Neural geometry in the human hippocampus enables generalization across spatial position and gaze

TL;DR

This study reveals that hippocampal neurons encode spatial and gaze information in orthogonal yet alignable subspaces, enabling generalization and abstraction across different agents and viewpoints during a virtual pursuit task.

Contribution

It demonstrates that hippocampal neural codes form geometrically related manifolds that support flexible generalization across spatial maps and gaze directions.

Findings

Neurons encode self, prey, predator positions, and gaze with mixed responses.

Neural codes occupy mostly orthogonal subspaces that can be aligned linearly.

Linear rules learned in one context transfer to others, enabling generalization.

Abstract

Hippocampal neurons track positions of self, others, and gaze direction. However, it is unclear how their respective neural codes differ enough to avoid confusion while allowing for abstraction. We recorded from populations of hippocampal neurons while participants performed a joystick-controlled virtual prey pursuit task involving multiple moving agents. We found that neurons have mixed selective responses that map positions of self, prey, and predator, as well as gaze. Their codes occupied mostly orthogonal subspaces, but these subspaces geometric structure allowed them to be aligned by simple linear transformations. Moreover, their geometry supported generalization across spatial maps, such that a linear rule learned on one agent transfers to another. This scheme enables reliable individuation and abstraction across both agent identity and viewpoint. Together, these findings suggest that hippocampal spatial knowledge is structured as a family of geometrically related manifolds that can be flexibly aligned to different agents and gaze directions.