Packing arrangement of the three cone classes in primate retina

Roorda, Austin; Metha, Andrew; Lennie, Peter; Williams, David R.

doi:10.1016/s0042-6989(01)00043-8

Cited by 214 publications

(183 citation statements)

References 33 publications

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“…Using methods similar to those described in detail by Roorda and Williams (1999) and Roorda et al (2001), we classified individual cones by comparing images taken when all of the photopigment was bleached with those taken when it was fully regenerated or when one class of photopigment was bleached selectively with either a 470 or 650 nm light. S cones absorb negligibly at our imaging wavelength of 550 nm (whereas the L and M cones absorb strongly), so they appear as dark cones in the absorptance image (which is defined as 1 minus the ratio of the dark adapted image to the corresponding fully bleached image) (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…To distinguish L from M cones, the polar angle () of each data point in the scatter plot was calculated. Because the radial spread of the data in the scatter plot is mainly related to the optical density of a cone and not its spectral identity (L or M) (Packer et al, 1996;Roorda and Williams, 1999;Roorda et al, 2001), we can restrict our attention to the of each data point in the scatter plot. The absorptance angle distribution was fit as a sum of two Gaussian functions representing L and M cones, and the absorptance angle at which they intersected was taken as the criterion for deciding whether an individual cone was likely to be L or M (for representative examples from our subjects, see Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, Roorda et al (2001) evaluated two human eyes at 1°e ccentricity using retinal densitometry combined with adaptiveoptics (AO) imaging to determine whether S cones were randomly distributed, whether they tended to be at discontinuities in the triangular cone mosaic, and whether they showed a tendency to neighbor either L or M cones. Here, we conducted the same analysis on a larger group of subjects, including two subjects with highly skewed L:M cone ratios.…”

Section: Arrangement Of S Cones In the Human Retinamentioning

confidence: 99%

“…Using microspectrophotometry, Mollon and Bowmaker (1992) observed a random arrangement of L and M cones in patches of talapoin monkey retina, and Bowmaker et al (2003) reported a random arrangement of L and M cones in patches of human retina, consistent with a random assignment of these pigments to cones (Nathans, 1999). Roorda et al (2001) also reported a random arrangement of human foveal L/M cones for two subjects. However, there is accumulating evidence for nonrandom arrangement of L and M cones.…”

Section: Introductionmentioning

confidence: 96%

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Organization of the Human Trichromatic Cone Mosaic

Hofer¹,

Carroll²,

Neitz³

et al. 2005

J. Neurosci.

413

311

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Using high-resolution adaptive-optics imaging combined with retinal densitometry, we characterized the arrangement of short-(S), middle-(M), and long-(L) wavelength-sensitive cones in eight human foveal mosaics. As suggested by previous studies, we found males with normal color vision that varied in the ratio of L to M cones (from 1.1:1 to 16.5:1). We also found a protan carrier with an even more extreme L:M ratio (0.37:1). All subjects had nearly identical S-cone densities, indicating independence of the developmental mechanism that governs the relative numerosity of L/M and S cones. L:M cone ratio estimates were correlated highly with those obtained in the same eyes using the flicker photometric electroretinogram (ERG), although the comparison indicates that the signal from each M cone makes a larger contribution to the ERG than each L cone. Although all subjects had highly disordered arrangements of L and M cones, three subjects showed evidence for departures from a strictly random rule for assigning the L and M cone photopigments. In two retinas, these departures corresponded to local clumping of cones of like type. In a third retina, the L:M cone ratio differed significantly at two retinal locations on opposite sides of the fovea. These results suggest that the assignment of L and M pigment, although highly irregular, is not a completely random process. Surprisingly, in the protan carrier, in which X-chromosome inactivation would favor L-or M-cone clumping, there was no evidence of clumping, perhaps as a result of cone migration during foveal development.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Arrangement Of S Cones In the Human Retinamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

See 2 more Smart Citations

Organization of the Human Trichromatic Cone Mosaic

Hofer¹,

Carroll²,

Neitz³

et al. 2005

J. Neurosci.

413

311

View full text Add to dashboard Cite

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“…This is clearly reflected in a number of obvious ways: the paucity of type I blue on-center cells in Wiesel and Hubel's study (15), the relatively low occurrence of S cones in the retina (20), the almost total absence of S cones in the fovea (20), and the low level of the sensitivity of the S cone in the photopic range compared to the L and M cones as illustrated in Fig. 3.…”

Section: Discussionmentioning

confidence: 91%

Modeling lateral geniculate nucleus cell response spectra and Munsell reflectance spectra with cone sensitivity curves

Romney

D’Andrade²

2005

Proc. Natl. Acad. Sci. U.S.A.

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We find that the cell response spectra of lateral geniculate nucleus cells, as well as the reflectance spectra of Munsell color chips, may be modeled by using the cone sensitivity functions of the long and medium cones. We propose a simple model for how the neural signals from the photoreceptors might be combined in the retina to closely approximate the reflectance spectra of Munsell color chips without input from the short cone.vision ͉ color perception R ecent research reveals that lateral geniculate nucleus (LGN) cell response spectra are fairly smoothly distributed in Munsell perceptual color space in a manner similar to the reflectance spectra of Munsell color chips (1). The findings were based on data reported by De Valois et al. (2), who measured spectral response data on 147 LGN cells. Neural signals reach the LGN through the optic nerve, which consists of the axons of ganglion cells in the retina. Each axon carries signals aggregated from a number of photoreceptors. The aim of this paper is to examine the LGN response spectra for clues as to how the responses of the long (L), medium (M), and short (S) cones (photoreceptors) are aggregated through the various layers of the retina to produce the LGN response spectra. From these clues, we construct a simple model of how the reflectance spectra of the Munsell chips might be represented by these aggregation processes.We assume that the color appearance of objects originates in their reflectance spectra that, together with the illumination source, determine the light stimuli reaching the photoreceptors or cones in the retina. We seek understanding of how photoreceptors are combined in the retina to produce a distribution of the LGN response spectra that is similar in distribution to the reflectance spectra of objects (1). First, we explore the contribution of the cones to the basis functions of the LGN spectral response curves. We recognize that these representations are statistical constructions and do not necessarily represent how the neural system actually functions. However, we can obtain some insight into the way the system might function from such an examination. We then propose a model of how the cone sensitivity curves might be combined to produce response spectra that closely approximate reflectance spectra of ordinary objects such as flowers, fruits, and Munsell color chips. We denote the data consisting of the response spectra of the 147 LGN cells as R NϫM , where N ϭ 147 cells and M ϭ 12 sampled wavelength locations with responses measured in spikes per second (each cell was measured at three light intensities, and the 147 values are means of the three measures). We used the raw recorded data uncorrected for base firing rates. In general, PCA or singular value decomposition analyzes a matrix such as R in the following way (5) Cone Contributions to the Basis Functions of the LGN Neuronswhere both UU T and VV T are identity matrices, and ⌬ KϫK ϭ ( ͌ k ) is a diagonal matrix. Values of k (Ͼ0) and v jk are obtained, respectively, as eigenvalues and ei...

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