Oscillatory Associative

Associative recall and completion of information is one of the astonishing abilities of intelligent living beings. The search for mechanisms which produce this ability of associative memory yielded a class of computational systems composed of many neuron-like, non-linear units. The neural units are connected to artiicial neural networks. The basic principle of associative information recall is a dissipative network dynamics which maps initial network states to a subset of nal states. According to dynamical systems theory, the asymptotic dynamics of such an assembly of neurons can be a xed point, a limit cycle or a chaotic attractor 8]. The best understood artiicial neural networks with associative abilities are networks with a xed point dynamics. The prototypical model of this class, suggested by Hoppeld 11], comprises a network of n binary units which are connected in a symmetric fashion and typically show a distributed activity pattern. The dissipative dynamics with synchronous or asynchronous (stochastic) update of neuron states drives the network into a stationary state, a xed point of the neural dynamics. These xed points are supposed to be identical or at least very similar to a set of p random patterns f ~ the information content of the associative memory. A connectivity which guarantees the closeness of pattern states and xed points for a moderate number of patterns (p=n < 0:14)