Event-based Neural Networks

Event-based Neural Networks are a type of artificial intelligence model that process information only when specific events or changes occur, rather than continuously analyzing data. Instead of receiving a constant stream of input, they activate and compute only when meaningful signals—like a change in an image or sensor reading—are detected. This approach allows for faster, more efficient processing, especially in real-time systems, by focusing resources on relevant data. They are inspired by biological systems and are particularly useful in applications like robotics, surveillance, and sensory data analysis where efficient, timely responses are critical.