The Topology Representing Network (TRN) model is extended by using an activation dynamics which implicitly orders the neurons according to the distance from the input pattern. This allows to apply the same Hebbian learning method to the thalamo-cortical and cortico-cortical connections. The model proposed combines a process of diffusion (via the excitatory topologically organized connections) and a process of competitive distribution of activation which tends to sharpen the active map region. The dynamics is analyzed taking into account the excitatory nature of the majority of cortical synapses and the puzzling presence of long-range competition without long-range inhibition. The model is shown to be more consistent than TRN or other self-organizing paradigms with a number of neurophysiological facts.