Department of Special Education, Chonnam National University, Buk-gu, Gwangju, 61186, Korea
Department of Electronics Engineering, Incheon National University, Yeonsu-gu, Incheon 22012, Korea
Neural Comput. 2020 Dec;32(12):2455-2485. doi: 10.1162/neco_a_01326. Epub 2020 Sep 18.
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.
在这项研究中，我们将神经编码和解码整合到一个统一的框架中，用于处理大脑中的空间信息。具体来说，海马体（HPC）和内嗅皮层（EC）中的自我位置的神经表示在空间导航中起着至关重要的作用。有趣的是，这些相邻脑区的神经表示显示出明显的差异。虽然 HPC 中的位置细胞以空间位置的单峰函数方式发射，但 EC 中的网格细胞显示出具有不同周期的周期性调谐曲线，用于不同的亚群（称为模块）。通过结合用于这种模块化神经表示的编码模型和基于置信度传播的现实解码模型，我们研究了 EC 中的神经元如何对自我位置进行编码，然后由 HPC 中的下游神经元进行解码。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的异构模块之间引入了更多的耦合，从而提供了更高的纠错能力。这也通过比较用于解码两模块和四模块代码的信念来证明。虽然前者导致完全解码失败，但后者即使从相同的输入也能正确恢复自我位置。对解码过程中置信度传播的进一步分析揭示了由于具有不同尺度的多个模块之间的相互作用而导致的信息更新中的复杂动态。因此，所提出的统一框架允许研究人员调查空间信息的整体流动，从而在大脑中封闭自我位置的编码和解码循环。
