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

Abstract

AbstractThe mammalian hippocampus contains a cognitive map that represents an animal’s position in the environment1and generates offline “replay”2,3for the purposes of recall4, planning5,6, and forming long term memories7. Recently, it’s been found that artificial neural networks trained to predict sensory inputs develop spatially tuned cells8, aligning with predictive theories of hippocampal function9–11. 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 sweeps12. 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.