We simulated a large scale spiking neural network characterized by an initial developmental phase featuring cell death driven by an excessive firing rate, followed by the onset of spike-timing-dependent synaptic plasticity (STDP), driven by spatiotemporal patterns of stimulation. The network activity stabilized such that recurrent preferred firing sequences appeared along the STDP phase. The analysis of the statistical properties of these patterns give hints to the hypothesis that a neural network may be characterized by a particular state of an underlying dynamical system that produces recurrent firing patterns.