Place cells and grid cells in the hippocampal formation are thought to integrate sensory and self-motion information into a representation of estimated spatial location, but the precise mechanism is unknown. We simulated a parallel attractor system in which place cells form an attractor network driven by environmental inputs and grid cells form an attractor network performing path integration driven by self-motion, with inter-connections between them allowing both types of input to influence firing in both ensembles. We show that such a system is needed to explain the spatial patterns and temporal dynamics of place cell firing when rats run on a linear track in which the familiar correspondence between environmental and self-motion inputs is changed. In contrast, the alternative architecture of a single recurrent network of place cells (performing path integration and receiving environmental inputs) cannot reproduce the place cell firing dynamics. These results support the hypothesis that grid and place cells provide two different but complementary attractor representations (based on self-motion and environmental sensory inputs, respectively). Our results also indicate the specific neural mechanism and main predictors of hippocampal map realignment and make predictions for future studies.

中文翻译：

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神经动力学表明位置和网格单元对环境和自运动信息的并行集成。

海马结构中的位置细胞和网格细胞被认为可以将感觉和自我运动信息整合到估计的空间位置的表示中，但其确切机制尚不清楚。我们模拟了一个并行吸引器系统，其中位置单元形成由环境输入驱动的吸引器网络，网格单元形成一个由自运动驱动的执行路径集成的吸引器网络，它们之间的相互连接允许两种类型的输入影响两者的发射合奏。我们表明，当大鼠在线性轨道上奔跑时，需要这样一个系统来解释位置细胞放电的空间模式和时间动态，其中环境和自我运动输入之间熟悉的对应关系发生了变化。相比之下，位置细胞的单个循环网络的替代架构（执行路径整合和接收环境输入）不能再现位置细胞放电动力学。这些结果支持网格和位置单元提供两种不同但互补的吸引子表示（分别基于自运动和环境感官输入）的假设。我们的结果还表明了海马图重新排列的特定神经机制和主要预测因素，并为未来的研究做出预测。