Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks. Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural networks have led to promising neuromorphic systems. However, developing compact parallel computing technology for integrating artificial neural networks into traditional hardware remains a challenge. Organic computational materials offer affordable, biocompatible neuromorphic devices with exceptional adjustability and energy-efficient switching. Here, the review investigates the advancements made in the development of organic neuromorphic devices. This review explores resistive switching mechanisms such as interface-regulated filament growth, molecular-electronic dynamics, nanowire-confined filament growth, and vacancy-assisted ion migration, while proposing methodologies to enhance state retention and conductance adjustment. The survey examines the challenges faced in implementing low-power neuromorphic computing, e.g., reducing device size and improving switching time. The review analyses the potential of these materials in adjustable, flexible, and low-power consumption applications, viz. biohybrid spiking circuits interacting with biological systems, systems that respond to specific events, robotics, intelligent agents, neuromorphic computing, neuromorphic bioelectronics, neuroscience, and other applications, and prospects of this technology.

Researchers from Sun Yat-sen University and Guangxi University have developed a machine learning-enhanced synchronization method for quantum key distribution, enabling secure communication over 200 km without the need for external clock synchronization or calibration. Combined with a simplified reference-frame-independent QKD protocol and a streamlined detector setup, the result is a compact, low-cost, and robust quantum communication system.

A new study by researchers at the Icahn School of Medicine at Mount Sinai, Memorial Sloan Kettering Cancer Center, and collaborators, suggests that artificial intelligence (AI) could significantly improve how doctors determine the best treatment for cancer patients—by enhancing how tumor samples are analyzed in the lab. The findings, published in the July 9 online edition of Nature Medicine, showed that AI can accurately predict genetic mutations from routine pathology slides—potentially reducing the need for rapid genetic testing in certain cases.