Long Short-Term Memory (LSTM) is a type of artificial neural network architecture that is specifically designed for tasks involving sequential data, such as natural language processing and speech recognition. It falls under the broader category of recurrent neural networks (RNNs), which are neural networks that have connections between neurons that form a directed cycle.

LSTM is particularly effective in capturing long-term dependencies in sequential data, which is a common challenge for traditional RNNs. It achieves this by introducing specialized memory cells that can store and access information over long periods of time. These memory cells are equipped with gating mechanisms that regulate the flow of information, allowing LSTM to selectively retain or discard information at each step of the sequence.

The core component of an LSTM architecture is the memory cell. It maintains an internal state, which is updated based on the incoming input and the previous state. The memory cell can choose to retain or forget information based on the relevance of the input. The gates in an LSTM network, such as the input gate, forget gate, and output gate, control this information flow.

The input gate determines how much of the new input is to be added to the memory cell, whereas the forget gate decides which information should be discarded from the memory cell. The output gate, on the other hand, regulates the amount of information that gets passed on to the next layer or the final output of the LSTM.

The ability of LSTM to capture long-term dependencies makes it a powerful tool in various applications. In natural language processing, LSTM networks can be trained to understand and generate coherent sentences. In speech recognition, LSTM-based models can effectively recognize speech patterns and convert them into text.

In summary, Long Short-Term Memory (LSTM) is an advanced neural network architecture that excels in handling sequential data. By utilizing memory cells and gating mechanisms, LSTM networks can process and retain information over long sequences, making them suitable for a wide range of applications.