The memristor is the resistor with dynamic characteristics. Its resistance value can be changed according to the variation of the external field, and it can maintain the original resistance value after the external field is removed. Memristors can be used to store synaptic weights thanks to their similar characteristics to the connection strength of biological synapses. In order to realize the function of recognition and learning of the IRIS dataset based on the temporal rule, we design a SPICE simulation circuit of neural networks which takes the bridge memristor as synapses. The circuit uses the single pulse encoding method, and the time of the pulse represents data information. The neural network circuit consists of 48 pulse input ports, 144 synapses and three output ports. The synaptic weights are modified based on the learning rules of the temporal rule. Simulation results show that the classification accuracy on the IRIS dataset can reach 93.33%, which proves that the proposed neural network circuit can be used in brain-like pulse neural networks.