Rooke Spencer, Wang Zhaoze, Di Tullio Ronald W, Balasubramanian Vijay
Departments of Physics.
of Computer and Information Science.
bioRxiv. 2025 Jan 11:2024.10.29.620785. doi: 10.1101/2024.10.29.620785.
Many animals learn cognitive maps of their environment - a simultaneous representation of context, experience, and position. Place cells in the hippocampus, named for their explicit encoding of position, are believed to be a neural substrate of these maps, with place cell "remapping" explaining how this system can represent different contexts. Briefly, place cells alter their firing properties, or "remap", in response to changes in experiential or sensory cues. Substantial sensory changes, produced, e.g., by moving between environments, cause large subpopulations of place cells to change their tuning entirely. While many studies have looked at the physiological basis of remapping, we lack explicit calculations of how the contextual capacity of the place cell system changes as a function of place field firing properties. Here, we propose a geometric approach to understanding population level activity of place cells. Using known firing field statistics, we investigate how changes to place cell firing properties affect the distances between representations of different environments within firing rate space. Using this approach, we find that the number of contexts storable by the hippocampus grows exponentially with the number of place cells, and calculate this exponent for environments of different sizes. We identify a fundamental trade-off between high resolution encoding of position and the number of storable contexts. This trade-off is tuned by place cell width, which might explain the change in firing field scale along the dorsal-ventral axis of the hippocampus. We demonstrate that clustering of place cells near likely points of confusion, such as boundaries, increases the contextual capacity of the place system within our framework and conclude by discussing how our geometric approach could be extended to include other cell types and abstract spaces.
许多动物会学习其环境的认知地图——一种对环境、经验和位置的同步表征。海马体中的位置细胞因其对位置的明确编码而得名，被认为是这些地图的神经基础，位置细胞的“重新映射”解释了该系统如何表征不同的环境。简而言之，位置细胞会根据经验或感官线索的变化改变其放电特性，即“重新映射”。例如，在不同环境之间移动所产生的显著感官变化会导致大量位置细胞亚群完全改变其调谐。虽然许多研究已经探讨了重新映射的生理基础，但我们缺乏关于位置细胞系统的上下文容量如何随位置野放电特性变化的明确计算。在此，我们提出一种几何方法来理解位置细胞的群体水平活动。利用已知的放电野统计数据，我们研究位置细胞放电特性的变化如何影响放电率空间中不同环境表征之间的距离。使用这种方法，我们发现海马体可存储的上下文数量随位置细胞数量呈指数增长，并计算了不同大小环境的该指数。我们确定了位置高分辨率编码与可存储上下文数量之间的基本权衡。这种权衡由位置细胞宽度调节，这可能解释了海马体背腹轴上放电野尺度的变化。我们证明，在可能的混淆点（如边界）附近位置细胞的聚类增加了我们框架内位置系统的上下文容量，并通过讨论我们的几何方法如何扩展以纳入其他细胞类型和抽象空间来得出结论。
