论文标题
广义的最终产品反馈电路感知高维环境波动
Generalized end-product feedback circuit senses high dimensional environmental fluctuations
论文作者
论文摘要
了解简单生物回路的计算能力，例如单细胞生物的调节回路，仍然是一个活跃的研究领域。最近的理论工作表明，基于最终产物抑制的简单调节结构可以通过学习一个或两个环境参数的波动统计来表现出预测行为。在这里，我们将此分析扩展到更高的维度。我们表明，随着输入的数量增加，电路的广义版本不仅可以学习波动的主要方向，如前所述，还可以学习次要波动模式。
Understanding computational capabilities of simple biological circuits, such as the regulatory circuits of single-cell organisms, remains an active area of research. Recent theoretical work has shown that a simple regulatory architecture based on end-product inhibition can exhibit predictive behavior by learning fluctuation statistics of one or two environmental parameters. Here we extend this analysis to higher dimensions. We show that as the number of inputs increases, a generalized version of the circuit can learn not only the dominant direction of fluctuations, as shown previously, but also the subdominant fluctuation modes.
