Sequential predictive learning is a unifying theory for hippocampal representation and replay

The mammalian hippocampus contains a cognitive map that represents an animal’s position in the environment ¹ and generates offline “replay” ^2,3 for the purposes of recall ⁴ , planning ^5,6 , and forming long term memories ⁷ . Recently, it’s been found that artificial neural networks trained to predict sensory inputs develop spatially tuned cells ⁸ , aligning with predictive theories of hippocampal function ^9–11 . However, whether predictive learning can also account for the ability to produce offline replay is unknown. Here, we find that spatially-tuned cells, which robustly emerge from all forms of predictive learning, do not guarantee the presence of a cognitive map with the ability to generate replay. Offline simulations only emerged in networks that used recurrent connections and head-direction information to predict multi-step observation sequences, which promoted the formation of a continuous attractor reflecting the geometry of the environment. These offline trajectories were able to show wake-like statistics, autonomously replay recently experienced locations, and could be directed by a virtual head direction signal. Further, we found that networks trained to make cyclical predictions of future observation sequences were able to rapidly learn a cognitive map and produced sweeping representations of future positions reminiscent of hippocampal theta sweeps ¹² . These results demonstrate how hippocampal-like representation and replay can emerge in neural networks engaged in predictive learning, and suggest that hippocampal theta sequences reflect a circuit that implements a data-efficient algorithm for sequential predictive learning. Together, this framework provides a unifying theory for hippocampal functions and hippocampal-inspired approaches to artificial intelligence.