Learning internal representations of pattern sequences in a neural network with adaptive time-delays

The Tempo-Network is an artificial neural network with both adaptive weights and adaptive time delays. U Bodenhausen (see ibid., vol.1, p.597-600, 1990) showed that the network is able to work as a autoassociator for sequences of patterns which are fed into the network one after the other. The patterns of the sequence are stepped through in time. The learning rules and the internal representations in the hidden layer are discussed. Simulations with the Tempo-Network are discussed. Neural networks with adaptive time delays seem to be interesting for tasks where the sequence contains nonadjacent events