论文标题
内在稳态的生物物理模型：发射速率及以后
Biophysical models of intrinsic homeostasis: Firing rates and beyond
论文作者
论文摘要
鉴于外部世界和内在大脑状态的不断变化的条件，保持计算的鲁棒性构成了挑战，我们才开始了解，这是一个充分的解决方案。在细胞中性特性的水平上，神经元的生物物理模型允许人们确定可以用作神经元兴奋性调节剂的相关生理底物，并测试反馈回路如何稳定关键变量，例如长期钙水平和解散率。数学理论还揭示了由不同的细胞动力学甚至在网络级别塑造处理引起的一系列互补计算特性。在这里，我们提供了有关最近探讨的稳态机制的概述，这些机制源自生物物理模型，并假设如何稳定地控制细胞的多个动力学特征，包括其内在的神经元兴奋性类别。
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.
