<unk>  is an extension to the<unk>  algorithm that is applicable to<unk>  neural networks. It is a type of supervised learning. It was described somewhat cryptically in Richard Feynman's senior thesis, and rediscovered independently in the context of artificial neural networks by both Fernando<unk>  and Luis B.<unk> . A<unk>  neural network for this algorithm consists of some input units, some output units and eventually some hidden units. For a given set of (input, target) states, the network is trained to settle into a stable activation state with the output units in the target state, based on a given input state clamped on the input units.
Almeida–Pineda recurrent backpropagation
Relapsing–remitting
Trousseau sign of latent tetany
Perforant path
Cyclic alternating pattern
Re-entry ventricular arrhythmia
Kanavel's cardinal signs
Holmes rebound phenomenon