1994 Special Issue: Modeling visual recognition from neurobiological constraints
Neural Networks - Special issue: models of neurodynamics and behavior
Singularities in primate orientation maps
Neural Computation
SCAN: a scalable model of attentional selection
Neural Networks
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Maplets for correspondence-based object recognition
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Image Representation by Complex Cell Responses
Neural Computation
Dynamics of cortical columns – sensitive decision making
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Rapid convergence to feature layer correspondences
Neural Computation
Dynamic Link Matching between Feature Columns for Different Scale and Orientation
Neural Information Processing
A marker-based model for the ontogenesis of routing circuits
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Visual object detection by specifying the scale and rotation transformations
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Self-organization of topographic bilinear networks for invariant recognition
Neural Computation
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Analyzing the design of networks for visual information routing is an underconstrained problem due to insufficient anatomical and physiological data. We propose here optimality criteria for the design of routing networks. For a very general architecture, we derive the number of routing layers and the fanout that minimize the required neural circuitry. The optimal fanout l is independent of network size, while the number k of layers scales logarithmically (with a prefactor below 1), with the number n of visual resolution units to be routed independently. The results are found to agree with data of the primate visual system.