Backpropagation through time is a gradient-based technique for training recurrent neural networks by unfolding a sequence across time steps. It computes how errors at later time steps depend on earlier states and uses those gradients to update shared model parameters.
A gradient-based technique for training certain types of recurrent neural networks, such as Elman networks. The algorithm was independently derived by numerous researchers.