Recent progress in memristors for stimulating synaptic plasticity

With the rapid expansion of data information, modern computers based on the von Neumann architecture are facing severe challenges. Intelligent computers that can learn, store, and process information flexibly like a human brain will be the direction and goal of computers development. Brain controls almost all the complex life activities of human beings, and information transmission between cerebral neurons relies on the structure called “synapse, whose outstanding property — synaptic plasticity— is thought to be an important molecular basis of learning and memory. Therefore, it is widely believed that emulation of synapse and synaptic plasticity is the first step to realize effective artificial neural networks. Owing to the birth and development of the fourth fundamental passive circuit elements, memristors, which have unique nonlinear synaptic electrical transmission characteristics, it is possible to achieve this goal. Thus, over the past few years, a great deal of efforts have been made in mimicking synapse functions though memristors. In this review, recent simulations of synaptic plasticity using different memristor devices and various methods are comprehensively summarized, including short-term plasticity (paired-pulse depression, paired-pulse facilitation, and post-tetanic potentiation), long-term plasticity, spiking-rate-dependent plasticity, spiking-timing-dependent plasticity, learning experience, associative memory, and synaptic scaling. Finally, the current problems faced in the research and the development prospects in this area are briefly discussed.