In contrast to FFNNs, recurrent networks introduce feedback connections, meaning the information is allowed to be carried to subsequent nodes across different time steps. These cyclic or feedback connections have the objective of providing the network with some kind of memory, making RNNs particularly suited for time- series data, natural language processing, speech recognition, and several other problems for which the order of the data is crucial. The RNN architectures vary greatly in how they manage information flow and memory in the network.