Rapid correspondence finding in networks of cortical columns

Authors:
Jörg Lücke;Christoph von der Malsburg
Affiliations:
Gatsby Computational Neuroscience Unit, UCL, London, UK;Institut für Neuroinformatik, Ruhr-Universität, Bochum, Germany
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Year:
2006

Citing 7
Cited 4

The role of topographical constraints in face recognition

Pattern Recognition Letters
The FERET Evaluation Methodology for Face-Recognition Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid processing and unsupervised learning in a model of the cortical macrocolumn

Neural Computation
Face Authentication Test on the BANCA Database

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Maplets for correspondence-based object recognition

Neural Networks - 2004 Special issue: New developments in self-organizing systems
Dynamics of cortical columns – sensitive decision making

ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Dynamics of cortical columns – self-organization of receptive fields

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 Visual Object Recognition System Invariant to Scale and Rotation

ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Learning of Neural Information Routing for Correspondence Finding

ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe a neural network able to rapidly establish correspondence between neural fields. The network is based on a cortical columnar model described earlier. It realizes dynamic links with the help of specialized columns that evaluate similarities between the activity distributions of local feature cell populations, are subject to a topology constraint, and gate the transfer of feature information between the neural fields. Correspondence finding requires little time (estimated to 10-40 ms in physiological terms) and is robust to noise in feature signals.