Homeostatic synaptic scaling in self-organizing maps
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Object recognition by artificial cortical maps
Neural Networks
Edge Detection Based on Spiking Neural Network Model
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Evolution of recollection and prediction in neural networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Extraction of salient contours in color images
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Hi-index | 0.00 |
Contour integration in low-level vision is believed to occur based on lateral interaction between neurons with similar orientation tuning. How such interactions could arise in the brain has been an open question. Our model suggests that the interactions can be learned through input-driven self-organization, i.e., through the same mechanism that underlies many other developmental and functional phenomena in the visual cortex. The model also shows how synchronized firing mediated by these lateral connections can represent the percept of a contour, resulting in performance similar to that of human contour integration. The model further demonstrates that contour integration performance can differ in different parts of the visual field, depending on what kinds of input distributions they receive during development. The model thus grounds an important perceptual phenomenon onto detailed neural mechanisms so that various structural and functional properties can be measured and predictions can be made to guide future experiments.