海马位置细胞中奖赏调节的空间编码的神经计算和动力机制。

IF 3.9 3区 工程技术 Q2 NEUROSCIENCES
Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-06-23 DOI:10.1007/s11571-025-10282-6
Qi Shao, Yihong Wang, Xuying Xu, Yaning Wang, Xiaochuan Pan, Ying Du, Rubin Wang
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引用次数: 0

摘要

海马位置细胞在哺乳动物空间导航、情景记忆形成和其他相关的空间认知功能中起着至关重要的作用。实验证据表明,当动物在真实或虚拟环境中执行空间导航任务时,目标或奖励位置附近区域的位置场数量明显高于远端区域,这种位置细胞表征现象被定义为“过度表征”。“过度表征”现象表现出空间表征的动态变化:当奖励或目标位置移动时,最大位置场密度的位置会转移到新的奖励位置,这一过程被称为“过度表征转移”。尽管在理解过度表征方面取得了重大进展,但目前的解释主要集中在定性描述上,缺乏一个全面的计算框架来系统地阐明过度表征的潜在神经机制。为了解决这个问题,我们基于连续吸引子网络框架开发了两个不同但相关的位置细胞子模型:位置集成模型,通过位置细胞活动动态编码空间位置,以及速度驱动模型,结合速度细胞编码动物的运动速度。两个子模型都成功实现了在啮齿类动物中观察到的路径整合功能。在这些基础模型的基础上,我们实现了一个与奖励位置相关的动态增益机制来模拟一维(1D)线性轨道和二维(2D)方形环境中的目标导向导航。这种机制根据奖励位置和动物位置之间的欧几里得距离动态调节神经活动增益。我们的模拟结果表明,位置细胞在奖励区域5-10 cm范围内表现出过度表征,并且位置场的空间分布动态跟踪奖励位置的变化。该框架成功再现了位置细胞中的过度表征和过度表征的动态转移,揭示了奖励位置如何塑造空间表征并触发位置场重组。这些发现增强了我们对基于奖励的空间导航海马机制的理解,并为研究经验依赖的神经重映射奠定了计算基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
Cognitive Neurodynamics
Cognitive Neurodynamics 医学-神经科学
CiteScore
6.90
自引率
18.90%
发文量
140
审稿时长
12 months
期刊介绍: Cognitive Neurodynamics provides a unique forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics, intelligent science and applications, bridging the gap between theory and application, without any preference for pure theoretical, experimental or computational models. The emphasis is to publish original models of cognitive neurodynamics, novel computational theories and experimental results. In particular, intelligent science inspired by cognitive neuroscience and neurodynamics is also very welcome. The scope of Cognitive Neurodynamics covers cognitive neuroscience, neural computation based on dynamics, computer science, intelligent science as well as their interdisciplinary applications in the natural and engineering sciences. Papers that are appropriate for non-specialist readers are encouraged. 1. There is no page limit for manuscripts submitted to Cognitive Neurodynamics. Research papers should clearly represent an important advance of especially broad interest to researchers and technologists in neuroscience, biophysics, BCI, neural computer and intelligent robotics. 2. Cognitive Neurodynamics also welcomes brief communications: short papers reporting results that are of genuinely broad interest but that for one reason and another do not make a sufficiently complete story to justify a full article publication. Brief Communications should consist of approximately four manuscript pages. 3. Cognitive Neurodynamics publishes review articles in which a specific field is reviewed through an exhaustive literature survey. There are no restrictions on the number of pages. Review articles are usually invited, but submitted reviews will also be considered.
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