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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 Zhiyong Yang Brain and Behavior Discovery Institute James and Jean Culver Vision Discovery Institute Department of Ophthalmology Georgia Regents University April4, 2013
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
A Model of Pattern Recognition • Features • Probability distributions • Decision rule
The Ventral Pathway • Kravitz et. al., 2013
Output pathways Occipitotemporal network
Three Cortico-subcortical Output Pathways • Occipitotemporo-neostriatal pathway reinforcement learning 2. Occipitotemporo-ventral striatum pathway value 3. Occipitotemporo-amygdaloid pathway emotion
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
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
Orientation Selectivity Hubel & Wiesel, 1968
Orientation Map • Nauhaus et. al., 2008
LNL Models of V1 Neurons simple cells complex cells
Shape Codes in V2 • Responses to single orientation 2. Responses to multiple orientations 3. Responses to shapes of intermediate complexity
Stimulus sets Grating stimuli Contour stimuli Hegde & Van Essen, 2007
Shape Codes in V4 • Responses selectively to curvature, orientation, and object-relative position 2. Evidence for a sparse coding scheme
Sparseness Index = 0.80 Sparseness Index = 0.36 Sparseness Index = 0.22 Sparseness Index = 0.11
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
2D contour shapes Brincat & Connor, 2004
2D contour shapes Brincat & Connor, 2004
3D shapes Yamane et. al., 2008
3D shapes Yamane et. al., 2008
Categorical Coding Kriegeskorte et. al., 2008
A perspective based on untanglingobject manifolds • Core object recognition and IT codes • Untangling object manifolds and a proposal • Open questions DiCarlo et. al., 2012
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.
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
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.
IT Single-Unit Properties and Their Relationship to Population Performance
A Neural Network Model of Object Recognition Serre et. al., 2007