Neuromorphic computing is an interdisciplinary research field and computational paradigm that investigates and develops hardware and software systems designed to mimic the structure and function of biological neural systems. Its key characteristics include massively parallel processing, event-driven communication, and the integration of memory and processing elements, aiming for high energy efficiency and adaptability. This approach holds significance for addressing complex computational tasks, such as pattern recognition, machine learning, and real-time processing, often with greater efficiency than conventional Von Neumann architectures.