Data from: A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans

Random search is a behavioral strategy used by organisms from bacteria to humans to locate food that is randomly distributed and undetectable at a distance. We investigated this behavior in the nematode Caenorhabditis elegans, an organism with a small, well-described nervous system. Here we formulate a mathematical model of random search abstracted from the C. elegans connectome and fit to a large-scale kinematic analysis of C. elegans behavior at submicron resolution. The model predicts behavioral effects of neuronal ablations and genetic perturbations, as well as unexpected aspects of wild type behavior. The predictive success of the model indicates that random search in C. elegans can be understood in terms of a neuronal flip-flop circuit involving reciprocal inhibition between two populations of stochastic neurons. Our findings establish a unified theoretical framework for understanding C. elegans locomotion and a testable neuronal model of random search that can be applied to other organisms.随机搜索是一类广泛存在于从细菌到人类等多种生物中的行为策略，用于定位随机分布且远距离无法探测的食物资源。我们以神经系统小巧且已被充分解析的模式生物秀丽隐杆线虫（Caenorhabditis elegans）为研究对象，对其随机搜索行为开展了系统性探究。本研究基于秀丽隐杆线虫连接组构建了随机搜索的数学模型，并通过亚微米分辨率下的大规模运动学分析对该模型进行参数拟合。该模型不仅能够精准预测神经元消融与遗传扰动所引发的行为效应，还能揭示野生型秀丽隐杆线虫行为中此前未被关注的特征。该模型的预测有效性证实，秀丽隐杆线虫的随机搜索行为可通过一类由两群随机神经元之间相互抑制构成的神经元触发器电路得到合理解释。本研究的发现为解析秀丽隐杆线虫的运动行为搭建了统一的理论框架，同时构建了一个可验证的随机搜索神经元模型，该模型可推广应用于其他生物的相关研究。