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Question Examples

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

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Question Examples

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

  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

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