1. Recurrent neural networks can model sequential data like time series by incorporating hidden state that has internal dynamics. This allows the model to store information for long periods of time.
2. Two key types of recurrent networks are linear dynamical systems and hidden Markov models. Long short-term memory networks were developed to address the problem of exploding or vanishing gradients in training traditional recurrent networks.
3. Recurrent networks can learn tasks like binary addition by recognizing patterns in the inputs over time rather than relying on fixed architectures like feedforward networks. They have been successfully applied to handwriting recognition.