Hidden layers are the intermediate processing stages between a network's input and output. Each unit takes weighted inputs from the previous layer, sums them, and applies a non-linear activation function. Early layers detect simple patterns; deeper layers compose them into increasingly abstract representations. Weights are adjusted via backpropagation to minimise prediction error.