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Neural Mechanisms of Object Perception

Neural Mechanisms of Object Perception. Zhiyong Yang Brain and Behavior Discovery Institute James and Jean Culver Vision Discovery Institute Department of Ophthalmology Georgia Regents University April 4, 2013. Outline. A model of pattern recognition

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Neural Mechanisms of Object Perception

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  1. Neural Mechanisms of Object Perception Zhiyong Yang Brain and Behavior Discovery Institute James and Jean Culver Vision Discovery Institute Department of Ophthalmology Georgia Regents University April4, 2013

  2. Outline • A model of pattern recognition 2. An updated view of the ventral pathway 3. Neural codes for object perception 3.1. V1 and V2 3.2. V4 3.3. IT 4. A perspective based on untangling object manifolds

  3. A Model of Pattern Recognition • Features • Probability distributions • Decision rule

  4. The Ventral Pathway • Kravitz et. al., 2013

  5. Output pathways Occipitotemporal network

  6. Three Cortico-subcortical Output Pathways • Occipitotemporo-neostriatal pathway reinforcement learning 2. Occipitotemporo-ventral striatum pathway value 3. Occipitotemporo-amygdaloid pathway emotion

  7. Three Cortico-corticalOutput Pathways 1. Occipitotemporo-medial temporal pathway long-term memory 2. Occipitotemporo-orbitofrontal pathway reward 3. Occipitotemporo-ventrolateral pathway working memory and executive function

  8. Neural codes for object perception

  9. Neural Codes in V1 • Responses selectively to a full range of visual features orientation, direction, disparity, speed, luminance, contrast, color, and spatial frequency 2. Functional maps retinotopic map, orientation map, ocular dominance map 3. Contextual modulation 4. Adaptive 5. Sparse and decorrelated relative to inputs

  10. Orientation Selectivity Hubel & Wiesel, 1968

  11. Orientation Map • Nauhaus et. al., 2008

  12. LNL Models of V1 Neurons simple cells complex cells

  13. Shape Codes in V2 • Responses to single orientation 2. Responses to multiple orientations 3. Responses to shapes of intermediate complexity

  14. Anzai et. al. 2007

  15. Stimulus sets Grating stimuli Contour stimuli Hegde & Van Essen, 2007

  16. Response profiles of exemplar V4 and V2 cells

  17. Shape Codes in V4 • Responses selectively to curvature, orientation, and object-relative position 2. Evidence for a sparse coding scheme

  18. Pasupathy & Connor 2002

  19. Carlson et. al., 2011

  20. Sparseness Index = 0.80 Sparseness Index = 0.36 Sparseness Index = 0.22 Sparseness Index = 0.11

  21. Neural Codes in IT • Structural, configurational, and compositional for both 2D and 3D objects 2. Position, orientation, curvature 3. Skeletal shape and boundary shape 3. Structural and holistic 4. Categorical clustering

  22. 2D contour shapes Brincat & Connor, 2004

  23. 2D contour shapes Brincat & Connor, 2004

  24. 3D shapes Yamane et. al., 2008

  25. 3D shapes Yamane et. al., 2008

  26. Categorical Coding Kriegeskorte et. al., 2008

  27. A perspective based on untanglingobject manifolds • Core object recognition and IT codes • Untangling object manifolds and a proposal • Open questions DiCarlo et. al., 2012

  28. Core Object Recognition • Discriminate a visual object from all other possible visual objects within <200 ms. • Discount changes due to changes in illumination, object position, size, scale, viewpoint, and visual context, and other structural variations. • Comprise between invariance and generalization. • There are ~30,000 natural objects. • Current models approach at best ~5% of human object perception.

  29. Untangling Object Representations

  30. The Ventral Visual PathwayEach area proportional to cortical surface area. Total number of neurons. Dimensionality of each representationPortion (color) dedicated toprocessing the central 10 deg of the visual fieldMedian response latency

  31. IT Neural Codes • Spike counts in ~50 msconvey information object identity 2. Object identity information is available ~100 ms after presentation 3. IT population presentation is untangled and object identity can be decoded by weighted summation codes. 4. These codes are quite general.

  32. IT Single-Unit Properties and Their Relationship to Population Performance

  33. Abstraction Layers and Their Potential Links

  34. Serial-Chain DiscriminativeModels of Object Recognition

  35. A Neural Network Model of Object Recognition Serre et. al., 2007

  36. A Model of Object Recognition

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