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Self-organizing neural networks endeavour to preserve the topology of an input space by means of competitive learning. This capacity is used for the representation of objects and their motion. In addition, these applications usually have real-time constraints imposed on them. This paper describes several variants of a Growing Neural Gas self-organizing network that accelerate the learning process. However, in some cases this acceleration causes a loss in topology preservation and, therefore, in the quality of the representation. Our study quantifies topology preservation using different measures to establish the most suitable learning parameters, depending on the size of the network and on the time available for adaptation.