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

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.

3. Moving checkerboard Daniel’s moving check

4. Motion induced blindness Motion-induced blindness (Bonneh)

5. Receptor density varies dramatically as we measure across the retina nasal temporal

6. Definition of visual angle

7. Photoreceptor gross anatomy

8. Receptor functional schematics

9. The visual photopigment regenerated (in vivo)

10. The visual photopigment bleached (in vivo)

11. Receptor models I would like to know the modern story about disk shedding and regeneration (phagocytosis).

12. Cone wavelength spectral measurements

13. Cone receptor mosaic visualization (Roorda and Williams, 1999)

14. Psychophysical estimation of S-cone sampling: (Williams, Macleod and Hayhoe)

15. S-Cones labeled with procion yellow (DeMonasterio et al.)

16. S cone sampling mosaic is lower density than the other types (image courtesy S. Schein)

17. The peripheral receptor mosaic L,M cones stained S-cones not stained Small cross-sections show rods Just outside the fovea

18. Foveal cone mosaic Even spacing All cones

19. S-cones shape is slightly different from that of the other cone types Ahnelt et al., 1987

20. Human photoreceptor density measurements (Curcio) FoveaPeriphery

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

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)

23. Methods Selectively bleach one photopigment Capture image immediately after and compare

24. Selected cones are dark

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

26. Pseudocolor mosaics

27. Questions Why isn’t 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?

28. End

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';

30. Example Result imshow(im.* sGrid)

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?

32. Drawings of aliasing from three authors Williams Byron Helmholtz

33. Drawings of Aliasing (Williams) 80 cpd 100 cpd

34. Young: double-slit experiments

35. Interference pattern

36. Interferometry apparatus used to measure L,M cone density

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-')

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')