A non-Hebbian code for episodic memory

Hebbian plasticity has long dominated neurobiological models of memory formation. Yet plasticity rules operating on one-shot episodic memory timescales rarely depend on both pre- and postsynaptic spiking, challenging Hebbian theory in this crucial regime. To address this, we present an episodic memory model governed by a simple non-Hebbian rule depending only on presynaptic activity. We show that this rule, capitalizing on high-dimensional neural activity with restricted transitions, naturally stores episodes as paths through complex state spaces like those underlying a world model. The resulting memory traces, which we term path vectors, are highly expressive and decodable with an odor-tracking algorithm. We show that path vectors are robust alternatives to Hebbian traces when created via spiking and support diverse one-shot sequential and associative recall tasks, and policy learning. Thus, non-Hebbian plasticity is sufficient for flexible memory and learning, and well-suited to encode episodes and policies as paths through a world model.