Core Viewpoint - Researchers from the National University of Singapore (NUS) have demonstrated that a single standard silicon transistor can mimic the behavior of biological neurons and synapses, bringing hardware-based artificial neural networks (ANN) closer to reality [1][3]. Group 1: Research Findings - The NUS research team, led by Professor Mario Lanza, provides a scalable and energy-efficient solution for hardware-based ANN, making neuromorphic computing more feasible [1][3]. - The study published in Nature on March 26, 2025, shows that a single silicon transistor can replicate neural firing and synaptic weight changes, which are fundamental mechanisms of biological neurons and synapses [3][4]. Group 2: Technical Innovations - The research achieved this by adjusting the resistance of the transistor to specific values, controlling two physical phenomena: impact ionization and charge trapping [4]. - The team developed a dual-transistor unit called "neuro-synaptic random access memory" (NS-RAM), which operates in neuron or synapse states [4]. Group 3: Advantages of the New Approach - The method utilizes commercial CMOS technology, ensuring scalability, reliability, and compatibility with existing semiconductor manufacturing processes [4]. - Experimental results show that NS-RAM units exhibit low power consumption, stable performance over multiple operational cycles, and consistent behavior across different devices, essential for building reliable ANN hardware [4].