RNNs are another type of neural networks used for sequential data. They are used for time series analysis, NLP and speech recognition.

Unlike feedforward neural networks, RNNs have connections which form directed cycles. They thus show dynamic temporal behaviour. They are able to process sequences of inputs and capture dependencies over time.

RNNs have the ability to maintain a hidden state. It retains information about previous inputs seen in the sequence. The hidden state is updated at each time step. It allows the network to capture context and long-range dependencies in sequential data.

RNNs suffer from vanishing gradient problem — it makes them struggle to capture long-term dependencies effectively.

RNNs overcome this by Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). Here the flow of information and gradients are regulated.