Which gate in a long short-term memory (LSTM) cell determines what information to retain in long-term memory?

The input gate in a long short-term memory (LSTM) cell plays a crucial role in determining what information is retained in long-term memory. It functions by deciding which values from the input data should be updated in the memory cell. In this context, the input gate takes a vector of input features, applies a sigmoid activation function, and thus produces output values that range between 0 and 1. These values serve as a filter that influences which aspects of the new input are significant and should be allowed into the cell state.

When the input gate outputs a value close to 1 for a particular feature, it indicates that this information is relevant and should be added to the memory. Conversely, values near 0 signal that the information is less important and should be ignored. This selective process allows an LSTM to effectively manage long-term dependencies in sequential data, which is essential for tasks like language modeling and time series prediction.

Other options such as the output gate and forget gate have distinct purposes: the output gate determines how much of the cell state should be exposed to the next layer in the neural network, while the forget gate decides which information from the cell state should be discarded. The tanh gate is associated with scaling the cell state values