Neuroscience research originated in the United Kingdom, at the University of Edinburgh, where Professor Langer Higgins theorized that three-layer neural networks (including input layer, hidden layer, and output layer) can theoretically approximate any continuous nonlinear function. However, in practical applications, Deep Neural Networks (DNNs) are widely adopted. The reason is that deep networks can more efficiently handle complex data structures, extract multi-level features, and perform better in terms of computational efficiency and model generalization. Imagine you are a painter with only three colors of paint—red, green, and blue. By mixing these three colors, you can create almost all the colors you need. However, relying solely on simple mixing techniques, you may not be able to quickly create delicate and complex paintings. If you introduce more layers and tools for mixing, such as airbrushes, palette knives, or even digital painting software, your creations will become more efficient and expressive. Similarly, while three-layer neural networks can accomplish basic function approximation tasks, deep neural networks provide a stronger capability to tackle complex real-world problems.