Speaker
Description
Navigation is a defining signature of autonomous behaviour that presents animals with fundamental computational challenges. Here we studied how the hippocampal-entorhinal system solves two such challenges. First, efficient navigation requires a continuously updated representation of uncertainty about one’s location. We formalised this uncertainty through an image computable Bayesian ideal observer model that infers location and heading direction as latent variables from self-motion, haptic (when available), and visual sensory inputs, the latter modelled as a retinal image obtained by a pin hole projection. The ideal observer's inferences about location were used to drive homing behaviour in spatial memory tasks and assumed to be represented in the population activity of entorhinal grid cells. Our model accounted for a wide range of ubiquitously described, but puzzling, forms of apparent suboptimalities in navigational behaviour and grid cell responses under deformed environmental geometries. Second, efficient navigation also requires rapid generalisation to novel tasks (goal locations or reward configurations) in a familiar environment. Here we propose the hierarchical successor representation (HSR) by incorporating temporal abstractions into the well-known successor representation (SR). HSR, unlike the classical SR, provides a policy-agnostic multi-scale map that effectively bridges model-free optimality and model-based flexibility, and scales well in topologically complex environments. Furthermore, the HSR successfully accounts for the multi-scale organisation of hippocampal place fields (the distribution of the number of place fields per cell, their sizes, and magnitudes, and how all these depend on the size and structure of the environment). These results suggest that widely described but seemingly idiosyncratic features of neural responses in the hippocampal formation are explained by the first principles of uncertainty representation and flexible generalisation.