A perceptual learning model based on topological representation

Proceedings of the International Joint Conference on Neural Networks

Volumn 1, Issue , 2001, Pages 406-411

  Milanova, M G ^a   Wachowiak, M P ^a   Rubin, S ^a   Elmaghraby, A S ^a  

^a University of Louisville   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CELLULAR NEURAL NETWORKS; COMPUTER SIMULATION; INDEPENDENT COMPONENT ANALYSIS; REDUNDANCY; SELF ORGANIZING MAPS; SPACE TIME ADAPTIVE PROCESSING;

NATURAL IMAGE SEQUENCES; PERCEPTUAL LEARNING MODEL; TOPOLOGICAL REPRESENTATION;

LEARNING ALGORITHMS;

EID: 0034844669     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Models of perceptual learning
2. Natural image statistics and neural representation
3. A mathematical theory of communication
4. What does the retina know about natural scenes?
5. Neural networks, principal components and subspaces
6. The independent components of natural scenes are edge filters
7. Topographic independent component analysis
8. Probabilistic framework for the adaptation and comparison of image codes
9. Sparse codes and spikes
10. What is the goal of sensory coding?
11. Emergence of simple-cell receptive field properties by learning a sparse code for natural images
12. Dynamic model of visual recognition predicts neural response properties in the visual cortex
13. Sensor encoding using lateral inhibited self-organized cellular neural networks
14. Cellular neural networks: Theory and applications
15. Object recognition in image sequences with cellular neural networks
16. Receptive-field dynamic in the central visual pathways