The primary visual area (V1) of the cerebral cortex is the first stage of cortical processing of visual information. Area V1 contains a complete map of the visual field covered by the eyes. It receives its main visual input from the lateral geniculate nucleus of the thalamus (LGN), and sends its main output to subsequent cortical visual areas (Maunsell and Newsome, 1987; Van Essen and Felleman, 1991).
Thanks to high neuronal density and large area, V1 contains a vast number of neurons. In humans, it contains about 140 million neurons per hemisphere (Wandell, 1995), i.e. about 40 V1 neurons per LGN neuron. Such divergence gives scope for extensive processing of the images received from LGN.
V1 is one of the best understood areas of the cerebral cortex, and constitutes a prime workbench for the study of cortical circuits and of computations. Numerous factors contribute to this fortunate position: we understand the nature of its main inputs, we know what stimuli make its neurons fire, and we can easily make those stimuli thanks to computer displays. Moreover, in many species area V1 lies at least partly on the surface, and is therefore particularly accessible for various imaging methods.
The discovery of primary visual cortex is attributed to Panizza, who published in 1855 his observations on the brains of patients who had become blind after strokes (Gross, 1998). He confirmed these observations by performing enucleation or cortical lesions in experimental animals. These results were not noticed, and further experiments were performed independently by Ferrier, by Munk, and by Schafer. The precise map of the visual field contained in area V1 was discovered in the 1920s in patients with strokes and bullet wounds.
The key properties of primary visual cortex were discovered by Hubel and Wiesel by recording electrical activity in experimental animals. Starting in the 1950s, they published a seminal series of papers on V1's cellular responses, anatomy, development, and connectivity (Hubel and Wiesel, 1959, 1962, 1968, 1977, 1979, 1998). Their work paved the way for substantial advances that continue to this day.
Position and connections
Area V1 is located in both hemispheres. V1 in the left hemisphere receives input from the left LGN, and thereby from the left portion of the two retinas, which capture images from the right visual field (Figure 1). Similarly, the right side of V1 processes images from the left visual field. The two sides of V1 are connected via the corpus callosum.
The main task of V1 is to process visual inputs from the LGN and send the results of this processing to higher visual areas and subcortical structures. In primates, these include areas V2, V3, MT, MST, and FEF (Van Essen and Felleman, 1991). V1 also sends output to subcortical brain regions, including LGN, thalamic reticular nucleus, superior colliculus, pulvinar, and pons.
As is usual in cortex, these feed-forward connections are typically accompanied by reciprocal feedback connections. Specifically, the higher visual areas send feedback signals into V1, and in turn V1 sends feedback signals back to the visual thalamus. The feedback connections from V1 to thalamus target both the LGN itself and the thalamic reticular nucleus (TRN), which in turn inhibits the LGN. The functional role of feedback connections is hitherto unexplained and is the subject of much research (Alitto and Usrey, 2003; Briggs and Usrey, 2008).
V1 also receives input from other brain regions, including pulvinar, claustrum, nucleus paracentralis, raphe system, locus coeruleus, and the nucleus basalis. The functional roles of these secondary inputs are only beginning to be understood, and are thought to be modulatory (Sherman and Guillery, 1998). For instance, cholinergic input from the nucleus basalis, which is thought to be related to alertness and attention, changes the excitability of V1 neurons (Harris and Thiele, 2011).
As in the rest of neocortex, area V1 is traditionally divided in 6 horizontal layers, with a characteristic distribution of inputs and outputs across layers (Douglas and Martin, 1998). Feed-forward inputs from LGN arrive in layer 4, with collaterals to layer 6, and feedback inputs from other cortical areas arrive mostly in superficial layers. Feed-forward outputs to other cortical areas depart from layer 2/3, feedback outputs to the thalamus depart from layer 6, and outputs to other subcortical targets depart from layer 5.
In primates such as the macaque, this distinction between layers can be further refined. For instance layer 4 of V1 is divided into sublayers 4A, 4B, 4Ca, and 4Cß. The main LGN inputs arrive in 4C, and segregate depending on the source: magnocellular LGN cells to 4Ca and parvocellular cells to 4Cß. Additional LGN inputs of the koniocellular kind arrive in layer 4A, 3, and 1 (Casagrande and Xu, 2004).
Area V1 differs from other cortical areas also by having a higher density of neurons, particularly in layer 4 (Rockel et al., 1980). This layer is packed with spiny stellate cells (Douglas and Martin, 1998). In primates including humans, layer 4 is visible to the naked eye even in an unstained section, thanks to a thick band of myelinated axons traveling from sublayers 4Ca to 4B, the stria of Gennari (Figure 2). Because of this band, V1 is historically called "striate cortex", and the term "extrastriate" denotes the rest of visual cortex.
Area V1 is present in the cortex of all mammalian species (Krubitzer, 2007) (Figure 3). It has been mostly studied in carnivores (cats and ferrets), rodents (mice), and primates (macaques and humans). In primates, V1 coincides with Brodmann’s area 17, also known as striate cortex (Wandell et al., 2009). In cats, it is taken to include both areas 17 and 18, because both receive direct input from the LGN (Payne and Peters, 2002).
Area V1 has retinotopic organization, meaning that it contains a complete [map of the visual field | visual map] covered by the two eyes. In most species V1 is considered to have a single map of the visual field, but in cats it contains two of them: one for area 17 and one for area 18.
Retinotopy is continuous (nearby points in cortex map nearby visual positions). Moreover, it is remarkably free of local distortions (Adams and Horton, 2003).
The local precision of the map coexists with global distortions, which are particularly pronounced in humans and other primates. One such distortion is magnification, which favors the central visual field at the expense of the periphery (Figure 4A). For instance, 50% of the area of human V1 is devoted to the central 2% of the visual field (Wandell, 1995). This emphasis is only partially inherited from the retina (Adams and Horton, 2003). Another distortion is geometrical, and it transforms concentric circles and radial lines in an image into vertical and horizontal lines in V1 (Figure 4A).
These effects can be summarized by a simple mathematical rule based on the complex logarithm (Schwartz, 1980) (Figure 4B). A consequence of this rule is that scaling or rotating an image simply translates its representation in V1, potentially helping subsequent stages to recognize images regardless of distance and orientation.
Neurons in area V1 are classically divided into two types: simple and complex (Hubel and Wiesel, 1959, 1962), based on the structure of their receptive field. In simple cells, receptive fields have separate ON and OFF subregions (Figure 5). ON and OFF subregions differ in their responses to the onset of stimuli on a gray background: ON subregions respond white bars, and OFF subregions respond to black bars. In complex cells, instead, ON and OFF regions are superimposed, i.e. every location in the receptive field responds both to white and black bars (Figure 6).
When thinking about the responses of simple and complex cells, it helps to consider two descriptive models (Figure 7). A simple cell (Figure 7A) operates weighted sums (linear filtering) on an image, with weights defined by the profile of the receptive field; the output of this filtering can be positive or negative, but only the portion that exceeds a threshold results in a response (Movshon et al., 1978c). A complex cell (Figure 7B), in turn, can be thought of as integrating the output of a multiple simple cells with overlapping receptive fields but different arrangements of ON and OFF regions (Movshon et al., 1978b).
V1 cells are commonly classified as simple or complex based on their responses to drifting visual gratings. In simple cells the responses are periodic, whereas in complex cells they are steady in time. When this assay is applied to the spike responses of the neurons, it classifies simple and complex cells as distinct groups (Skottun et al., 1991). However, in terms of the underlying synaptic inputs V1 cells seem to fall along a continuum, suggesting that the distinction between simple and complex may be one of degree rather than kind (Priebe et al., 2004).
In addition to stimulus position, V1 neurons are selective for a number of attributes, including orientation, direction of motion, spatial and temporal frequency. In many species they are also selective for binocular depth and color.
- Orientation. A key characteristic of the responses of V1 neurons is their high selectivity for stimulus orientation. This selectivity was discovered by Hubel and Wiesel (1959), and must arise from computations that take place within cortex, because LGN responses are not selective for orientation. Orientation selectivity is arguably the most studied example of cortical elaboration of thalamic input. The orientation selectivity of simple cells derives directly from the shape of their receptive field (Figure 5): ON and OFF subregions are elongated, so their preferred stimulus is similarly elongated. For complex cells, orientation selectivity cannot be directly predicted from the profile of the receptive field (Figure 6), but it is no less pronounced than for simple cells. This follows straightforwardly from the descriptive model of complex cells (Figure 7b): all the simple cells providing input to the complex cell are selective for the same orientation, endowing the complex cell with the same orientation selectivity.
- Spatial frequency. In addition to orientation, V1 neurons are typically sharply selective for the spatial frequency of a stimulus. Spatial frequency is best defined for a grating pattern, where frequency is the inverse of the distance between bars. Just as this orientation, this selectivity arises naturally from the shape of the receptive fields. These receptive fields have multiple ON and OFF regions, and the more regions there are the more selective the neurons are for spatial frequency (Figure 8). Consequently, neurons in V1 tend to be much more selective for spatial frequency than LGN neurons (De Valois and De Valois, 1988).
- Direction. Cells in area V1 are commonly selective for direction of stimulus motion, and this selectivity can again be explained by the receptive field, by extending this concept to the dimension of time (Figure 9). For example, a vertical bar drifting from left to right can be seen as a solid in a 3-dimensional space given by the two dimensions of spatial extent and the one dimension of time (Figure 9, left). Different velocities result in different tilts in space-time (reviewed in DeAngelis et al., 1995; Carandini et al., 1999). If the bar were going faster or slower, its space-time representation would have been more tilted towards the horizontal or the vertical. Likewise, receptive fields of V1 neurons are thought to be slanted in space-time (Figure 9, right). For instance, a receptive field slanted towards the right in space-time confers selectivity for stimuli moving rightward. Support for this view comes from studies that have measured the space-time receptive fields of simple cells and found the they are indeed slanted in space-time. Moreover, studies by multiple laboratories confirmed that simple cells perform simple summation of their inputs, with weights prescribed by the receptive field in space-time (see Carandini et al., 1999 for a review).
- Temporal frequency. The slant of receptive fields in space-time confers V1 neurons with some selectivity for stimulus speed, but this selectivity depends on the spatial pattern of a stimulus (Movshon et al., 1978a). Rather than speed, V1 neurons are typically thought to be selective for temporal frequency, which is the inverse of the period between temporal oscillations between dark and light. V1 neurons typically prefer lower temporal frequencies than those that can drive LGN neurons.
- Disparity. In animals with front-facing eyes (such as carnivores and primates), much of the visual field is covered joinly by both eyes. This poses a challenge as signals need to be integrated, but also an opportunity for computing binocular depth (stereoscopy). The signals from corresponding regions in the two eyes are kept separate in the LGN, and are combined in V1. Neurons receive subthreshold input from both eyes (Priebe et al., 2004), but the relative ability of each eye alone to drive spike responses varies: some neurons are driven mostly by one eye or the other, whereas other neurons are driven by both eyes (Hubel and Wiesel, 1962). In many neurons this arrangement is accompanied by selectivity for binocular depth, or disparity (Cumming and DeAngelis, 2001). Each V1 neuron has two receptive fields, one per each eye. These receptive fields cover the same region of visual space, but differ slightly so as to endow each neuron with a preferred distance, as determined by stereopsis (Cumming and DeAngelis, 2001).
- Color. In primates, the retina contains cones responsive to three bands of wavelengths (trichromacy). Retinal ganglion cells rearrange these responses along one of three “cardinal directions”, known informally as red-green, blue-yellow, and black-white. The chromatic selectivity of V1 neurons, was initially thought to be much more varied (Gegenfurtner and Kiper, 2003), but later data indicated that it is also organized along "cardinal directions" (Horwitz and Hass, 2012).
The selectivity for orientation, spatial frequency, direction, and temporal frequency can be viewed in an integrated fashion by thinking of receptive fields in frequency space (reviewed in Mante and Carandini, 2005) (Figure 10). Frequency space has three dimensions: two of spatial frequency Fx and Fy, and one of temporal frequency, Ft. In this space, a receptive field in space-time (e.g. Figure 9) is simply represented by a ball (Figure 10, left). The height of the ball from the ground indicates preferred temporal frequency, the distance from the middle vertical indicates spatial frequency, and the angle on the ground plane indicates preferred orientation and direction of motion. Different V1 neurons have different preferences, and their preferences are thought to tile the frequency space (Figure 10, right).
Many of the forms of selectivity exhibited by V1 neurons are novel, in that they are not inherited from LGN. The mechanisms and circuits creating this selectivity are in most cases not known. For instance, we know little about the mechanisms by which V1 achieves direction selectivity. Conversely, a rich literature investigates the circuits that generate orientation selectivity.
The mechanisms of orientation selectivity are typically studied in simple cells that receive direct LGN input. These cells are thought to obtain their orientation selectivity through appropriate summation of LGN inputs (Hubel and Wiesel, 1962) (Figure 11). This model is supported by evidence for specificity of synaptic input from LGN to V1 (Reid and Alonso, 1995) (Figure 12). Connectivity from LGN to V1, however, may not be the only factor contributing to orientation selectivity. Other factors that are thought to contribute include spike threshold, intracortical excitation, and intracortical inhibition (Douglas and Martin, 2007; Ferster and Miller, 2000; Finn et al., 2007; Priebe and Ferster, 2008; Katzner et al., 2011; Atallah et al., 2012). The role of inhibition, in particular, is hotly debated and may depend on species: in mouse inhibition shows little selectivity for orientation, but in cat it is typically as selective as excitation (Isaacson and Scanziani, 2011).
Another area of study is the circuitry producing the receptive fields of complex cells. The classical view is that complex cells sum the output of a pool of simple cells (Hubel and Wiesel, 1962; Alonso and Martinez, 1998) (Figure 13 left). Alternatively, complex cells might obtain their receptive fields by mutual excitation (Chance et al., 1999). Increasing mutual excitation would turn a collection of simple cells with similar orientation preferences and receptive field locations into a collection of complex cells (Figure 13, middle and right).
Yet another area of study concerns the role of long-range horizontal axons. These axons extend over many millimeters and tend to connect neurons with similar orientation preference (Bosking et al 1997; Stettler et al 2002). Their role could involve the integration of information across different receptive fields (Stettler et al 2002) or more prosaically the construction of the receptive fields themselves (Angelucci et al. 2002; Cavanaugh et al 2002).
In addition to the map of retinotopy, in carnivores and primates V1 neurons are organized according to maps of selectivity for various stimulus attributes. Collectively these maps go under the name of functional architecture. These maps are striking in their organization and precision, but their significance is unknown. Many species such as rodents lack them, and even in species where they are present their features can vary widely across individuals (Horton and Adams, 2005).
One of the most studied maps is that of orientation selectivity (Figure 14) , where neurons with similar orientation preference are arranged next to each other (Hubel and Wiesel, 1962; Blasdel and Salama, 1986; Bonhoeffer and Grinvald, 1991; Ohki et al., 2006).
Maps of selectivity have been identified for various other stimulus attributes. There is a map of ocular dominance, typically composed of alternating stripes where neuronal responses are dominated by one input or the other (Hubel and Wiesel, 1968). There is a map of direction selectivity (Weliky et al., 1996; Ohki et al., 2005). There are also proposals for maps of selectivity for spatial frequency (Issa et al., 2000) and for disparity (Kara and Boyd, 2009).
The relationships between these various maps is the subject of substantial research. For instance, the maps of orientation selectivity and ocular dominance are thought to intersect at right angles (Obermayer and Blasdel, 1993). Moreover, there are proposals that the maps of orientation selectivity and retinotopy may distort one another (Das and Gilbert, 1997). Other data, however, suggest that the interaction between maps is minimal (Bosking et al., 2002; Benucci et al., 2007).
Functional architecture shows marked changes during development. For example the map of direction selectivity appears only following visual stimulation (Li, 2008). Moreover, functional architecture shows increased plasticity during development. For instance, there is a critical period of development during which monocular deprivation enlarges the ocular dominance columns of the remaining eye, a finding that has clinical implications.
The basic operation that V1 is thought to perform on images is simple filtering to enhance edges and contours. Simple cells are thought to perform linear filtering (Figure 8a), i.e. weighted sums of the intensity values in an image, with weights given by the receptive field profile (Movshon et al., 1978c). Complex cells, in turn are thought to sum the rectified output of simple cell-like filters (Figure 8b), thus computing the overall energy of an image in a band of frequency and orientation (Movshon et al., 1978b).
The result of these image processing operations can be summarized by taking an example image and producing a “neural image” that summarizes the output of an array of visual neurons (Figure 15). The original image is filtered by center-surround receptive fields in retina and LGN to obtain a neural image that enhances contours. This enhancement is much more pronounced in the output of V1 cells, which enhance oriented contours.
This view of V1 neurons performing linear filtering, i.e. weighted sums, is quantitatively correct only for extremely simple stimuli such as flashed bars and drifting gratings. For more complex images and especially for time-changing stimuli there are a number of corrections that need to be applied (see review in Carandini et al., 2005). First, there are a number of nonlinearities in the temporal domain (reviewed in Carandini, 2006). Second, there are nonlinear effects of summation seen when stimuli are superimposed (cross-orientation suppression, reviewed in Carandini et al., 1999) or surrounded by other stimuli (surround suppression, Sceniak et al., 1999; Fitzpatrick, 2000; Angelucci et al., 2002; Cavanaugh et al., 2002). Many of these deviations from simple linear filtering can be summarized with a simple model in which neurons don’t respond independently of each other, but rather control each other’s responsiveness in a divisive fashion (normalization model, reviewed in Carandini and Heeger, 2012).
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