The activity of neurons in the brain can be modelled statistically. Each neuron at any time is either active + or inactive −. The active neurons are those that send an action potential down the axon in any given time window, and the inactive ones are those that do not. Because the neural activity at any one time is modelled by independent bits, Hopfield suggested in 1982 that a dynamical Ising model would provide a first approximation to a neural network which is capable of learning. This learning recurrent neural network was published by Shun'ichi Amari in 1972.