The interaction between Hebbian and homeostatic plasticity in neuronal networks has recently received a lot of attention. Hebbian synaptic plasticity like STDP, known for its associative properties, tends to lead to instabilities in recurrent networks, and different homeostatic mechanisms have been proposed to stabilize the learning process. While slow homeostatic plasticity has been observed in experiments, a mechanism fast enough to compensate instabilities on short time scales remains to be found . The goal of this work is to contribute another aspect to the understanding of this interaction and show that associative properties can also emerge from a rule solely based on homeostatic principles, and that is not explicitly dependent on correlations between the activity of pairs of neurons. By simulating neural networks of spiking neurons and a computational model of structural plasticity based on homeostasis of firing rate [3,4], we have shown that cell assemblies of strongly connected neurons can be formed upon neuronal stimulation . Moreover, we could demonstrate that this effect is larger for repetitive stimulation  and it is long-lasting, i.e. the emerging structure decays only slowly when the specific external stimulation is turned off. While the existence of an assembly has only a small effect on the spontaneous activity of the network it is part of, the evoked activity is larger upon stimulation of the cell assembly, allowing for a simple readout of the memory.