  A central challenge faced by memory systems is the robust retrieval of a
stored pattern in the presence of interference due to other stored patterns and
noise. A theoretically well-founded solution to robust retrieval is given by
attractor dynamics, which iteratively clean up patterns during recall. However,
incorporating attractor dynamics into modern deep learning systems poses
difficulties: attractor basins are characterised by vanishing gradients, which
are known to make training neural networks difficult. In this work, we avoid
the vanishing gradient problem by training a generative distributed memory
without simulating the attractor dynamics. Based on the idea of memory writing
as inference, as proposed in the Kanerva Machine, we show that a
likelihood-based Lyapunov function emerges from maximising the variational
lower-bound of a generative memory. Experiments shows it converges to correct
patterns upon iterative retrieval and achieves competitive performance as both
a memory model and a generative model.
