Technological advances in profiling cells along genetic, anatomical and physiological axes have fomented interest in identifying all neuronal cell types. This goal nears completion in specialized circuits such as the retina, while remaining more elusive in higher order cortical regions. We propose that this differential success of cell type identification may not simply reflect technological gaps in co-registering genetic, anatomical and physiological features in the cortex. Rather, we hypothesize it reflects evolutionarily driven differences in the computational principles governing specialized circuits versus more general-purpose learning machines. In this framework, we consider the question of cell types in medial entorhinal cortex (MEC), a region likely to be involved in memory and navigation. While MEC contains subsets of identifiable functionally defined cell types, recent work employing unbiased statistical methods and more diverse tasks reveals unsuspected heterogeneity and adaptivity in MEC firing patterns. This suggests MEC may operate more as a generalist circuit, obeying computational design principles resembling those governing other higher cortical regions.

中文翻译：

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用于我们的位置感的单元格类型：我们在哪里以及我们要去哪里。

沿着遗传，解剖学和生理学轴分析细胞的技术进步引起了人们对识别所有神经元细胞类型的兴趣。这个目标在诸如视网膜的专用回路中接近完成，而在更高阶的皮质区域中仍然难以捉摸。我们建议，这种细胞类型识别成功的差异可能不能简单地反映皮质中遗传，解剖和生理特征共配准中的技术空白。相反，我们假设它反映了控制专用电路的计算原理与更通用的学习机的进化驱动差异。在此框架中，我们考虑内侧内嗅皮层（MEC）中的细胞类型问题，该区域可能参与记忆和导航。虽然MEC包含可识别的功能定义的细胞类型的子集，但最近采用无偏统计方法和更多样化任务的工作揭示了MEC激发模式中的意外异质性和适应性。这表明，MEC可能更像通才电路，遵循类似于控制其他较高皮层区域的计算设计原则。