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Thalamocortical processing in vision

Published online by Cambridge University Press:  20 June 2017

REECE MAZADE  and
JOSE MANUEL ALONSO
REECE MAZADE
Affiliation:
Department of Biological and Visual Sciences, State University of New York, College of Optometry, New York, New York 10036
JOSE MANUEL ALONSO*
Affiliation:
Department of Biological and Visual Sciences, State University of New York, College of Optometry, New York, New York 10036
*
*Address correspondence to: Jose-Manuel Alonso, State University of New York, College of Optometry, Department of Biological and Visual Sciences, 33 West, 42nd street, 17th floor New York, NY 10036. E-mail: jalonso@sunyopt.edu
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Abstract

Visual information reaches the cerebral cortex through a major thalamocortical pathway that connects the lateral geniculate nucleus (LGN) of the thalamus with the primary visual area of the cortex (area V1). In humans, ∼3.4 million afferents from the LGN are distributed within a V1 surface of ∼2400 mm2, an afferent number that is reduced by half in the macaque and by more than two orders of magnitude in the mouse. Thalamocortical afferents are sorted in visual cortex based on the spatial position of their receptive fields to form a map of visual space. The visual resolution within this map is strongly correlated with total number of thalamic afferents that V1 receives and the area available to sort them. The ∼20,000 afferents of the mouse are only sorted by spatial position because they have to cover a large visual field (∼300 deg) within just 4 mm2 of V1 area. By contrast, the ∼500,000 afferents of the cat are also sorted by eye input and light/dark polarity because they cover a smaller visual field (∼200 deg) within a much larger V1 area (∼400 mm2), a sorting principle that is likely to apply also to macaques and humans. The increased precision of thalamic sorting allows building multiple copies of the V1 visual map for left/right eyes and light/dark polarities, which become interlaced to keep neurons representing the same visual point close together. In turn, this interlaced arrangement makes cortical neurons with different preferences for stimulus orientation to rotate around single cortical points forming a pinwheel pattern that allows more efficient processing of objects and visual textures.

Keywords

ThalamusCortexPrimary visual cortexLateral geniculate nucleus

Information

Type
Review Article
Information
Visual Neuroscience , Volume 34 , 2017 , E007
Copyright
Copyright © Cambridge University Press 2017 

The ability to resolve image detail is an important visual function that animals use to guide their behaviors and survive in their environments. Visual acuity can be maximized by increasing the size of the eye to enlarge the images that are projected on the photoreceptor array. The eye size of vertebrates increases with body size and its axial length can range from just 1 mm in the American toad to 107 mm in the blue whale (Hughes, Reference Hughes and Crescitelli1977; Howland et al., Reference Howland, Merola and Basarab2004). Eye size also increases with other factors such as maximum running speed probably to fulfill an important visual function, which is to avoid collisions when moving (Hughes, Reference Hughes and Crescitelli1977; Heard-Booth & Kirk, Reference Heard-Booth and Kirk2012). When comparing animals with similar size, the fast cheetah (110 km/h) has the eyes with the largest axial length among carnivores (37 mm) while the flightless penguins have the smallest among birds (21 mm). In primates, the fastest monkey (patas monkey, 55 km/h) has eyes with axial lengths (22.5 mm) that are comparable to much bigger primates such as gorillas (22.5 mm), and humans (25 mm) (Howland et al., Reference Howland, Merola and Basarab2004; Heard-Booth & Kirk, Reference Heard-Booth and Kirk2012).

Cortical visual acuity

Among animals with similar eye size, visual acuity increases as a function of photoreceptor density and the number of axons from retinal ganglion cells leaving the optic nerve. Moreover, as the diameter of the optic nerve increases, the volume of the visual thalamus and primary visual cortex also becomes larger. Data from seven different mammals indicate that the primary visual cortex increases by roughly 800 mm2 for every million of LGN neurons (Fig. 1, top left). The relation is ∼400 mm2 for ∼0.5 million in cats, ∼1200 mm2 for ∼1.5 million in macaques, and ∼2400 mm2 for ∼3 million in humans (Table 1). Cortical visual acuity for central vision also increases with the number of LGN neurons (Fig. 1 top right, ∼2 cpd for one million), the size of the V1 area (Fig. 1 bottom left, ∼2 cpd for 1000 mm2) and the horizontal extent of the binocular visual field (Fig. 1 bottom right, ∼2 cpd for 50%). The relation between cortical visual acuity and binocular vision is consistent with ecological studies demonstrating a tendency for active predators with frontal eyes to have higher visual acuity than herbivores with lateral eyes (Veilleux & Kirk, Reference Veilleux and Kirk2014).

Fig. 1.

Thalamocortical visual function in different animals: primates, carnivores, scandentia, lagomorphs, and rodents. Top. The number of LGN cells is correlated to the size of area V1 (left) and cortical visual acuity (right). Bottom. Cortical visual acuity is also correlated to the size of area V1 (left, expected from correlations shown at the top), and the horizontal extent of the binocular field (shown as a percentage of the total horizontal extent of the visual field). Size of area V1 refers to only one hemisphere. See Table 1 for references.

Table 1.

Comparison of visual systems across different animals: primates, carnivores, scandentia, lagomorphs, and rodents. Columns 1–9 are taken from references cited below. Column 10 is calculated as column 8 times column 9; column 11 is calculated as column 10 divided by column 7; column 12 is calculated as column 6 times one million divided by column 8. Columns are numbered from left to right, from [1] for body weight to [12] for LGN-axon density in V1. All areas and volumes refer only to one hemisphere. Human: (Howland et al., Reference Howland, Merola and Basarab2004) for [1], [2]; (Curcio & Allen, Reference Curcio and Allen1990) for [3], [4], [5]; (Selemon & Begovic, Reference Selemon and Begovic2007) for [6]; (Andrews et al., Reference Andrews, Halpern and Purves1997) for [7], [8], [10]. Macaque: (Howland et al., Reference Howland, Merola and Basarab2004) for [1]; (Perry & Cowey, Reference Perry and Cowey1985) for [2]; (Kong et al., Reference Kong, Wang, Sun and Witt2010) for [3]; (Kim et al., Reference Kim, Tom and Spear1996) for [4]; (Perry & Cowey, Reference Perry and Cowey1985) for [5]; (Connolly & Van Essen, Reference Connolly and Van Essen1984) for [6]; (Malpeli & Baker, Reference Malpeli and Baker1975) for [7]; (Adams et al., Reference Adams, Sincich and Horton2007) for [8]; (Hubel & Wiesel, Reference Hubel and Wiesel1968) for [9]. Cat: (Howland et al., Reference Howland, Merola and Basarab2004) for [1], [2]; (Hughes, Reference Hughes1975) for [3], [5]; (Hughes & Wassle, Reference Hughes and Wassle1976) for [4]; (Williams et al., Reference Williams, Cavada and Reinoso-Suarez1993) for [5], [6], [7]; (Tusa et al., Reference Tusa, Palmer and Rosenquist1978) for [8]; (Hubel & Wiesel, Reference Hubel and Wiesel1962) for [9]. Tree Shrew: (Howland et al., Reference Howland, Merola and Basarab2004) for [1], [2]; (Engelmann & Peichl, Reference Engelmann and Peichl1996) for [3]; (Drenhaus et al., Reference Drenhaus, Von Gunten and Rager1997) for [4]; (Petry et al., Reference Petry, Fox and Casagrande1984) for [5]; (Conway & Schiller, Reference Conway and Schiller1983) for [6], [7]; (Sesma et al., Reference Sesma, Casagrande and Kaas1984) for [8]; (Muly & Fitzpatrick, Reference Muly and Fitzpatrick1992) for [9]. Ferret: (Howland et al., Reference Howland, Merola and Basarab2004) for [1], [2]; (Henderson, Reference Henderson1985) for [3], [4]; (Vitek et al., Reference Vitek, Schall and Leventhal1985) for [5]; (Gautschi & Clarke, Reference Gautschi and Clarke2007) for [6]; (Zahs & Stryker, Reference Zahs and Stryker1985) for [7]; (Law et al., Reference Law, Zahs and Stryker1988) for [8], [9]. Rabbit: (Howland et al., Reference Howland, Merola and Basarab2004) for [1], [2]; (Oyster et al., Reference Oyster, Takahashi and Hurst1981) for [3], [4], [5]; (Najdzion et al., Reference Najdzion, Wasilewska, Bogus-Nowakowska, Rowniak, Szteyn and Robak2009) for [6], [7]; (Hughes, Reference Hughes1971) for [8], [9]. Mouse: (Howland et al., Reference Howland, Merola and Basarab2004) for [1], [2]; (Drager and Olsen, Reference Drager and Olsen1981) for [3]; (Williams et al., Reference Williams, Strom, Rice and Goldowitz1996) for [4]; (Jeon et al., Reference Jeon, Strettoi and Masland1998) for [5]; (Seecharan et al., Reference Seecharan, Kulkarni, Lu, Rosen and Williams2003) for [6], [7]; (Garrett et al., Reference Garrett, Nauhaus, Marshel and Callaway2014) for [8]; (Antonini et al., Reference Antonini, Fagiolini and Stryker1999) for [9]. RGC: retinal ganglion cells

As the number of LGN neurons increases, the number of V1 neurons also increases but the relation is not linear (Stevens, Reference Stevens2001). V1 becomes larger than what would be expected from an equal expansion of thalamus and visual cortex. This overexpansion of area V1 makes the density of LGN axons per mm2 of cortical area to be lower in animals with larger V1 areas. The density is ∼4 times lower for the ∼1.3 million afferents of macaques (1093 axons/mm2) and the ∼3.4 million afferents of humans (1417 axons/mm2) than the ∼20,000 LGN afferents of the mouse (5000 axons/mm2) as shown in Table 1. The close relation between V1 size and LGN afferent number could explain why cortical visual acuity is better related with the number of LGN afferents (R 2 = 0.98, Fig. 1) than the retinal area (R 2 = 0.70), number of retinal ganglion cells (R 2 = 0.54), or even the peak density of retinal ganglion cells (R 2 = 0.67), as can be verified by comparing the values from Tables 1 and 2. For example, cortical visual acuity is ∼4 times higher in cats than rabbits (Table 2) but their retinal areas are similar (498 mm2 in rabbits versus 500 mm2 in cats, Table 1) and the number of retinal ganglion cells is actually ∼2 times higher in rabbits (Table 1). Such discrepancies between retinal resources and visual cortical acuity are likely to be less pronounced among animals of the same species. For example, Spanish wildcats, Felis silvestris tartessia, have higher central cone densities, higher peak density of retinal ganglion cells, more axons in the optic nerve, and more LGN neurons than domestic cats of similar weights (Williams et al., Reference Williams, Cavada and Reinoso-Suarez1993) and, since cortical visual acuity is strongly correlated with the number of LGN afferents (R 2 = 0.98, Fig. 1), Spanish wildcats should also have higher cortical visual acuity than domestic cats. Therefore, while the number of retinal ganglion cells is a good predictor of cortical visual acuity when comparing animals that are closely related, the number of LGN afferents is a better predictor when comparing species with different visual needs such as rabbits and cats. The mismatch between cortical visual acuity and retinal ganglion cell number is likely explained by differences in visual behavior. Although rabbits have more retinal ganglion cells than cats, most of these cells are likely to serve other functions than visual acuity, such as generating quick reflexes to run away from moving targets that abruptly enter their extensive visual field (Table 2) or avoiding collisions when running at fast speeds away from predators. Cats have fewer retinal ganglion cells than rabbits but more of them project to the LGN (Wassle & Illing, Reference Wassle and Illing1980; Vaney et al., Reference Vaney, Peichl, Wassle and Illing1981), which explains why the LGN volume is ∼3 times larger (Table 1) and the cortical visual acuity ∼4 times higher in cats than rabbits (Table 2).

Table 2.

Cortical mapping of visual space in different animals: primates, carnivores, scandentia, lagomorphs, and rodents. Columns 1–2, 5, 9–10 are taken from references cited below. Column 3 is calculated as column 2 divided by column 1 times 100; column 4 is calculated as column 8 from Table 1 divided by column 1 in this table; column 6 is calculated as column 4 times column 5; column 7 is calculated as column 1 divided by column 5; column 8 is calculated as column 2 divided by column 5. Columns are numbered from left to right as in Table 1. Macaque: (Adams et al., Reference Adams, Sincich and Horton2007) for [1], [2]; (Levitt et al., Reference Levitt, Schumer, Sherman, Spear and Movshon2001) for [5]; (De Valois et al., Reference De Valois, Albrecht and Thorell1982) for [9]; (Hubel & Wiesel, Reference Hubel and Wiesel1968) for [10]. Cat: (Sherman, Reference Sherman1974) for [1], [2]; (Cai et al., Reference Cai, Deangelis and Freeman1997) for [5]; (Movshon et al., Reference Movshon, Thompson and Tolhurst1978) for [9]; (Hubel & Wiesel, Reference Hubel and Wiesel1962) for [10]. Tree shrew: (Kaas et al., Reference Kaas, Hall, Killackey and Diamond1972) for [1], [2]; (Holdefer & Norton, Reference Holdefer and Norton1995) for [5]; (Johnson et al., Reference Johnson, Van Hooser and Fitzpatrick2010) for [9]; (Humphrey & Norton, Reference Humphrey and Norton1980) for [10]. Ferret: (Law et al., Reference Law, Zahs and Stryker1988) for [1], [2]; (Zahs & Stryker, Reference Zahs and Stryker1985) for [5]; (Baker et al., Reference Baker, Thompson, Krug, Smyth and Tolhurst1998) for [9]; (Law et al., Reference Law, Zahs and Stryker1988) for [10]. Rabbit: (Hughes, Reference Hughes1971) for [1], [2]; (Bezdudnaya et al., Reference Bezdudnaya, Cano, Bereshpolova, Stoelzel, Alonso and Swadlow2006) for [5]; (Zhuang et al., Reference Zhuang, Stoelzel, Bereshpolova, Huff, Hei, Alonso and Swadlow2013) for [9]; (Murphy & Berman, Reference Murphy and Berman1979) for [10]. Mouse: (Scholl et al., Reference Scholl, Burge and Priebe2013) for [1]; (Heesy, Reference Heesy2004) for [2]; (Tang et al., Reference Tang, Ardila Jimenez, Chakraborty and Schultz2016) for [5]; (Niell & Stryker, Reference Niell and Stryker2008) for [9]; (Drager, Reference Drager1975) for [10]. RF: receptive field

While the extent of the rabbit visual field is enormous and approaches 360 deg [(Hughes, Reference Hughes1971), Table 2], animals that need to hunt for prey (or search for small fruits) compromise the size of their visual fields to enhance binocular vision and visual acuity. This evolutionary process explains the strong correlation between cortical visual acuity and binocular field size (Fig. 1) and why the ratio between V1 size and visual field size is two orders of magnitude larger in macaques (5.95 mm2/deg) than mice (0.01 mm2/deg, Table 2). Because LGN central receptive fields are much larger in mice (6.5 deg, Table 2) than macaques (0.09 deg in parafovea and even smaller in fovea), tiling a horizontal line across the mouse visual field can be done with just 49 LGN receptive fields but it requires 2222 LGN parafoveal receptive fields in the macaque (Table 2, Fig. 2a). Similarly, a horizontal line across the binocular field requires just 6 LGN receptive fields in the mouse but 1556 in the macaque (Table 2). Interestingly, cortical visual acuity is slightly better correlated with the number of receptive fields required to tile a horizontal line in the binocular field (Table 2, R 2 = 0.87, power law with 1.27 exponent) than the tiling for the entire visual field (R 2 = 0.74, power law with 0.79 exponent), which highlights once more the intimate relation between binocular vision and cortical visual acuity.

Fig. 2.

Thalamocortical networks underlying the organization of visual cortical maps. (a) Number of non-overlapping LGN receptive fields needed to tile a horizontal line across the visual field (shown on top left corner of each rectangle, taken from column 7 in Table 2). Only 1/10 of the horizontal visual field is represented for illustration purposes. (b) Number of nonoverlapping LGN-axon-patches needed to tile a horizontal line through area V1 (shown on top left corner of each rectangle, taken from column 3 in Table 3). Green and orange colors represent two different afferents. (c) The largest spacing between two LGN-axon-patches covering one LGN receptive field in visual cortex (shown on top left corner of each rectangle, taken from column 5 in Table 3). For simplicity, axon patches are represented as squares. (d) Cortical optimal visual acuity across 11 different species (values obtained from the references cited below). Colored circles indicate pinwheel orientation maps in visual cortex. Mouse: (Niell & Stryker, Reference Niell and Stryker2008). Rat, Gray squirrel, Bush baby, Owl monkey: (Heimel et al., Reference Heimel, Van Hooser and Nelson2005). Rabbit: (Zhuang et al., Reference Zhuang, Stoelzel, Bereshpolova, Huff, Hei, Alonso and Swadlow2013). Ferret: (Baker et al., Reference Baker, Thompson, Krug, Smyth and Tolhurst1998). Tree shrew: (Johnson et al., Reference Johnson, Van Hooser and Fitzpatrick2010). Marmoset: (Forte et al., Reference Forte, Hashemi-Nezhad, Dobbie, Dreher and Martin2005). Cat: (Movshon et al., Reference Movshon, Thompson and Tolhurst1978). Macaque: (De Valois et al., Reference De Valois, Albrecht and Thorell1982). VF: visual field. RF: receptive field.

Overexpansion of the V1 thalamocortical network

Although the reduction in LGN receptive field size from mice to macaques is remarkable, the overexpansion of area V1 is even more pronounced. The ratio between V1 size and LGN receptive field size is almost one order of magnitude larger in the macaque (0.54 mm2/RF) than in the mouse (0.08 mm2/RF, Table 2) and, because the average area of an LGN axon patch is similar (mice: 0.24 mm2, macaques: 0.23 mm2, averaging all axons in mouse and parvocellular/magnocellular axons in macaques), the axon patches of LGN afferents with overlapping receptive fields must overlap in mouse cortex but can be horizontally segregated in macaque cortex (Table 3). Similarly, nearly all LGN afferents with overlapping receptive fields project to the same cortical point in rabbits [>90%, (Stoelzel et al., Reference Stoelzel, Bereshpolova, Gusev and Swadlow2008)] but at least 40% project to different cortical points in cats (Jin et al., Reference Jin, Weng, Yeh, Gordon, Ruthazer, Stryker, Swadlow and Alonso2008; Jin et al., Reference Jin, Wang, Swadlow and Alonso2011).

Table 3.

Comparison of LGN axon-patch density across different animals: primates, carnivores, scandentia, and rodents. Column 1 is taken from references cited below. Column 2 is calculated as the square root of column 1 times 1000; column 3 is calculated as column 8 in Table 1 divided by column 1 in this table; column 4 is calculated as column 6 in Table 1 times column 1 in this table times one million and divided by column 8 in Table 1; column 5 is calculated as the square root of the ratio between column 6 of Table 2 and column 1 of this table. Columns are numbered from left to right as in Table 1. Macaque: (Blasdel & Lund, Reference Blasdel and Lund1983) for [1]. Cat (area 17): (Humphrey et al., Reference Humphrey, Sur, Uhlrich and Sherman1985a) for [1]. Tree shrew: (Raczkowski & Fitzpatrick, Reference Raczkowski and Fitzpatrick1990) for [1]. Mouse: (Antonini et al., Reference Antonini, Fagiolini and Stryker1999) for [1]. RF: receptive field

Our understanding of the horizontal organization of the thalamocortical axon patches in cortex is still very limited in great part because reconstructions from single LGN axons are scarce. However, available measurements from macaques (Blasdel & Lund, Reference Blasdel and Lund1983), cats (Freund et al., Reference Freund, Martin and Whitteridge1985; Humphrey et al., Reference Humphrey, Sur, Uhlrich and Sherman1985a; Humphrey et al., Reference Humphrey, Sur, Uhlrich and Sherman1985b), tree shrews (Fitzpatrick & Raczkowski, Reference Fitzpatrick and Raczkowski1990; Raczkowski & Fitzpatrick, Reference Raczkowski and Fitzpatrick1990), and mice (Antonini et al., Reference Antonini, Fagiolini and Stryker1999) allow some rough estimates of axon patch coverage and spacing (Table 3). These data indicate that just 17 non-overlapping LGN-axon-patches are enough to tile a line across area V1 in mice but 5170 are needed to tile a line across area V1 in macaques (Table 3, Fig. 2b). Consequently, the largest horizontal separation between two LGN axon patches with overlapping receptive fields is less than one axon patch in mice but ∼2 axon patches in tree shrews, cats, and macaques (Table 3, Fig. 2c).

Based on these estimates, we propose that frequent separating gaps between axon patches of LGN afferents with overlapping receptive fields are a unique feature of animals with high visual acuity and have important consequences for the organization of visual cortical maps. In mice, area V1 is not large enough to allow many horizontal gaps between thalamic axon patches with overlapping receptive fields (Table 3) and the density of thalamic afferents is very high (5000 axons/mm2, Table 1). In contrast, area V1 in macaques and cats can accommodate many thalamic afferents with overlapping receptive fields that do not have overlapped patches in cortex (Table 3) and the density of thalamic afferents is ∼5 times lower than in mice (Table 1). The separating gaps between LGN axon patches with overlapping receptive fields allow sorting the thalamic afferents within the cortex by functional properties that are not just spatial position but also eye input and dark/light polarity. We argue that this more precise thalamic sorting causes a major reorganization of the visual cortical map.

Pinwheel cortical maps for stimulus orientation

The spacing between thalamic axon patches with overlapping receptive fields is large enough in primates, carnivores, and scandentia (∼2 axon patches, Table 3) to allow clustering afferents of the same type within different cortical domains (e.g., by eye input and contrast polarity). As demonstrated by recent results (Kremkow et al., Reference Kremkow, Jin, Wang and Alonso2016; Lee et al., Reference Lee, Huang and Fitzpatrick2016), these thalamic clusters tend to rotate around each other probably to minimize the amount of wiring and cortical volume needed to represent each visual point (Mitchison, Reference Mitchison1991; Cajal, Reference Cajal1995; Chklovskii et al., Reference Chklovskii, Schikorski and Stevens2002). For example, changes in orientation preference within a horizontal track of cat visual cortex are associated with modest changes in receptive field position for one contrast polarity (OFF in Fig. 3, top row of receptive fields) and a rotation in receptive field position for the opposite polarity (ON in Fig. 3, middle row of receptive fields). In this example, the OFF receptive fields anchor the spatial position represented at the cortical domain while the ON receptive fields rotate with the changes in orientation preference (Fig. 3, bottom row of receptive fields).

Fig. 3.

Changes in orientation preference and receptive field position along a horizontal track of cat visual cortex. From top to bottom, the rows show OFF receptive fields mapped with dark stimuli (in blue), ON receptive fields mapped with light stimuli (in red), the ON–OFF receptive field difference (diff.), and the orientation/direction preference measured with moving bars (circles show the orientation preference predicted from the receptive field maps). From left to right, the columns show cortical measurements separated by 0.1 mm from 0 to 1.2 mm distance. The column on the right shows the receptive field average along the entire horizontal track (top three receptive fields) and the central positions of ON (red circles) and OFF receptive fields (blue circles). The position of these ON and OFF cortical receptive fields is determined by the receptive field population of ON and OFF thalamic afferents. Reproduced from Kremkow et al. (Reference Kremkow, Jin, Wang and Alonso2016).

It remains unknown if such ON–OFF rotation in receptive field position is due to the mosaic organization of ON and OFF ganglion cells in the retina (Wassle et al., Reference Wassle, Boycott and Illing1981; Soodak, Reference Soodak1987; Paik & Ringach, Reference Paik and Ringach2011) or the stronger correlated firing between ON and OFF thalamic afferents (Goodhill, Reference Goodhill1993; Miller, Reference Miller1994; Goodhill & Lowel, Reference Goodhill and Lowel1995; Nakagama et al., Reference Nakagama, Saito and Tanaka2000). However, it is now clear that the ON–OFF receptive field rotation is closely related (Kremkow et al., Reference Kremkow, Jin, Wang and Alonso2016; Lee et al., Reference Lee, Huang and Fitzpatrick2016) to the pinwheel organization of cortical orientation maps [Fig. 4, (Bonhoeffer & Grinvald, Reference Bonhoeffer and Grinvald1991; Blasdel, Reference Blasdel1992; Bosking et al., Reference Bosking, Zhang, Schofield and Fitzpatrick1997; Ohki et al., Reference Ohki, Chung, Kara, Hubener, Bonhoeffer and Reid2006)], and that such pinwheel organization allows a more efficient processing of visual textures in animals with high visual acuity (Goris et al., Reference Goris, Simoncelli and Movshon2015; Koch et al., Reference Koch, Jin, Alonso and Zaidi2016). Therefore, given the close relationship between visual acuity, ON–OFF receptive field position and the pinwheel organization of cortical orientation preference, we would like to propose that pinwheel orientation maps originate when area V1 expands enough to generate high visual acuity and thalamic clustering by eye input and ON–OFF polarity.

Fig. 4.

Pinwheel pattern of the cortical map for stimulus orientation. Colors illustrate the different orientations represented on the cortical surface (cat area 17, 2 × 2 mm). A square of 0.5 × 0.5 mm is shown centered on a cortical pinwheel.

While this proposal still needs to be rigorously tested, it is consistent with available data. First, as shown here, pinwheel orientation maps emerge as cortical visual acuity increases and area V1 becomes larger (Table 2, Fig. 2d). Second, pinwheel orientation maps are not restricted to mammals but include other animals with high visual acuity. For example, barn owls have similar visual acuity to cats (Orlowski et al., Reference Orlowski, Harmening and Wagner2012) and also have pinwheel orientation maps in their visual Wulst, the avian brain structure that receives the bulk of thalamic afferents (Liu & Pettigrew, Reference Liu and Pettigrew2003). Third, all animals that have thalamic afferents sorted by eye input in area V1 (ocular dominance columns) also have pinwheel orientation maps, and the spacing between ocular dominance and pinwheel centers is remarkably similar (Table 4). Interestingly, the spacing of both ocular dominance columns and pinwheel centers is narrowly restricted to a range of about 0.4 to 0.5 mm in most animals (Table 4), and the thalamic clusters representing a single whisker in the somatosensory cortex of rodents and lagomorphs are also ∼0.5 mm wide (Woolsey & Van der Loos, Reference Woolsey and Van Der Loos1970; Bosman et al., Reference Bosman, Houweling, Owens, Tanke, Shevchouk, Rahmati, Teunissen, Ju, Gong, Koekkoek and De Zeeuw2011), which suggests a common organizing principle of sensory cortical topography. Moreover, although the thalamic axon spacing is narrowly constrained to 0.4–0.5 mm in most animals, exceptions of larger spacing (wider ocular dominance columns) are usually associated with larger distance between pinwheel centers (Table 4). For example, the cat visual cortex has several areas receiving thalamic input, and the area that has the wider ocular dominance columns, area 18, has also the larger pinwheel spacing (Table 4). Our proposal also predicts that the direction maps in the superior colliculus of frogs implanted with a third eye should be organized in a pinwheel pattern because ocular dominance bands emerge with the arrival of inputs from the third eye (Constantine-Paton & Law, Reference Constantine-Paton and Law1978), however, this prediction has not yet been tested.

Table 4.

Cortical spacing for ocular dominance and orientation pinwheels in area V1 of primates, carnivores, scandentia, and rodents. Column 2 is calculated as the square root of 1/pinwheel density. References for the column on the left [1] and on the right [2] are provided below. Human: (Adams et al., Reference Adams, Sincich and Horton2007) for [1]; (Yacoub et al., Reference Yacoub, Harel and Ugurbil2008) for [2]. Macaque: (Adams et al., Reference Adams, Sincich and Horton2007) for [1]; (Obermayer & Blasdel, Reference Obermayer and Blasdel1993) for [2]. Cat area 17: (Lowel, Reference Lowel1994) for [1]; (Bonhoeffer et al., Reference Bonhoeffer, Kim, Malonek, Shoham and Grinvald1995) for [2]. Cat area 18: (Lowel, Reference Lowel1994) for [1]; (Bonhoeffer & Grinvald, Reference Bonhoeffer and Grinvald1993) for [2]. Owl monkey: (Takahata et al., Reference Takahata, Miyashita, Tanaka and Kaas2014) for [1]; (Xu et al., Reference Xu, Bosking, Sary, Stefansic, Shima and Casagrande2004) for [2]. Bush baby: (Xu et al., Reference Xu, Bosking, White, Fitzpatrick and Casagrande2005) for [1], [2]. Marmoset: (Roe et al., Reference Roe, Fritsches and Pettigrew2005) for [1]; (Liu & Pettigrew, Reference Liu and Pettigrew2003) for [2]. Squirrel monkey: (Adams & Horton, Reference Adams and Horton2003) for [1]; (Obermayer & Blasdel, Reference Obermayer and Blasdel1997) for [2]. Ferret: (Law et al., Reference Law, Zahs and Stryker1988) for [1]; (Rao et al., Reference Rao, Toth and Sur1997) for [2]

Pinwheel orientation maps are found in animals with large V1 areas and LGN volumes such as macaques (1189 mm2, 77 mm3) and cats (380 mm2, 19.4 mm3) but also in tree shrews, which have a much smaller LGN volume than rabbits (2.4 mm3 versus 6 mm3, Table 1). In spite of their small LGN volume, however, tree shrews are more similar to carnivores and primates than lagomorphs in that they have large binocular fields of vision and low V1 thalamic density. The binocular field of tree shrews is similar to ferrets and two times larger than rabbits (60 deg, Table 2), and their V1 thalamic density is lower than in ferrets, rabbits, and mice (1515 axons/mm2, Table 1). The thalamic axon patches of tree shrews also have a remarkably small area (0.08 mm2, Table 3), which is almost as small as the parvocellular axon patches of the macaque [0.07 mm2, (Blasdel & Lund, Reference Blasdel and Lund1983)]. The thalamocortical organization of the tree shrew is very intriguing and, in many aspects, is radically different from that of primates, carnivores, lagomorphs, and rodents. The thalamic axon patches of tree shrews are extremely asymmetric in shape; they can spread as much 0.9 mm in the lateral dimension but only 0.14 mm in the anteroposterior dimension (Raczkowski & Fitzpatrick, Reference Raczkowski and Fitzpatrick1990). They also show a pronounced inter-ocular asymmetry, with thalamic axons from the ipsilateral eye being 3 times wider in lateral extent (and less abundant) than axons from the contralateral eye (Raczkowski & Fitzpatrick, Reference Raczkowski and Fitzpatrick1990). Finally, the thalamic afferents of tree shrews are segregated by eye input and ON–OFF polarity through the depth of the middle layers of the cortex instead of horizontally as in carnivores and primates (Harting et al., Reference Harting, Diamond and Hall1973; Hubel, Reference Hubel1975; Norton et al., Reference Norton, Rager and Kretz1985).

It is unclear why the thalamocortical pathway of tree shrews is so different. An attractive hypothesis is that cortical maps are optimized to keep all thalamic axons with overlapping receptive fields as close as possible within area V1 (Hubel & Wiesel, Reference Hubel and Wiesel1977; Durbin & Mitchison, Reference Durbin and Mitchison1990; Swindale et al., Reference Swindale, Shoham, Grinvald, Bonhoeffer and Hubener2000; Carreira-Perpinan & Goodhill, Reference Carreira-Perpinan and Goodhill2002; Kremkow et al., Reference Kremkow, Jin, Wang and Alonso2016), and this optimization requires different compromises in different animals. Perhaps, because area V1 in tree shrews is so small (66 mm2, Table 1), the best compromise is to make the thalamic axon patches as restricted as possible, sort them through the depth of layer 4, and reduce the number of ipsilateral axons while compensating the reduction by increasing its patch size. Carnivores may reach a different compromise to have both high visual acuity to search for prey and high-quality vision while running at fast speeds to hunt (110 km/h in the cheetah). The best compromise for cats may be to accommodate thalamic afferents in separate cortical areas, a large one specialized in visual acuity [area 17: 380 mm2, (Tusa et al., Reference Tusa, Palmer and Rosenquist1978)] and a smaller one specialized in processing fast movement [area 18: 98 mm2, (Tusa et al., Reference Tusa, Rosenquist and Palmer1979)]. Because running creates motion blur, cats may have a special need for Y thalamic afferents with large receptive fields and fast response latencies (Cleland et al., Reference Cleland, Dubin and Levick1971) that are also common in other animals including rodents, lagomorphs, scandentia (Sherman et al., Reference Sherman, Norton and Casagrande1975; Swadlow & Weyand, Reference Swadlow and Weyand1985; Price & Morgan, Reference Price and Morgan1987; Krahe et al., Reference Krahe, El-Danaf, Dilger, Henderson and Guido2011), and primates (Schiller & Malpeli, Reference Schiller and Malpeli1978; Kaplan & Shapley, Reference Kaplan and Shapley1982). However, the receptive fields of Y afferents projecting to area 18 in cats are 2–3 times larger than those from afferents projecting to area 17 (Yeh et al., Reference Yeh, Stoelzel and Alonso2003). Therefore, such Y afferents would have to cover a huge cortical region if they projected to area 17 (∼2 mm2/deg, Table 2) but can remain more restricted (although still large) within area 18, which is ∼4 times smaller and has a much coarser retinotopic precision (∼0.5 mm2/deg). Interestingly, in spite of such unique cortical specialization, thalamic axon patches are larger in cats than in any other animal studied to date (Table 3, Fig. 2).

Concluding remarks

The thalamocortical pathway plays an important role in building visual cortical maps and maximizing visual spatial resolution within the cerebral cortex. During evolution, the expansion of primary visual cortex is associated with an increase in visual acuity, a reduction in the cortical density of thalamic afferents, and an increase in the cortical separation between axon patches of thalamic afferents with overlapping receptive fields. This increased separation allows sorting thalamic afferents within the visual cortex not only by their spatial position but also by their eye input and contrast polarity (ON or OFF). We propose that this increased separation between thalamic axon patches, and the resulting thalamic sorting, leads to a major reorganization of the visual cortical map and allows for the emergence of a pinwheel pattern in the cortical representation of stimulus orientation. While this proposal is consistent with available data, anatomical reconstructions from single thalamic axons remain unfortunately scarce, which limits our ability to build realistic models of cortical architecture. It is surprising that, in the era of the connectome, we still have to rely on heroic reconstructions from single thalamic axons obtained more than two decades ago with more limited resources. Advancing progress in understanding thalamocortical processing would greatly benefit from a renewed effort to fully reconstruct the thalamocortical network, which provides the structural framework for visual function.

Acknowledgment

Supported by NIH grants EY027157 (RM) and EY05253 (JMA).

References

Adams, , , D.L. & Horton, , , J.C. (2003). Capricious expression of cortical columns in the primate brain. Nature Neuroscience 6, 113–114.CrossRefGoogle ScholarPubMed
Adams, , , D.L., Sincich, , , L.C. & Horton, , , J.C. (2007). Complete pattern of ocular dominance columns in human primary visual cortex. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 27, 10391–10403.CrossRefGoogle ScholarPubMed
Andrews, , , T.J., Halpern, , , S.D. & Purves, D. (1997). Correlated size variations in human visual cortex, lateral geniculate nucleus, and optic tract. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 17, 2859–2868.Google Scholar
Antonini, , , A., Fagiolini, , , M. & Stryker, , , M.P. (1999). Anatomical correlates of functional plasticity in mouse visual cortex. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 19, 4388–4406.Google Scholar
Baker, , , G.E., Thompson, , , I.D., Krug, , , K., Smyth, , , D. & Tolhurst, , , D.J. (1998). Spatial-frequency tuning and geniculocortical projections in the visual cortex (areas 17 and 18) of the pigmented ferret. The European Journal of Neuroscience 10, 2657–2668.Google Scholar
Bezdudnaya, , , T., Cano, , , M., Bereshpolova, , , Y., Stoelzel, , , C.R., Alonso, , , J.M. & Swadlow, , , H.A. (2006). Thalamic burst mode and inattention in the awake LGNd. Neuron 49, 421–432.CrossRefGoogle ScholarPubMed
Blasdel, G.G. (1992). Orientation selectivity, preference, and continuity in monkey striate cortex. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 12, 3139–3161.CrossRefGoogle ScholarPubMed
Blasdel, , , G.G. & Lund, , , J.S. (1983). Termination of afferent axons in macaque striate cortex. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 3, 1389–1413.CrossRefGoogle ScholarPubMed
Bonhoeffer, , , T. & Grinvald, A. (1991). Iso-orientation domains in cat visual cortex are arranged in pinwheel-like patterns. Nature 353, 429–431.Google Scholar
Bonhoeffer, , , T. & Grinvald, A. (1993). The layout of iso-orientation domains in area 18 of cat visual cortex: Optical imaging reveals a pinwheel-like organization. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 13, 4157–4180.Google Scholar
Bonhoeffer, , , T., Kim, , , D.S., Malonek, , , D., Shoham, , , D. & Grinvald, A. (1995). Optical imaging of the layout of functional domains in area 17 and across the area 17/18 border in cat visual cortex. The European Journal of Neuroscience 7, 1973–1988.CrossRefGoogle ScholarPubMed
Bosking, , , W.H., Zhang, , , Y., Schofield, , , B. & Fitzpatrick, D. (1997). Orientation selectivity and the arrangement of horizontal connections in tree shrew striate cortex. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 17, 2112–2127.CrossRefGoogle ScholarPubMed
Bosman, , , L.W., Houweling, , , A.R., Owens, , , C.B., Tanke, , , N., Shevchouk, , , O.T., Rahmati, , , N., Teunissen, , , W.H., Ju, , , C., Gong, , , W., Koekkoek, , , S.K. & De Zeeuw, C.I. (2011). Anatomical pathways involved in generating and sensing rhythmic whisker movements. Frontiers in Integrative Neuroscience 5, 53.Google Scholar
Cai, , , D., Deangelis, , , G.C. & Freeman, , , R.D. (1997). Spatiotemporal receptive field organization in the lateral geniculate nucleus of cats and kittens. Journal of Neurophysiology 78, 1045–1061.Google Scholar
Cajal, S.R. (1995). Histology of the Nervous System. Vol. 1. New York: Oxford University Press.CrossRefGoogle Scholar
Carreira-Perpinan, , , M.A. & Goodhill, , , G.J. (2002). Are visual cortex maps optimized for coverage? Neural Computation 14, 1545–1560.CrossRefGoogle ScholarPubMed
Chklovskii, D.B., Schikorski, , , T. & Stevens, , , C.F. (2002). Wiring optimization in cortical circuits. Neuron 34, 341–347.Google Scholar
Cleland, , , B.G., Dubin, , , M.W. & Levick, , , W.R. (1971). Sustained and transient neurones in the cat’s retina and lateral geniculate nucleus. The Journal of Physiology 217, 473–496.CrossRefGoogle ScholarPubMed
Connolly, , , M. & Van Essen, D. (1984). The representation of the visual field in parvicellular and magnocellular layers of the lateral geniculate nucleus in the macaque monkey. The Journal of Comparative Neurology 226, 544–564.Google Scholar
Constantine-Paton, , , M. & Law, , , M.I. (1978). Eye-specific termination bands in tecta of three-eyed frogs. Science 202, 639–641.Google Scholar
Conway, , , J.L. & Schiller, , , P.H. (1983). Laminar organization of tree shrew dorsal lateral geniculate nucleus. Journal of Neurophysiology 50, 1330–1342.Google Scholar
Curcio, , , C.A. & Allen, , , K.A. (1990). Topography of ganglion cells in human retina. The Journal of Comparative Neurology 300, 5–25.Google Scholar
De Valois, , , R.L., Albrecht, , , D.G. & Thorell, , , L.G. (1982). Spatial frequency selectivity of cells in macaque visual cortex. Vision Research 22, 545–559.Google Scholar
Drager, U.C. (1975). Receptive fields of single cells and topography in mouse visual cortex. The Journal of Comparative Neurology 160, 269–290.Google Scholar
Drager, , , U.C. & Olsen, , , J.F. (1981). Ganglion cell distribution in the retina of the mouse. Investigative Ophthalmology & Visual Science 20, 285–293.Google ScholarPubMed
Drenhaus, , , U., Von Gunten, , A. & Rager, G. (1997). Classes of axons and their distribution in the optic nerve of the tree shrew (Tupaia belangeri). The Anatomical Record 249, 103–116.Google Scholar
Durbin, , , R. & Mitchison, G. (1990). A dimension reduction framework for understanding cortical maps. Nature 343, 644–647.Google Scholar
Engelmann, , , R. & Peichl, L. (1996). Unique distribution of somatostatin-immunoreactive cells in the retina of the tree shrew (Tupaia belangeri). The European Journal of Neuroscience 8, 220–228.Google Scholar
Fitzpatrick, , , D. & Raczkowski, D. (1990). Innervation patterns of single physiologically identified geniculocortical axons in the striate cortex of the tree shrew. Proceedings of the National Academy of Sciences of the United States of America 87, 449–453.Google Scholar
Forte, , , J.D., Hashemi-Nezhad, , , M., Dobbie, , , W.J., Dreher, , , B. & Martin, , , P.R. (2005). Spatial coding and response redundancy in parallel visual pathways of the marmoset Callithrix jacchus. Visual Neuroscience 22, 479–491.Google Scholar
Freund, , , T.F., Martin, , , K.A. & Whitteridge, D. (1985). Innervation of cat visual areas 17 and 18 by physiologically identified X- and Y-type thalamic afferents. I. Arborization patterns and quantitative distribution of postsynaptic elements. The Journal of Comparative Neurology 242, 263–274.Google Scholar
Garrett, , , M.E., Nauhaus, , , I., Marshel, , , J.H. & Callaway, , , E.M. (2014). Topography and areal organization of mouse visual cortex. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 34, 12587–12600.CrossRefGoogle ScholarPubMed
Gautschi, , , M. & Clarke, , , P.G. (2007). Neuronal death in the lateral geniculate nucleus of young ferrets following a cortical lesion: Time-course, age dependence and involvement of caspases. Brain Research 1167, 20–30.Google Scholar
Goodhill, G.J. (1993). Topography and ocular dominance: A model exploring positive correlations. Biological Cybernetics 69, 109–118.Google Scholar
Goodhill, , , G.J. & Lowel, S. (1995). Theory meets experiment: Correlated neural activity helps determine ocular dominance column periodicity. Trends in Neurosciences 18, 437–439.Google Scholar
Goris, , , R.L., Simoncelli, , , E.P. & Movshon, , , J.A. (2015). Origin and function of tuning diversity in macaque visual cortex. Neuron 88, 819–831.Google Scholar
Harting, , , J.K., Diamond, , , I.T. & Hall, , , W.C. (1973). Anterograde degeneration study of the cortical projections of the lateral geniculate and pulvinar nuclei in the tree shrew (Tupaia glis). The Journal of Comparative Neurology 150, 393–440.CrossRefGoogle ScholarPubMed
Heard-Booth, , , A.N. & Kirk, , , E.C. (2012). The influence of maximum running speed on eye size: A test of Leuckart’s law in mammals. Anatomical Record 295, 1053–1062.Google Scholar
Heesy, C.P. (2004). On the relationship between orbit orientation and binocular visual field overlap in mammals. The Anatomical Record. Part A, Discoveries in Molecular, Cellular, and Evolutionary Biology 281, 1104–1110.Google Scholar
Heimel, , , J.A., Van Hooser, , , S.D. & Nelson, , , S.B. (2005). Laminar organization of response properties in primary visual cortex of the gray squirrel (Sciurus carolinensis). Journal of Neurophysiology 94, 3538–3554.Google Scholar
Henderson, Z. (1985). Distribution of ganglion cells in the retina of adult pigmented ferret. Brain Research 358, 221–228.CrossRefGoogle ScholarPubMed
Holdefer, , , R.N. & Norton, , , T.T. (1995). Laminar organization of receptive field properties in the dorsal lateral geniculate nucleus of the tree shrew (Tupaiaglis belangeri). The Journal of Comparative Neurology 358, 401–413.CrossRefGoogle ScholarPubMed
Howland, , , H.C., Merola, , , S. & Basarab, , , J.R. (2004). The allometry and scaling of the size of vertebrate eyes. Vision Research 44, 2043–2065.Google Scholar
Hubel, D.H. (1975). An autoradiographic study of the retino-cortical projections in the tree shrew (Tupaia glis). Brain Research 96, 41–50.Google Scholar
Hubel, , , D.H. & Wiesel, , , T.N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology 160, 106–154.Google Scholar
Hubel, , , D.H. & Wiesel, , , T.N. (1968). Receptive fields and functional architecture of monkey striate cortex. The Journal of Physiology 195, 215–243.Google Scholar
Hubel, , , D.H. & Wiesel, , , T.N. (1977). Ferrier lecture. Functional architecture of macaque monkey visual cortex. Proceedings of the Royal Society of London. Series B, Biological Sciences 198, 1–59.Google ScholarPubMed
Hughes, A. (1971). Topographical relationships between the anatomy and physiology of the rabbit visual system. Documenta Ophthalmologica 30, 33–159.Google Scholar
Hughes, A. (1975). A quantitative analysis of the cat retinal ganglion cell topography. The Journal of Comparative Neurology 163, 107–128.CrossRefGoogle ScholarPubMed
Hughes, A. (1977). The topography of vision in mammals of contrasting life style: Comparative optics and retinal organisation. In Handbook of Sensory Physiology, VII/5: The Visual System in Vertebrates, ed. Crescitelli, F., pp. 613–756. Berlin: Springer-Verlag.Google Scholar
Hughes, , , A. & Wassle, H. (1976). The cat optic nerve: Fibre total count and diameter spectrum. The Journal of Comparative Neurology 169, 171–184.Google Scholar
Humphrey, , , A.L. & Norton, , , T.T. (1980). Topographic organization of the orientation column system in the striate cortex of the tree shrew (Tupaia glis). I. Microelectrode recording. The Journal of Comparative Neurology 192, 531–547.Google Scholar
Humphrey, , , A.L., Sur, , , M., Uhlrich, , , D.J. & Sherman, , , S.M. (1985a). Projection patterns of individual X- and Y-cell axons from the lateral geniculate nucleus to cortical area 17 in the cat. The Journal of Comparative Neurology 233, 159–189.CrossRefGoogle ScholarPubMed
Humphrey, , , A.L., Sur, , , M., Uhlrich, , , D.J. & Sherman, , , S.M. (1985b). Termination patterns of individual X- and Y-cell axons in the visual cortex of the cat: Projections to area 18, to the 17/18 border region, and to both areas 17 and 18. The Journal of Comparative Neurology 233, 190–212.Google Scholar
Jeon, , , C.J., Strettoi, , , E. & Masland, , , R.H. (1998). The major cell populations of the mouse retina. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 18, 8936–8946.CrossRefGoogle ScholarPubMed
Jin, , , J., Wang, , , Y., Swadlow, , , H.A. & Alonso, , , J.M. (2011). Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex. Nature Neuroscience 14, 232–238.Google Scholar
Jin, , , J.Z., Weng, , , C., Yeh, , , C.I., Gordon, , , J.A., Ruthazer, , , E.S., Stryker, , , M.P., Swadlow, , , H.A. & Alonso, , , J.M. (2008). On and off domains of geniculate afferents in cat primary visual cortex. Nature Neuroscience 11, 88–94.CrossRefGoogle ScholarPubMed
Johnson, , , E.N., Van Hooser, , S.D. & Fitzpatrick, D. (2010). The representation of S-cone signals in primary visual cortex. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 30, 10337–10350.Google Scholar
Kaas, , , J.H., Hall, , , W.C., Killackey, , , H. & Diamond, , , I.T. (1972). Visual cortex of the tree shrew (Tupaia glis): Architectonic subdivisions and representations of the visual field. Brain Research 42, 491–496.CrossRefGoogle ScholarPubMed
Kaplan, , , E. & Shapley, , , R.M. (1982). X and Y cells in the lateral geniculate nucleus of macaque monkeys. The Journal of Physiology 330, 125–143.CrossRefGoogle ScholarPubMed
Kim, , , C.B., Tom, , , B.W. & Spear, , , P.D. (1996). Effects of aging on the densities, numbers, and sizes of retinal ganglion cells in rhesus monkey. Neurobiology of Aging 17, 431–438.Google Scholar
Koch, , , E., Jin, , , J., Alonso, , , J.M. & Zaidi, Q. (2016). Functional implications of orientation maps in primary visual cortex. Nature Communications 7, 13529.Google Scholar
Kong, , , X., Wang, , , K., Sun, , , X. & Witt, , , R.E. (2010). Comparative study of the retinal vessel anatomy of rhesus monkeys and humans. Clinical & Experimental Ophthalmology 38, 629–634.Google Scholar
Krahe, , , T.E., El-Danaf, , , R.N., Dilger, , , E.K., Henderson, , , S.C. & Guido, W. (2011). Morphologically distinct classes of relay cells exhibit regional preferences in the dorsal lateral geniculate nucleus of the mouse. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 31, 17437–17448.Google Scholar
Kremkow, , , J., Jin, , , J., Wang, , , Y. & Alonso, , , J.M. (2016). Principles underlying sensory map topography in primary visual cortex. Nature 533, 52–57.Google Scholar
Law, , , M.I., Zahs, , , K.R. & Stryker, , , M.P. (1988). Organization of primary visual cortex (area 17) in the ferret. The Journal of Comparative Neurology 278, 157–180.Google Scholar
Lee, , , K.S., Huang, , , X. & Fitzpatrick, D. (2016). Topology of ON and OFF inputs in visual cortex enables an invariant columnar architecture. Nature 533, 90–94.Google Scholar
Levitt, , , J.B., Schumer, , , R.A., Sherman, , , S.M., Spear, , , P.D. & Movshon, , , J.A. (2001). Visual response properties of neurons in the LGN of normally reared and visually deprived macaque monkeys. Journal of Neurophysiology 85, 2111–2129.Google Scholar
Liu, , , G.B. & Pettigrew, , , J.D. (2003). Orientation mosaic in barn owl’s visual Wulst revealed by optical imaging: Comparison with cat and monkey striate and extra-striate areas. Brain Research 961, 153–158.Google Scholar
Lowel, S. (1994). Ocular dominance column development: Strabismus changes the spacing of adjacent columns in cat visual cortex. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 14, 7451–7468.Google Scholar
Malpeli, , , J.G. & Baker, , , F.H. (1975). The representation of the visual field in the lateral geniculate nucleus of Macaca mulatta. The Journal of Comparative Neurology 161, 569–594.Google Scholar
Miller, K.D. (1994). A model for the development of simple cell receptive fields and the ordered arrangement of orientation columns through activity-dependent competition between ON- and OFF-center inputs. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 14, 409–441.Google Scholar
Mitchison, G. (1991). Neuronal branching patterns and the economy of cortical wiring. Proceedings of the Royal Society B: Biological Sciences 245, 151–158.Google Scholar
Movshon, , , J.A., Thompson, , , I.D. & Tolhurst, , , D.J. (1978). Spatial and temporal contrast sensitivity of neurones in areas 17 and 18 of the cat’s visual cortex. The Journal of Physiology 283, 101–120.Google Scholar
Muly, , , E.C. & Fitzpatrick, D. (1992). The morphological basis for binocular and ON/OFF convergence in tree shrew striate cortex. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 12, 1319–1334.Google Scholar
Murphy, , , E.H. & Berman, N. (1979). The rabbit and the cat: A comparison of some features of response properties of single cells in the primary visual cortex. The Journal of Comparative Neurology 188, 401–427.CrossRefGoogle Scholar
Najdzion, , , J., Wasilewska, , , B., Bogus-Nowakowska, , , K., Rowniak, , , M., Szteyn, , , S. & Robak, A. (2009). A morphometric comparative study of the lateral geniculate body in selected placental mammals: The common shrew, the bank vole, the rabbit, and the fox. Folia Morphologica 68, 70–78.Google ScholarPubMed
Nakagama, , , H., Saito, , , T. & Tanaka, S. (2000). Effect of imbalance in activities between ON- and OFF-center LGN cells on orientation map formation. Biological Cybernetics 83, 85–92.Google Scholar
Niell, , , C.M. & Stryker, , , M.P. (2008). Highly selective receptive fields in mouse visual cortex. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 28, 7520–7536.Google Scholar
Norton, , , T.T., Rager, , , G. & Kretz, R. (1985). ON and OFF regions in layer IV of striate cortex. Brain Research 327, 319–323.Google Scholar
Obermayer, , , K. & Blasdel, , , G.G. (1993). Geometry of orientation and ocular dominance columns in monkey striate cortex. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 13, 4114–4129.Google Scholar
Obermayer, , , K. & Blasdel, , , G.G. (1997). Singularities in primate orientation maps. Neural Computation 9, 555–575.Google Scholar
Ohki, , , K., Chung, , , S., Kara, , , P., Hubener, , , M., Bonhoeffer, , , T. & Reid, , , R.C. (2006). Highly ordered arrangement of single neurons in orientation pinwheels. Nature 442, 925–928.Google Scholar
Orlowski, , , J., Harmening, , , W. & Wagner, H. (2012). Night vision in barn owls: Visual acuity and contrast sensitivity under dark adaptation. Journal of Vision 12, 4.Google Scholar
Oyster, , , C.W., Takahashi, , , E.S. & Hurst, , , D.C. (1981). Density, soma size, and regional distribution of rabbit retinal ganglion cells. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 1, 1331–1346.CrossRefGoogle ScholarPubMed
Paik, , , S.B. & Ringach, , , D.L. (2011). Retinal origin of orientation maps in visual cortex. Nature Neuroscience 14, 919–925.Google Scholar
Perry, , , V.H. & Cowey, A. (1985). The ganglion cell and cone distributions in the monkey’s retina: Implications for central magnification factors. Vision Research 25, 1795–1810.Google Scholar
Petry, , , H.M., Fox, , , R. & Casagrande, , , V.A. (1984). Spatial contrast sensitivity of the tree shrew. Vision Research 24, 1037–1042.CrossRefGoogle ScholarPubMed
Price, , , D.J. & Morgan, , , J.E. (1987). Spatial properties of neurones in the lateral geniculate nucleus of the pigmented ferret. Experimental Brain Research 68, 28–36.Google Scholar
Raczkowski, , , D. & Fitzpatrick, D. (1990). Terminal arbors of individual, physiologically identified geniculocortical axons in the tree shrew’s striate cortex. The Journal of Comparative Neurology 302, 500–514.Google Scholar
Rao, , , S.C., Toth, , , L.J. & Sur, M. (1997). Optically imaged maps of orientation preference in primary visual cortex of cats and ferrets. The Journal of Comparative Neurology 387, 358–370.Google Scholar
Roe, , , A.W., Fritsches, , , K. & Pettigrew, , , J.D. (2005). Optical imaging of functional organization of V1 and V2 in marmoset visual cortex. The anatomical record. Part A, Discoveries in Molecular, Cellular, and Evolutionary Biology 287, 1213–1225.Google Scholar
Schiller, , , P.H. & Malpeli, , , J.G. (1978). Functional specificity of lateral geniculate nucleus laminae of the rhesus monkey. Journal of Neurophysiology 41, 788–797.Google Scholar
Scholl, , , B., Burge, , , J. & Priebe, , , N.J. (2013). Binocular integration and disparity selectivity in mouse primary visual cortex. Journal of Neurophysiology 109, 3013–3024.Google Scholar
Seecharan, , , D.J., Kulkarni, , , A.L., Lu, , , L., Rosen, , , G.D. & Williams, , , R.W. (2003). Genetic control of interconnected neuronal populations in the mouse primary visual system. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 23, 11178–11188.Google Scholar
Selemon, , , L.D. & Begovic, A. (2007). Stereologic analysis of the lateral geniculate nucleus of the thalamus in normal and schizophrenic subjects. Psychiatry Research 151, 1–10.Google Scholar
Sesma, , , M.A., Casagrande, , , V.A. & Kaas, , , J.H. (1984). Cortical connections of area 17 in tree shrews. The Journal of Comparative Neurology 230, 337–351.Google Scholar
Sherman, S.M. (1974). Permanence of visual perimetry deficits in monocularly and binocularly deprived cats. Brain Research 73, 491–501.Google Scholar
Sherman, , , S.M., Norton, , , T.T. & Casagrande, , , V.A. (1975). X- and Y-cells in the dorsal lateral geniculate nucleus of the tree shrew (Tupaia glis). Brain Research 93, 152–157.Google Scholar
Soodak, R.E. (1987). The retinal ganglion cell mosaic defines orientation columns in striate cortex. Proceedings of the National Academy of Sciences of the United States of America 84, 3936–3940.Google Scholar
Stevens, C.F. (2001). An evolutionary scaling law for the primate visual system and its basis in cortical function. Nature 411, 193–195.Google Scholar
Stoelzel, , , C.R., Bereshpolova, , , Y., Gusev, , , A.G. & Swadlow, , , H.A. (2008). The impact of an LGNd impulse on the awake visual cortex: Synaptic dynamics and the sustained/transient distinction. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 28, 5018–5028.Google Scholar
Swadlow, , , H.A. & Weyand, , , T.G. (1985). Receptive-field and axonal properties of neurons in the dorsal lateral geniculate nucleus of awake unparalyzed rabbits. Journal of Neurophysiology 54, 168–183.Google Scholar
Swindale, , , N.V., Shoham, , , D., Grinvald, , , A., Bonhoeffer, , , T. & Hubener, M. (2000). Visual cortex maps are optimized for uniform coverage. Nature Neuroscience 3, 822–826.Google Scholar
Takahata, , , T., Miyashita, , , M., Tanaka, , , S. & Kaas, , , J.H. (2014). Identification of ocular dominance domains in New World owl monkeys by immediate-early gene expression. Proceedings of the National Academy of Sciences of the United States of America 111, 4297–4302.Google Scholar
Tang, , , J., Ardila Jimenez, S.C., Chakraborty, , , S. & Schultz, , , S.R. (2016). Visual receptive field properties of neurons in the mouse lateral geniculate nucleus. PLoS One 11, e0146017.Google Scholar
Tusa, , , R.J., Palmer, , , L.A. & Rosenquist, , , A.C. (1978). The retinotopic organization of area 17 (striate cortex) in the cat. The Journal of Comparative Neurology 177, 213–235.Google Scholar
Tusa, , , R.J., Rosenquist, , , A.C. & Palmer, , , L.A. (1979). Retinotopic organization of areas 18 and 19 in the cat. The Journal of Comparative Neurology 185, 657–678.Google Scholar
Vaney, , , D.I., Peichl, , , L., Wassle, , , H. & Illing, , , R.B. (1981). Almost all ganglion cells in the rabbit retina project to the superior colliculus. Brain Research 212, 447–453.Google Scholar
Veilleux, , , C.C. & Kirk, , , E.C. (2014). Visual acuity in mammals: Effects of eye size and ecology. Brain, Behavior and Evolution 83, 43–53.Google Scholar
Vitek, , , D.J., Schall, , , J.D. & Leventhal, , , A.G. (1985). Morphology, central projections, and dendritic field orientation of retinal ganglion cells in the ferret. The Journal of Comparative Neurology 241, 1–11.Google Scholar
Wassle, , , H., Boycott, , , B.B. & Illing, , , R.B. (1981). Morphology and mosaic of on- and off-beta cells in the cat retina and some functional considerations. Proceedings of the Royal Society of London. Series B, Biological sciences 212, 177–195.Google Scholar
Wassle, , , H. & Illing, , , R.B. (1980). The retinal projection to the superior colliculus in the cat: A quantitative study with HRP. The Journal of Comparative Neurology 190, 333–356.Google Scholar
Williams, , , R.W., Cavada, , , C. & Reinoso-Suarez, F. (1993). Rapid evolution of the visual system: A cellular assay of the retina and dorsal lateral geniculate nucleus of the Spanish wildcat and the domestic cat. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 13, 208–228.Google Scholar
Williams, , , R.W., Strom, , , R.C., Rice, , , D.S. & Goldowitz, D. (1996). Genetic and environmental control of variation in retinal ganglion cell number in mice. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 16, 7193–7205.Google Scholar
Woolsey, , , T.A. & Van Der Loos, H. (1970). The structural organization of layer IV in the somatosensory region (SI) of mouse cerebral cortex. The description of a cortical field composed of discrete cytoarchitectonic units. Brain Research 17, 205–242.Google Scholar
Xu, , , X., Bosking, , , W., Sary, , , G., Stefansic, , , J., Shima, , , D. & Casagrande, V. (2004). Functional organization of visual cortex in the owl monkey. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 24, 6237–6247.Google Scholar
Xu, , , X., Bosking, , , W.H., White, , , L.E., Fitzpatrick, , , D. & Casagrande, , , V.A. (2005). Functional organization of visual cortex in the prosimian bush baby revealed by optical imaging of intrinsic signals. Journal of Neurophysiology 94, 2748–2762.CrossRefGoogle ScholarPubMed
Yacoub, , , E., Harel, , , N. & Ugurbil, K. (2008). High-field fMRI unveils orientation columns in humans. Proceedings of the National Academy of Sciences of the United States of America 105, 10607–10612.Google Scholar
Yeh, , , C.I., Stoelzel, , , C.R. & Alonso, , , J.M. (2003). Two different types of Y cells in the cat lateral geniculate nucleus. Journal of Neurophysiology 90, 1852–1864.Google Scholar
Zahs, , , K.R. & Stryker, , , M.P. (1985). The projection of the visual field onto the lateral geniculate nucleus of the ferret. The Journal of Comparative Neurology 241, 210–224.Google Scholar
Zhuang, , , J., Stoelzel, , , C.R., Bereshpolova, , , Y., Huff, , , J.M., Hei, , , X., Alonso, , , J.M. & Swadlow, , , H.A. (2013). Layer 4 in primary visual cortex of the awake rabbit: Contrasting properties of simple cells and putative feedforward inhibitory interneurons. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 33, 11372–11389.Google Scholar
Figure 0

Fig. 1. Thalamocortical visual function in different animals: primates, carnivores, scandentia, lagomorphs, and rodents. Top. The number of LGN cells is correlated to the size of area V1 (left) and cortical visual acuity (right). Bottom. Cortical visual acuity is also correlated to the size of area V1 (left, expected from correlations shown at the top), and the horizontal extent of the binocular field (shown as a percentage of the total horizontal extent of the visual field). Size of area V1 refers to only one hemisphere. See Table 1 for references.

Figure 1

Table 1. Comparison of visual systems across different animals: primates, carnivores, scandentia, lagomorphs, and rodents. Columns 1–9 are taken from references cited below. Column 10 is calculated as column 8 times column 9; column 11 is calculated as column 10 divided by column 7; column 12 is calculated as column 6 times one million divided by column 8. Columns are numbered from left to right, from [1] for body weight to [12] for LGN-axon density in V1. All areas and volumes refer only to one hemisphere. Human: (Howland et al., 2004) for [1], [2]; (Curcio & Allen, 1990) for [3], [4], [5]; (Selemon & Begovic, 2007) for [6]; (Andrews et al., 1997) for [7], [8], [10]. Macaque: (Howland et al., 2004) for [1]; (Perry & Cowey, 1985) for [2]; (Kong et al., 2010) for [3]; (Kim et al., 1996) for [4]; (Perry & Cowey, 1985) for [5]; (Connolly & Van Essen, 1984) for [6]; (Malpeli & Baker, 1975) for [7]; (Adams et al., 2007) for [8]; (Hubel & Wiesel, 1968) for [9]. Cat: (Howland et al., 2004) for [1], [2]; (Hughes, 1975) for [3], [5]; (Hughes & Wassle, 1976) for [4]; (Williams et al., 1993) for [5], [6], [7]; (Tusa et al., 1978) for [8]; (Hubel & Wiesel, 1962) for [9]. Tree Shrew: (Howland et al., 2004) for [1], [2]; (Engelmann & Peichl, 1996) for [3]; (Drenhaus et al., 1997) for [4]; (Petry et al., 1984) for [5]; (Conway & Schiller, 1983) for [6], [7]; (Sesma et al., 1984) for [8]; (Muly & Fitzpatrick, 1992) for [9]. Ferret: (Howland et al., 2004) for [1], [2]; (Henderson, 1985) for [3], [4]; (Vitek et al., 1985) for [5]; (Gautschi & Clarke, 2007) for [6]; (Zahs & Stryker, 1985) for [7]; (Law et al., 1988) for [8], [9]. Rabbit: (Howland et al., 2004) for [1], [2]; (Oyster et al., 1981) for [3], [4], [5]; (Najdzion et al., 2009) for [6], [7]; (Hughes, 1971) for [8], [9]. Mouse: (Howland et al., 2004) for [1], [2]; (Drager and Olsen, 1981) for [3]; (Williams et al., 1996) for [4]; (Jeon et al., 1998) for [5]; (Seecharan et al., 2003) for [6], [7]; (Garrett et al., 2014) for [8]; (Antonini et al., 1999) for [9]. RGC: retinal ganglion cells

Figure 2

Table 2. Cortical mapping of visual space in different animals: primates, carnivores, scandentia, lagomorphs, and rodents. Columns 1–2, 5, 9–10 are taken from references cited below. Column 3 is calculated as column 2 divided by column 1 times 100; column 4 is calculated as column 8 from Table 1 divided by column 1 in this table; column 6 is calculated as column 4 times column 5; column 7 is calculated as column 1 divided by column 5; column 8 is calculated as column 2 divided by column 5. Columns are numbered from left to right as in Table 1. Macaque: (Adams et al., 2007) for [1], [2]; (Levitt et al., 2001) for [5]; (De Valois et al., 1982) for [9]; (Hubel & Wiesel, 1968) for [10]. Cat: (Sherman, 1974) for [1], [2]; (Cai et al., 1997) for [5]; (Movshon et al., 1978) for [9]; (Hubel & Wiesel, 1962) for [10]. Tree shrew: (Kaas et al., 1972) for [1], [2]; (Holdefer & Norton, 1995) for [5]; (Johnson et al., 2010) for [9]; (Humphrey & Norton, 1980) for [10]. Ferret: (Law et al., 1988) for [1], [2]; (Zahs & Stryker, 1985) for [5]; (Baker et al., 1998) for [9]; (Law et al., 1988) for [10]. Rabbit: (Hughes, 1971) for [1], [2]; (Bezdudnaya et al., 2006) for [5]; (Zhuang et al., 2013) for [9]; (Murphy & Berman, 1979) for [10]. Mouse: (Scholl et al., 2013) for [1]; (Heesy, 2004) for [2]; (Tang et al., 2016) for [5]; (Niell & Stryker, 2008) for [9]; (Drager, 1975) for [10]. RF: receptive field

Figure 3

Fig. 2. Thalamocortical networks underlying the organization of visual cortical maps. (a) Number of non-overlapping LGN receptive fields needed to tile a horizontal line across the visual field (shown on top left corner of each rectangle, taken from column 7 in Table 2). Only 1/10 of the horizontal visual field is represented for illustration purposes. (b) Number of nonoverlapping LGN-axon-patches needed to tile a horizontal line through area V1 (shown on top left corner of each rectangle, taken from column 3 in Table 3). Green and orange colors represent two different afferents. (c) The largest spacing between two LGN-axon-patches covering one LGN receptive field in visual cortex (shown on top left corner of each rectangle, taken from column 5 in Table 3). For simplicity, axon patches are represented as squares. (d) Cortical optimal visual acuity across 11 different species (values obtained from the references cited below). Colored circles indicate pinwheel orientation maps in visual cortex. Mouse: (Niell & Stryker, 2008). Rat, Gray squirrel, Bush baby, Owl monkey: (Heimel et al., 2005). Rabbit: (Zhuang et al., 2013). Ferret: (Baker et al., 1998). Tree shrew: (Johnson et al., 2010). Marmoset: (Forte et al., 2005). Cat: (Movshon et al., 1978). Macaque: (De Valois et al., 1982). VF: visual field. RF: receptive field.

Figure 4

Table 3. Comparison of LGN axon-patch density across different animals: primates, carnivores, scandentia, and rodents. Column 1 is taken from references cited below. Column 2 is calculated as the square root of column 1 times 1000; column 3 is calculated as column 8 in Table 1 divided by column 1 in this table; column 4 is calculated as column 6 in Table 1 times column 1 in this table times one million and divided by column 8 in Table 1; column 5 is calculated as the square root of the ratio between column 6 of Table 2 and column 1 of this table. Columns are numbered from left to right as in Table 1. Macaque: (Blasdel & Lund, 1983) for [1]. Cat (area 17): (Humphrey et al., 1985a) for [1]. Tree shrew: (Raczkowski & Fitzpatrick, 1990) for [1]. Mouse: (Antonini et al., 1999) for [1]. RF: receptive field

Figure 5

Fig. 3. Changes in orientation preference and receptive field position along a horizontal track of cat visual cortex. From top to bottom, the rows show OFF receptive fields mapped with dark stimuli (in blue), ON receptive fields mapped with light stimuli (in red), the ON–OFF receptive field difference (diff.), and the orientation/direction preference measured with moving bars (circles show the orientation preference predicted from the receptive field maps). From left to right, the columns show cortical measurements separated by 0.1 mm from 0 to 1.2 mm distance. The column on the right shows the receptive field average along the entire horizontal track (top three receptive fields) and the central positions of ON (red circles) and OFF receptive fields (blue circles). The position of these ON and OFF cortical receptive fields is determined by the receptive field population of ON and OFF thalamic afferents. Reproduced from Kremkow et al. (2016).

Figure 6

Fig. 4. Pinwheel pattern of the cortical map for stimulus orientation. Colors illustrate the different orientations represented on the cortical surface (cat area 17, 2 × 2 mm). A square of 0.5 × 0.5 mm is shown centered on a cortical pinwheel.

Figure 7

Table 4. Cortical spacing for ocular dominance and orientation pinwheels in area V1 of primates, carnivores, scandentia, and rodents. Column 2 is calculated as the square root of 1/pinwheel density. References for the column on the left [1] and on the right [2] are provided below. Human: (Adams et al., 2007) for [1]; (Yacoub et al., 2008) for [2]. Macaque: (Adams et al., 2007) for [1]; (Obermayer & Blasdel, 1993) for [2]. Cat area 17: (Lowel, 1994) for [1]; (Bonhoeffer et al., 1995) for [2]. Cat area 18: (Lowel, 1994) for [1]; (Bonhoeffer & Grinvald, 1993) for [2]. Owl monkey: (Takahata et al., 2014) for [1]; (Xu et al., 2004) for [2]. Bush baby: (Xu et al., 2005) for [1], [2]. Marmoset: (Roe et al., 2005) for [1]; (Liu & Pettigrew, 2003) for [2]. Squirrel monkey: (Adams & Horton, 2003) for [1]; (Obermayer & Blasdel, 1997) for [2]. Ferret: (Law et al., 1988) for [1]; (Rao et al., 1997) for [2]