[1] NTMs combine the fuzzy pattern matching capabilities of neural networks with the algorithmic power of programmable computers.
An NTM has a neural network controller coupled to external memory resources, which it interacts with through attentional mechanisms.
The memory interactions are differentiable end-to-end, making it possible to optimize them using gradient descent.
[2] An NTM with a long short-term memory (LSTM) network controller can infer simple algorithms such as copying, sorting, and associative recall from examples alone.
[1] The first stable open-source implementation was published in 2018 at the 27th International Conference on Artificial Neural Networks, receiving a best-paper award.