We consider a fully-connected network of leaky integrate-and-fire neuronswith spike-timing-dependent plasticity. The plasticity is controlled by aparameter representing the expected weight of a synapse between neurons thatare firing randomly with the same mean frequency. For low values of theplasticity parameter, the activities of the system are dominated by noise,while large values of the plasticity parameter lead to self-sustaining activityin the network. We perform event-driven simulations on finite-size networkswith up to 128 neurons to find the stationary synaptic weight conformations fordifferent values of the plasticity parameter. In both the low and high activityregimes, the synaptic weights are narrowly distributed around the plasticityparameter value consistent with the predictions of mean-field theory. However,the distribution broadens in the transition region between the two regimes,representing emergent network structures. Using a pseudophysical approach forvisualization, we show that the emergent structures are of "path" or "hub"type, observed at different values of the plasticity parameter in thetransition region.
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