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Neuroscientist OnlineFirst, published on October 2, 2007 as doi:10.1177/1073858407306597

REVIEW „

Similarity and Diversity in Visual Cortex: Is There a Unifying Theory of Cortical Computation? STEPHEN D. VAN HOOSER Duke University Medical Center, Durham, North Carolina

The cerebral cortex, with its conserved 6-layer structure, has inspired many unifying models of function. However, recent comparative studies of primary visual cortex have revealed considerable structural diversity, raising doubts about the possibility of an all-encompassing theory. This review examines similarities and differences in V1 across mammals. Gross laminar interconnections are relatively conserved. Major functional response classes are found universally or nearly universally. Orientation and spatial frequency tuning bandwidths are quite similar despite an enormous range of visual resolution across species, and orientation tuning is contrast-invariant. Nevertheless, there is considerable diversity in the abundance of different cell classes, laminar organization, functional architecture, and functional connectivity. Orientation-selective responses arise in different layers in different species. Some mammals have elaborate columnar architecture like orientation maps and ocular dominance bands, but others lack this organization with no apparent impact on single cell properties. Finally, local functional connectivity varies according to map structure: similar cells are connected in smooth map regions but dissimilar cells are linked in animals without maps. If there is a single structure/function relation for cortex, it must accommodate significant variations in cortical circuitry. Alternatively, natural selection may craft unique circuits that function differently in each species. NEUROSCIENTIST XX(X):xx–xx, XXXX. DOI: 10.1177/1073858407306597 KEY WORDS

Primate, Rodent, Carnivore, Tree shrew, Comparative study, Evolution

A fundamental goal of scientific inquiry is to provide compact, unifying descriptions of natural phenomena. In neuroscience, perhaps no brain region has inspired as many attempts to provide synthesizing theories of function as the mammalian neocortex. It is easy to understand why this is the case, as the gross 6-layer structure of cortex and its major interconnections are quite similar across brain areas and across species (Gilbert 1983; Casagrande and Kaas 1994; Douglas and Martin 2004; Hirsch and Martinez 2006). In addition, early studies of cortex in primates and carnivores uncovered exquisite columnar organization, raising hopes that cortex might consist of elegant and simple modules that are repeated across the cortical surface (Mountcastle 1957; Hubel and Wiesel 1963; Shatz and Stryker 1978; Horton and Hubel 1981). If one could understand the wiring of one module, then one might understand the mechanisms underlying all cortical computation.

This work was supported by NIH EY018064. Thanks to David Fitzpatrick, Sacha Nelson, Alexander Heimel, Elizabeth Johnson, Mark Mazurek, Ye Li, Wei Wu, Paul Tiesinga, and Leonard White for helpful discussions and comments on the manuscript. Address correspondence to: Stephen D. Van Hooser, Duke University Medical Center, Box 3209, Durham, NC 27710 (vanhooser@ neuro.duke.edu).

Volume XX, Number X, XXXX Copyright © 2007 Sage Publications ISSN 1073-8584

Comparative studies have now provided a comprehensive description of functional cell types and functional architecture of one cortical area, primary visual cortex, in several mammalian orders. This review examines these features of V1 in detail and attempts to identify what single cell and network features are common across mammals and which vary from species to species. Although most functional cell types in V1 are similar across mammals, recent studies have revealed a surprising diversity in the abundance of different cell types, their laminar and spatial organization, and network connectivity. The diversity of cortical architecture may require a reevaluation of the notion of a unified theory of cortical function. On one hand, if there is a single structure/ function relationship, then it must be constructed such that the significant differences in structure across mammals are irrelevant. On the other hand, the process of natural selection creates differences in homologous structures that benefit each species, so it should not be surprising if structure/function relationships differ from animal to animal. If there is diversity in underlying mechanisms, then the study of cortex may become more like the study of algorithms in computer engineering. In electrical circuits, the same core devices (transistors) can be wired in different ways to generate a variety of input/output relationships, and more than one algorithm can compute a particular function.

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Copyright 2007 by SAGE Publications.

1

Table 1.

Major Features of Primary Visual Cortex in Selected Mammals

Animal

Rat

Gray Squirrel

Tree Shrew

Cat

Order Rodentia Rodentia Scandentia Carnivora Adult weight (kg) 0.35 0.6 0.15 3 Retinal photorecptor 98.5/1.5 40/60 5/95 >95/5 ratio (rod %/cone %) Cone activation peak 359, 512 444, 543 444, 556 440, 555 wavelengths (nm) Life rhythm Nocturnal Diurnal Diurnal Crepuscular Eye position Lateral Lateral Lateral Frontal Acuity (cycle/°) 1.2 2.8, 3.9 2.4 6.0 V1 single neuron properties: Orientation tuning width (°) ~30 28 24 19-25 Orientation selective (%) 84-93 75 75 95-99 Direction selective (%) 59 22 20.2 64 Optimal spatial frequency (cycles per degree) 0.1 0.21 0.9 Spatial frequency bandwidth (octaves) 2 2.3 1.49 V1 spatial organization: Area (mm2) 7 ~80 380 Retinotopic map Yes Yes Yes Yes –1 ~25 3.5-4 4-4.5 1.5 Magnification (°/mm) Orientation maps No No Yes Yes Ocular dominance bands No No No Yes Cytochrome oxydase blobs No No No Yes

Owl Monkey

Macaque

Primates 0.8 98/2

Primates 7 0/100 (ctr) 95/5 (all) 430, 535, 565

543 Nocturnal Frontal 10

Diurnal Frontal 46.0

27 90 48

24 69-95 41

0.7

1.5-4.2

2.1

1.5

275 Yes 1.25 Yes Yes Yes

1325 Yes 0.077 Yes Yes Yes

Mammals vary widely in photoreceptor sensitivity and composition, behavioral acuity, percentage of orientation- and direction-selective cells, optimal spatial frequency, and V1 area and spatial organization. Despite these differences, 2 important single cell properties are relatively conserved across mammals: tuning width of orientation-selective neurons and bandwidth of spatial frequency tuning. Retinotopic maps are also found universally. Tuning width is median half width at half height for neurons showing some orientation selectivity (criteria varies by study), direction-selective is at least 2:1 ratio of response in preferred vs. nonpreferred direction, V1 area is one hemisphere, inverse magnification factor is averaged over central 10° of vision. Mean luminance for behavioral tests (in cd/m2): 51 rat, 3.4 and 340 gray squirrel, 35 tree shrew, 17.1 cat, 11.4 owl monkey, 17.1 macaque. Rod/cone ratios: (Steinberg and others 1973; Ogden 1975; West and Dowling 1975; La Vail 1976; Schneider and Zrenner 1986; Muller and Peichl 1989; Wikler and others 1990), cone activation wavelengths (Jacobs and Neitz 1986; Blakeslee and others 1988; Schnapf and others 1988; Deegan and Jacobs 1993; Jacobs and others 1993; Jacobs and Deegan 1997), macaque direction selectivity from Orban and others (1986). All other data except tree shrew (Humphrey and others 1977; Kaufmann and Somjen 1979; Humphrey and others 1980; Petry and others 1984; Wong-Riley and Norton 1988; Bosking and others 1997) adapted from Van Hooser and others (2005) and Heimel and others (2005).

V1 and its Inputs Primary visual cortex, variously called V1, striate cortex, or area 17, is found in all mammals (Krubitzer and Kaas 2005), from highly visual primates to subterranean animals with subcutaneous eyes such as the blind mole rat (Cooper and others 1993). V1 can be uniquely identified as a region in the occipital portion of the neocortex that 1) has a retinotopic map of space, 2) receives a major projection from the lateral geniculate nucleus, and 3) possesses a highly granulated layer 4 (Rosa and Krubitzer 1999). On the basis of these multiple similarities, it is widely believed that V1 is homologous across mammals. In all mammals, V1 receives visual input from the retina via the lateral geniculate nucleus (LGN). The basic laminar 2

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structure of the retina and morphological cell types are generally conserved across mammals, although there is wide variation in photoreceptors and their distributions (see Table 1, for a review, see Peichl 2005). In all examined mammals, there are 3 primary classes of LGN neurons (Sherman and others 1976; Shapley and Perry 1986; Casagrande and Norton 1991; Holdefer and Norton 1995; Hendry and Reid 2000; Van Hooser and others 2003). In monkeys, these classes have been termed parvocellular (P), magnocellular (M), and koniocellular (K), whereas in other mammals they are called X, Y, and W cells. The homological relationships among these classes remain unclear, although there are functional parallels between P and X cells, M and Y cells, and K and W cells, respectively. P cells and X cells respond to constant visual Similarity and Diversity in Visual Cortex

stimulation in a sustained manner, whereas M and Y cells respond only transiently. P/X and M/Y cells all respond reliably to visual stimulation and have a center-surround receptive field organization. In many primates, the P cells also carry color signals that are derived from long wavelength cones. The third “class” of cells, the K/W cells, is a heterogeneous mix of several subtypes, including longlatency cells, unreliable cells, blue-ON cells (Martin and others 1997), and some cells that do not show centersurround antagonism. The 3 classes of LGN neurons make connections with specific layers of the visual cortex. In all mammals, the P/X and M/Y cells primarily target cortical neurons in the granular layer 4 and make smaller projections to cortical layer 6.* However, the sublaminar organization of P/X and M/Y connections are species-specific. In monkeys, the M cells target the upper portion of layer 4, layer 4A, whereas the P cells project to layer 4B (Casagrande and Kaas 1994). Some monkeys such as the macaque and owl monkey have an extra P projection into layer 3Bβ. In the cat, X and Y cell projections are highly overlapping, although many Y cell contacts are superficial to X cell contacts (Leventhal 1979; Humphrey and others 1985). In the tree shrew, layer 4 inputs are not segregated into X and Y but instead are divided into a superficial ON layer (4A) and deeper OFF layer (4B). In most mammals, such as primates, squirrels, and tree shrews, the P/X and M/Y projections are restricted to primary visual cortex, although carnivores have an unusual arrangement in which some Y cells make a substantial projection to the second topographic map of visual space, area 18. Owing to its substantial LGN input, it is unclear whether area 18 in carnivores should be considered a primary or secondary visual area (Tretter and others 1975). The K and W-like cells make connections with the superficial layers, and once again the finer details of these connections vary with species. Some mammals such as carnivores and primates have periodic patches or “blobs” in the superficial layers that stain heavily for cytochrome oxidase (Horton and Hubel 1981), and in these animals the K/W cells specifically target the blobs (Fitzpatrick and others 1983; Boyd and Matsubara 1996). In the macaque monkey, these K/W projections are monocular, with each blob receiving input from one eye exclusively (Hendry and Yoshioka 1994). However, in other primates such as the squirrel monkey, the K/W projection to each blob is binocular (Adams and Horton 2006). In animals that lack blobs, *In some monkeys such as the macaque and owl monkey, the definition of layer 4 is somewhat complicated because there is an additional superficial granular layer, separated from the main granular layer, that receives input from P cells (Casagrande and Kaas 1994). A popular laminar terminology due to Brodmann includes this displaced granular layer as a subdivision of layer 4 called 4A. Under Brodmann’s scheme, the intervening space is labeled layer 4B, and the primary M/P recipient zone is divided into layers 4Cα and 4Cβ. This terminology is a bit clumsy for comparative discussions, as Brodmann’s 4B is comprised of large pyramidal neurons that make projections to other visual cortical areas, which are characteristics more typical of layer 2/3 neurons in other mammals. Therefore, in this article I will use the laminar terminology of Hassler, who labeled the major LGN recipient layer as layers 4A and 4B, Brodmann’s 4A as 3Bβ, and Brodmann’s 4B as 3C.

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such as the squirrel and the tree shrew, W projections to the superficial layers are diffuse (Harting and Huerta 1983; Usrey and others 1992). Unlike the P/X and M/Y cells, which project exclusively to V1 in most mammals, the K/W projection extends beyond area V1 to include extra striate areas (Rodman and Dieguez 2003; Nassi and Callaway 2006). Intrinsic connectivity within V1 is grossly similar among mammals (Burkhalter 1989; Casagrande and Kaas 1994; Fitzpatrick 1996; Binzegger and others 2004). Layer 4 neurons make short-range projections to overlying layer 2/3 neurons. Layer 2/3 cells send local and long-range connections to neighboring V1 layer 2/3 neurons, neurons in V1 layer 5, and neurons in higher cortical areas. In turn, layer 5 cells make short- and long-range projections within cortex to other layer 5 neurons and to layers 2/3 and 6, and in addition make projections to subcortical areas such as the superior colliculus, pulvinar, and the pons. Finally, layer 6 neurons send feedback signals to all cortical layers and some subcortical structures such as LGN and claustrum; the projections to cortical layer 4 and the LGN are particularly large. Functional Cell Types Are Similar across Mammalian V1 Over the last half-century, visual neuroscientists have examined responses of individual V1 neurons to many types of visual stimuli, including flashing dots and bars, sinusoidal gratings, white noise, and natural stimuli. Although cortical neurons are quite heterogeneous in their responses, these studies have revealed functional cell types that respond to visual stimulation in stereotypical ways. These cortical cell types are defined coarsely, meaning there is variability among individual cells of a given type, and there are not strict boundaries where one cell type ends and another begins (Mechler and Ringach 2002; Priebe and others 2004). Nevertheless, it is remarkable that when these studies have been repeated in different mammals, the cell classes that emerge are quite similar. In every mammal that has been examined, there are neurons in V1 that respond to images of bars or edges at a particular orientation (see Fig. 1). Surprisingly, the orientation tuning width (half width at half height) of orientationselective neurons is highly conserved across mammals, with median values ranging from about 19° to 25° in cat to about 30° in rat (see Table 1). This invariance is significant considering the enormous differences in visual acuity among these animals in behavior and in spatial resolution of individual V1 neurons. Many subclasses of orientation-selective cells have been identified. One class, termed simple cells by Hubel and Weisel, respond strongly to bars of light at particular positions within their receptive fields (Hubel and Wiesel 1959). At each position, a simple cell responds to light of a specific sign, either a light bar that is brighter than the background illumination or a dark bar that is darker than background. Another group of cells, complex cells, are selective for orientation but are less selective for bar position. Although a properly oriented bar THE NEUROSCIENTIST

3

40

20

20

10

D

200 Direction (°)

0

Number of cells

30

0

C

40

60

0

0

40 20

0

^ 1 Orientation index

E Width at 75% contrast (°)

10

5

0

N = 194

60

0

200 Direction (°)

15 Response (Hz)

Contrast invariant tuning

B

80 Response (Hz)

Orientation selectivity

A

0

100

200 Direction (°)

F

N = 194 60

40 20

0

300

0

20 40 60 Width at 50% contrast (°)

G 40

N = 192 Number of cells

20 Response (Hz)

Spatial frequency tuning

80

0 15 10

60 40 20 0 1

5

3

5

lowpass

Bandwidth (octaves)

0 0.03 0.1 0.3 1 Spatial frequency (cpd) Fig. 1. Many visual response properties are conserved in V1. All mammals have orientation-selective neurons in V1, and the orientation tuning width is invariant to stimulus contrast. In addition, a majority of mammalian V1 neurons show bandpass spatial frequency tuning. (A) Responses of an orientation-selective neuron from gray squirrel visual cortex to sinusoidal grating stimulation. The direction of motion was orthogonal to grating orientation. (B) Tuning curve of a typical cell with median orientation tuning in squirrel. Note that while the cell responds more to its preferred orientation, it responds to stimulation at all angles. (C) Orientation index histogram for 195 neurons in squirrel visual cortex; caret indicates median value. Orientation selectivity is common but heterogeneous. (D) Orientation tuning curves for one squirrel V1 cell measured at 25%, 50%, and 75% contrast. (E) Tuning width at 50% contrast vs. 75% contrast. The data fall along a line, indicating invariant tuning. (F, top) Typical band-pass spatial frequency tuning curve for V1 neuron. (F, bottom) A low-pass spatial frequency tuning curve. This low-pass shape is found in LGN neurons and a minority of V1 neurons. (G) Distribution of bandwidth for 192 squirrel V1 neurons. Adapted from Van Hooser and others (2005) and Heimel and others (2005) with permission from the Society for Neuroscience and American Physiological Society. 4

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Similarity and Diversity in Visual Cortex

must be within a complex cell’s receptive field to elicit a response, the complex cell will respond regardless of the precise position or sign of the bar. Both of these cell types can additionally show direction selectivity, which means they respond preferentially to motion in a particular direction (Hubel and Wiesel 1959). Simple and complex cells have been found in many mammalian orders, including carnivores (Hubel and Wiesel 1959), primates (Hubel and Wiesel 1968), rodents (Girman and others 1999; Heimel and others 2005), and marsupials (Ibbotson and others 2005). Many cortical neurons modulate their responses based on stimulation outside the central receptive field. Initial experiments of surround modulation measured responses to high contrast bars or gratings of varying lengths. Some cells, called end-stopped cells, decrease their responses when the bars or gratings become longer than the central receptive field (Gilbert 1977). Other cells, called lengthsumming cells, continue to increase their responses as bar or grating length increases. All examined mammals, including primates, carnivores (Gilbert 1977), rodents (Girman and others 1999; Heimel and others 2005; Van Hooser and others 2006), and marsupials (Oliveira and others 2002; Ibbotson and Mark 2003) have cells that exhibit endstopped and length-summing behavior. Recent studies have discovered that the extent to which cells are end-stopped or length-summing often depends on stimulus contrast (e.g, Sceniak and others 1999), but in this review I will use the older classifications based on high contrast stimulation because more comparative data are available. Responses of V1 neurons are sensitive to stimulus contrast, spatial frequency, and temporal frequency. Just as the cell types themselves show similarities across mammals, the manner in which contrast, spatial, and temporal frequency impact responses of cortical neurons is conserved. Most cells of the mammalian visual system from the retina to the cortex respond primarily to local stimulus contrast rather than absolute brightness. In V1, the tuning width of orientation-selective neurons remains constant as stimulus contrast is increased (Sclar and Freeman 1982), as shown in Figure 1. This relationship is notable because the naïve prediction of purely feed-forward excitatory input of LGN center-surround cells is that tuning width should increase with contrast due to the presence of the cortical spike threshold (Somers and others 1995; Troyer and others 1998). Therefore, the mechanism of contrastinvariant tuning must be cortical in origin. Contrast-invariant orientation tuning seems to be a universal property in visual cortex, because this feature has been found in all mammals examined including macaque monkey (Albrecht and Hamilton 1982), cat (Sclar and Freeman 1982), and gray squirrel (Van Hooser and others 2005). The influence of spatial frequency on cortical responses is similar to that of stimulus orientation, because spatial frequency tuning also arises in cortex and tuning widths are highly conserved. The majority of V1 spatial frequency tuning curves have a characteristic band-pass shape as shown in Figure 1. Although the absolute peak and high cutoff frequencies in any given species are strongly correlated

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with its density of retinal ganglion cells and the animal’s overall behavioral acuity, spatial bandwidth is highly conserved over the 50-fold range in absolute visual acuity (Heimel and others 2005), ranging from ~1.5 octaves in cats (Movshon and others 1978) and macaque monkeys (Foster and others 1985) to 2.6 octaves in bush baby (DeBruyn and others 1993). Band-pass tuning is not a property of the cells that provide input to cortical cells, because retinal ganglion and LGN cells show low-pass spatial frequency tuning (Enroth-Cugell and Robson 1966). Furthermore, band-pass selectivity is not a necessary mathematical consequence of orientation selectivity, because it is possible to build an orientation-selective cell that exhibits low-pass spatial frequency tuning using a single, elongated receptive field region that receives both ON and OFF input. Nevertheless, nature has constructed band-pass selectivity in V1 of every mammalian order that has been examined, including carnivores, primates, rodents (Heimel and others 2005), and marsupials (Ibbotson and Mark 2003). The transformation in temporal frequency tuning from the LGN to cortex is similar to that of spatial frequency. LGN neurons exhibit low-pass tuning, but the majority of cortical neurons display band-pass tuning curves (Hawken and others 1996; Heimel and others 2005). Peak preferred temporal frequencies are quite similar between LGN and V1, but the highest temporal frequency that neurons can follow is on average slightly lower in the cortex as compared to LGN. Absolute temporal frequency peak values are strongly correlated with the animal’s waking behavior (Heimel and others 2005). For example, median peak temporal frequency tuning values have been reported to be 2.4 Hz in the crepuscular cat (Bierer and Freeman 2003) and 2.9 Hz in nocturnal bush baby (DeBruyn and others 1993), whereas the same values are 6.6 Hz in the diurnal gray squirrel and 10 Hz in the diurnal macaque monkey. Finally, some neurons in V1 are selective for stimulus color. Most color vision physiology has been in trichromatic primates, where a majority of color-selective neurons receive signals from middle- (M-) and long- (L-) wavelength cones. Color cells are similar to those just described in that response properties are heterogeneous, but again one can coarsely divide cells into classes. One class of cells, called single cone opponent cells, resemble the parvocellular LGN neurons in that they respond to one cone type with a particular sign (ON or OFF) in one receptive field region, and respond to the other cone type in another region with the opposite sign (OFF or ON) (Johnson and others 2001; Conway and Livingstone 2006). Although single cone opponent cells are sensitive to color, they do not signal the presence of chromatic boundaries. Another class, called double opponent cells (Livingstone and Hubel 1984; Johnson and others 2001; Conway and Livingstone 2006), responds vigorously to chromatic boundaries and shows opponency in both space and color. For example, a double opponent cell might receive L ON and M OFF input in one region of its receptive field, and receive L OFF and M ON input in another region.

THE NEUROSCIENTIST

5

There has been less cortical work on the short- (S-) wavelength cone system, because relatively few neurons in primates appear to be strongly driven by S-cone input. Nevertheless, several primate studies have identified both single and double opponent neurons that show chromatic antagonism between S-cones and M- and L-cones (Johnson and others 2001; Conway and Livingstone 2006). Most mammals have dichromatic vision that is mediated by comparisons between S- and L-cones (Peichl 2005). One study of the dichromatic gray squirrel identified single and double opponent cell types (Heimel and others 2005), suggesting that these cell classes are common to both trichromatic and dichromatic mammals. Laminar Organization of Cell Types Varies across Mammals Although properties of functional cell types appear to be largely conserved across mammals, the relative abundance of each cell type and the organization within the cortical layers is more variable. The distribution of orientation-selective cells varies considerably across the mammalian orders. Although tuning widths of orientation-selective cells are quite similar across mammals, reports of the percentage of orientationselective cells range from about 75% in studies of squirrel, tree shrew, and monkey to 95%-99% in studies of cat (see Table 1). Furthermore, although the majority of cells in the upper and lower layers of cortex in all mammals are selective for orientation, there is wide variation in the presence of orientation selectivity in the cortical input layer as shown in Figure 2. In carnivores such as the cat and the ferret, the vast majority of cells in layer 4 are orientation selective (Hubel and Wiesel 1959; Usrey and others 2003). The same is true in rodents such as the rat (Girman and others 1999) and the squirrel (Heimel and others 2005), which do not show a strong tendency for orientation selectivity to vary across the cortical layers. A different pattern is found in the tree shrew, where a majority of layer 4 neurons have a center-surround receptive field organization, similar to LGN cells, and are not orientation selective (Humphrey and Norton 1980; Chisum and others 2003). The organization of primate layer 4 has elements of both patterns (Hubel and Wiesel 1968; Blasdel and Fitzpatrick 1984; Leventhal and others 1995; Ringach and others 2002). Orientation index values for cells in layer 4 are generally lower than the other layers, although some cells in macaque layer 4A do show orientation selectivity. Recordings of cells in layer 4B have suggested that the vast majority of these neurons are not orientation selective, much like tree shrew layer 4. In their original study of simple and complex cells, Hubel and Wiesel suggested that an elongated pattern of input from nonoriented LGN ON and OFF cells could produce orientation-selective simple cells, and that input from many simple cells with different position preferences could add together to yield complex receptive fields that are position invariant (Hubel and Wiesel 1959). The organization of simple and complex cells in many mammals is consistent with this idea. In cat

6

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(Gilbert 1977), gray squirrel (Heimel and others 2005), Tammer wallaby (Ibbotson and Mark 2003), and macaque monkey (Hubel and Wiesel 1968; Schiller and others 1976; Ringach and others 2002), simple cells are more common in layers 4 and 6, which receive direct input from the LGN. The layer 4 neurons make synaptic connections with cells in the superficial layers, which are dominated by complex cells. However, some mammals deviate from this plan. In macaque layer 4B and tree shrew layer 4, neurons are not orientation selective and therefore cannot be considered simple cells. Nevertheless, in both animals, these nonoriented layer 4 cells make projections to the superficial layers, which are dominated by orientation-selective complex cells (Hubel and Wiesel 1968; Chisum 2003). It remains to be seen whether these nonoriented cells project to a minority population of simple cells in the superficial layers, and thus follow the progression of nonoriented to simple to complex, or if they directly project to complex cells without intervening simple cells. One of the most dramatic differences across mammals is the percentage and distribution of direction-selective cells. Carnivores such as the cat and the ferret appear to be primarily wired for direction selectivity, because more than two-thirds of cells respond more than twice as much to stimulation in their preferred direction as compared to the opposite direction (Gilbert 1977). However, in primates, less than half of cells show direction selectivity (Orban and others 1986; Hawken and others 1988; O’Keefe and others 1998), and in the gray squirrel, direction-selective cells comprise only 22% of neurons in V1 (Heimel and others 2005). In carnivores, direction-selective cells make up the majority of cells in all cortical layers (Gilbert 1977), but in other mammals direction-selective cells are restricted to particular layers. Direction selective cells are primarily found in layers 3C, 4A, and 6 in macaques and owl monkeys, and in layer 6 in the gray squirrel. Another major difference is in the number and laminar locations of end-stopped and length-summing cells. In the cat, about one-third of cells in layer 2/3 are end stopped, and end-stopped cells are largely absent from layer 6 (Gilbert 1977). In the macaque monkey (Hubel and Wiesel 1968; Hawken and others 1988), endstopped cells are most common in the upper layers and 3C in particular and, like the cat, are uncommon in layer 6 (Sceniak and others 2001). End-stopped cell organization is very different in the squirrel and the tree shrew. In the squirrel, only 16% of cells show end-stopped behavior (Van Hooser and others 2006). Further, a recent study in the tree shrew found no evidence at all for endstopping in layer 2/3, but about 40% of layer 4 cells were end stopped in this animal (Chisum and others 2003). Length-summing cells are relatively rare in carnivores, although they are quite common in layer 2/3 of the gray squirrel and tree shrew. In cat, length-summation is largely restricted to about half of layer 6 cells that receive longrange horizontal connections from layer 5 (Bolz and Gilbert 1989). Length summation is found in almost half of squirrel layer 2/3 neurons (Yu and others 2005; Van Hooser and others 2006), and all neurons examined in

Similarity and Diversity in Visual Cortex

Macaque monkey

Cat

2,3A,3Bα 2/3 3B β (4A) 3C (4B) 4A (4Cα) 4

4B (4Cβ) 5

5

6

6

Tree shrew

Gray squirrel

2/3

2/3

4A (ON) 4 4B (OFF) 5

?

?

?

?

?

5

6

?

?

?

?

?

6

>25% >40%

d pe op g st in dm En um -s th ng Le ex pl om C e e iv pl ct m le Si se e niv io ct ct le ire se D nio at nt rie O

d pe op g st in dm En um -s th ng Le ex pl om C e e iv pl ct m le Si se e niv io ct ct le ire se D nio at nt rie O

Fig. 2. Laminar distributions of functional cell types in 4 mammals illustrate similarities and differences in functional organization. Bright colors indicate cell type exceeds 40% of population; dim colors indicate cell type exceeds 25%. Only relevant sublaminae are shown and “?” indicates no applicable data. Macaque laminae are due to Hassler (Brodmann in parentheses). Orientation-selective cells are common in most cortical layers but are absent in macaque 4B and tree shrew layer 4. Direction selectivity dominates in the cat but is only common in specific layers of macaque and squirrel. Simple cells are generally found in cortical input layers but are not present in macaque 4B and tree shrew 4, whereas complex cells comprise the majority in the upper and lower layers of all mammals. The tree shrew and squirrel both exhibit many length-summing cells in the upper and lower layers, whereas end-stopped cells are common in layer 2/3 of macaque and cat.

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7

Cat L2/3

Tree shrew

Receptive field: Topographic feed-forward input:

L4

Receptive field: Topographic feed-forward input:

LGN

Receptive field:

Fig. 3. In cat and tree shrew, orientation selectivity arises in analogous cell groups, demonstrating that functional properties can be computed at different laminar stages in different species. Orange indicates pattern of retinotopic sampling from antecedent cells. Bars indicate preferences of orientation-selective cells; spots indicate nonoriented cells; color indicates likely homological relationships: LGN (yellow), layer 4 (green), layer 2/3 (red). In cat L4 neurons and tree shrew L2/3 neurons, orientation selectivity is generated in part from elongated input from nonoriented afferents. Despite similarities in function and description of mechanism, orientation selectivity in cat L4 and tree shrew L2/3 neurons likely represents convergent evolution. The transformation from macaque 4B to L2/3 may be similar to that found in tree shrew (see text).

layer 2/3 of the tree shrew showed length-summing behavior (Chisum and others 2003). In the macaque monkey, layer 3C and layer 6 cells frequently show length summation (Sceniak and others 2001). Are Similar Functional Cell Types Homologous across Mammals? Many properties of individual cell types, such as the tuning width of orientation-selective cells and the bandwidth of spatial frequency tuning, are highly conserved across mammals. One might imagine that this similarity in function implies that these cell types are homologous across mammals and share underlying mechanisms and synaptic connections. However, it is also possible that the statistics of natural scenes drives convergent evolution with the result that functional properties of analogous cell types are similar. For example, suppose that a mutation in an ancestor generated an orientation-selective neuron type with a median tuning width of 75°. If there were a computational advantage to a median tuning width of 25° to 30° based on natural scene statistics, then there would be evolutionary pressure over time to narrow the orientation tuning width to these values. Therefore, similarities in functional cell properties by themselves neither support nor refute ideas about homology. The diversity in laminar organization of orientationselective cells gets more to the core of this issue. In the cat, orientation selectivity is present in layer 4, whereas

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in the tree shrew, orientation selectivity is not found until layer 2/3. The mechanism underlying the generation of orientation selectivity in both cell types is identical in description: there is biased feed-forward input from centersurround neurons such that the majority of input arises from cells whose receptive fields are along the orientationselective cell’s axis of preferred orientation (see Fig. 3). In the case of the cat, the center-surround cells reside in the LGN (Reid and Alonso 1995), whereas in the tree shrew these cells are found in layer 4 (Mooser and others 2004). Despite the similarities in orientation tuning width and the description of the mechanism, it is important to note that these 2 populations of orientation-selective cells are very unlikely to be homologous: the layer 2/3 cells in the tree shrew resemble those of other mammals and make longrange horizontal intrinsic connections and project to layer 5 and neighboring cortical areas, whereas the cat layer 4 neurons make shorter horizontal connections and project to layer 2/3. These analogous cell types—different cell types that happen to share functional and mechanistic features—demonstrate that the same receptive field property can be computed in different ways or at different laminar stages in different species. Homological relationships among neurons may become clearer as we learn more about the cellular identities of neurons that comprise cortical circuits. Neuronal cell types can be defined by many parameters, such as morphology, neurotransmitters, intrinsic currents, synaptic dynamics, or position in a circuit (Nelson 2002; Markram and others

Similarity and Diversity in Visual Cortex

2004; Nelson and others 2006b; Soltesz 2006). These techniques have identified many excitatory and inhibitory neurons that seem to be clearly homologous across mammals (Tyler and others 1998; Hof and Sherwood 2005). For example, in rodents, carnivores, and primates, layer 5 neurons that project to the superior colliculus are always large, bitufted, pyramidal cells that extend their apical dendries into layer one (Hubener and Bolz 1988; Hubener and others 1990; Yoneshima and others 2006). By contrast, layer 5 pyramids that make corticocortical connections are smaller and have shorter apical dendrites that do not extend beyond layer 2/3. Recently, some investigators have applied microarray technology to examine the relative expression of thousands of genes in specific classes of neurons that have been highlighted by genetic markers (Sugino and others 2006). One might imagine that these gene expression profiles may unambiguously define specific neuronal circuit elements in cortex (Nelson and others 2006a). By examining similarities and differences in gene expression profiles in cell classes across mammals, it may be possible to identify homologies among neurons in different species. By closely examining gene profiles of homologous neurons, one may further be able to uncover the differences in protein expression that underlie differences in synaptic connectivity from one circuit to another, thus gaining insight about changes in mechanisms that have been made through evolution. On the other hand, evolutionary drift among some cortical cell types could be so great that it might be difficult to identify homologies. There is evidence for significant species variation in cell morphology and the abundance of different cell types (DeFelipe and others 2002). For example, many spiny neurons in layer 4 of some mammals like the cat and marsupial quokka are pyramidal with apical dendrites that extend into the superficial layers, whereas the vast majority of spiny layer 4 dendrites in some other mammals such as the marsupial dunnart, tree shrew, and the macaque are smaller and are primarily restricted to layer 4 (Tyler and others 1998). At least one distinct neuronal subtype, the inhibitory double bouquet cell, is found in some mammals but not others. This cell, which can be recognized by its “horse tail”-shaped axon that extends vertically through many cortical layers, is present in human and monkey V1, but is less common in carnivore V1 and is absent in rodents, lagomorphs, and ungulates (Feldman and Peters 1978; Yanez and others 2005). It will be fascinating to learn whether variable presence of neuronal cell types is common in cortical evolution or if the double bouquet cell is an unusual exception. A more complete understanding of gene expression in cortical cell types could allow a circuit-level exploration of developmental or plastic abilities of cortex to compensate for particular changes. For example, if one manipulates kitten layer 4 cells to remove convergence of ON-center and OFF-center LGN input, thereby eliminating orientation selectivity in layer 4, will inherent developmental interactions produce orientation selectivity in layer 2/3 as is found in tree shrew? Or, are other compensatory changes required for this to occur? Previous experiments

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in ferrets that have rerouted retinal inputs to auditory thalamus have shown that many functional cell types and organizational features normally found in visual cortex can be recapitulated in auditory cortex, suggesting that a significant degree of development depends on sensory input (Newton and others 2007). However, when visual input is normally routed to the visual cortex in tree shrews and squirrels, the resulting circuits are different from ferret visual cortex, so clearly there may be processes that differ across mammals. Manipulations along these lines within the V1 circuit could provide insight into the question of universal versus diverse mechanisms of development and plasticity. Horizontal Columnar Organization All mammals have some degree of columnar structure in visual cortex: neurons below a particular position on the cortical surface have similar functional properties, and these properties tend to change smoothly as one moves across the cortical surface. For example, all examined mammals have a retinotopic map of visual space in V1 (Kaas 1997). Other forms of cortical organization vary considerably from animal to animal. Pioneering physiological studies of visual cortex identified several forms of columnar organization that are related to the response properties of individual cells. Many mammals, including carnivores (Hubel and Wiesel 1963; Grinvald and others 1986; McConnell and LeVay 1986; Rao and others 1997), sheep (Clarke and others 1976), primates (Hubel and others 1978; Blasdel and Salama 1986), and tree shrews (Humphrey and others 1980; Bosking and others 1997), have smooth, repeating columnar maps of orientation selectivity in V1, as shown in Figure 4. At any given location on the map, neurons have similar orientation preferences, and the preferred angle of orientation selectivity changes smoothly across the cortical surface. These orientation maps repeat at 550-µm intervals in macaque monkeys and tree shrews, and the repeat interval is about 800-900 µm in cat and ferret. In carnivores, the orientation map also contains a smooth map of direction selectivity; in these animals, orientation domains are divided into 2 direction domains at an abrupt fracture where direction preference reverses (Weliky and others 1996; Ohki and others 2005). In addition to orientation maps, carnivores and primates have alternating regions of input from the 2 eyes in layer 4 (Hubel and Wiesel 1969) called ocular dominance columns or ocular dominance bands. In a given column or band, the majority of cells receive monocular visual input that arises from either the ipsilateral eye or contralateral eye but not both. In the upper and lower layers of cortex, the input becomes mixed. Finally, primates and carnivores have yet another form of functional architecture in the form of periodic patches or “blobs” that show increased staining for the enzyme cytochrome oxidase (Horton and Hubel 1981). Cytochrome oxidase is present in mitochondria, suggesting that metabolic demands in these areas are elevated relative to surrounding tissue. These cytochrome oxidase blobs

THE NEUROSCIENTIST

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Macaque orientation map

B

Macaque ocular dominance bands

C

Macaque cytochrome oxidase blobs

D

Tree shrew orientation map

Ferret direction map

500µm No orientation map in rat

100µm

A

Fig. 4. Functional architecture is highly variable across mammalian visual cortex. (A, top) False color map of orientation preference in macaque monkey derived from voltage sensitive dye imaging. Color indicates orientation preference. (A, middle) Ocular dominance bands in the same region of cortex as above. White areas indicate responses to the right eye, and black areas indicate left eye responses. (A, bottom) Cytochrome oxidase blobs are evident in the same region of tissue after histological staining. From Blasdel and Salama (1986) with permission of the Nature Publishing Group. (B) False color map of orientation preference in tree shrew V1 (Bosking and others, 1997) with permission from the Society for Neuroscience. (C) Carnivores have an additional map of direction selectivity layered on top of the orientation map. False color map of orientation preference, key as in (B). Arrows indicate local direction preference. Numbers indicate locations of abrupt fractures in direction preference that can occur within smooth orientation domains. For example, at 1, red region indicates that local orientation preference is horizontal, but neurons to right prefer upward motion and neurons to left prefer downward motion. From Weliky and others (1986), with permission from the Nature Publishing Group. (D) Many mammals lack functional architecture such as orientation maps and ocular dominance bands. Two-photon image of orientation selectivity of many individual neurons in rat V1 (Ohki and others 2005). Cells with significant responses have been colored according to orientation preference. Note many adjacent cells have different orientation preferences. Reproduced with permission from the Nature Publishing Group. Despite differences in functional organization, many single cell properties in animals with and without maps are relatively similar (see Table 1, text).

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Similarity and Diversity in Visual Cortex

receive projections from koniocellular/W cells of the LGN, and in macaque monkeys the blobs are centered on ocular dominance bands. After the discovery of functional architecture like segregated columns of superficial and deep receptor responses in somatosensory cortex (Mountcastle 1957), and orientation maps, ocular dominance bands, and cytochrome oxidase blobs in visual cortex, there was some hope that all of the cortex might be organized into repeating columnar modules. Understanding the specific rules of one module would allow one to understand the function of the entire cortex. However, the attractive idea of a universal modular architecture has faded over the years because a steady trickle of studies have failed to identify any role of columnar architecture in determining functional properties of single cells (Purves and others 1992; Horton and Adams 2005). Studies as long ago as the 1970s noted that mice (Dräger 1974), rabbits (Hollander and Halbig 1980), squirrels (Weber and others 1977), tree shrews (Hubel 1975), and sheep (Pettigrew and others 1984) lacked ocular dominance bands, so this architecture is not general across mammals. Recent work in squirrel monkey found that the presence or absence of ocular dominance bands varied from individual animal to animal (Adams and Horton 2003). Interestingly, some of these monkeys had ocular dominance bands in a portion of V1 but lacked bands in other regions of V1. This variation across species and within individual animals implies ocular dominance bands are unlikely to be critical for any particular visual function. If one had to imagine a function for these columns, one might guess that ocular dominance bands might be necessary for the existence of layer 4 cells that receive purely monocular input, and that animals without these bands would only have cells that received binocular input. However, a single unit recording study that examined individual layer 4 neurons in squirrel monkey found that animals lacking ocular dominance columns had numerous cells that received monocular input (Adams and Horton 2006). An analogous story has emerged regarding orientation maps. In the 1970s, investigators identified orientationselective neurons in the hamster (Tiao and Blakemore 1976) and the rabbit (Murphy and Berman 1979) but did not find evidence for columnar orientation maps in these animals. Later studies also did not find orientation maps in mice (Metín and others 1988) or rats (Girman and others 1999; Ohki and others 2005). The lack of orientation maps in nocturnal, burrowing mammals might have been dismissed as degeneration of an otherwise important mammalian feature, but a recent study found orientationselective cells but no orientation maps in the gray squirrel, which has better visual acuity than many mammals with orientation maps (Van Hooser and others 2005). Further work in the rat and squirrel has established the presence of all major cell types, including simple, complex, direction-selective, length-summing, end-stopped cells, and color-selective cells (Girman and others 1999; Heimel and others 2005; Van Hooser and others 2006), suggesting

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that orientation maps are not necessary for the existence of these cells. Finally, median orientation tuning widths are not very different in animals with and without orientation maps, suggesting that orientation maps do not substantially improve single cell orientation tuning in animals where maps are present. The function of the blobs, if any, also remains an enigma (Horton and Adams 2005). Once again, many animals lack blobs, such as the tree shrew (Wong-Riley and Norton 1988) and rodents such as the gray squirrel (Kaas and others 1989), so this structure cannot be critical for receptive field properties that are found in common across these animals. One early single electrode study in macaque suggested that color-selective cells are localized to the blobs and that orientation-selective cells are not present in blob regions (Livingstone and Hubel 1984). One subsequent study in macaque came to similar conclusions (Ts’o and Gilbert 1988), but other studies have found no relationship between blobs and color-selective and orientation-selective cells (Lennie and others 1990; Leventhal and others 1995). A major prediction of the idea that blobs lack orientationselective neurons is that optical recordings of orientation selectivity in V1 should show periodic regions of weak orientation selectivity that correspond to the blobs (Horton and Adams 2005). However, no such regions have ever been identified in intrinsic signal or voltage-sensitive dye imaging, which casts doubt on a strong correlation between low orientation selectivity and cytochrome oxidase blobs. Finally, evidence in other species has never found strong links between the presence or absence of color-selective or orientation-selective cells and blobs (DeBruyn and others 1993; O’Keefe and others 1998), suggesting that blobs are not a key component of these properties, at least in some species. In summary, no major differences in individual cell properties have been reported between animals that have functional maps and those that do not. Although cats and monkeys have orientation maps and squirrels and rats do not, tuning widths of orientation-selective neurons are very similar in all of these animals. Likewise, squirrel monkeys without ocular dominance bands have monocularly driven neurons in layer 4. Furthermore, animals with and without maps seem to have the same basic functional cell types, including orientation-selective, direction-selective, simple/complex, length-summing, and end-stopped cells. Therefore, the presence of columnar architecture cannot be critical for these properties. These and other results have led many scientists to abandon the notion of a repeating modular organization as providing significant insight into cortical function (Horton and Adams 2005). Horizontal Connectivity and Functional Maps Although functional architecture varies across mammals, perhaps a more important issue for function is whether or not connectivity among neurons is conserved or variable. Of course, the spatial organization of neurons does not necessarily have an impact on connectivity. If one has a network of neurons with a particular set

THE NEUROSCIENTIST

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A

V1 horizontal connectivity in mammals with orientation maps

B

Possible horizontal connectivity in mammals without maps Local/unselective

C

Orientation-selective

Possible horizontal connectivity at map fractures Local/unselective

Orientation-selective

Fig. 5. Horizontal connectivity in mammals with and without orientation maps. (A) In animals with maps, there are dense short-range connections among nearby neighbors and long-range connections with other cells that have similar orientation preferences. (B, C) In animals without maps or at map discontinuities in animals that have maps, it is unclear which rules govern local connectivity. Cells cannot, with the same synapses, contact their local neighbors and contact cells with similar orientation preferences. One possibility (left) is that cells are unselectively connected with their near neighbors without regard to orientation preference. Or (right), cells may selectively contact only their close neighbors that show similar preferences. Evidence from small tracer injections, intracellular recordings, and spike timing correlation studies (see text) have suggested that local connections are unselective (left), but new single cell tracing methods may be able to address these possibilities more precisely.

of synaptic connections, one can physically arrange the neurons in many ways while preserving the connections among them. Do all mammals have similar functional connectivity with different physical arrangements, or does connectivity follow different rules in different species? The connections that are most relevant for differences in functional architecture are the short- and long-range horizontal connections in the upper and lower layers that can extend for millimeters across the cortical surface. Short-range horizontal connections (