Memristors have been favored in artificial intelligence, neural networks, and chaotic circuits, especially in neural synapses because of their unique advantages such as resistance variability, nonvolatile nature, and nanometer size. Benefits such as integration scale and low power consumption contribute toward simulating the biological synaptic function. Compared with memory association circuits using traditional CMOS transistors, memristors will reduce the complexity of the circuit and the power consumption. Therefore, it is greatly promising to use memristors as synapses to construct neural networks to mimic human brain functions. This paper successfully establishes a recognition circuit based on memristors to recognize some characteristics (size, color, shape, and smooth) of fruits, which is a learning function. After a few seconds, the output signal voltage drops, and this is a forgetting function. Through the establishment of a recognition circuit, the neural network and human complex behavior were simulated. This work lays the foundation for further research of human neural networks.

In artificial neural networks, the fourth passive element memristor can be utilized as an electronic synapse that serves as the interface between neurons. The artificial neuron composed of the memristor bridge synapse not only has the characteristics of low power consumption and high integration but also has a more simplified circuit and weight change conditions. Particularly, it has the ability of bionic intelligent information processing. This paper established two novel synaptic structures on the basis of memristor bridges (type 1 and type 2) and then synthetically analyzed how to realize the artificial neuron perceptron. Herein, the artificial synapses (type 1 and type 2) have the following characteristics: continuous changes in synaptic weights (positive, negative, and zero) and memory properties. Among them, the type 2 memristor bridge has the advantage of a wider range of weight updates for the synaptic circuit, which can realize the function of the artificial neuron perceptron with less error. This work lays the foundation for the future exploitation of artificial intelligence.