An innovative biomimetic technology: Memristors mimic human sensation

Abstract

As a device with tunable resistance states, the memristor has demonstrated significant potential in emulating the plasticity of biosynapses. In recent years, the application of memristors in biomimetic sensory systems has gained widespread attention. This work reviews the research progress of memristors in simulating human senses, particularly in systems involving vision, touch, smell, and hearing. Memristors can not only simulate the perception, storage, and processing of various sensory signals, but also it can integrate with neuromorphic computing and self-learning algorithms to construct multimodal sensory systems. These systems, by integrating information from different sensory channels, can perceive the external environment more intelligently and have wide application prospects in many fields, such as robotics, smart healthcare, neural prosthetics, and augmented reality. Although current research on memristor-based sensory systems faces challenges such as manufacturing variability, randomness in conduction mechanisms, and power consumption during high-frequency operation, continuous developments in materials, structural design, and algorithm optimization are expected to lead to breakthroughs in the future. This work will facilitate the transition of memristor-based sensory systems from laboratory research to real-world applications, driving innovation and progress in biomimetic sensory systems and neuromorphic computing.