In this study, we integrated neural encoding and decoding into a unified framework for spatial information processing in the brain. Specifically, the neural representations of self-location in the hippocampus (HPC) and entorhinal cortex (EC) play crucial roles in spatial navigation. Intriguingly, these neural representations in these neighboring brain areas show stark differences. Whereas the place cells in the HPC fire as a unimodal function of spatial location, the grid cells in the EC show periodic tuning curves with different periods for different subpopulations (called modules). By combining an encoding model for this modular neural representation and a realistic decoding model based on belief propagation, we investigated the manner in which self-location is encoded by neurons in the EC and then decoded by downstream neurons in the HPC. Through the results of numerical simulations, we first show the positive synergy effects of the modular structure in the EC. The modular structure introduces more coupling between heterogeneous modules with different periodicities, which provides increased error-correcting capabilities. This is also demonstrated through a comparison of the beliefs produced for decoding two- and four-module codes. Whereas the former resulted in a complete decoding failure, the latter correctly recovered the self-location even from the same inputs. Further analysis of belief propagation during decoding revealed complex dynamics in information updates due to interactions among multiple modules having diverse scales. Therefore, the proposed unified framework allows one to investigate the overall flow of spatial information, closing the loop of encoding and decoding self-location in the brain.

中文翻译：

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迈向认知地图的统一框架

在这项研究中，我们将神经编码和解码整合到一个统一的大脑空间信息处理框架中。具体而言，海马体 (HPC) 和内嗅皮层 (EC) 中自我定位的神经表征在空间导航中起着至关重要的作用。有趣的是，这些相邻大脑区域的神经表征显示出明显的差异。尽管 HPC 中的位置单元作为空间位置的单峰函数进行发射，但 EC 中的网格单元显示不同亚群（称为模块）的不同周期的周期性调谐曲线。通过将这种模块化神经表示的编码模型与基于置信传播的现实解码模型相结合，我们研究了自我定位由 EC 中的神经元编码，然后由 HPC 中的下游神经元解码的方式。通过数值模拟的结果，我们首先展示了 EC 中模块化结构的积极协同效应。模块化结构在具有不同周期性的异构模块之间引入了更多耦合，从而提供了增强的纠错能力。这也通过比较为解码二模块和四模块代码而产生的信念来证明。前者导致完全解码失败，后者即使从相同的输入中也能正确恢复自定位。由于具有不同尺度的多个模块之间的交互，对解码过程中信念传播的进一步分析揭示了信息更新的复杂动态。因此，提议的统一框架允许人们调查空间信息的整体流动，