So my understanding of their paper is that they have a kind of (doubly) linked-list memory, which records a state vector at some time instant, and is linked to a state vector from the previous time and will get linked to the next state vector that gets added, so you have a 'timeline' of state vectors.
The memory is also 'content addressable' in that you can present it with a state vector and it will find the closest match. This could form the beginning of a kind of episodic memory, where you take some current input state and you can then find the best match in the memory, and replay what happened before or after that, in sequence.
That seems like a very useful mechanism for any intelligent system to have, indeed it seems like a requirement for any higher intelligence. The question is how does something like integrate with learning machinery.
Are these episodic memory units distributed locally around some set of neurons or regions of layers, or can they record input from anyplace in the system?