文献类型：期刊文献

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

作者：Shao, Qi[1];Wang, Yihong[1,2];Xu, Xuying[1,2];Wang, Yaning[1];Pan, Xiaochuan[1,2];Du, Ying[1,2];Wang, Rubin[1]

机构：[1]East China Univ Sci & Technol, Inst Cognit Neurodynam, Sch Math, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Ctr Intelligent Comp, Sch Math, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2025

卷号：19

期号：1

外文期刊名：COGNITIVE NEURODYNAMICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001513576200002)】；

基金：This study was supported by the National Natural Science Foundation of China (Grant Nos. 12172132, 12272136 and 12472054) and Science and Technology Commission of Shanghai Municipality (No. 24JS2810400). The authors thank Ms. Shuang Xu for helpful discussion regarding the simulation code.

语种：英文

外文关键词：Place cells; Neurodynamics; Continuous attractor network; Reward coding; Over-representation; Goal-directed navigation

摘要：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.