Complex cell pooling and the statistics of natural images
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Summary
This model is novel in that it is the first to analyze optimal subspace size and how this size is influenced by contrast normalization and shows that the optimal nonlinearity for the pooling is squaring.
- Type
- article
- Published
- 2007-01-01
- Cited by
- 80
- References
- 28
- Access
- Open access
- OpenAlex
- https://openalex.org/W17852755
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:7825665
Keywords
Pension, Bond, Bond market, Finance, Economics
References
- Natural signal statistics and sensory gain control
- The "independent components" of natural scenes are edge filters.
- Functional connectivity between simple cells and complex cells in cat striate cortex
- Complex cells as cortically amplified simple cells
- Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2
- A 2.|E|-Bit Distributed Algorithm for the Directed Euler Trail Problem
- Lp-norm spherical distribution
- Quadratic forms in natural images
- Learning higher-order structures in natural images
- Independent component filters of natural images compared with simple cells in primary visual cortex
- Sparse coding with an overcomplete basis set: a strategy employed by V1?
- Learning the Nonlinearity of Neurons from Natural Visual Stimuli
- Spatiotemporal energy models for the perception of motion.
- Information-theoretic inequalities for contoured probability distributions
- Training Products of Experts by Minimizing Contrastive Divergence
- Neural mechanisms for processing binocular information I. Simple cells.
- Symmetric Multivariate and Related Distributions
- How are complex cell properties adapted to the statistics of natural stimuli?
- Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces
- Fast and robust fixed-point algorithms for independent component analysis
Cited by
- Temporal Adaptation Enhances Efficient Contrast Gain Control on Natural Images
- A Deep Learning Pipeline for Image Understanding and Acoustic Modeling
- How Much Can Orientation Selectivity and Contrast Gain Control Reduce the Redundancies in Natural Images
- Orientation Selectivity and Contrast Gain Control in Representations of Natural Images (Orientierungsselektivität und Kontrastverstärkungsregelung in Repräsentationen von natürlichen Bildern)
- A Probabilistic Approach to the Primary Visual Cortex
- The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction
- Theoretical models of neural circuit development.
- Why do linear SVMs trained on HOG features perform so well?
- Natural Image Coding in V1: How Much Use Is Orientation Selectivity?
- A Two-Layer Model of Natural Stimuli Estimated with Score Matching
- Learning Topographic Representations of Nature Images with Pairwise Cumulant
- Natural image statistics: Energy-based models estimated by score matching
- Lp-Nested Symmetric Distributions
- FastISA: A fast fixed-point algorithm for independent subspace analysis
- Learning invariant features through topographic filter maps
- Learning Adaptive Correlations of Independent Components for Complex Cell Modeling
- Hierarchical Modeling of Local Image Features through L_p-Nested Symmetric Distributions
- Learning Feature Hierarchies for Object Recognition
- A Pairwise Cumulant-Based Model for Topographic Representations of Nature Image: A Pairwise Cumulant-Based Model for Topographic Representations of Nature Image
- Sparse Coding Model Based on Structural Similarity
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