Small-world structure induced by spike-timing-dependent plasticity in networks with critical dynamics

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.

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