Spatial reasoning via recurrent neural dynamics in mouse retrosplenial cortex

Abstract

From visual perception to language, sensory stimuli change their meaning depending on prior experience. Recurrent neural dynamics can interpret stimuli based on externally cued context, but it is unknown whether similar dynamics can compute and employ internal hypotheses to resolve ambiguities. Here, we show that mouse retrosplenial cortex (RSC) can form hypotheses over time and perform spatial reasoning through recurrent dynamics. In our task, mice navigated using ambiguous landmarks that are identified through their mutual spatial relationship, requiring sequential refinement of hypotheses. Neurons in RSC and in artificial neural networks encoded mixtures of hypotheses, location, and sensory information, and were constrained by robust low dimensional dynamics. RSC encoded hypotheses as locations in activity space with divergent trajectories for identical sensory inputs, enabling their correct interpretation. Our results indicate that interactions between internal hypotheses and external sensory data in recurrent circuits can provide a substrate for complex sequential cognitive reasoning.