A Recurrent Neural Network (RNN) is a type of neural network designed to process sequential data by maintaining a memory or state of previous inputs. Unlike feedforward neural networks, where information flows in one direction from input to output, RNNs have connections that loop back on themselves, allowing them to retain information about previous inputs and incorporate context into their predictions. This makes RNNs particularly well-suited for tasks involving sequential or time-series data, such as natural language processing, speech recognition, handwriting recognition, and time series prediction. One of the key features of RNNs is their ability to handle input sequences of variable length, making them flexible and adaptable to a wide range of applications.

However, traditional RNNs suffer from the vanishing gradient problem, where gradients diminish exponentially over time, limiting their ability to capture long-term dependencies. To address this issue, more advanced architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) have been developed, which are capable of learning and retaining information over longer sequences more effectively.

Note: This concise definition provides an overview of Recurrent Neural Network (RNN). For further information, a more in-depth search on Google is recommended.