Neuromorphic computing, also known as "brain-inspired computing," is an emerging field that aims to change how computers operate by drawing inspiration from the human brain. This approach focuses on creating systems that are more energy-efficient and better at handling complex tasks, much like the brain itself. Unlike traditional computers, which separate memory and processing units, the human brain integrates these functions within its neurons and synapses, using energy only when needed. This natural efficiency is something researchers hope to replicate in computing technology. Traditional computer chips face a challenge known as the Von Neumann bottleneck, where the separate memory and processing units require significant energy to transfer data back and forth. Neuromorphic computing addresses this by bringing memory and processing closer together, using sparse data representations and performing computation only when necessary—known as event-driven computing. This method has led to the development of event cameras, which are inspired by the human retina. These sensors respond only to changes in a scene, drastically reducing power use and improving performance in low-light or fast-moving environments. The applications of neuromorphic computing are expanding rapidly. In autonomous vehicles, for example, the technology could improve obstacle detection in extreme lighting conditions. In space environments, where power is limited and lighting can be harsh, neuromorphic systems offer a practical solution. Australia's BrainChip has already begun commercial shipments of a neuromorphic processor tailored for low-power applications, such as cameras and sensors. Beyond energy efficiency, neuromorphic computing also enhances data privacy and cybersecurity by allowing sensitive data to be processed locally on devices, rather than being sent to the cloud. This reduces the risk of data breaches and enables devices to operate independently in areas with unreliable connectivity. IBM has demonstrated the potential of neuromorphic computing with its TrueNorth chip, which uses up to 10,000 times less energy than traditional chips for event-driven tasks. However, neuromorphic computing is not expected to replace conventional processors and GPUs entirely. Instead, it may serve as a complementary technology, handling tasks where its efficiency is most advantageous. The global market for neuromorphic technology is projected to grow significantly, reaching nearly $20 billion by 2030. In the UK, NeuroWare—a collaborative innovation hub involving multiple universities—has developed a roadmap for the technology up to 2050. This roadmap emphasizes the need for open-access research facilities, coordinated investment, and a skilled workforce with expertise in neuroscience, electronics, and computer science to support the development and adoption of neuromorphic computing on a larger scale.