Model-free information-theoretic approach to infer leadership in pairs of zebrafish
Model-free information-theoretic approach to infer leadership in pairs of zebrafish
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DOI：
10.1103/physreve.93.042411
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发表时间：
2016-04-18
影响因子：
2.4
通讯作者：
Porfiri, Maurizio
中科院分区：
文献类型：
--
作者：
Butail, Sachit;Mwaffo, Violet;Porfiri, Maurizio
集体行为为鱼类在躲避捕食者、觅食、交配和游泳方面提供了几个优势。虽然鱼群传统上被认为是平等主义的超级有机体，但许多经验观察表明，群居群体中出现了领导力。发现和分类领导-追随者关系对于阐明领导的行为和生理原因以及理解其后果至关重要。在这里，我们展示了一种信息论的方法，从鱼类游泳的位置数据推断领导。在这个框架中，我们通过传递熵的数学结构来衡量鱼对之间的社会互动，传递熵量化了一个时间序列预测另一个可能耦合的时间序列的预测能力。我们专注于斑马鱼模式生物，由于其与人类的遗传相似性和相对于哺乳动物降低的神经生物学复杂性，斑马鱼正迅速成为临床前研究的选择物种。为了克服实验混淆并生成测试数据集，我们可以彻底评估我们的方法，我们调整和校准了一个数据驱动的斑马鱼运动随机模型，用于模拟斑马鱼对的耦合动力系统。在这个合成数据集中，鱼之间的耦合程度和方向在一个广泛的参数范围内系统地变化，以证明传递熵在推断领导能力方面的准确性和可靠性。我们的方法有望帮助分析集体行为，提供一个数据驱动的视角来理解社会互动。
Collective behavior affords several advantages to fish in avoiding predators, foraging, mating, and swimming. Although fish schools have been traditionally considered egalitarian superorganisms, a number of empirical observations suggest the emergence of leadership in gregarious groups. Detecting and classifying leader-follower relationships is central to elucidate the behavioral and physiological causes of leadership and understand its consequences. Here, we demonstrate an information-theoretic approach to infer leadership from positional data of fish swimming. In this framework, we measure social interactions between fish pairs through the mathematical construct of transfer entropy, which quantifies the predictive power of a time series to anticipate another, possibly coupled, time series. We focus on the zebrafish model organism, which is rapidly emerging as a species of choice in preclinical research for its genetic similarity to humans and reduced neurobiological complexity with respect to mammals. To overcome experimental confounds and generate test data sets on which we can thoroughly assess our approach, we adapt and calibrate a data-driven stochastic model of zebrafish motion for the simulation of a coupled dynamical system of zebrafish pairs. In this synthetic data set, the extent and direction of the coupling between the fish are systematically varied across a wide parameter range to demonstrate the accuracy and reliability of transfer entropy in inferring leadership. Our approach is expected to aid in the analysis of collective behavior, providing a data-driven perspective to understand social interactions.