After training, the NN is tested by giving it only input values, not target values, and seeing how close it comes to outputting the correct target values. In unsupervised learning, the NN is not provided with the correct results during training. Unsupervised NNs usually perform some kind of data compression, such as dimensionality reduction or clustering. The distinction between supervised and unsupervised methods is not always clear-cut. An unsupervised method can learn a summary of a probability distribution, then that summarized distribution can be used to make predictions.
Many unsupervised methods are equivalent to auto-associative supervised methods.
In a feedforward NN, the connections between units do not form cycles. Feedforward NNs usually produce a response to an input quickly. In a feedback or recurrent NN, there are cycles in the connections. In some feedback NNs, each time an input is presented, the NN must iterate for a potentially long time before it produces a response. Feedback NNs are usually more difficult to train than feedforward NNs.