What defines a neural network in the context of AI?

A neural network is defined as a computational model inspired by the human brain. This concept is central to understanding how AI mimics certain cognitive functions. Neural networks consist of interconnected nodes, or "neurons," that process information in layers. These models are designed to learn from data by adjusting the connections between neurons based on the input they receive and the output they predict.

The architecture of neural networks allows them to identify patterns and relationships within complex datasets, making them particularly powerful for tasks such as image and speech recognition, natural language processing, and more. By leveraging structures similar to biological neural networks, AI can improve its performance through a process called training, where the model learns from examples.

The other options provided do not accurately characterize a neural network. A database structure pertains to how data is organized and stored, a programming language relates to the syntax and structure used to communicate with a computer, and a graphical user interface refers to the visual elements that allow users to interact with software applications. These concepts, while relevant to technology, do not capture the essence of what a neural network is in the context of artificial intelligence.