The circular variance data highlight the poor orientation-selectivity of the single-opponent neurons (Figure 8A). Furthermore, the distributions across the double-opponent population of CV for equiluminant stimuli and cone-contrast-matched luminance stimuli are very similar (Figure 8C, D), supporting the earlier results using the O/P and bandwidth measures of orientation-selectivity that revealed that double-opponent neurons are orientation-selective for red-green as for achromatic stimuli. The results with CV generally support what we found with the O/P ratio, which is not a surprise since we showed earlier that O/P ratio and circular variance are highly correlated (Ringach et al., 2002). To compare the overall selectivity of neurons that respond reliably to color stimuli (single- and double-opponent groups) with those neurons that respond to achromatic stimuli (double-opponent and non-opponent groups) we have compiled composite histograms (Figure 8E, F). These data could be useful for comparisons with fMRI in humans (Schluppeck and Engel, 2002).

Figure 8
Population analysis of circular variance in single-, double-, and non-opponent populations

Discussion

Classes of chromatically opponent neurons and the double-opponent model

The orientation-selectivity documented in this study, in addition to the bandpass spatial frequency tuning for equiluminant chromatic gratings (Thorell et al., 1984; Johnson et al., 2001; 2004), adds considerable support to the spatial model of double-opponent neurons (Figure 9A) we proposed previously (Shapley and Hawken, 2002; Johnson et al., 2004; see also Box 4 in Solomon and Lennie, 2007). The two-dimensional spatial structure of the double-opponent model for simple cells is further supported by the direct measurements of the first-order chromatic kernels (Figure 1A and B, Supplementary Figure 1A and B). Other models of double-opponent neurons with a circularly symmetric receptive field organization that is either single-opponent with a non-opponent surround (Ts’o and Gilbert, 1988; see also Box 4 in Solomon and Lennie, 2007;) or chromatically double-opponent (Figure 9B; Conway and Livingstone, 2006; Billock, 1991) do not provide a satisfactory account of the “double-opponent” neurons we have described that are orientation- and spatial frequency-selective with odd-symmetric receptive fields (Johnson et al., 2004). Receptive fields of neurons in the population of chromatically opponent neurons that are not orientation-selective and respond best to low spatial frequency color stimuli are well described by single-opponent models as shown in Figure 9C (Hubel and Wiesel, 1968; Lennie et al., 1990; Shapley and Hawken, 2002; see Box 4 in Solomon and Lennie, 2007).

Figure 9
Models of double-opponent and single-opponent V1 neurons

One of the critical components that distinguishes various proposals of double-opponent receptive field structure is the symmetry of the subunit structure (Shapley and Hawken, 2002; Solomon and Lennie, 2007). Analysis of a subset of the double-opponent simple cell receptive fields showed that many were odd-symmetric or asymmetric (Johnson et al., 2004), which is consistent with the predominance of asymmetric simple cell receptive fields mapped using achromatic stimuli in macaque V1 (Ringach, 2002). Girard and Morrone (1995) inferred from their results on human visual evoked potentials to gratings modulated in either luminance or red-green color that both color and luminance mechanisms have receptive fields that include asymmetric spatial substructure. It is also worth noting that Thorell et al. (1984) suggested a receptive field organization like the one in Figure 9A from their results on responses to flashed colored bars. Suppose that the cortex adopts the same design for non-opponent and chromatically opponent simple cell receptive fields; it would not be surprising that there is a close correspondence between the underlying receptive field structures of non-opponent and double-opponent neurons described in this study. It is important to note that there are differences in tuning between the double-opponent and non-opponent cortical receptive fields. In general, one feature of the non-opponent population is a lower average O/P ratio. We and our colleagues found that a non-orientation-selective, spatially low-pass, suppressive mechanism (called untuned suppression) plays an important role in establishing this facet of selectivity (Ringach et al., 2002; 2003; Xing et al., 2005). Perhaps untuned suppression is stronger in non-opponent than in double-opponent neurons.

Solomon and Lennie (2007) refer to the neurons with receptive fields we have called double-opponent cells as “weakly opponent.” However, we have presented evidence that double-opponent cells have a range of cone-opponent ratios and that there often are strongly opposing cone responses at the peak of double-opponent cells’ spatial tuning curves (in Johnson et al., 2004). The wide range of cone-opponent ratios in the double-opponent population will make different double-opponent cells selective for different colors. Such diversity in V1 color selectivities also has been observed by others (Lennie et al., 1990; Friedman et al., 2003) and is possibly a neural mechanism for the multiplicity of color-selective channels inferred from psychophysics (Webster and Mollon, 1994).

Function of single-opponent and double-opponent cells in color vision

There is an important role for edge-sensitive cells in color vision (Johnson et al., 2001; Friedman et al., 2003). Color induction—the complementary color appearance of a neutral gray area surrounded by a colored region—can be a strong effect (Gordon and Shapley, 2006). A striking demonstration of color induction is given in Supplementary Figure 2, an example of a color Chevreul illusion (cf. Ratliff, 1992). The edge-sensitive, double-opponent cells in V1 should support color induction. Besides color induction there are other perceptual phenomena that appear to require orientation-tuned color signals, for example, the perception of 3-D shape in orientation flow patterns (Zaidi and Li, 2006) and the perception of geometric illusions under chromatic equiluminant conditions (Wilson and Switkes, 2005; Hamburger et al., 2007). There are many other psychophysical and perceptual connections between color and form: spatially-tuned masking with pure color patterns (Switkes et al., 1988; Losada and Mullen, 1994); orientation discrimination with pure color patterns (Webster et al., 1990; Beaudot and Mullen, 2005); tilt illusion with pure color patterns (Clifford et al., 2003); color filling-in (Krauskopf, 1963).

Comparisons with previous work

Some early neurophysiological studies supported the concept of a spatially-challenged color system. The older view was based in part on human psychophysics: the low-pass human color contrast sensitivity function (Mullen, 1985; DeValois and DeValois, 1988). Such psychophysical results were interpreted to mean that the neural tuning for color was likewise low-pass in spatial frequency. However, there is now a wealth of psychophysical evidence for orientation-sensitive, spatial-frequency-selective color-responsive cells (Switkes et al., 1988; Webster et al., 1990; Losada and Mullen, 1994; Clifford et al., 2003; Beaudot and Mullen, 2005). Recent human fMRI results on V1 cortex also have indicated the existence of orientation-tuned, color-responsive neurons in V1 (Engel, 2005; Sumner et al., 2008).

Hubel and Wiesel (1968) reported that many color-responsive neurons in macaque V1 lacked orientation specificity, although they did find some color-selective cells with orientation-selectivity. Livingstone and Hubel (1987, 1988) suggested a link between color processing and the cytochrome oxidase (CO-) rich patches (blobs) and CO-poor interpatches (interblobs) in layer 2/3 of V1 (Wong-Riley, 1983; Livingstone and Hubel, 1984). Livingstone and Hubel (1984) proposed that the color-responsive cells in the blobs were double-opponent neurons with circular receptive fields that were not selective for stimulus orientation. Lennie et al. (1990) reported that some color-responsive V1 neurons showed evidence of spatial-frequency and orientation-selectivity, but concluded that most V1 neurons preferred luminance modulation, and that V1 neurons that were most responsive to chromatic modulation had poor orientation-selectivity and responded best to spatially-uniform chromatic fields.

Another reading of cortical neurophysiology suggests that color information is not processed separately from spatial attributes in V1. Four previous studies suggested that color-responsive neurons can be orientation-selective (Thorell et al., 1984; Leventhal et al., 1995; Yoshioka and Dow, 1996; Horwitz et al., 2007). Friedman et al. (2003) studied the responses to squares or bars of color of cells in V1 upper layers and V2 in the awake monkey. They found a substantial proportion (64%) of color-selective neurons in the upper layers of V1; most of these were most responsive to edges. They also found a smaller proportion of “color-surface-responsive cells,” cells that were not especially sensitive to edges. Their edge-responsive color cells were mostly orientation-selective, while the surface-responding cells were not. Though more research is needed to establish the connection, it is a reasonable hypothesis that Friedman et al.’s (2003) edge-sensitive, color-selective cells were double-opponent cells while their surface-responding cells were single-opponent neurons.

Conway and Livingstone (2006) reported roughly circularly-symmetric double-opponent cells in macaque cortex. Their experiments involve mapping receptive fields by reverse correlation with cone-isolating stimuli which were flashed, colored squares on a gray background. They selected neurons for study that responded well to the flashed, colored squares. It is possible that many, perhaps all, double-opponent cells that we studied do not respond to such stimuli. Most cells that Conway and Livingstone (2006) called double-opponent had very weak surround effects, and they stated that some of their double-opponent cells were “weakly orientation-selective.” Therefore, it is possible that most of the cells they studied were what we would classify as single-opponent. Because of their weak surrounds, the double-opponent cells Conway and Livingstone describe would be inadequate to explain many of the color-form interactions we have discussed above.

Contrast-invariance and orientation selectivity

Sclar and Freeman (1982) reported that orientation tuning was invariant with contrast, and since the papers of Ben-Yishai et al. (1995) and Troyer et al. (1998) there has been interest in the theoretical importance of this invariance (for example Finn et al., 2007). Our results on orientation selectivity, color, and contrast reveal that contrast-invariance is mostly a floor effect. Neurons of intermediate orientation-selectivity become more selective at high contrast, as shown in Figure 7. Contrast non-invariance was observed both for non-opponent cells as well as for double-opponent cells. Our findings replicate in monkey V1 the findings of Alitto and Usrey (2004) in ferret V1. We do not know the mechanism for the sharpening of orientation selectivity at higher contrast, but suspect it is related to the contrast-dependence of cortico-cortical interactions. Whatever the mechanism, the greater orientation-selectivity at higher contrast had important consequences in our experiments. The implication of contrast-non-invariance is that it is necessary to compare orientation-selectivities of V1 neurons to patterns of comparable cone contrast in order to assess the relative efficacy of colored and achromatic stimuli for conveying orientation-dependent signals. When we did the matched comparison, we found that the orientation-selectivity of the double-opponent population was about the same for color and luminance.

Supplementary Material

S1

supplement 1

Acknowledgments

This work was supported by National Institutes of Health Grants EY-01472, EY-8300, MH-12430–01 and Core Grant EY-P031-13079. We thank Drs. Andy Henrie, Patrick Williams, Dajun Xing, and Siddhartha Joshi for helping with the experiments, and Dr. Stephen Van Hooser and Prof. James Gordon for helpful comments on the manuscript.

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