What describes the function of hidden layers in a neural network?

Hidden layers in a neural network serve as the processing units that transform the input data into something the output layer can use. Each hidden layer consists of multiple neurons that receive inputs from the previous layer's neurons, apply weights and activation functions, and pass the transformed data to the next layer.

The characteristic of hidden layers is that they are not directly connected to the input or output layers; instead, they work as intermediaries to learn patterns and representations of the data. This allows the network to make more complex decisions without being directly influenced by the input or output data, which is crucial for capturing non-linear relationships in the data.

This layered approach enables the network to learn hierarchical representations, where lower layers may capture basic features while higher layers capture more complex features. Such an architecture is what makes neural networks effective at processing data for tasks like classification, regression, and more.

The other options do not accurately encapsulate the role of hidden layers. For instance, the notion that hidden layers are linked directly to the output is misleading, as they serve as intermediaries rather than direct connections. The claim that hidden layers store the final decisions is incorrect because the output layer is responsible for producing the final output. Lastly, while hidden layers connect to every node from