Gamma rhythms in the visual cortex: functions and mechanisms.

Gamma-band activity, peaking around 30-100 Hz in the local field potential's power spectrum, has been found and intensively studied in many brain regions. Although gamma is thought to play a critical role in processing neural information in the brain, its cognitive functions and neural mechanisms remain unclear or debatable. Experimental studies showed that gamma rhythms are stochastic in time and vary with visual stimuli. Recent studies further showed that multiple rhythms coexist in V1 with distinct origins in different species. While all these experimental facts are a challenge for understanding the functions of gamma in the visual cortex, there are many signs of progress in computational studies. This review summarizes and discusses studies on gamma in the visual cortex from multiple perspectives and concludes that gamma rhythms are still a mystery. Combining experimental and computational studies seems the best way forward in the future.

Signals from Single-Opponent Cortical Cells in the Human cVEP.

We used the chromatic visual evoked potential (cVEP) to study responses in human visual cortex evoked by equiluminant color stimuli for 6 male and 11 female observers. Large-area, colored squares were used to stimulate Single-Opponent cells preferentially, and fine color-checkerboard stimuli were used to activate Double-Opponent responses preferentially. Stimuli were modulated along the following two directions in color space: (1) the cardinal direction, L-M or M-L of DKL (Derrington, Krauskopf, and Lennie) space; and (2) the line from the white point to the color of the Red LED in the display screen, which was approximately intermediate between the L-M and -S directions in DKL space in cone-contrast coordinates. The amplitudes of cVEPs to large squares were smaller than those to checkerboards, and the latency of the cVEP response to squares was significantly less than the checkerboard latency. The latency of cVEP responses to the squares varied little with cone-contrast unlike the steep reduction of latency with cone-contrast observed in responses to color checkerboard patterns. The dynamic differences between cVEPs to squares and checkerboards support the hypothesis that a distinct neuronal mechanism responded to squares: Single-Opponent cells. Response amplitude, latency, and transientness-and their dependence on cone-contrast-were similar in the responses in the L-M and Red color directions. The similarity supports the hypothesis that the Single-Opponent signals in the cVEP come from a distinct population of cells that receives subtractive inputs from L and M cones, either L-M or M-L.SIGNIFICANCE STATEMENT This article is about characterizing the visual behavior of a distinct population of neurons in the human visual cortex, the Single-Opponent color cells. Based on single-cell results in the visual cortex of macaque monkeys, we used large uniformly colored stimuli to isolate the responses of Single-Opponent cells in the chromatic visual evoked potential (cVEP) recorded on the scalp of human observers. VEP signals recorded under conditions believed to reveal Single-Opponent responses are small and transient. Their time course is relatively unaffected by cone-contrast, and they are relatively insensitive to stimulus modulation of short wavelength-sensitive S cones. Because Single-Opponent cells convey signals that can be used to judge the color of scene illumination, knowing their visual properties is important for understanding color vision.

A Computational Model of Direction Selectivity in Macaque V1 Cortex Based on Dynamic Differences between On and Off Pathways.

This paper is about neural mechanisms of direction selectivity (DS) in macaque primary visual cortex, V1. We present data (on male macaque) showing strong DS in a majority of simple cells in V1 layer 4Cα, the cortical layer that receives direct afferent input from the magnocellular division of the lateral geniculate nucleus (LGN). Magnocellular LGN cells are not direction-selective. To understand the mechanisms of DS, we built a large-scale, recurrent model of spiking neurons called DSV1. Like its predecessors, DSV1 reproduces many visual response properties of V1 cells including orientation selectivity. Two important new features of DSV1 are (1) DS is initiated by small, consistent dynamic differences in the visual responses of OFF and ON Magnocellular LGN cells, and (2) DS in the responses of most model simple cells is increased over those of their feedforward inputs; this increase is achieved through dynamic interaction of feedforward and intracortical synaptic currents without the use of intracortical direction-specific connections. The DSV1 model emulates experimental data in the following ways: (1) most 4Cα Simple cells were highly direction-selective but 4Cα Complex cells were not; (2) the preferred directions of the model's direction-selective Simple cells were invariant with spatial and temporal frequency (TF); (3) the distribution of the preferred/opposite ratio across the model's population of cells was very close to that found in experiments. The strong quantitative agreement between DS in data and in model simulations suggests that the neural mechanisms of DS in DSV1 may be similar to those in the real visual cortex.SIGNIFICANCE STATEMENT Motion perception is a vital part of our visual experience of the world. In monkeys, whose vision resembles that of humans, the neural computation of the direction of a moving target starts in the primary visual cortex, V1, in layer 4Cα that receives input from the eye through the lateral geniculate nucleus (LGN). How direction selectivity (DS) is generated in layer 4Cα is an outstanding unsolved problem in theoretical neuroscience. In this paper, we offer a solution based on plausible biological mechanisms. We present a new large-scale circuit model in which DS originates from slightly different LGN ON/OFF response time-courses and is enhanced in cortex without the need for direction-specific intracortical connections. The model's DS is in quantitative agreement with experiments.

A theory of direction selectivity for macaque primary visual cortex.

This paper offers a theory for the origin of direction selectivity (DS) in the macaque primary visual cortex, V1. DS is essential for the perception of motion and control of pursuit eye movements. In the macaque visual pathway, neurons with DS first appear in V1, in the Simple cell population of the Magnocellular input layer 4Cα. The lateral geniculate nucleus (LGN) cells that project to these cortical neurons, however, are not direction selective. We hypothesize that DS is initiated in feed-forward LGN input, in the summed responses of LGN cells afferent to a cortical cell, and it is achieved through the interplay of 1) different visual response dynamics of ON and OFF LGN cells and 2) the wiring of ON and OFF LGN neurons to cortex. We identify specific temporal differences in the ON/OFF pathways that, together with item 2, produce distinct response time courses in separated subregions; analysis and simulations confirm the efficacy of the mechanisms proposed. To constrain the theory, we present data on Simple cells in layer 4Cα in response to drifting gratings. About half of the cells were found to have high DS, and the DS was broadband in spatial and temporal frequency (SF and TF). The proposed theory includes a complete analysis of how stimulus features such as SF and TF interact with ON/OFF dynamics and LGN-to-cortex wiring to determine the preferred direction and magnitude of DS.

A multiplicity of color-responsive cortical mechanisms revealed by the dynamics of cVEPs.

Our results connect higher-order color mechanisms deduced from psychophysics with the known diversity of populations of double-opponent, color-responsive cells in V1. We used the chromatic visual evoked potential, the cVEP, to study responses in human visual cortex to equiluminant color patterns. Stimuli were modulated along three directions in color space: the cardinal directions, L-M and S, and along the line in color space from the white point to the color of the Red LED in the display screen (the Red direction). The Red direction is roughly intermediate between L-M and S in DKL space in cone-contrast coordinates. While cVEP response amplitude, latency, and width--and their dependences on cone contrast-- were similar in the L-M and Red directions, the Transientness of the Red response was significantly greater than for responses to stimuli in the L-M direction and in the S direction. This difference in response dynamics supports the concept that there are multiple, distinct neuronal populations, so-called higher- order color mechanisms, for color perception within human V1 cortex.

Distinct spatiotemporal mechanisms underlie extra-classical receptive field modulation in macaque V1 microcircuits.

Complex scene perception depends upon the interaction between signals from the classical receptive field (CRF) and the extra-classical receptive field (eCRF) in primary visual cortex (V1) neurons. Although much is known about V1 eCRF properties, we do not yet know how the underlying mechanisms map onto the cortical microcircuit. We probed the spatio-temporal dynamics of eCRF modulation using a reverse correlation paradigm, and found three principal eCRF mechanisms: tuned-facilitation, untuned-suppression, and tuned-suppression. Each mechanism had a distinct timing and spatial profile. Laminar analysis showed that the timing, orientation-tuning, and strength of eCRF mechanisms had distinct signatures within magnocellular and parvocellular processing streams in the V1 microcircuit. The existence of multiple eCRF mechanisms provides new insights into how V1 responds to spatial context. Modeling revealed that the differences in timing and scale of these mechanisms predicted distinct patterns of net modulation, reconciling many previous disparate physiological and psychophysical findings.

Contrast response in a comprehensive network model of macaque V1.

The response to contrast is one of the most important functions of the macaque primary visual cortex, V1, but up to now there has not been an adequate theory for it. To fill this gap in our understanding of cortical function, we built and analyzed a new large-scale, biologically constrained model of the input layer, 4Cα, of macaque V1. We called the new model CSY2. We challenged CSY2 with a three-parameter family of visual stimuli that varied in contrast, orientation, and spatial frequency. CSY2 accurately simulated experimental data and made many new predictions. It accounted for 1) the shapes of firing-rate-versus-contrast functions, 2) orientation and spatial frequency tuning versus contrast, and 3) the approximate contrast-invariance of cortical activity maps. Post-analysis revealed that the mechanisms that were needed to produce the successful simulations of contrast response included strong recurrent excitation and inhibition that find dynamic equilibria across the cortical surface, dynamic feedback between L6 and L4, and synaptic dynamics like inhibitory synaptic depression.

Functional Clusters of Neurons in Layer 6 of Macaque V1.

Layer 6 appears to perform a very important role in the function of macaque primary visual cortex, V1, but not enough is understood about the functional characteristics of neurons in the layer 6 population. It is unclear to what extent the population is homogeneous with respect to their visual properties or if one can identify distinct subpopulations. Here we performed a cluster analysis based on measurements of the responses of single neurons in layer 6 of primary visual cortex in male macaque monkeys (Macaca fascicularis) to achromatic grating stimuli that varied in orientation, direction of motion, spatial and temporal frequency, and contrast. The visual stimuli were presented in a stimulus window that was also varied in size. Using the responses to parametric variation in these stimulus variables, we extracted a number of tuning response measures and used them in the cluster analysis. Six main clusters emerged along with some smaller clusters. Additionally, we asked whether parameter distributions from each of the clusters were statistically different. There were clear separations of parameters between some of the clusters, particularly for f1/f0 ratio, direction selectivity, and temporal frequency bandwidth, but other dimensions also showed differences between clusters. Our data suggest that in layer 6 there are multiple parallel circuits that provide information about different aspects of the visual stimulus.SIGNIFICANCE STATEMENT The cortex is multilayered and is involved in many high-level computations. In the current study, we have asked whether there are subpopulations of neurons, clusters, in layer 6 of cortex with different functional tuning properties that provide information about different aspects of the visual image. We identified six major functional clusters within layer 6. These findings show that there is much more complexity to the circuits in cortex than previously demonstrated and open up a new avenue for experimental investigation within layers of other cortical areas and for the elaboration of models of circuit function that incorporate many parallel pathways with different functional roles.

Population rate-coding predicts correctly that human sound localization depends on sound intensity.

Human sound localization is an important computation performed by the brain. Models of sound localization commonly assume that sound lateralization from interaural time differences is level invariant. Here we observe that two prevalent theories of sound localization make opposing predictions. The labelled-line model encodes location through tuned representations of spatial location and predicts that perceived direction is level invariant. In contrast, the hemispheric-difference model encodes location through spike-rate and predicts that perceived direction becomes medially biased at low sound levels. Here, behavioral experiments find that softer sounds are perceived closer to midline than louder sounds, favoring rate-coding models of human sound localization. Analogously, visual depth perception, which is based on interocular disparity, depends on the contrast of the target. The similar results in hearing and vision suggest that the brain may use a canonical computation of location: encoding perceived location through population spike rate relative to baseline.

A case study in the functional consequences of scaling the sizes of realistic cortical models.

Neuroscience models come in a wide range of scales and specificity, from mean-field rate models to large-scale networks of spiking neurons. There are potential trade-offs between simplicity and realism, versatility and computational speed. This paper is about large-scale cortical network models, and the question we address is one of scalability: would scaling down cell density impact a network's ability to reproduce cortical dynamics and function? We investigated this problem using a previously constructed realistic model of the monkey visual cortex that is true to size. Reducing cell density gradually up to 50-fold, we studied changes in model behavior. Size reduction without parameter adjustment was catastrophic. Surprisingly, relatively minor compensation in synaptic weights guided by a theoretical algorithm restored mean firing rates and basic function such as orientation selectivity to models 10-20 times smaller than the real cortex. Not all was normal in the reduced model cortices: intracellular dynamics acquired a character different from that of real neurons, and while the ability to relay feedforward inputs remained intact, reduced models showed signs of deficiency in functions that required dynamical interaction among cortical neurons. These findings are not confined to models of the visual cortex, and modelers should be aware of potential issues that accompany size reduction. Broader implications of this study include the importance of homeostatic maintenance of firing rates, and the functional consequences of feedforward versus recurrent dynamics, ideas that may shed light on other species and on systems suffering cell loss.

Rhythm and Synchrony in a Cortical Network Model.

We studied mechanisms for cortical gamma-band activity in the cerebral cortex and identified neurobiological factors that affect such activity. This was done by analyzing the behavior of a previously developed, data-driven, large-scale network model that simulated many visual functions of monkey V1 cortex (Chariker et al., 2016). Gamma activity was an emergent property of the model. The model's gamma activity, like that of the real cortex, was (1) episodic, (2) variable in frequency and phase, and (3) graded in power with stimulus variables like orientation. The spike firing of the model's neuronal population was only partially synchronous during multiple firing events (MFEs) that occurred at gamma rates. Detailed analysis of the model's MFEs showed that gamma-band activity was multidimensional in its sources. Most spikes were evoked by excitatory inputs. A large fraction of these inputs came from recurrent excitation within the local circuit, but feedforward and feedback excitation also contributed, either through direct pulsing or by raising the overall baseline. Inhibition was responsible for ending MFEs, but disinhibition led directly to only a small minority of the synchronized spikes. As a potential explanation for the wide range of gamma characteristics observed in different parts of cortex, we found that the relative rise times of AMPA and GABA synaptic conductances have a strong effect on the degree of synchrony in gamma.SIGNIFICANCE STATEMENT Canonical computations used throughout the cerebral cortex are performed in primary visual cortex (V1). Providing theoretical mechanisms for these computations will advance understanding of computation throughout cortex. We studied one dynamical feature, gamma-band rhythms, in a large-scale, data-driven, computational model of monkey V1. Our most significant conclusion is that the sources of gamma band activity are multidimensional. A second major finding is that the relative rise times of excitatory and inhibitory synaptic potentials have strong effects on spike synchrony and peak gamma band power. Insight gained from studying our V1 model can shed light on the functions of other cortical regions.

Cortical Double-Opponent Cells in Color Perception: Perceptual Scaling and Chromatic Visual Evoked Potentials.

In the early visual cortex V1, there are currently only two known neural substrates for color perception: single-opponent and double-opponent cells. Our aim was to explore the relative contributions of these neurons to color perception. We measured the perceptual scaling of color saturation for equiluminant color checkerboard patterns (designed to stimulate double-opponent neurons preferentially) and uniformly colored squares (designed to stimulate only single-opponent neurons) at several cone contrasts. The spatially integrative responses of single-opponent neurons would produce the same response magnitude for checkerboards as for uniform squares of the same space-averaged cone contrast. However, perceived saturation of color checkerboards was higher than for the corresponding squares. The perceptual results therefore imply that double-opponent cells are involved in color perception of patterns. We also measured the chromatic visual evoked potential (cVEP) produced by the same stimuli; checkerboard cVEPs were much larger than those for corresponding squares, implying that double-opponent cells also contribute to the cVEP response. The total Fourier power of the cVEP grew sublinearly with cone contrast. However, the 6-Hz Fourier component's power grew linearly with contrast-like saturation perception. This may also indicate that cortical coding of color depends on response dynamics.

Nonlinear dynamics of cortical responses to color in the human cVEP.

The main finding of this paper is that the human visual cortex responds in a very nonlinear manner to the color contrast of pure color patterns. We examined human cortical responses to color checkerboard patterns at many color contrasts, measuring the chromatic visual evoked potential (cVEP) with a dense electrode array. Cortical topography of the cVEPs showed that they were localized near the posterior electrode at position Oz, indicating that the primary cortex (V1) was the major source of responses. The choice of fine spatial patterns as stimuli caused the cVEP response to be driven by double-opponent neurons in V1. The cVEP waveform revealed nonlinear color signal processing in the V1 cortex. The cVEP time-to-peak decreased and the waveform's shape was markedly narrower with increasing cone contrast. Comparison of the linear dynamics of retinal and lateral geniculate nucleus responses with the nonlinear dynamics of the cortical cVEP indicated that the nonlinear dynamics originated in the V1 cortex. The nature of the nonlinearity is a kind of automatic gain control that adjusts cortical dynamics to be faster when color contrast is greater.

Orientation Selectivity from Very Sparse LGN Inputs in a Comprehensive Model of Macaque V1 Cortex.

A new computational model of the primary visual cortex (V1) of the macaque monkey was constructed to reconcile the visual functions of V1 with anatomical data on its LGN input, the extreme sparseness of which presented serious challenges to theoretically sound explanations of cortical function. We demonstrate that, even with such sparse input, it is possible to produce robust orientation selectivity, as well as continuity in the orientation map. We went beyond that to find plausible dynamic regimes of our new model that emulate simultaneously experimental data for a wide range of V1 phenomena, beginning with orientation selectivity but also including diversity in neuronal responses, bimodal distributions of the modulation ratio (the simple/complex classification), and dynamic signatures, such as gamma-band oscillations. Intracortical interactions play a major role in all aspects of the visual functions of the model. SIGNIFICANCE STATEMENT: We present the first realistic model that has captured the sparseness of magnocellular LGN inputs to the macaque primary visual cortex and successfully derived orientation selectivity from them. Three implications are (1) even in input layers to the visual cortex, the system is less feedforward and more dominated by intracortical signals than previously thought, (2) interactions among cortical neurons in local populations produce dynamics not explained by single neurons, and (3) such dynamics are important for function. Our model also shows that a comprehensive picture is necessary to explain function, because different visual properties are related. This study points to the need for paradigm shifts in neuroscience modeling: greater emphasis on population dynamics and, where possible, a move toward data-driven, comprehensive models.

Noise masking of White's illusion exposes the weakness of current spatial filtering models of lightness perception.

Spatial filtering models are currently a widely accepted mechanistic account of human lightness perception. Their popularity can be ascribed to two reasons: They correctly predict how human observers perceive a variety of lightness illusions, and the processing steps involved in the models bear an apparent resemblance with known physiological mechanisms at early stages of visual processing. Here, we tested the adequacy of these models by probing their response to stimuli that have been modified by adding narrowband noise. Psychophysically, it has been shown that noise in the range of one to five cycles per degree (cpd) can drastically reduce the strength of some lightness phenomena, while noise outside this range has little or no effect on perceived lightness. Choosing White's illusion (White, 1979) as a test case, we replicated and extended the psychophysical results, and found that none of the spatial filtering models tested was able to reproduce the spatial frequency specific effect of narrowband noise. We discuss the reasons for failure for each model individually, but we argue that the failure is indicative of the general inadequacy of this class of spatial filtering models. Given the present evidence we do not believe that spatial filtering models capture the mechanisms that are responsible for producing many of the lightness phenomena observed in human perception. Instead we think that our findings support the idea that low-level contributions to perceived lightness are primarily determined by the luminance contrast at surface boundaries.

Representation of Color Surfaces in V1: Edge Enhancement and Unfilled Holes.

The neuronal mechanism underlying the representation of color surfaces in primary visual cortex (V1) is not well understood. We tested on color surfaces the previously proposed hypothesis that visual perception of uniform surfaces is mediated by an isomorphic, filled-in representation in V1. We used voltage-sensitive-dye imaging in fixating macaque monkeys to measure V1 population responses to spatially uniform chromatic (red, green, or blue) and achromatic (black or white) squares of different sizes (0.5°-8°) presented for 300 ms. Responses to both color and luminance squares early after stimulus onset were similarly edge-enhanced: for squares 1° and larger, regions corresponding to edges were activated much more than those corresponding to the center. At later times after stimulus onset, responses to achromatic squares' centers increased, partially "filling-in" the V1 representation of the center. The rising phase of the center response was slower for larger squares. Surprisingly, the responses to color squares behaved differently. For color squares of all sizes, responses remained edge-enhanced throughout the stimulus. There was no filling-in of the center. Our results imply that uniform filled-in representations of surfaces in V1 are not required for the perception of uniform surfaces and that chromatic and achromatic squares are represented differently in V1. SIGNIFICANCE STATEMENT: We used voltage-sensitive dye imaging from V1 of behaving monkeys to test the hypothesis that visual perception of uniform surfaces is mediated by an isomorphic, filled-in representation. We found that the early population responses to chromatic and achromatic surfaces are edge enhanced, emphasizing the importance of edges in surface processing. Next, we show for color surfaces that responses remained edge-enhanced throughout the stimulus presentation whereas response to luminance surfaces showed a slow neuronal 'filling-in' of the center. Our results suggest that isomorphic representation is not a general code for uniform surfaces in V1.

Testing the role of luminance edges in White's illusion with contour adaptation.

White's illusion is the perceptual effect that two equiluminant gray patches superimposed on a black-and-white square-wave grating appear different in lightness: A test patch placed on a dark stripe of the grating looks lighter than one placed on a light stripe. Although the effect does not depend on the aspect ratio of the test patches, and thus on the amount of border that is shared with either the dark or the light stripe, the context of each patch must, in a yet to be specified way, influence their lightness. We employed a contour adaptation paradigm (Anstis, 2013) to test the contribution of each of the test patches' edges to the perceived lightness of the test patches. We found that adapting to the edges that are oriented parallel to the grating slightly increased the lightness illusion, whereas adapting to the orthogonal edges abolished, or for some observers even reversed, the lightness illusion. We implemented a temporal adaptation mechanism in three spatial filtering models of lightness perception, and show that the models cannot account for the observed adaptation effects. We conclude that White's illusion is largely determined by edge contrast across the edge orthogonal to the grating, whereas the parallel edge has little or no influence. We suggest mechanisms that could explain this asymmetry.

Brightness-color interactions in human early visual cortex.

The interaction between brightness and color causes there to be different color appearance when one and the same object is viewed against surroundings of different brightness. Brightness contrast causes color to be desaturated, as has been found in perceptual experiments on color induction and color-gamut expansion in human vision. However, it is not clear yet where in the cerebral cortex the brightness-color interaction that causes these major perceptual effects is located. One hypothesis is that brightness and color signals are processed separately and in parallel within the primary visual cortex V1 and only interact in extrastriate cortex. Another hypothesis is that color and brightness contrast interact strongly already within V1. We localized the brightness-color interaction in human V1 by means of recording the chromatic visual-evoked potential. The chromatic visual-evoked potential measurements decisively support the idea that brightness-color interaction arises in a recurrent inhibitory network in V1. Furthermore, our results show that the inhibitory signal for brightness-color interaction is generated by local brightness contrast at the boundary between target and surround, instead of by the luminance difference between the interior of the color target and its large background.

Context affects lightness at the level of surfaces.

Visual perception of object attributes such as surface lightness is crucial for successful interaction with the environment. How the visual system assigns lightness to image regions is not yet understood. It has been shown that the context in which a surface is embedded influences its perceived lightness, but whether that influence involves predominantly low-, mid-, or high-level visual mechanisms has not been resolved. To answer this question, we measured whether perceptual attributes of target image regions affected their perceived lightness when they were placed in different contexts. We varied the sharpness of the edge while keeping total target flux fixed. Targets with a sharp edge were consistent with the perceptual interpretation of a surface, and in that case, observers perceived significant brightening or darkening of the target. Targets with blurred edges rather appeared to be spotlights instead of surfaces; for targets with blurred edges, there was much less of a contextual effect on target lightness. The results indicate that the effect of context on the lightness of an image region is not fixed but is strongly affected by image manipulations that modify the perceptual attributes of the target, implying that a mid-level scene interpretation affects lightness perception.

A contrast and surface code explains complex responses to black and white stimuli in V1.

We investigated the cortical mechanisms underlying the visual perception of luminance-defined surfaces and the preference for black over white stimuli in the macaque primary visual cortex, V1. We measured V1 population responses with voltage-sensitive dye imaging in fixating monkeys that were presented with white or black squares of equal contrast around a mid-gray. Regions corresponding to the squares' edges exhibited higher activity than those corresponding to the center. Responses to black were higher than to white, surprisingly to a much greater extent in the representation of the square's center. Additionally, the square-evoked activation patterns exhibited spatial modulations along the edges and corners. A model comprised of neural mechanisms that compute local contrast, local luminance temporal modulations in the black and white directions, and cortical center-surround interactions, could explain the observed population activity patterns in detail. The model captured the weaker contribution of V1 neurons that respond to positive (white) and negative (black) luminance surfaces, and the stronger contribution of V1 neurons that respond to edge contrast. Also, the model demonstrated how the response preference for black could be explained in terms of stronger surface-related activation to negative luminance modulation. The spatial modulations along the edges were accounted for by surround suppression. Overall the results reveal the relative strength of edge contrast and surface signals in the V1 response to visual objects.