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Dynamic neural fields are recurrent neural networks which aim at modeling cortical activity evolution both in space and time. A self-organized formation of these fields has been rarely explored previously. The main reason for this is that learning-induced changes in effective connectivity constitute a severe problem with respect to network stability. In this paper, we present a novel network model which is able to self-organize even in face of experience-driven changes in the synaptic strengths of all connections. Key to the model is the incorporation of homeostatic mechanisms which explicitly address network stability. These mechanisms regulate activity of individual neurons in a similar manner as cortical activity is controlled. Namely, our model implements the homeostatic principles of synaptic scaling and intrinsic plasticity. By using fully plastic within-field connections our model further decouples learning from topological constraints. For this reason, we propose to incorporate an additional process which facilitates the development of topology preserving mappings. This process minimizes the wiring length between neurons. We thoroughly evaluated the model using artificial data as well as continuous speech. Our results demonstrate that the network is able to self-organize, maintains stable activity levels, and remains adaptive to variations in input strength and input distribution.