High‐level cognitive abilities such as navigation and spatial memory are thought to rely on the activity of grid cells in the medial entorhinal cortex (MEC), which encode the animal's position in space with periodic triangular patterns. Yet the neural mechanisms that underlie grid‐cell activity are still unknown. Recent in vitro and in vivo experiments indicate that grid cells are embedded in highly structured recurrent networks. But how could recurrent connectivity become structured during development? And what is the functional role of these connections? With mathematical modeling and simulations, we show that recurrent circuits in the MEC could emerge under the supervision of weakly grid‐tuned feedforward inputs. We demonstrate that a learned excitatory connectivity could amplify grid patterns when the feedforward sensory inputs are available and sustain attractor states when the sensory cues are lost. Finally, we propose a Fourier‐based measure to quantify the spatial periodicity of grid patterns: the grid‐tuning index.

中文翻译：

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网格细胞活动的反复放大

导航和空间记忆等高级认知能力被认为依赖于内侧内嗅皮层 (MEC) 中网格细胞的活动，这些网格细胞以周期性的三角形图案对动物在空间中的位置进行编码。然而，网格细胞活动背后的神经机制仍然未知。最近的体外和体内实验表明网格细胞嵌入高度结构化的循环网络中。但是，如何在开发过程中构建循环连接呢？这些连接的功能作用是什么？通过数学建模和模拟，我们表明 MEC 中的循环电路可以在弱网格调谐前馈输入的监督下出现。我们证明，当前馈感觉输入可用时，学习的兴奋性连接可以放大网格模式，并在感觉线索丢失时维持吸引子状态。最后，我们提出了一种基于傅立叶的度量来量化网格模式的空间周期性：网格调整指数。