C语言的自动化、预测性和可解释性推理。线虫逃逸动力学
Automated, predictive, and interpretable inference of C. elegans escape dynamics
摘要：
蛔虫C.线虫对迅速上升的温度表现出强烈的逃避行为。这种行为持续几秒钟，表现出历史依赖性，涉及感觉和运动系统，并且太复杂，无法使用现有知识进行机械建模。相反，我们从现象学的角度对这个过程进行建模，我们使用艾萨克爵士的动态推理平台，直接从实验数据中以完全自动化的方式推断模型。推断的模型需要纳入一个未观察到的动态变量，是生物学解释。该模型对蠕虫行为的动力学做出了准确的预测，它可以用来描述逃逸反应背后的动力学系统的功能逻辑。这项工作说明了现代人工智能的力量，以帮助发现复杂自然系统的准确和可解释的模型。
英文摘要：
The roundworm C. elegans exhibits robust escape behavior in response to rapidly rising temperature. The behavior lasts for a few seconds, shows history dependence, involves both sensory and motor systems, and is too complicated to model mechanistically using currently available knowledge. Instead we model the process phenomenologically, and we use the Sir Isaac dynamical inference platform to infer the model in a fully automated fashion directly from experimental data. The inferred model requires incorporation of an unobserved dynamical variable, and is biologically interpretable. The model makes accurate predictions about the dynamics of the worm behavior, and it can be used to characterize the functional logic of the dynamical system underlying the escape response. This work illustrates the power of modern artificial intelligence to aid in discovery of accurate and interpretable models of complex natural systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
