What mechanism allows RNNs to handle sequences of data effectively?

The mechanism that allows recurrent neural networks (RNNs) to handle sequences of data effectively is backpropagation through time. This is a specialized variant of the backpropagation algorithm that is used for training RNNs by enabling them to remember information about previous inputs in a sequence.

In an RNN, the output at each time step is dependent not only on the current input but also on the hidden state that encodes information from previous time steps. Backpropagation through time facilitates the updating of the weights across the multiple steps of the sequence during training. By "unrolling" the RNN in time and applying the standard backpropagation algorithm at each time step, it ensures that the gradients are correctly computed and propagated backward through the sequence. This capability to maintain and adjust the state over time allows RNNs to model temporal dependencies in sequential data effectively, which is key for tasks such as speech recognition, language modeling, and more.

The other options, while relevant in various contexts, do not specifically address the core mechanism that enables RNNs to manage sequences. Gated recurrent units improve the handling of long-term dependencies within RNNs but do not describe the training process. Aggregation functions and pooling layers are typically used in feed