[AlexCoventry]

It separates the concern of memorization from those of training and processing. In most current neural architectures, patterns in the training data are implicitly represented in the trained neural weights, and the net is implicitly forced to develop recall of past events by transmitting them from each time step to the next via neural net outputs.

The framework in this paper trains a neural net which interacts with a memory bank in a manner similar to a CPU. That means it can save and recall data on request, which could lead to more flexible architectures (you can give a trained net different data to recall) and easier training (since a memory-based architecture means the neural weights no longer have to learn the data along with the processing algorithm.)