Why do RNNs need to "loop back" during their operations?

The need for recurrent neural networks (RNNs) to "loop back" during their operations is primarily to retain past information for current processing. This characteristic is crucial because RNNs are designed to work with sequential data where context from previous inputs can be important for correctly interpreting and predicting the current output. By incorporating feedback connections, RNNs can maintain a hidden state that captures information about previous inputs in the sequence. This allows the network to remember and utilize relevant details from earlier inputs as it processes the ongoing sequence, making RNNs particularly effective for tasks such as language modeling, time-series prediction, and sequence generation.

This looping mechanism enables the networks to create a form of memory that is essential for understanding context over time, which is a key advantage in applications involving sequential data. Other options do not capture this fundamental aspect of RNN functionality.