What is a key characteristic of hidden layers in a neural network?

The key characteristic of hidden layers in a neural network being that they can have an unlimited number of links to input and output nodes is crucial for understanding how neural networks operate. This characteristic allows hidden layers to capture complex patterns and relationships within the data by receiving inputs from various nodes (either from the input layer or from previous hidden layers) and providing outputs to subsequent layers.

Having an unlimited number of connections enhances the flexibility and capability of the network to learn from diverse datasets. This means that each node in a hidden layer can interact with many other nodes, allowing for the modeling of intricate functions and enabling deeper insights into the data through effective feature extraction and transformation.

The other options do not accurately reflect the characteristics of hidden layers in neural networks. Hidden layers do not have to consist of a single layer, and they are not restricted in the number of nodes; rather, they often include multiple nodes to perform effective learning. Furthermore, hidden layers indeed contribute to output as it is through their activations that the final output of the network is derived, making them essential in the overall functioning of the neural network.