Today, Neural network, a powerful learning model, has archived amazing results. However, the current Von Neumann computing system–based implementations of neural networks are suffering from memory wall and communication bottleneck problems ascribing to the Complementary Metal Oxide Semiconductor (CMOS) technology scaling down and communication gap. Memristor, a two terminal nanosolid state nonvolatile resistive switching, can provide energy‐efficient neuromorphic computing with its synaptic behavior. Crossbar architecture can be used to perform neural computations because of its high density and parallel computation. Thus, neural networks based on memristor crossbar will perform better in real world applications. Neural networks can be classified into three generations. The initial generation of these structures (the perceptron) consists of a single artificial nerve cell that can be educated. It is the output of the perceptron system when the neurons cross a specified threshold value (threshold), such as 0 or 1. Second-generation neural networks are Artificial Neural Networks (ANN) structures that are still in use today. Third-generation neural networks are the Spiking Neural Networks (SNN) structure, which attempts to fully mimic the working principles of the human brain and communicate via spikes. The Spiking Neural Network (SNN) can be constructed using specific network topologies. It becomes smarter and more energy-efficient. In this paper, the design of Spiking neural networks based on memristors is introduced. SNN design, architecture, computing circuits, and the training algorithms are presented by instances. The potential applications and the prospects of memristor‐based neural network system are discussed.

Today, Neural network, a powerful learning model, has archived amazing results.