The LSTM manages a cell state vector \({\bf c}^t\) expressing the story of the sequence so far and informing the future decisions. The LSTM can act on this vector to indicate regime transitions and forget past information (or simply stay the course) and it embodies the Long-Short Term Memory functionality indicated in the name. The input of the decision process is made up of both the current sequence input data \({\bf x}^t\) and the recurring latent state vector \({\bf h}^{t-1}\). The output of the decision process is a new latent state vector \({\bf h}^t\), from which the actual output can be extracted.