👉 Nervous computing, also known as neural computing or brain-inspired computing, is a computational paradigm that mimics the structure and function of biological neural networks found in the human brain. It leverages artificial neural networks (ANNs) and spiking neural networks (SNNs) to process information in a manner similar to how neurons communicate via electrical and chemical signals. These networks consist of interconnected nodes (neurons) that process inputs, perform computations, and transmit outputs through weighted connections, enabling parallel processing and efficient handling of complex, unstructured data. Unlike traditional computing, which relies on binary logic (0s and 1s), nervous computing uses continuous values and temporal dynamics, allowing for more adaptive and energy-efficient processing. This approach excels in tasks like pattern recognition, sensory data interpretation, and decision-making, making it particularly valuable for applications such as robotics, autonomous systems, and advanced AI. By emulating the brain's parallelism and plasticity, nervous computing offers a promising path toward more robust, scalable, and biologically plausible artificial intelligence systems.
