Although recent experiments suggested that a synchronous neural activity may play a significant functional role in cognitive processes, it is still unclear how the spike synchronization affects the network behavior in neural systems. In the first approach, we consider a specific problem: how the retrieval state of associative memory model of neural network is affected by incoming spike synchronization. Considering a simple network of Leaky-Integrate-and-Fire neurons, organizing through the learning rule by spike-timing dependent plasticity (STDP), we present that this network exhibits the nature of associative memory as a standard Hopfield model for asynchronous input, whereas a transition between the learned spike-trains can be triggered by a brief incoming spike-synchrony.