  In recent years, Neural Turing Machines have gathered attention by joining
the flexibility of neural networks with the computational capabilities of
Turing machines. However, Neural Turing Machines are notoriously hard to train,
which limits their applicability. We propose reservoir memory machines, which
are still able to solve some of the benchmark tests for Neural Turing Machines,
but are much faster to train, requiring only an alignment algorithm and linear
regression. Our model can also be seen as an extension of echo state networks
with an external memory, enabling arbitrarily long storage without
interference.
