Data-driven modelling of social forces and collective behaviour in zebrafish
Data-driven modelling of social forces and collective behaviour in zebrafish
复制标题
DOI：
10.1016/j.jtbi.2018.01.011
复制
发表时间：
2018-04-14
影响因子：
2
通讯作者：
Di Bernardo, Mario
中科院分区：
文献类型：
--
作者：
Zienkiewicz, Adam K.;Ladu, Fabrizio;Di Bernardo, Mario
斑马鱼正迅速成为一种强大的模式生物，在假设驱动的研究中针对许多功能和功能失调的过程。斑马鱼行为的数学模型可以通过前所未有的在计算机上进行试点试验的能力，为实验设计提供信息。与此同时，通过对关键神经行为因素的系统调查，计算机实验可以帮助改进对真实数据的分析。在这里，我们建立了一个数据驱动的斑马鱼社会互动模型。具体来说，我们推导了一套相互作用规则来捕捉实验观察到的主要反应机制。与以往的研究相反，除了转向响应，我们还包括动态速度调节，它们共同提供个体之间的吸引力，排斥性和对齐相互作用。由此产生的多智能体模型提供了一个新颖的，自下而上的框架来描述斑马鱼的自发运动和个体水平的相互作用动力学，直接从实验观察中推断出来。(C) 2018 Elsevier Ltd.版权所有。
Zebrafish are rapidly emerging as a powerful model organism in hypothesis-driven studies targeting a number of functional and dysfunctional processes. Mathematical models of zebrafish behaviour can inform the design of experiments, through the unprecedented ability to perform pilot trials on a computer. At the same time, in-silico experiments could help refining the analysis of real data, by enabling the systematic investigation of key neurobehavioural factors. Here, we establish a data-driven model of zebrafish social interaction. Specifically, we derive a set of interaction rules to capture the primary response mechanisms which have been observed experimentally. Contrary to previous studies, we include dynamic speed regulation in addition to turning responses, which together provide attractive, repulsive and alignment interactions between individuals. The resulting multi-agent model provides a novel, bottom-up framework to describe both the spontaneous motion and individual-level interaction dynamics of zebrafish, inferred directly from experimental observations. (C) 2018 Elsevier Ltd. All rights reserved.