The hidden neurons act as feature detectors; as such, they play a critical role in the operation of a multiplayer perceptron. As the learning process progresses across the multilayer perceptron, the hidden neurons begin to gradually "discover" the salient features that characterize the training data. They do so by performing a nonlinear transformation on the input data into a new space called the feature space. In this new space, the classes of interest in a pattern-classification task, for example, may be more easily separated from each other than could be the case in the original input data space. Indeed, it is the formation of this feature space through supervised learning that distinguishes the multiplayer perceptron from Rosenblatt's perceptron.