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Table of Contents
Summary; 1 Neural Mechanisms of Saliency, Attention, and Orienting; Abstract; 1 Overview; 2 The Visual Orienting Network; 2.1 Superior Colliculus; 2.2 Occipital Cortex; 2.3 Fronto-Parietal Cortices; 2.4 Basal Ganglia; 2.5 Brainstem; 3 Neural Representations of Visual Saliency; 4 Neural Representations of Behavioral Priority; 4.1 Spatial Attention; 4.2 Target Selection; 5 Conclusion; References; 2 Insights on Vision Derived from Studying Human Single Neurons; 1 Latency; 2 Visual Selectivity of Neurons in the Human MTL; 3 Invariance; 4 Grandmother Cells; 5 Topography of Tuning.
6 Internally Generated Responses and Consciousness7 Memory; 8 Closing Remarks; References; 3 Recognition of Occluded Objects; 1 Visual System Hierarchy; 2 The Computational Problem of Object Completion; 2.1 Amodal Completion; 2.2 From Amodal Completion to Recognition of Occluded Objects; 3 Neural Representation of Occluded Objects; 4 Computational Models of Occluded Object Recognition; 4.1 Performance of Feed-Forward Models in Recognizing Occluded Objects; 4.2 Beyond Feed-Forward Models; References; Towards a Theory of Computation in the Visual Cortex.
1 Cortical Filter Models of Form Processing1.1 The Linear-Nonlinear (LN) Model; 1.2 Divisive Normalization; 1.3 LN Cascade; 2 Cortical Filter Models Across Visual Cues; 2.1 Color Processing; 2.2 Binocular Disparity Processing; 2.3 Motion Processing; 3 Completing the Hierarchy: Models of the Visual Cortex; 3.1 Hubel and Wiesel Model; 3.2 Hierarchical Models: Formalism; 3.3 Models of Object Recognition; 3.4 Models Across Visual Cues; 4 Discussion and Concluding Remarks; 4.1 Why Hierarchies?; 4.2 Limitations; References; Invariant Recognition Predicts Tuning of Neurons in Sensory Cortex.
1 Appendix1.1 Retinal Processing; 1.2 Additional Evidence for Gabor Shapes as Templates in V1; 1.3 Hebbian Rule and Gabor-Like Functions; 1.4 Motion Determines a Consistent Orientation of the Gabor-Like Eigenfunctions; 1.5 Phase of Gabor RFs; References; Speed Versus Accuracy in Visual Search: Optimal Performance and Neural Implementations; 1 The Phenomenology of Visual Search; 2 Ideal Observers; 2.1 Sensory Input; 2.2 Optimality; 3 The Sequential Probability Ratio Test; 3.1 Notations; 3.2 S(Xt) for Homogeneous Discrimination; 3.3 S(Xt) for Homogeneous Search.
3.4 S(Xt) for Heterogeneous Search4 Model Prediction and Human Psychophysics; 4.1 Qualitative Fits; 4.2 Quantitative Fits; 5 Optimality Analysis; 5.1 Solving for the Ideal Observer; 5.2 Dynamic Programming; 5.3 Comparison with SPRT; 6 Spiking Network Implementation; 7 Chapter Summary; References; 7 The Pupil as Marker of Cognitive Processes; 1 The Pupil Is a Readily Accessible Marker of Neural Processes; 2 Modulation of the Pupil's Response to Light by Cognitive Factors; 2.1 Awareness and Imaginary Light Sources Modulate the Pupil Light Reflex.
6 Internally Generated Responses and Consciousness7 Memory; 8 Closing Remarks; References; 3 Recognition of Occluded Objects; 1 Visual System Hierarchy; 2 The Computational Problem of Object Completion; 2.1 Amodal Completion; 2.2 From Amodal Completion to Recognition of Occluded Objects; 3 Neural Representation of Occluded Objects; 4 Computational Models of Occluded Object Recognition; 4.1 Performance of Feed-Forward Models in Recognizing Occluded Objects; 4.2 Beyond Feed-Forward Models; References; Towards a Theory of Computation in the Visual Cortex.
1 Cortical Filter Models of Form Processing1.1 The Linear-Nonlinear (LN) Model; 1.2 Divisive Normalization; 1.3 LN Cascade; 2 Cortical Filter Models Across Visual Cues; 2.1 Color Processing; 2.2 Binocular Disparity Processing; 2.3 Motion Processing; 3 Completing the Hierarchy: Models of the Visual Cortex; 3.1 Hubel and Wiesel Model; 3.2 Hierarchical Models: Formalism; 3.3 Models of Object Recognition; 3.4 Models Across Visual Cues; 4 Discussion and Concluding Remarks; 4.1 Why Hierarchies?; 4.2 Limitations; References; Invariant Recognition Predicts Tuning of Neurons in Sensory Cortex.
1 Appendix1.1 Retinal Processing; 1.2 Additional Evidence for Gabor Shapes as Templates in V1; 1.3 Hebbian Rule and Gabor-Like Functions; 1.4 Motion Determines a Consistent Orientation of the Gabor-Like Eigenfunctions; 1.5 Phase of Gabor RFs; References; Speed Versus Accuracy in Visual Search: Optimal Performance and Neural Implementations; 1 The Phenomenology of Visual Search; 2 Ideal Observers; 2.1 Sensory Input; 2.2 Optimality; 3 The Sequential Probability Ratio Test; 3.1 Notations; 3.2 S(Xt) for Homogeneous Discrimination; 3.3 S(Xt) for Homogeneous Search.
3.4 S(Xt) for Heterogeneous Search4 Model Prediction and Human Psychophysics; 4.1 Qualitative Fits; 4.2 Quantitative Fits; 5 Optimality Analysis; 5.1 Solving for the Ideal Observer; 5.2 Dynamic Programming; 5.3 Comparison with SPRT; 6 Spiking Network Implementation; 7 Chapter Summary; References; 7 The Pupil as Marker of Cognitive Processes; 1 The Pupil Is a Readily Accessible Marker of Neural Processes; 2 Modulation of the Pupil's Response to Light by Cognitive Factors; 2.1 Awareness and Imaginary Light Sources Modulate the Pupil Light Reflex.