  We extend the capabilities of neural networks by coupling them to external
memory resources, which they can interact with by attentional processes. The
combined system is analogous to a Turing Machine or Von Neumann architecture
but is differentiable end-to-end, allowing it to be efficiently trained with
gradient descent. Preliminary results demonstrate that Neural Turing Machines
can infer simple algorithms such as copying, sorting, and associative recall
from input and output examples.
