Most descriptions of modern RNNs present a "folded" characterisation, that is to say, a single cell with a loop back to itself transmitting the hidden state from one step to the next. However, in implementations the RNN is computed "unfolded", so a new cell is created for every step of the sequence up to some maximum sequence length, and the state is passed from one cell to the next.
My question is: are the learned parameters shared between all the cells in the unfolded sequence? E.g. in the case of a stack of LSTMs, does each LSTM have its own set of forget, input-gate, candidate and output parameters, or does the whole stack share and update a common set?