  The small-world property in the context of complex networks implies
structural benefits to the processes taking place within a network, such as
optimal information transmission and robustness. In this paper, we study a
model network of integrate-and-fire neurons that are subject to
activity-dependent synaptic plasticity. We find the learning rule that gives
rise to a small-world structure when the collective dynamics of the system
reaches a critical state which is characterised by power-law distributions of
activity clusters. Moreover, by analysing the motif profile of the networks, we
observe that bidirectional connectivity is impaired by the effects of this type
of plasticity.
