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Recently it has been pointed out that in simple animals like flies a motor neuron can have a visual receptive field [H.G. Krapp, S.J. Huston, Encoding self-motion: From visual receptive fields to motor neuron response maps, in: H. Zimmermann, K. Krieglstein (Eds.), Proceedings of the sixth Meeting of the German Neuroscience Society/30th Gottingen Neurobiology Conference 2005, Gottingen, 2005, p. S16-3] [4]. Such receptive fields directly generate behaviour which, through closing the perception-action loop, will feed back to the sensors again. In more complex animals an increasingly complex hierarchy of visual receptive fields exists from early to higher visual areas, where visual input becomes more and more indirect. Here we will show that it is possible to develop receptive fields in simple behavioural systems by ways of a temporal sequence learning algorithm. The main goal is to demonstrate that learning generates stable behaviour and that the resulting receptive fields are also stable as soon as the newly learnt behaviour is successful.