PowerPoint-Pr

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Sensation Perception
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-Discussion Section- Session 4 Visual Cortex
I Icecube (Hubel Wiesel) Pinwheel (Issa
Stryker)
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Administrative stuff
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Presentation 1 Receptive fields and
functional architecture of monkey striate cortex
(1968)presented by John Scott-Railton

Presentation 2 Spatial Frequency Maps in Cat
Visual Cortex (2000)presented by Jasmine Kwong
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Next week
Week 7 11/08/2004 Higher visual perception
Contours Paper 1 (Classic) von der Heydt et
al., 1984 (Perla) Paper 2 (Modern) Bakin et
al., 2000 (?)
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BACKGROUND
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This weeks issue
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How can one effectively represent 6 stimulus
dimensions on a 2.5 dimensional sheet?
Orientation Ocular dominance Spatial frequency
Motion Depth Color
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Essentially a mathematical optimization problem

• Similarly
• How to put as much of the sheet as possible into
  a sphere? (Cortex into skull)

Solution?
Folding!
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Thats why cortex is folded

• It optimizes area while minimizing volume
• So far, so good.
• But what about the basic functional unit of
  cortex organization?
• How to represent all stimulus dimensions
  effectively in all locations?

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Two proposals
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1. The Iceblock model (Hypercolumns)
Nobel prize in Physiology, 1981
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2. The Pinwheel model
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Optical imaging

• Rationale
• Deoxygenated blood is darker than oxygenated
  blood.
• This is particularly true in the range of red
  light.
• Active neurons use up more oxygen than neurons
  that are less active.
• This leads to a local, spatial distribution of
  blood saturation levels.
• ? Regions of active neurons should appear darker.
  

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Optical Imaging
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Pro and Con

• Pro
• Very high spatial resolution
• Allows to visualize large scale activity patterns
• Relatively direct link to electrical activity of
  neurons
• Better time course than fMRI

• Con
• Still relatively bad time course (linked to
  oxygen dynamics)
• Invasive
• Very low signal to noise ? need to average many,
  many trials to see signal.
• Hemodynamics indirect, nonlinear.

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BOLD time course
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The case for spatial frequency
Orientation Ocular dominance Spatial frequency
Motion Depth Color
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Picture representation in V1
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The concept
Luminance
Wavelength Spatial Frequency
Amplitude Contrast
Space
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Looks like
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Fouriers theorem

• EVERY waveform can be decomposed into simple
  sine-waves with the right amplitude and
  frequency.
• EVERY waveform can be synthesized by adding
  sine-waves with the right amplitude and frequency.

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V1 neurons are apt to represent sine waves of
different frequency
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Philosophical implication

• The representation of the visual world by neurons
  in V1 is VERY different from our phenomenological
  experience. Each neuron only represents a tiny
  slice of oriented spatial frequency!