Slide 1

Structure of the lens The fibrous nature of the lens is evident in low- magnification scanning Lens fibers (micrograph 2) are mature cells that have lost their organelles, including nuclei, and are packed with soluble structural proteins called crystallins. The age- related decrease in the ability of the lens to accommodate for near vision is, in part, related to the accumulation of more lens fibers, but it is due primarily to decreased elasticity of the capsule. Special cellular properties that permit the lens to stretch Slide 2 Socket and knob interdigitations maintain lens organization Mature lens fibers are tightly packed and join with one another via knob- and socketlike associations (k, micrograph 2). These elaborate cell interdigitations maintain the lens organization during shape changes associated with accommodation. In addition, close packing of cells prevents excess light scattering and facilitates communication between adjacent cells. Slide 3 Moving checkerboard Daniels moving check Slide 4 Motion induced blindness Motion-induced blindness (Bonneh) Slide 5 Receptor density varies dramatically as we measure across the retina nasal temporal Slide 6 Definition of visual angle Slide 7 Photoreceptor gross anatomy Slide 8 Receptor functional schematics Slide 9 The visual photopigment regenerated (in vivo) Slide 10 The visual photopigment bleached (in vivo) Slide 11 Receptor models I would like to know the modern story about disk shedding and regeneration (phagocytosis). Slide 12 Cone wavelength spectral measurements Slide 13 Cone receptor mosaic visualization (Roorda and Williams, 1999) Slide 14 Psychophysical estimation of S-cone sampling: (Williams, Macleod and Hayhoe) Slide 15 S-Cones labeled with procion yellow (DeMonasterio et al.) Slide 16 S cone sampling mosaic is lower density than the other types (image courtesy S. Schein) Slide 17 The peripheral receptor mosaic L,M cones stained S-cones not stained Small cross-sections show rods Just outside the fovea Slide 18 Foveal cone mosaic Even spacing All cones Slide 19 S-cones shape is slightly different from that of the other cone types Ahnelt et al., 1987 Slide 20 Human photoreceptor density measurements (Curcio) FoveaPeriphery Slide 21 High degree of variability in the very central fovea, less 1 deg out (Curcio et al. data) Cones per square millimeter Eccentricity (mm) 300,000 100,000 0.20.40.6 12.3 mm/deg Slide 22 Viewing the retinal cone classes Roorda & Williams, 1999 The task Determine the distribution of the three retinal cone classes The problem Blur in the eye due to wave-front aberrations The solution Adaptive optics (discussed earlier) Slide 23 Methods Selectively bleach one photopigment Capture image immediately after and compare Slide 24 Selected cones are dark Slide 25 Results Large individual differences in L/M distribution Random distribution of L and M cones Although there is some clumping These clumps may explain misjudgment of small color objects Beneficial for viewing high-frequency patterns Slide 26 Pseudocolor mosaics Slide 27 Questions Why isnt there a more regular distribution pattern? Why are there individual differences in distribution patterns? Why are there individual differences between individuals but not between eyes? Genetic? Environmental? Slide 28 End Slide 29 Two-Dimensional Examples x = [1:256]/256; f = 50; y = sin(2*pi*f*x); im = y(ones(1,256),:); s = [1:5:256]; y = zeros(size(x)); y(s) = 1; sGrid = y(ones(1,256),:); sGrid = sGrid.* sGrid'; Slide 30 Example Result imshow(im.* sGrid) Slide 31 Two Dimensional Aliasing How about making an aliasing demonstration tool? The samples could be drawn from different types of animal eyes, different orientations, and it could include the effects of motion? Slide 32 Drawings of aliasing from three authors Williams Byron Helmholtz Slide 33 Drawings of Aliasing (Williams) 80 cpd 100 cpd Slide 34 Young: double-slit experiments Slide 35 Interference pattern Slide 36 Interferometry apparatus used to measure L,M cone density Slide 37 Undersampled harmonic x = [1:16]/16; freq = 15; y = cos(2*pi*freq*x); plot(x,y,'b-o);hold on x = [1:256]/256; freq = 15; y = cos(2*pi*freq*x); plot(x,y,'r-') Slide 38 Irregularly sampled harmonic x = [1:256]/256; freq = 15; y = cos(2*pi*freq*x); plot(x,y,'r-') x = [1:16 ]/ 16; freq = 11.2; y= cos(2*pi*freq*x); plot(x,y,'b-o')