Neuromorphic computing is an emerging computing technology inspired by the operational principles of the human brain. By employing neuromorphic devices to emulate neuronal functions and construct neural networks, it effectively overcomes the von-Neumann bottleneck, demonstrating remarkable energy efficiency. Due to their unique electrical properties, compact structures, and extremely low power consumption, memristive devices have become a core focus in neuromorphic computing based on memristive devices have become a core focus in neuromorphic device research. This review highlights recent advances in neuromorphic computing based on memristive devices, covering the working principles, key functions, and performance metrics of memristive devices, the structure and typical applications of memristive arrays, and the architectures and performance of neuromorphic chips. It also analyzes the intrinsic links among memristive devices, memristive arrays, and neuromorphic chips. 

A international joint research team of KAIST and the University of Michigan developed a digital biomarker for predicting symptoms of depression based on data collected by smartwatches

Osaka Metropolitan University scientists have developed an artificial photosynthesis technology that produces precursors of biodegradable nylon from biomass-derived compounds and ammonia.

The team proposed a prenatal depression detection method using the semantically enhanced option embedding (SEOE) model to represent questionnaire options. It can quantitatively determine the relationship and patterns between options and depression.

In nature, phenomena involving multiple fluctuations often arise interactively. At the ASDEX Upgrade^*1 experimental device in Germany, two plasma fluctuations driven by energetic particles have been observed in conjunction. Assistant Professor Hao Wang and Professor Yasushi Todo of the National Institute for Fusion Science (NIFS), in collaboration with researchers from the Max Planck Institute for Plasma Physics in Germany, successfully reproduced the coupled generation of the two fluctuations using a simulation code developed at NIFS and performed on a supercomputer. Through detailed analysis, they clarified that as the first fluctuation grew, the distribution function^*2 of the energetic particles was significantly deformed, which caused the second fluctuation to occur in conjunction. Energetic particles and fluctuations are deeply involved in maintaining the high-temperature plasma necessary for the realization of fusion energy. This achievement will greatly contribute to research in these areas.

DNA-nanoparticle motors are exactly as they sound: tiny artificial motors that use the structures of DNA and RNA to propel motion by enzymatic RNA degradation. Essentially, chemical energy is converted into mechanical motion by biasing the Brownian motion. The DNA-nanoparticle motor uses the "burnt-bridge" Brownian ratchet mechanism. In this type of movement, the motor is being propelled by the degradation (or "burning") of the bonds (or "bridges") it crosses along the substrate, essentially biasing its motion forward.

These nano-sized motors are highly programmable and can be designed for use in molecular computation, diagnostics, and transport. Despite their genius, DNA-nanoparticle motors don't have the speed of their biological counterparts, the motor protein, which is where the issue lies. This is where researchers come in to analyze, optimize, and rebuild a faster artificial motor using single-particle tracking experiment and geometry-based kinetic simulation.