Pseudorehearsal is a mechanism proposed by Robins which alleviates catastrophic forgetting in multi-layer perceptron networks. In this paper, we extend the exploration of pseudorehearsal to a Hopfield-type net. The same general principles apply: old information can be rehearsed if it is available, and if it is not available, then generating and rehearsing approximations of old information that 'map' the behaviour of the network can also be effective at preserving the actual old information itself. The details of the pseudorehearsal mechanism, however, benefit from being adapted to the dynamics of Hopfield nets so as to exploit the extra attractors created in state space during learning. These attractors are usually described as 'spurious' or 'cross-talk', and regarded as undesirable, interfering with the retention of the trained population items. Our simulations have shown that, in another sense, such attractors can in fact be useful in preserving the learned population. In general terms, a solution to the catastrophic forgetting problem enables the on-going or sequential learning of information in artificial neural networks, and consequently also provides a framework for the modelling of lifelong learning/developmental effects in cognition.