The concept of memory cell is what differentiates LSTM (Long Short-Term Memory) models from other recurrent neural networks (RNNs). In traditional RNNs, each hidden state at a given time step depends solely on the current input and the previous hidden state. However, LSTM introduces a memory cell that allows the network to retain and access information over longer sequences.

  The memory cell in LSTM is a separate unit that stores information and decides when to forget or update it. It consists of three main components: the input gate, the forget gate, and the output gate. These gates regulate the flow of information in and out of the memory cell, providing better control over storing and accessing information.

  The input gate determines how much new information should be stored in the memory cell based on the current input and the previous hidden state. The forget gate decides which information from the memory cell should be discarded based on the current input and the previous hidden state. The output gate determines how much of the memory cell's content should be exposed to the next hidden state.

  By using these gates, LSTM is able to selectively remember or forget information based on its relevance and importance in the sequence. This helps in addressing the vanishing or exploding gradient problem, which can occur in traditional RNNs and hamper their ability to capture long-term dependencies.

  In summary, the concept of the memory cell in LSTM models allows for the storage and access of information over longer sequences, providing better control over retaining or forgetting information. This makes LSTM models more effective at capturing long-term dependencies compared to other recurrent neural networks.