1. Question Examples If you were a neurosurgeon and you needed to take out part of the cortex of a patient, which technique would you use to identify the function of that part and why. What is the most important drawback to the fMRI technique What does the Stroop Effect tell us about how the brain works?

2. 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

3. 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

4. Visual Pathways Primary visual cortex receives input from LGN –also known as “striate” because it appears striped on some micrographs –also known as V1 –also known as Brodmann Area 17

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

6. 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!

7. 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?

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

9. Visual Neuron Responses V1 maintains a map of orientations across the retina because each small area on the retina has a corresponding cortical module that contains cells with the entire range of orientation tunings

10. 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?

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

12. 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 neither of these are true! Representing boundaries must be more complicated than simple edge detection!

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

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

15. Visual Neuron Responses Boundaries between objects can be defined by motion