In this paper, we investigate how Spike-Timing Dependent Plasticity, when applied to a random recurrent neural network of leaky integrate-and-fire neurons, can affect its dynamical regime. We show that in an autonomous network with self-sustained activity, STDP has a regularization effect and simplifies the dynamics. We then look at two different ways to present stimuli to the network: potential-based input and current-based input. We show that in the first case STDP can lead to either synchronous or asynchronous periodical activity, depending on the network's internal parameters. However, in the latter case, synchronization can only appear when the input is presented to a fraction of the neurons instead of the whole.