Which statement about neural networks is incorrect?

The statement regarding neural networks that indicates as hidden layers of a neural network increase, the model classification capability gradually weakens is indeed incorrect. In fact, adding hidden layers to a neural network generally enhances its ability to model complex functions and capture intricate patterns in the data. This capability is often referred to as the model's expressiveness.

A neural network with an appropriate number of hidden layers can progressively learn higher-level features and abstractions from the input data, allowing it to classify data more effectively, especially for complex tasks. While it is true that overly deep networks can lead to issues like overfitting, poor training, or vanishing gradients, the general consensus is that increased depth, up to a point, typically improves performance when managed correctly.

The other statements are correct: neurons within the same layer of a feedforward neural network are indeed not interconnected; single-layer perceptrons cannot resolve XOR problems due to their linearity, which limits them to linearly separable data; and a feedforward neural network can be aptly represented as a directed acyclic graph that illustrates the flow of information through layers in a one-way manner.