A Hopfield neural network is a type of artificial neural network invented by John Hopfield in 1982. It usually works by first learning a number of binary patterns and then returning the one that is the most similar to a given input. What defines a Hopfield network: It is composed of only one layer of nodes or units each of which is connected to all the others but not itself. It is therefore a feedback network, which means that its outputs are redirected to its inputs. Every unit also acts as an input and an output of the network. Thus the number of nodes, inputs, outputs of the network are equal. Additionally, each one of the neurons in a has a binary state or activation value, usually represented as 1 or -1, which is its particular output. The state of each node generally converges, meaning that the state of each node becomes fixed after a certain number of updates.