- REVIEW Communicated by Terrence J. Sejnowski How Close Are We to Understanding V1?
- Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
- Pattern recognition, attention, and information bottlenecks in the primate visual system
- Learning Horizontal Connections in a Sparse Coding Model of Natural Images
- 44 CHAPTER 2. DYNAMIC ROUTING CIRCUITS Feedback Feedforward
- Learning Sparse Image Codes using a Wavelet Pyramid Architecture
- REFERENCES 109 von der Malsburg C, Bienenstock E (1986) Statistical coding and shortterm synaptic plas
- Learning sparse codes with a mixtureofGaussians prior
- Directed Visual Attention and the Dynamic Control of Information Flow
- Probabilistic framework for the adaptation and comparison of image codes
- The Journal of Neuroscience, November 1993, 13(11): 4700-4719 A Neurobiological Model of Visual Attention and Invariant Pattern
- 2.5. SUMMARY OF THE MODEL 49 Figure 2.25: Simulation of the stack circuit.
- The recognition of partially visible natural objects in the presence and absence of their occluders
- A graphical anatomical database of neural connectivity
- 34 CHAPTER 2. DYNAMIC ROUTING CIRCUITS between the V i and the inputs I mem
- To appear in The Visual Neurosciences, L.M. Chalupa, J.S. Werner, Eds. MIT Press. 1 Principles of Image Representation in Visual Cortex
- Bilinear Models of Natural Images Bruno A. Olshausena, Charles Cadieub, Jack Culpepperc, and David K. Warlandd
- LEARNING SPARSE, OVERCOMPLETE REPRESENTATIONS OF TIME-VARYING NATURAL IMAGES
- 13 Sparse Codes and Spikes Bruno A. Olshausen
- In: The Visual Neurosciences, L.M. Chalupa, J.S. Werner, Eds. MIT Press, 2003: 1603-1615. Principles of Image Representation in Visual Cortex
- The following text on page 242 of Vision and the Coding of Natural Images by B.A. Olshausen and D.J. Field, American Scientist, vol. 88 (2000) is partially obscured
- Forward to Map-seeking circuits in visual Bruno A. Olshausen
