title: Rapid convergence to feature layer correspondences.
abstract: 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.
authors.affiliation: Gatsby Computational Neuroscience Unit, University College London, London WC1N 3AR, U.K. lucke@gatsby.ucl.ac.uk
authors.forename: Jörg
authors.initials: J
authors.lastname: Lücke
authors.forename: Christian
authors.initials: C
authors.lastname: Keck
authors.forename: Christoph
authors.initials: C
authors.lastname: von der Malsburg
mesh_terms: D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*