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Rapid Convergence to Feature Layer Correspondences

lucke{at}gatsby.ucl.ac.uk Gatsby Computational Neuroscience Unit, University College London, London WC1N 3AR, U.K.

Christian Keck

keck{at}fias.uni-frankfurt.de Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum, Germany

Christoph von der Malsburg

malsburg{at}fias.uni-frankfurt.de Frankfurt Institute for Advanced Studies, Goethe-Universität Frankfurt, 60438 Frankfurt am Main, Germany

We describe a neural network able to rapidly establish correspondence between neural feature layers. Each of the network's two layers consists of interconnected cortical columns, and each column consists of inhibitorily coupled subpopulations of excitatory neurons. The dynamics of the system builds on a dynamic model of a single column, which is consistent with recent experimental findings. The network realizes dynamic links between its layers 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 can gate the transfer of feature information between the neural layers. The system can robustly be applied to natural images, and correspondences are found in time intervals estimated to be smaller than 100 ms in physiological terms.