title: Robust autoassociative memory with coupled networks of Kuramoto-type oscillators.
abstract: Uncertain recognition success, unfavorable scaling of connection complexity, or dependence on complex external input impair the usefulness of current oscillatory neural networks for pattern recognition or restrict technical realizations to small networks. We propose a network architecture of coupled oscillators for pattern recognition which shows none of the mentioned flaws. Furthermore we illustrate the recognition process with simulation results and analyze the dynamics analytically: Possible output patterns are isolated attractors of the system. Additionally, simple criteria for recognition success are derived from a lower bound on the basins of attraction.
authors.affiliation: Physics Department, Technical University of Munich, 85748 Garching, James-Franck-Straße 1, Germany.
authors.forename: Daniel
authors.initials: D
authors.lastname: Heger
authors.affiliation: Physics Department, Technical University of Munich, 85748 Garching, James-Franck-Straße 1, Germany.
authors.forename: Katharina
authors.initials: K
authors.lastname: Krischer