Neural networks mimic human brain function by using interconnected nodes (neurons) to process data similarly to biological neural connections. They consist of multiple layers, including an input layer, hidden layers, and an output layer, where data passes through and is transformed at each stage. Neural networks learn patterns in data through training, adjusting the connections' weights to improve accuracy over time. This ability to recognize complex relationships enables neural networks to excel in tasks such as image recognition, natural language processing, and predictive analytics, closely resembling human cognition.