Title: Investigating the Influence of Reward Location on Hippocampal Path-Integration Computations

Navigating to rewards such as food sites and shelters is a crucial behavior for an animal’s survival. This goal-directed behavior depends on the animal’ ability to continuously localize itself relative to the reward and adapt to changing conditions. Animals can achieve such self-localization through “path integration”, a neural process that tracks the animal’s movements to update an internal representation of its location. For accurate and adaptive self-localization – critical for successful goal-directed navigation – the relationship between the animal’s physical movement and the path integration-based updating of self-location representations must be finely tuned. Salient landmarks, such as visual cues, provide the necessary positional feedback for this fine-tuning. However, it remains unclear how the path-integration process is finely tuned when animals navigate toward a reward, especially in environments lacking visual cues. The present proposal seeks an answer to this question through virtual reality experiments that probe and disrupt neural activity within hippocampus – a brain area recognized as a hub for encoding self-location via path integration. In doing so, I will apply principles and analytical techniques of control-systems engineering to address two hypothesis-driven aims that are formulated based on my preliminary data and modeling