  The medial temporal lobe (MTL), a brain region containing the hippocampus and
nearby areas, is hypothesized to be an experience-construction system in
mammals, supporting both recall and imagination of temporally-extended
sequences of events. Such capabilities are also core to many recently proposed
``world models" in the field of AI research. Taking inspiration from this
connection, we propose a novel variant, the Dual Stream World Model (DSWM),
which learns from high-dimensional observations and dissociates them into
context and content streams. DSWM can reliably generate imagined trajectories
in novel 2D environments after only a single exposure, outperforming a standard
world model. DSWM also learns latent representations which bear a strong
resemblance to place cells found in the hippocampus. We show that this
representation is useful as a reinforcement learning basis function, and that
the generative model can be used to aid the policy learning process using
Dyna-like updates.
