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• W2898817166 abstract "•V1 neurons carry poor orientation information at low spatial frequencies (SFs)•This study reveals a retinotopic representation of low-SF luminance stimuli in V1•This distributed representation carries high-quality orientation information•This signal is likely to contribute to our scale-invariant visual capabilities Humans have remarkable scale-invariant visual capabilities. For example, our orientation discrimination sensitivity is largely constant over more than two orders of magnitude of variations in stimulus spatial frequency (SF). Orientation-selective V1 neurons are likely to contribute to orientation discrimination. However, because at any V1 location neurons have a limited range of receptive field (RF) sizes, we predict that at low SFs V1 neurons will carry little orientation information. If this were the case, what could account for the high behavioral sensitivity at low SFs? Using optical imaging in behaving macaques, we show that, as predicted, V1 orientation-tuned responses drop rapidly with decreasing SF. However, we reveal a surprising coarse-scale signal that corresponds to the projection of the luminance layout of low-SF stimuli to V1’s retinotopic map. This homeomorphic and distributed representation, which carries high-quality orientation information, is likely to contribute to our striking scale-invariant visual capabilities. Humans have remarkable scale-invariant visual capabilities. For example, our orientation discrimination sensitivity is largely constant over more than two orders of magnitude of variations in stimulus spatial frequency (SF). Orientation-selective V1 neurons are likely to contribute to orientation discrimination. However, because at any V1 location neurons have a limited range of receptive field (RF) sizes, we predict that at low SFs V1 neurons will carry little orientation information. If this were the case, what could account for the high behavioral sensitivity at low SFs? Using optical imaging in behaving macaques, we show that, as predicted, V1 orientation-tuned responses drop rapidly with decreasing SF. However, we reveal a surprising coarse-scale signal that corresponds to the projection of the luminance layout of low-SF stimuli to V1’s retinotopic map. This homeomorphic and distributed representation, which carries high-quality orientation information, is likely to contribute to our striking scale-invariant visual capabilities. In natural visual scenes, relevant information regarding pattern geometry (e.g., contour orientation) is distributed over a wide range of spatial scales. Similarly, the spatial scale of the retinal image of an object can vary dramatically with its distance from the observer. Therefore, to support our interactions with complex natural environments, our visual system must be able to finely discriminate visual patterns over a broad range of spatial frequencies (SFs). Behavioral measurements suggest that this is indeed the case (Burr and Wijesundra, 1991Burr D.C. Wijesundra S.-A. Orientation discrimination depends on spatial frequency.Vision Res. 1991; 31: 1449-1452Crossref PubMed Scopus (44) Google Scholar, Jamar and Koenderink, 1983Jamar J.H.T. Koenderink J.J. Sine-wave gratings: scale invariance and spatial integration at suprathreshold contrast.Vision Res. 1983; 23: 805-810Crossref PubMed Scopus (25) Google Scholar). For example, we can discriminate between gratings whose orientation differs by less than one degree, and this sensitivity is only modestly affected by changes in SF and size over more than two orders of magnitude (Burr and Wijesundra, 1991Burr D.C. Wijesundra S.-A. Orientation discrimination depends on spatial frequency.Vision Res. 1991; 31: 1449-1452Crossref PubMed Scopus (44) Google Scholar). What are the neural mechanisms that underline these remarkable scale-invariant capabilities? Neurons in primate primary visual cortex (V1) are selective to the orientation of contrast edges within their receptive fields (RFs) (Hubel and Wiesel, 1968Hubel D.H. Wiesel T.N. Receptive fields and functional architecture of monkey striate cortex.J. Physiol. 1968; 195: 215-243Crossref PubMed Scopus (4413) Google Scholar). These orientation-selective responses are likely to play an important role in pattern discrimination. However, due to the limited spatial spread of the geniculo-cortical projections to V1 (Blasdel and Lund, 1983Blasdel G.G. Lund J.S. Termination of afferent axons in macaque striate cortex.J. Neurosci. 1983; 3: 1389-1413Crossref PubMed Google Scholar) and the connections between the input and output layers within V1 (Angelucci et al., 2002Angelucci A. Levitt J.B. Lund J.S. Anatomical origins of the classical receptive field and modulatory surround field of single neurons in macaque visual cortical area V1.Prog. Brain Res. 2002; 136: 373-388Crossref PubMed Scopus (152) Google Scholar, Levitt and Lund, 2002Levitt J.B. Lund J.S. The spatial extent over which neurons in macaque striate cortex pool visual signals.Vis. Neurosci. 2002; 19: 439-452Crossref PubMed Scopus (120) Google Scholar, Lund et al., 2003Lund J.S. Angelucci A. Bressloff P.C. Anatomical substrates for functional columns in macaque monkey primary visual cortex.Cereb. Cortex. 2003; 13: 15-24Crossref PubMed Scopus (169) Google Scholar), at any location in V1, neurons have a limited range of RF sizes. As a consequence, when the spatial scale of an oriented stimulus exceeds this range of RF sizes at the corresponding location, individual V1 neurons should not be able to represent edge orientation reliably (Figure 1A). Surprisingly, this fundamental “aperture problem” for the computation of orientation, phase, and SF of large-scale static patterns has not received the same attention as the aperture problem for the computation of direction and speed of large-scale moving patterns (Adelson and Movshon, 1982Adelson E.H. Movshon J.A. Phenomenal coherence of moving visual patterns.Nature. 1982; 300: 523-525Crossref PubMed Scopus (870) Google Scholar, Marr, 1982Marr D. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. The MIT Press, 1982Google Scholar). Therefore, our first goal was to test the prediction that the quality of orientation-selective signals in macaque V1 deteriorates for low-SF stimuli. If the quality of orientation signals carried by individual V1 neurons drops at low SFs, what could account for the high behavioral sensitivity in orientation discrimination at low SFs? Here, we explore the possibility that information regarding the orientation of low-SF stimuli is encoded by the collective pattern of population responses in V1 rather than by the activity of individual V1 neurons (Figure 1A). In addition to orientation, V1 neurons are also tuned to SF (De Valois et al., 1982De Valois R.L. Albrecht D.G. Thorell L.G. Spatial frequency selectivity of cells in macaque visual cortex.Vision Res. 1982; 22: 545-559Crossref PubMed Scopus (1157) Google Scholar). However, most V1 neurons continue to respond (albeit weakly) to the onset of visual patterns with SFs that are much lower than their preferred SF (De Valois et al., 1982De Valois R.L. Albrecht D.G. Thorell L.G. Spatial frequency selectivity of cells in macaque visual cortex.Vision Res. 1982; 22: 545-559Crossref PubMed Scopus (1157) Google Scholar). Because each neuron has access only to a small portion of the stimulus (Figure 1A), these responses must be driven by the abrupt change in the local mean luminance within the neuron’s receptive field. As discussed above, these responses are likely to be non-selective for orientation. Although these untuned luminance-related responses are relatively small in single neurons, they may be large at the population level because they are shared by most neurons (in contrast to the response to a stimulus that is optimal for a small subset of V1 neurons but elicits weak or no response from the vast majority of the other V1 neurons). Such an untuned luminance-related population response could provide a substrate for a distributed representation of orientation at low SFs. Therefore, our second goal was to determine whether there is a robust V1 population response to low-SF stimuli. V1 neurons are topographically organized so that neurons with similar response properties are clustered together. At a large (millimeters) scale, V1 neurons are organized based on their receptive field location into a map of visual-retinal space (“retinotopic map”; Van Essen et al., 1984Van Essen D.C. Newsome W.T. Maunsell J.H. The visual field representation in striate cortex of the macaque monkey: asymmetries, anisotropies, and individual variability.Vision Res. 1984; 24: 429-448Crossref PubMed Scopus (686) Google Scholar, Hubel and Wiesel, 1974Hubel D.H. Wiesel T.N. Uniformity of monkey striate cortex: a parallel relationship between field size, scatter, and magnification factor.J. Comp. Neurol. 1974; 158: 295-305Crossref PubMed Scopus (623) Google Scholar). At a finer (submillimeter) scale, neurons with similar preferred orientation are organized into a map of orientation columns (Blasdel, 1992Blasdel G.G. Orientation selectivity, preference, and continuity in monkey striate cortex.J. Neurosci. 1992; 12: 3139-3161Crossref PubMed Google Scholar, Bonhoeffer and Grinvald, 1991Bonhoeffer T. Grinvald A. Iso-orientation domains in cat visual cortex are arranged in pinwheel-like patterns.Nature. 1991; 353: 429-431Crossref PubMed Scopus (649) Google Scholar, Hubel and Wiesel, 1968Hubel D.H. Wiesel T.N. Receptive fields and functional architecture of monkey striate cortex.J. Physiol. 1968; 195: 215-243Crossref PubMed Scopus (4413) Google Scholar). We hypothesized that variations in the level of the untuned luminance-related population responses across V1’s retinotopic map provide useful information regarding the orientation of low-SF stimuli. A central goal of the current study was to test this hypothesis. If there are systematic variations in the amplitude of the untuned luminance-related population response across V1’s retinotopic map, downstream circuits could extract orientation information from these variations by performing computations on this luminance-retinotopic representation that are similar to those that V1 neurons perform on the retinal image. A downstream mechanism that relies on the spatial pattern of V1 population responses at the retinotopic scale (rather than the tuning properties of individual V1 neurons) could also account for our ability to discriminate the orientation of second-order patterns created by low-SF modulations in the local contrast of high-SF textures (Chubb and Sperling, 1988Chubb C. Sperling G. Drift-balanced random stimuli: a general basis for studying non-Fourier motion perception.J. Opt. Soc. Am. A. 1988; 5: 1986-2007Crossref PubMed Scopus (561) Google Scholar, Landy and Graham, 2004Landy M. Graham N. Visual perception of texture.in: Chalupa L.M. Werner J.S. The Visual Neurosciences. MIT, 2004: 1106-1118Google Scholar, Lin and Wilson, 1996Lin L.M. Wilson H.R. Fourier and non-Fourier pattern discrimination compared.Vision Res. 1996; 36: 1907-1918Crossref PubMed Scopus (50) Google Scholar). Our final goal was to compare V1 retinotopic representation of stimuli with large-scale variations in luminance to the representation of visual stimuli with large-scale variations in local contrast. Due to the limited range of V1 neurons’ RF sizes at a given visual field eccentricity, we predicted that the sensitivity of orientation-selective V1 responses will drop as the stimulus’ SF decreases. To test this prediction, we used wide-field imaging of voltage-sensitive dye (VSD) signals (Chen et al., 2006Chen Y. Geisler W.S. Seidemann E. Optimal decoding of correlated neural population responses in the primate visual cortex.Nat. Neurosci. 2006; 9: 1412-1420Crossref PubMed Scopus (148) Google Scholar, Seidemann et al., 2002Seidemann E. Arieli A. Grinvald A. Slovin H. Dynamics of depolarization and hyperpolarization in the frontal cortex and saccade goal.Science. 2002; 295: 862-865Crossref PubMed Scopus (110) Google Scholar, Shoham et al., 1999Shoham D. Glaser D.E. Arieli A. Kenet T. Wijnbergen C. Toledo Y. Hildesheim R. Grinvald A. Imaging cortical dynamics at high spatial and temporal resolution with novel blue voltage-sensitive dyes.Neuron. 1999; 24: 791-802Abstract Full Text Full Text PDF PubMed Scopus (283) Google Scholar), which measures the aggregate membrane potential changes in the superficial cortical layers. We imaged a V1 area (∼9 × 9 mm2, corresponding to ∼2 × 2 degrees of visual angle squared [dva2], at eccentricity of ∼2–4 dva; Figure 1B), while monkeys viewed large sinusoidal gratings (6 × 6 dva2) flashed at two orthogonal orientations (0° and 90°), two opposite phases (0° and 180°), and three SFs (0.5, 2, and 8 cycle per dva [cpd]). Because V1 neurons are organized in cortical columns based on their orientation preference (Blasdel, 1992Blasdel G.G. Orientation selectivity, preference, and continuity in monkey striate cortex.J. Neurosci. 1992; 12: 3139-3161Crossref PubMed Google Scholar, Bonhoeffer and Grinvald, 1991Bonhoeffer T. Grinvald A. Iso-orientation domains in cat visual cortex are arranged in pinwheel-like patterns.Nature. 1991; 353: 429-431Crossref PubMed Scopus (649) Google Scholar, Hubel and Wiesel, 1968Hubel D.H. Wiesel T.N. Receptive fields and functional architecture of monkey striate cortex.J. Physiol. 1968; 195: 215-243Crossref PubMed Scopus (4413) Google Scholar), the quality of the population response at the columnar scale can serve as a proxy for the average quality of the orientation signal at the single-neuron level. We first investigated how the orientation tuned columnar-scale signal changes with stimulus SF (Figure 1C). As a reference, we measured responses to stimuli with medium SF (2 cpd, which is close to the average preferred SF of V1 neurons in our imaging area). We compared responses to medium SF stimuli to the responses to stimuli with four-fold lower and four-fold higher SFs (0.5 and 8 cpd, respectively). For every stimulus SF, we measured the preference, strength, and reliability of the orientation-tuned response across the imaged cortical area by calculating the pixel-by-pixel signed d′ (see STAR Methods) across responses to orthogonal visual gratings. As expected, these “d′ maps” display a patchy texture representing the columnar response layout with an average cortical periodicity of ∼1.2 cyc/mm (Chen et al., 2012Chen Y. Palmer C.R. Seidemann E. The relationship between voltage-sensitive dye imaging signals and spiking activity of neural populations in primate V1.J. Neurophysiol. 2012; 107: 3281-3295Crossref PubMed Scopus (31) Google Scholar). As predicted, the quality of the columnar-scale orientation signal was high at medium and high SFs but dropped dramatically at low SF, as evidenced by the lower difference between the bright and dark patches in Figures 1C and 1D (left panels). Similar results were obtained using wide-field calcium imaging (using the virally expressed calcium indicator GCaMP6f), a signal that is more closely related to the pooled spiking response in V1 (Seidemann et al., 2016Seidemann E. Chen Y. Bai Y. Chen S.C. Mehta P. Kajs B.L. Geisler W.S. Zemelman B.V. Calcium imaging with genetically encoded indicators in behaving primates.eLife. 2016; 5: 1-19Crossref Scopus (45) Google Scholar; Figure 1E). To obtain a quantitative measure of the overall orientation discriminability of the columnar-scale neural responses, we developed a simple linear orientation decoder, which pools the single-trial responses with weights equal to the thresholded d′ maps in Figure 1D (see STAR Methods). The population discriminability (d′pop) is the signal-to-noise ratio of the pooled signals for discriminating between the two orthogonal orientations (with cross-validation; see STAR Methods). Consistent with our predictions and with the quality of the maps in Figures 1C–1E, we find that the overall discriminability of the columnar-scale orientation signal is exceedingly high at medium and high SFs and drops dramatically at low SF (Figures 1F and 1G). In 53 VSD sessions from three hemispheres of two macaque monkeys, the average columnar-scale orientation discriminability was maximal at medium SF and decreased slightly at high SF (15% average decrease) but dropped dramatically at low SF (70% average decrease; Figure 1G; see Figure S1 for results from individual monkeys). These results support our prediction that the quality of orientation-tuned V1 responses should drop rapidly when stimulus’ SF is decreased from the optimal value. In addition, to the best of our knowledge, these results are the first demonstration of reliable decoding of stimulus orientation based on single-trial columnar-scale responses in macaque V1 (see Figure S2 for examples of single-trial columnar responses). The drop in the quality of orientation-tuned V1 responses at low SF is not due to a decrease in the overall neural response to low-SF stimuli. As discussed in the introduction, V1 population responses may contain a large untuned response to low-SF stimuli. To test this possibility, we quantified the overall detectability of the stimulus based on the same neural responses (the discriminability between the responses to a grating and a blank stimulus; see STAR Methods) and found no significant decrease in detectability for low SF compared to the medium SF condition and a decrease for high SF in one monkey (Figures 2 and S4). These results show that there are strong untuned population responses to low-SF stimuli in V1. Our next goal was to examine the spatial variations of these responses at the retinotopic scale in V1. How can we be highly sensitive to orientation at low SFs if individual V1 neurons and columns carry low-quality information about orientation? We hypothesized that a new emergent neural population signal at the retinotopic scale encodes orientation information of low-SF patterns. The large untuned neural population response at low SF (Figure 2) could provide the substrate for such a signal. To test this possibility, we carefully analyzed the spatial layout of the evoked VSD responses to low-SF gratings. Consistent with our hypothesis, we found a surprising coarse modulation of V1 population responses that varies with the orientation and phase of low-SF stimuli (Figure 3A). Each grating elicits a spatial modulation of the V1 population response that resembles the projection of the stimulus’ luminance pattern onto the retinotopic map of the imaged area. To test whether the observed response corresponds to the retinotopic projection of the stimulus’ spatial luminance pattern, we used a VSD imaging protocol to measure the precise retinotopic coordinates in each V1 imaged area (Yang et al., 2007Yang Z. Heeger D.J. Seidemann E. Rapid and precise retinotopic mapping of the visual cortex obtained by voltage-sensitive dye imaging in the behaving monkey.J. Neurophysiol. 2007; 98: 1002-1014Crossref PubMed Scopus (31) Google Scholar; Figure 3B). We then projected the visual grating from visual space to the cortex, testing two possible polarities, where V1 response is either positively or negatively correlated with the local luminance at the corresponding retinotopic location (i.e., stronger response to bright or dark stimulus regions, respectively). We found that the predicted pattern of responses assuming stronger V1 population responses to the dark regions of the stimulus (Figure 3C) closely matches the observed responses (Figure 3A). Our next goal was to investigate the luminance-retinotopic signal in the response to the other stimulus SFs. To better visualize the luminance-retinotopic signal, we calculated the pixel-by-pixel signed d′ across responses to stimuli with the same orientation and SF but opposite stimulus phase. The results from the example VSD experiment resemble the projection of the stimulus to the retinotopic map for the low- and medium-SF conditions; however, the amplitude of the luminance-retinotopic signal drops rapidly as SF increases and approaches zero for the high SF condition (Figure 3D). This drop in amplitude with increasing SF is an expected byproduct of the cortical point image (CPI) and can be simulated by convolving the projection of the stimulus to the retinotopic map with a 2D Gaussian with the diameter of the subthreshold CPI (Palmer et al., 2012Palmer C.R. Chen Y. Seidemann E. Uniform spatial spread of population activity in primate parafoveal V1.J. Neurophysiol. 2012; 107: 1857-1867Crossref PubMed Scopus (26) Google Scholar; Figure 3E). Using the same approach, we were also able to reveal the spatiotemporal dynamics of the luminance-retinotopic signal to low-SF drifting gratings (Video S1). Finally, we used wide-field GCaMP imaging in one monkey to confirm that the luminance-retinotopic signal is not unique to subthreshold responses (Figures 3F and 3G). eyJraWQiOiI4ZjUxYWNhY2IzYjhiNjNlNzFlYmIzYWFmYTU5NmZmYyIsImFsZyI6IlJTMjU2In0.eyJzdWIiOiJlMzAxOGZhNDdkMGNmNjNhMzYyZWIwYTA2MzdiOGJlMiIsImtpZCI6IjhmNTFhY2FjYjNiOGI2M2U3MWViYjNhYWZhNTk2ZmZjIiwiZXhwIjoxNjc5MTMyMTU5fQ.spn-tE3BrLm56li6kMG7EIpwzXC-XeClj41ArVN8_nHA6OOso_HMqNlW4vi1hn6wDPEgwNfVz-jYLuAj9aBJOZwA2JYfo2KlH6MfzulFvSa0sgYZ23R6LrHeY9lbaLSNsP4K1DDmFKmnkR3MUAoKhwT4fhSY015MA1jjiklPmVwHmqeZf3CkruF3pKwMqV_d1p_nuwvMimTPmrVqAj9e1tjGhMiydCNReAH3bpGwXbKBjmkvCZN3D01xl-GCAsZAxC6xqXd2LRh0Inyi4dcHYhnU7oEi55SGhculiAbFN4du5o9PHJl1fBLdLuIa_85yuBamkBg7kFW7-fDMyaS9aw Download .mp4 (2.6 MB) Help with .mp4 files Video S1. Time Course of Luminance-Retinotopic Responses to Two Low-SF Grating Stimuli Drifting in Orthogonal Directions, Related to Figure 3Central panel displays an image of the entire cranial window in the right hemisphere of monkey HO. Scale bar 2mm. Brain orientation spatial references on the top-left: M: medial, L: lateral, R: rostral, C: caudal. Movie sequence: (1) Cortical areas (V1,V2 and V4 cortex) and anatomical references (V1-V2 border, Lunate Sulcus) are labeled in red. (2) Labels disappear and the time-course of the luminance-retinotopic response to the first stimulus (vertical drifting grating, SF:0.5cpd, TF:4cps, Size:6dva2) is presented (lapse time on top of the panel). (3) Anatomical labels are presented again. (4) Time-course of the response to the second stimulus (horizontal drifting grating) is presented. Response colors represent the pixel-by-pixel discriminability (d′ map) across VSD luminance-retinotopic responses (after low pass spatial filtration < 0.8cyc/mm) to opposite stimulus phases. For each time-frame we calculated the d′ map with respect to the time-frame of the same stimulus condition where the stimulus phase was opposite (half-cycle forward, + 125 ms). Only d′ < −0.8 and d′ > 0.8 is visible, color-bar on the right. Top-right inset shows the time-course of the visual stimulus in the corresponding area of visual field (bottom left portion of the visual field). Because the luminance-retinotopic signal reflects the geometry of the visual stimulus, it implicitly encodes orientation information that could be extracted by downstream mechanisms and contribute to orientation discrimination. To quantify the quality of the orientation information encoded in these signals, we developed a linear orientation decoder similar to the columnar decoder described above but one that uses only the luminance-retinotopic signals (see STAR Methods). The average orientation discriminability based on the luminance-retinotopic signal in the example experiment is strong at low-SF stimuli; this discriminability drops by 84% at medium SF and is near zero at high SF (Figure 4A). Similar results are seen in the summary across all imaging sessions (Figure 4B). Although direct comparison between the quality of the luminance-retinotopic and columnar signals (Figures 4C and 4D) is not warranted (because these signals could be affected by different sources of measurement noise), the relative discriminability as a function of SF within each signal shows a complementary trend, consistent with the possibility that the luminance-retinotopic signals contribute to pattern discrimination at low SF. Our results demonstrate that the luminance signal creates a blurred homeomorphic representation of the retinal image at the level of the retinotopic map in V1. Downstream circuits in V2 and other extrastriate areas, having larger RFs, could extract orientation information from large-scale variations in V1 responses by performing on this luminance-retinotopic representation similar computations to those that V1 neurons perform on the retinal image. Could such a mechanism be useful for discriminating other types of visual stimuli? Similar large-scale variations in V1 population responses at the retinotopic scale could be produced by a different class of visual stimuli—patterns created by low-SF modulations of the stimulus’ contrast (e.g., the patterns in Figure 5A, where the contrast of a high-SF texture is modulated by a low-SF sinusoid). These patterns are considered “second order” because their local mean luminance is constant and their contrast is varying over space (Landy and Graham, 2004Landy M. Graham N. Visual perception of texture.in: Chalupa L.M. Werner J.S. The Visual Neurosciences. MIT, 2004: 1106-1118Google Scholar). Because the responses of V1 neurons increase monotonically with contrast, such second-order stimuli are expected to strongly drive V1 neurons at the retinotopic locations overlapping the high-contrast portion of the stimulus and elicit weak or no response at the regions overlapping the zero-contrast portion of the stimulus. Indeed, a different type of second-order stimulus (one in which a mixture of drifting square wave gratings are viewed through complementary elongated apertures) was used by Blasdel and Campbell, 2001Blasdel G. Campbell D. Functional retinotopy of monkey visual cortex.J. Neurosci. 2001; 21: 8286-8301Crossref PubMed Google Scholar to measure the organization of the retinotopic map in V1 of anesthetized macaques. Note that these second-order stimuli with large-scale variations in local contrast are fundamentally different from the low-SF first-order stimuli discussed so far, where local contrast is effectively constant (and near zero) over space and the observed modulations in V1 responses are due to variations in local luminance. Because the responses to second-order stimuli are likely to be similar to the luminance-retinotopic responses described above, a downstream mechanism that relies on the spatial pattern of V1 population responses at the retinotopic scale (rather than the tuning properties of individual V1 neurons and columns) could also account for our ability to discriminate the orientation of second-order stimuli. Such a downstream mechanism could also account for the selectivity of some extrastriate neurons to second-order stimuli (El-Shamayleh and Movshon, 2011El-Shamayleh Y. Movshon J.A. Neuronal responses to texture-defined form in macaque visual area V2.J. Neurosci. 2011; 31: 8543-8555Crossref PubMed Scopus (59) Google Scholar, Li et al., 2014Li G. Yao Z. Wang Z. Yuan N. Talebi V. Tan J. Wang Y. Zhou Y. Baker Jr., C.L. Form-cue invariant second-order neuronal responses to contrast modulation in primate area V2.J. Neurosci. 2014; 34: 12081-12092Crossref PubMed Scopus (28) Google Scholar). In the second-order stimuli in Figure 5A, the local orientation of the texture is random and independent of the orientation of the global contrast modulation; thus, we expect individual V1 neurons and columns to carry no information regarding the global orientation. On the other hand, as discussed above, we expect these stimuli to elicit clear retinotopic-scale modulation of V1 population responses due to the spatial variations in contrast. Our final goal was to test these predictions. Our measurements of V1 responses to second-order gratings confirm these predictions and show that low-SF (0.5 cpd) second-order gratings evoke similar retinotopic responses to low-SF first-order gratings (Figure 5B). A summary of the results shows high-quality orientation-selective signals at the retinotopic scale and virtually no orientation-selective signals at the columnar scale (Figures 5C–5F). These results provide further support to the notion that orientation-tuned responses at the single neuron and single column levels in V1 are not necessary for the orientation discrimination of large-scale stimuli. Here, we report that the quality of orientation-selective columnar responses to grating stimuli drops quickly when their SF is decreased (Figure 1). This phenomenon, which is expected given the limited size of V1 RFs, creates an “aperture problem” for the computation of orientation of low-SF static stimuli. However, we show that there is a large untuned component of the population response at low SFs (Figure 2). This untuned population response produces a blurred homeomorphic representation of the retinal image in the cortex, with stronger response in the retinotopic areas that correspond to the darker stimulus areas (Figure 3). This distributed representation of the stimulus’ local luminance is surprising because neural activity in V1 has generally been assumed to represent primarily local contrast (i.e., local variations in luminance rather than the average local luminance itself). The observed luminance-retinotopic response is probably related to the imbalance between ON and OFF responses in V1 and is similar to responses to drifting gratings observed in the visual cortex of anesthetized cats (Onat et al., 2011Onat S. Nortmann N. Rekauzke S. König P. Jancke D. Independent encoding of grating motion across stationary feature maps in primary visual cortex visualized with vol" @default.
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• W2898817166 title "Scale-Invariant Visual Capabilities Explained by Topographic Representations of Luminance and Texture in Primate V1" @default.
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• W2898817166 doi "https://doi.org/10.1016/j.neuron.2018.10.020" @default.
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