Neuromorphic computing, an innovative and multidisciplinary field that draws inspiration from the complex architecture and remarkable functioning of biological neural networks such as the human brain, endeavors to create highly efficient, low-power, adaptable artificial intelligence systems through the development of specialized hardware known as neuromorphic chips, which incorporate numerous advanced features such as spiking neural networks that communicate using discrete signals or “spikes” akin to the action potentials observed in biological neurons, event-driven processing that only processes and transmits information when there is a change in input, leading to significantly reduced power consumption and remarkably faster response times, parallel architectures that closely emulate the billions of neurons in the human brain working simultaneously for the efficient processing of vast amounts of data, local memory integration where memory and processing units are combined within the same structure, reminiscent of the brain’s synapses, thus eliminating the need for data to travel long distances between separate memory and processing units, consequently lowering power consumption and increasing overall efficiency, and adaptability and plasticity that allow these neuromorphic chips to learn from new experiences, adjust their synaptic weights based on the input they receive, and improve their performance over time, ultimately enabling an extensive range of applications in diverse fields such as robotics, computer vision, natural language processing, sensor networks, and even healthcare, all with the transformative potential to revolutionize the field of AI by providing systems that can learn, adapt, and evolve in real-time while consuming considerably less power and operating with unparalleled efficiency in comparison to traditional AI approaches.