A model of formal neural networks for unsupervised learning of binary temporal sequences

Proposes an unsupervised model of formal neural networks to learn and recognize binary temporal sequences. In this model, time is represented by its effect on processing and not as an additional dimension of inputs: synaptic efficacy of a connection is the integration time of the signal passing through the connection. The only parameters subject to learning are connection integration times. A local and unsupervised learning of temporal sequences is achieved by assuming that any cell of the network can have a 'spontaneous' activity instead of an only 'evoked' activity as in other models of formal neurons. The only parameters subject to learning are connection integration times. An example of such a network is described and the results of the simulation are discussed. >