Pulsed neural network models can account for the cognitive processes in which internal states change rapidly and abruptly. This study constructed a pulsed neural network model for selective visual attention based on the temporal tagging hypothesis. According to the temporal tagging hypothesis, the effect of visual attention reflects the modulation of spatiotemporal correlation of neural activities, not the modulation of firing rates. The proposed model produced the temporal synchrony of spikes purely within the simple spike response model framework. The model could simulate the data from Moran & Desimone (1985), and showed that simple pulsed neural networks without excessive neurophysiological details were at the appropriate level for the models of visual cognition. Compared with traditional connectionist models, pulsed neural network models appear to be the better framework to deal with real-time temporal dynamics of cognitive process including feature binding.