Brain-inspired Algorithms

Brain-inspired algorithms are computational methods that mimic how our brain learns and processes information. They use structures similar to neural networks—interconnected nodes that resemble neurons—to recognize patterns, make decisions, and solve problems. These algorithms learn from data by adjusting connections, much like synapses strengthen with experience. By modeling brain functions, they enable machines to perform tasks like image recognition, language understanding, and decision-making more efficiently and adaptively. Overall, brain-inspired algorithms aim to create smarter, more flexible AI systems that learn and improve much like humans do.