Predictive learning induces Bayesian cognitive maps in the hippocampus

Navigation requires perception: location must be inferred from noisy and ambiguous egocentric sensory inputs, as in visual estimation of distance. However, many classical models of spatial representation implicitly assume that allocentric location is directly observable, thereby neglecting perceptual uncertainty. Here, we compare such a model with a Bayesian ideal observer that explicitly incorporates perceptual inference. We find that the Bayesian observer's beliefs over location more accurately reproduce key properties of place cell activity, including place field width, area, and density, within and across environments. Using analytic arguments and numerical simulations, we show that recurrent neural networks trained to predict the next egocentric sensory input learn representations resembling Bayesian beliefs and yield place cell-like activity in both familiar and unfamiliar environments, outperforming autoencoders trained to reproduce the current input. Together, these results suggest that hippocampal circuits may construct Bayesian cognitive maps from experience through predictive perceptual learning.