1. Midterm 1 Oct. 6 in class Review Session after class on Monday

2. Read this article for Friday Oct 8th!

3. Mental Representations Mental representations can start with sensory input and progress to more abstract forms –Local features such as colors, line orientation, brightness, motion are represented at low levels How might a neuron “represent” the presence of this line?

4. Mental Representations Mental representations can start with sensory input and progress to more abstract forms –Local features such as colors, line orientation, brightness, motion are represented at low levels A “labeled line” -Activity on this unit “means” that a line is present -Does the line actually have to be present?

5. Mental Representations Mental representations can be “embellished” - Kaniza Triangle is represented in a way that is quite different from the actual stimulus -the representation is embellished and extended

6. First Principles What are some ways that information might be represented by neurons?

7. First Principles What are some ways that information might be represented by neurons? –Magnitude might be represented by firing rate (e.g. brightness) –Presence or absence of a feature or piece of information might be represented by whether certain neurons are active or not – the “labeled line” (e.g. color, orientation, pitch) –Conjunctions of features might be represented by coordinated activity between two such labeled lines –Binding of component features might be represented by synchronization of units in a network

8. V I S I O N S C I E N C E

9. Visual Pathways Themes to notice: –Contralateral nature of visual system –Information is organized: According to spatial location According to features and kinds of information

10. Visual Pathways Image is focused on the retina Fovea is the centre of visual field –highest acuity Peripheral retina receives periphery of visual field –lower acuity –sensitive under low light

11. Visual Pathways Retina has distinct layers

12. Visual Pathways Retina has distinct layers Photoreceptors –Rods and cones respond to different wavelengths

13. Visual Pathways Retina has distinct layers Amacrine and bipolar cells perform “early” processing –converging / diverging input from receptors –lateral inhibition leads to centre/surround receptive fields - first step in shaping “tuning properties” of higher- level neurons

14. Visual Pathways Retina has distinct layers –signals converge onto ganglion cells which send action potentials to the Lateral Geniculate Nucleus (LGN) –two kinds of ganglion cells: Magnocellular and Parvocellular visual information is already being shunted through functionally distinct pathways as it is sent by ganglion cells

15. Visual Pathways visual hemifields project contralaterally –exception: bilateral representation of fovea! Optic nerve splits at optic chiasm about 90 % of fibers project to cortex via LGN about 10 % project through superior colliculus and pulvinar –but that’s still a lot of fibers! Note: this will be important when we talk about visuospatial attention

16. Visual Pathways Lateral Geniculate Nucleus maintains segregation: –of M and P cells (mango and parvo) –of left and right eyes P cells project to layers 3 - 6 M cells project to layers 1 and 2

17. Visual Pathways Primary visual cortex receives input from LGN –also known as “striate” because it appears striped when labeled with some dyes –also known as V1 –also known as Brodmann Area 17

18. Visual Pathways W. W. Norton Primary cortex maintains distinct pathways – functional segregation M and P pathways synapse in different layers

19. The Role of “Extrastriate” Areas Different visual cortex regions contain cells with different tuning properties

20. The Role of “Extrastriate” Areas Consider two plausible models: 1.System is hierarchical: –each area performs some elaboration on the input it is given and then passes on that elaboration as input to the next “higher” area 2.System is analytic and parallel: –different areas elaborate on different features of the input

21. The Role of “Extrastriate” Areas Functional imaging (PET) investigations of motion and colour selective visual cortical areas Zeki et al. Subtractive Logic –stimulus alternates between two scenes that differ only in the feature of interest (i.e. colour, motion, etc.)

22. The Role of “Extrastriate” Areas Identifying colour sensitive regions Subtract Voxel intensities during these scans… …from voxel intensities during these scans …etc. Time ->

23. The Role of “Extrastriate” Areas result –voxels are identified that are preferentially selective for colour –these tend to cluster in anterior/inferior occipital lobe

24. The Role of “Extrastriate” Areas similar logic was used to find motion-selective areas Subtract Voxel intensities during these scans… …from voxel intensities during these scans …etc. Time -> MOVING STATIONARY MOVING STATIONARY

25. The Role of “Extrastriate” Areas result –voxels are identified that are preferentially selective for motion –these tend to cluster in superior/dorsal occipital lobe near TemporoParietal Junction –Akin to Human V5

26. The Role of “Extrastriate” Areas Thus PET studies doubly-dissociate colour and motion sensitive regions

27. Electrical response (EEG) to direction reversals of moving dots generated in (or near) V5 This activity is absent when dots are isoluminant with background The Role of “Extrastriate” Areas

28. V4 and V5 are doubly-dissociated in lesion literature:

29. The Role of “Extrastriate” Areas V4 and V5 are doubly-dissociated in lesion literature: –achromatopsia (color blindness): there are many forms of color blindness cortical achromatopsia arises from lesions in the area of V4 singly dissociable from motion perception deficit - patients with V4 lesions have other visual problems, but motion perception is substantially spared

30. The Role of “Extrastriate” Areas V4 and V5 are doubly-dissociated in lesion literature: –akinetopsia (motion blindness): bilateral lesions to area V5 (extremely rare) severe impairment in judging direction and velocity of motion - especially with fast-moving stimuli visual world appeared to progress in still frames similar effects occur when M-cell layers in LGN are lesioned in monkeys

31. How does the visual system represent visual information? How does the visual system represent features of scenes? Vision is analytical - the system breaks down the scene into distinct kinds of features and represents them in functionally segregated pathways but… the spike timing matters too!

32. Visual Neuron Responses Unit recordings in LGN reveal a centre/surround receptive field many arrangements exist, but the “classical” RF has an excitatory centre and an inhibitory surround these receptive fields tend to be circular - they are not orientation specific How could the outputs of such cells be transformed into a cell with orientation specificity?

33. Visual Neuron Responses LGN cells converge on “simple” cells in V1 imparting orientation (and location) specificity

34. Visual Neuron Responses LGN cells converge on simple cells in V1 imparting orientation specificity Thus we begin to see how a simple representation - the orientation of a line in the visual scene - can be maintained in the visual system –increase in spike rate of specific neurons indicates presence of a line with a specific orientation at a specific location on the retina –Why should this matter?

35. Visual Neuron Responses Edges are important because they are the boundaries between objects and the background or objects and other objects

36. Visual Neuron Responses This conceptualization of the visual system was “static” - it did not take into account the possibility that visual cells might change their response selectivity over time –Logic went like this: if the cell is firing, its preferred line/edge must be present and… –if the preferred line/edge is present, the cell must be firing We will encounter examples in which these don’t apply! Representing boundaries must be more complicated than simple edge detection!

37. Visual Neuron Responses Boundaries between objects can be defined by color rather than brightness

38. Visual Neuron Responses Boundaries between objects can be defined by texture

39. Visual Neuron Responses Boundaries between objects can be defined by motion and depth cues