The neural computational and dynamical mechanisms of reward-modulated spatial coding in hippocampal place cells

Hippocampal place cells play a critical role in mammalian spatial navigation, episodic memory formation, and other relevant spatial cognitive functions. Experimental evidences suggest that when animals perform spatial navigation tasks in real or virtual environments, the number of place fields in the region adjacent to the target or reward location is significantly higher than in distal regions, a place cell representation phenomenon defined as "over-representation". The "over-representation" phenomenon shows dynamic changes in spatial representation: when the reward or target location moves, the location of maximum place field density shifts to the new reward position - a process termed "over-representation shift". Despite significant progress in understanding over-representation, current explanations predominantly focus on qualitative descriptions, lacking a comprehensive computational framework to systematically elucidate underlying neural mechanisms of over-representation. To address this question, we developed two distinct but related place cell sub-models based on the continuous attractor network framework: the Position-Integrated Model, which dynamically encodes spatial locations through place cell activity, and the Velocity-Driven Model, which incorporates speed cells to encode animal's movement speed. Both sub-models successfully achieved the path integration function observed in rodents. Building upon these foundational models, we implemented a reward-location-dependent dynamic gain mechanism to simulate goal-directed navigation in one-dimensional (1D) linear tracks and two-dimensional (2D) square environments. This mechanism dynamically modulates neural activity gains according to the Euclidean distance between reward locations and the animal's position. Our simulations revealed that place cells exhibit over-representation within 5-10 cm of reward zones, and the spatial distribution of place fields dynamically tracking reward location changes. This framework successfully reproduces over-representation and the dynamic shift of over-representation in place cells, revealing how reward locations shape spatial representations and trigger place field reorganization. These findings enhance our comprehension of hippocampal mechanisms in reward-based spatial navigation and establish a computational basis for studying experience-dependent neural remapping.