1. The Human Visual System Background on Vision Human vision – the best system around Deep network models

4. Hemifield neglect

5. Eye

6. Recording Spikes

7. Receptors Density - Fovea

9. Image Capture Huge dynamic range 10 -8 – 10 +6 μW/cm 2 Photons: poisson process. Noisy at low levels For low light: large receptors, slow integration Rods/cones, local adaptation,change of amplitude and time constant, motion deblur

10. Poisson Distribution

11. Adaptation Effect

12. Dynamic Range

14. Visual receptor types

16. Retina Mosaic

17. Color Mixing

18. Retina Output: Ganglion Receptive Fields Output = image * DoG

20. Cortex

24. Bi-directional Computation

25. Physiological Recording

26. Primary visual area V1 This is an important part of Class’ a Nobel prize was awarded to Hubel and Wiesel for these findings. The main properties of V1: Each cell responds to a small region of the visual field, called the ‘receptive field’ of the neuron Cells responds to edges and bars in their receptive fields Each cell is selective to the orientation of the edge or bar Most cells are also selective to the direction of motion of the edge, they will respond to motion in one direction but not to the opposite direction

27. Orientation Selectivity

28. V1: Direction selectivity Modified from PSY280F

29. Visual Areas

30. Contours and Boundaries (V1, V2)

31. Stimuli tested in V2, V4

32. Neuronal Responses,V2

33. Stimuli tested in V2, V4 Models (SIFT, HoG) do not represent such features

37. Tanaka IT clusters Kiani, et al J. Neurophysiol 2007 Object Category Structure in Response Patterns of Neuronal Population in Monkey Inferior Temporal Cortex.

38. fMRI Magnet

39. fMRI Activation Slice

40. fMRI Activation

41. Fusiform (red, yellow)

43. Face-Pace Rivalry

44. House-Face fMRI Transitions Rivalry differences similar to monocular but less pronounced Activity is higher for the perceived stimulus Dominance is partial, significant activity for the non-dominant stimulus.

46. Four short questions: Is color vision obtained by the eye or by the bran? Explain What is computed in the first visual area, V1? How can useful features for recognition be selected automatically, give an example What is a HoG descriptor of an image patch?