  In neuroscience, classical Hopfield networks are the standard biologically
plausible model of long-term memory, relying on Hebbian plasticity for storage
and attractor dynamics for recall. In contrast, memory-augmented neural
networks in machine learning commonly use a key-value mechanism to store and
read out memories in a single step. Such augmented networks achieve impressive
feats of memory compared to traditional variants, yet their biological
relevance is unclear. We propose an implementation of basic key-value memory
that stores inputs using a combination of biologically plausible three-factor
plasticity rules. The same rules are recovered when network parameters are
meta-learned. Our network performs on par with classical Hopfield networks on
autoassociative memory tasks and can be naturally extended to continual recall,
heteroassociative memory, and sequence learning. Our results suggest a
compelling alternative to the classical Hopfield network as a model of
biological long-term memory.
