The activation function typically used in LSTM (Long Short-Term Memory) cells is the hyperbolic tangent function (tanh).

  LSTM cells are a type of recurrent neural network (RNN) designed to address the vanishing gradient problem, which occurs when the gradients diminish exponentially over time, making it difficult for the network to learn long-term dependencies. The hyperbolic tangent function in the LSTM cell allows for the activation of the cell state to be in the range of -1 to 1, which helps alleviate the vanishing gradient problem.

  The tanh function is advantageous because it is symmetric around the origin, meaning that both positive and negative values are equally amplified, allowing the LSTM cell to capture both positive and negative information. When the input to the tanh function is close to zero, the output is close to zero, indicating that the LSTM cell is not significantly activating the cell state. When the input is positive or negative, the output approaches 1 or -1, respectively, indicating a strong activation of the cell state.

  The choice of the tanh activation function also allows LSTM cells to control the flow of information through its gates: input gate, forget gate, and output gate. These gates are responsible for determining what information to add, forget, or output from the cell state. The hyperbolic tangent function provides the necessary non-linearity for these gates to perform their functions effectively.

  In summary, the activation function typically used in LSTM cells is the hyperbolic tangent function (tanh), which helps address the vanishing gradient problem and allows for control of information flow through the gates.