A long-standing challenge in biological and artifcial intelligence is to understand how new knowledge
can be constructed from known building blocks in a way that is amenable for computation by neuronal
circuits. Here we focus on the task of storage and recall of structured knowledge in long-term memory.
Specifcally, we ask how recurrent neuronal networks can store and retrieve multiple knowledge
structures. We model each structure as a set of binary relations between events and attributes
(attributes may represent e.g., temporal order, spatial location, role in semantic structure), and map
each structure to a distributed neuronal activity pattern using a vector symbolic architecture scheme.
We then use associative memory plasticity rules to store the binarized patterns as fxed points in
a recurrent network. By a combination of signal-to-noise analysis and numerical simulations, we
demonstrate that our model allows for efcient storage of these knowledge structures, such that the
memorized structures as well as their individual building blocks (e.g., events and attributes) can be
subsequently retrieved from partial retrieving cues. We show that long-term memory of structured
knowledge relies on a new principle of computation beyond the memory basins. Finally, we show that
our model can be extended to store sequences of memories as single attractors.