In view of ever-changing conditions both in the external world and in intrinsic brain states, maintaining the robustness of computations poses a challenge, adequate solutions to which we are only beginning to understand. At the level of cell-intrinsic properties, biophysical models of neurons permit one to identify relevant physiological substrates that can serve as regulators of neuronal excitability and to test how feedback loops can stabilize crucial variables such as long-term calcium levels and firing rates. Mathematical theory has also revealed a rich set of complementary computational properties arising from distinct cellular dynamics and even shaping processing at the network level. Here, we provide an overview over recently explored homeostatic mechanisms derived from biophysical models and hypothesize how multiple dynamical characteristics of cells, including their intrinsic neuronal excitability classes, can be stably controlled.

中文翻译：

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内在稳态的生物物理模型：发射率及更高。

鉴于外部世界和内在大脑状态不断变化的条件，保持计算的稳健性提出了挑战，我们才刚刚开始理解适当的解决方案。在细胞内在特性的水平上，神经元的生物物理模型允许人们识别相关的生理底物，这些底物可以作为神经元兴奋性的调节剂，并测试反馈回路如何稳定关键变量，如长期钙水平和放电率。数学理论还揭示了一组丰富的互补计算特性，这些特性源于不同的细胞动力学，甚至是网络级别的整形处理。这里，