Do all the cells in a recurrent neural network share learned parameters?
Indeed, the copies of a cell in an unfolded version share their learning parameters.

Why is it done this way? If the sequence processed by the LSTM is always the same lenght, we could conceivably get a better result with different parameters, but there are two key caveats:


Shared parameters are faster to learn
We want to be able to process cases when the lenght of the sequence processed is not fixed!