What are Elman networks? What are LSTMs? What are the differences between them?

 Elman networks and Long Short-Term Memory (LSTM) networks are both types of Recurrent Neural Networks (RNNs), which are a class of neural networks that are designed to process sequential data.

An Elman network, also known as an Elman recurrent neural network (ERNN), is a type of RNN that was introduced by Jeffrey Elman in 1990. It has a simple architecture, composed of an input layer, a hidden layer, and an output layer. The hidden layer has a "context unit" that stores the previous hidden state, allowing the network to maintain information across time steps.

LSTMs, on the other hand, was introduced by Hochreiter and Schmidhuber in 1997 as a solution to the vanishing and exploding gradients problem of standard RNNs. LSTMs are more complex than Elman networks and consist of a series of "memory cells" that are connected to "gates" that control the flow of information. These gates allow LSTMs to selectively retain, forget, or update information, which enables them to maintain long-term dependencies in the data.

In summary, Elman networks are a simpler version of RNNs that store previous hidden states in a context unit, whereas LSTMs are a more advanced version of RNNs that use memory cells and gates to selectively retain, forget, or update information, allowing them to maintain long-term dependencies. Due to this, LSTMs are more powerful than Elman networks in tasks that require long-term memory, such as language modeling and speech recognition.