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Some Background on Visual Neuroscience

Some Background on Visual Neuroscience. Why do things look as they do?. Because the WORLD is as it is. Because the BRAIN is as it is. Because the World is as it is…. We can see trillions of different things. Each at infinitely many viewing angles.

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Some Background on Visual Neuroscience

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  1. Some Background on Visual Neuroscience

  2. Why do things look as they do? • Because the WORLD is as it is. • Because the BRAIN is as it is.

  3. Because the World is as it is… • We can see trillions of different things. • Each at infinitely many viewing angles. • Each under infinitely many lighting conditions.

  4. Because the Brain is as it is… • How many Brain Cells do we have? • How many Brain Cells “do vision”?

  5. The Visual Infinite Problem • How do we see … • Trillions of things • At infinitely many viewing angles • Under infinitely many lighting condition • …with just finite brain cells?

  6. Idea! • “If we only had”… • A visual “alphabet”, and • Rules for combining those “letters” • We could solve the visual infinite problem!

  7. What’s the Alphabet? • Answer: Sine Waves! 2. More specifically, sine waves of light!

  8. Evidence for a Sine Wave “Alphabet” Electrical Recordings from the Primate Occipital Lobe: Visual neurons LOVE (respond maximally too) sine waves of light!

  9. What are the rules? • Answer: Pattern Analyzers! • Answer: Pattern Analyzers! • Answer: Pattern Analyzers! • Answer: Pattern Analyzers! • Answer: Pattern Analyzers!

  10. What are the rules? • Fourier Analyzers. • Fourier’s Analysis- Sine waves of various sizes and orientations can be combined to produce any image, and at any scale!

  11. Fourier Analysis • The cycles of waves can be described by four features, or “parameters”. • These are Frequency, Amplitude, Phase, and Orientation. • A helpful acronym is F.A.P.O.. • Let’s see examples of how each parameter…

  12. These Differ In Frequency Low Spatial Frequency Fat Bars: Few Cycles Per Degree C.P.D. High Spatial Frequency Thin Bars: Many Cycles Per Degree C.P.D.

  13. These Differ In Amplitude (or Contrast) Low Amplitude (Low Contrast) High Amplitude (High Contrast)

  14. These Differ In Phase (Relative Position) Zero Phase (“Start With Black”) 180 Degree Phase Shift (“Start With White”)

  15. These Differ In Orientation Vertical Orientation Horizontal Orientation

  16. FAPO in the Occipital Lobe

  17. Tuning Curves

  18. Visual neurons respond best when the size (SF) of the stimulus matchesthe size (SF) of the receptive field. Stimulus “b” is the best match here.

  19. Spatial Frequency Filtering Low Pass High Pass Original Image

  20. An Inverse Problem(The Bayesian Brain?)&“Reverse Correlation”

  21. Divinci (1452-1519) and Divinci stereopsis

  22. Orthodox Stats: (p)Data | Hypothesis

  23. Electrophysiologists solve “their problem” with tuning curves!

  24. Bayesian Stats: (p) Hypothesis | Data

  25. Inverse Problem: Bayesian Brain • Computational Neuroscience…. • In principle, the brain could solve the inverse problem computationally by using Bayes’ Theorem: P(R|S) P(S) P(S|R) = -------------------- P(R) “In principle” is my favorite prepositional phrase, because it sets up a testable prediction.

  26. If You Like History… • Thomas Bayes (1701 - 1761): • English Mathematician & Presbyterian Minister

  27. If You Like Stats… • The Likelihood Principle (Birnbaum, 1962)- All information relevant to inference (about the stimulus, S) contained in data (the response, R) is provided by the likelihood. (Likelihood is instantaneous, not cumulative.) • The “likelihood” is defined as P(R|S).The “prior” is defined as P(S). “Posterior” = Likelihood * Prior. The “base rate” is defined as P(R). P(R|S)P(S) P(S|R) = -------------------- P(R) [ ]

  28. Engineering The Brain • Evolution favored brains that were better able to solve the inverse problem. (p)S | R • The Four F’s of Evolution • Feeding • Fighting • Fleeing • Reproduction

  29. The (Fab) Four Goals of Science • And speaking of fab four…. • Four Goals of Science • Description • Prediction (Correlation) • Causation • Application

  30. Reverse Correlation • We can reverse engineer the brain by using Reverse-Correlation Techniques • Article 1 - NeuroImage, 2010 • Name that tune: Decoding Music from the Listening Brain • Article 2 - Psychological Science, 2008 • Ethnic Out-Group Faces Are Biased in the Prejudiced Mind • Article 3 - Psychological Science, 2013 • Political Attitudes Bias the Mental Representation of a Presidential Candidate’s Face

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