Oscillatory associative memories
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)