We introduce a neural network model to study the effect of synchronization on the encoding capacity and spreading spikes in more realistic situation. In this model the neural network is composed of excitatory and inhibitory neurons and long-lasting synaptic current operated by heterogeneous Poisson synaptic input. Based on numerical integration, we find three different regimes depending on the ratio of coupling strength between the inhibitory and the excitatory neurons. In each regime, we study the relationship among the synchronization, information coding capacity, and spreading of spikes.