Learning Lie groups for invariant visual perception
Proceedings of the 1998 conference on Advances in neural information processing systems II
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
Separating Style and Content with Bilinear Models
Neural Computation
A parallel computation that assigns canonical object-based frames of reference
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
A multifactor winner-take-all dynamics
Neural Computation
Self-organization of topographic bilinear networks for invariant recognition
Neural Computation
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One way to handle the perception of images that change in position (or size, orientation or deformation) is to invoke rapidly changing fiber projections to project images into a fixed format in a higher cortical area. We propose here a model for the ontogenesis of the necessary control structures. For simplicity we limit ourselves to fiber projections between two one-dimensional chains of units. Our system is a direct extension of a mathematical model [1] for the ontogenesis of retinotopy. Our computer experiments are guided by stability analysis and show the establishment of multiple topographic mappings implementing different translations, each projection associated with a single control unit. The model relies on neural signals with appropriate correlation structure, signals that can be generated by the network as spontaneous noise, so that the proposed mechanism could act prenatally.