The Neurological Engineering Framework can be used to create systems that use memory in interesting ways. One system (Spaun) is able to memorize (and forget) lists much in the same way as humans do. Another system can learn tasks and apply the knowledge to subtasks using hierarchical reinforcement learning. Both of these tasks involve memory, but how is memory contained within the Neurological Engineering Framework?
The Neurological Engineering Framework does not explicitly state a mechanism for memory. There is no "hard-drive" in the brain for easy retrieval and access. Rather, memory is captured in the connection weights between neural populations and the dynamics of the network.
In the Hierarchical Reinforcement Learning example, linked to in the previous question, the "memory" of the map for the navigation task is saved by modifying the connections weights of the neurons representing the environment.
In the serial list memorisation task, the working memory for saving the semantic pointer list items is preserved in an integrator, which is essentially a neuron population that feeds back on itself.
These are examples of working memory, in other words, the memory that allows you to drive a car without really remembering how exactly you drove it. Long term memory modeling requires quite a few more mechanisms (memory reconsolidation) and structures (hippocampus, amygdala) that haven't been captured in any system built with the NEF yet.