👉 Metabolism computing, also known as neuromorphic computing, is a paradigm that mimics the structure and function of biological neural networks to process information. Unlike traditional computing, which relies on sequential processing and binary logic, metabolism computing leverages parallelism and event-driven architectures to replicate the brain's efficiency in handling complex tasks. It uses specialized hardware, such as spiking neural networks (SNNs), to simulate neurons and synapses, allowing for low-power, real-time data processing akin to the human brain. This approach excels in tasks like pattern recognition, sensory processing, and decision-making, making it particularly suitable for applications requiring adaptive learning and energy efficiency. By emulating the brain's parallel and distributed nature, metabolism computing promises to revolutionize fields like artificial intelligence, robotics, and edge computing.
