👉 Neural Computing, often referred to as Neuromorphic Computing or Brain-Inspired Computing, is a computational paradigm inspired by the structure and function of biological neural networks in the human brain. Unlike traditional computing, which relies on sequential processing and binary logic, Neural Computing mimics the parallel, distributed, and adaptive nature of neurons and synapses in the brain. This approach enables more efficient processing of complex, unstructured data, such as images, sounds, and sensory inputs, by leveraging concepts like spiking neural networks (SNNs) and synaptic plasticity. These systems can learn and adapt in real-time, making them particularly suitable for tasks like pattern recognition, decision-making, and robotics. By emulating the brain's energy-efficient and robust architecture, Neural Computing holds promise for advancing artificial intelligence, enhancing machine learning capabilities, and developing more intelligent and adaptive computing systems.
