arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1708.00385q-bio.PEq-bio.QM
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个体和集体动物行为的概率模型

Probabilistic models of individual and collective animal behavior

Katarina Bodova, Gabriel J. Mitchell, Roy Harpaz, Elad Schneidman, Gasper Tkacik

摘要:

自动跟踪的最新发展允许不间断地、高分辨率地记录动物轨迹,有时还可以识别身体姿势或其他感兴趣的行为的定型变化。这些数据的分析和解释是一个挑战:动物行为的时间可能是随机的,并通过运动学变量,与环境或动物群体内的同种动物的相互作用,并依赖于个体的内部认知或行为状态进行调制。现有的集体运动模型通常未能纳入行为的离散性,随机性和内部状态依赖性方面,而专注于个体动物行为的模型通常忽略了问题的空间方面。在这里,我们提出了一个概率建模框架,以解决这一差距。每种动物都可以在不同的行为状态之间随机切换,每种状态都可能导致不同的空间运动规律。行为转变的转换率可以以一种非常普遍的方式依赖于环境的影响以及动物之间的相互作用,我们试图从数据中确定这一点。我们表示的开关动态作为一个广义线性模型,并表明:(i)前向模拟多个相互作用的动物是可能的,使用一个变种的Gillespie的随机模拟算法;(ii)制定适当的,开关率函数的最大似然推断是可追踪的梯度下降求解;(iii)模型选择可用于识别调节行为状态切换的因素,并根据数据适当地调整模型复杂性。为了说明我们的框架,我们将其应用到两个合成模型的动物运动和真正的斑马鱼跟踪数据。

英文摘要:

Recent developments in automated tracking allow uninterrupted, high-resolution recording of animal trajectories, sometimes coupled with the identification of stereotyped changes of body pose or other behaviors of interest. Analysis and interpretation of such data represents a challenge: the timing of animal behaviors may be stochastic and modulated by kinematic variables, by the interaction with the environment or with the conspecifics within the animal group, and dependent on internal cognitive or behavioral state of the individual. Existing models for collective motion typically fail to incorporate the discrete, stochastic, and internal-state-dependent aspects of behavior, while models focusing on individual animal behavior typically ignore the spatial aspects of the problem. Here we propose a probabilistic modeling framework to address this gap. Each animal can switch stochastically between different behavioral states, with each state resulting in a possibly different law of motion through space. Switching rates for behavioral transitions can depend in a very general way, which we seek to identify from data, on the effects of the environment as well as the interaction between the animals. We represent the switching dynamics as a Generalized Linear Model and show that: (i) forward simulation of multiple interacting animals is possible using a variant of the Gillespie's Stochastic Simulation Algorithm; (ii) formulated properly, the maximum likelihood inference of switching rate functions is tractably solvable by gradient descent; (iii) model selection can be used to identify factors that modulate behavioral state switching and to appropriately adjust model complexity to data. To illustrate our framework, we apply it to two synthetic models of animal motion and to real zebrafish tracking data.

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。

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