共 4 条
Recovery of dynamical similarity from lossy representations of collective behavior of midge swarms
被引:0
|作者:
Aung, Eighdi
[1
]
Abaid, Nicole
[2
]
Jantzen, Benjamin
[3
]
机构:
[1] Virginia Tech, Engn Mech Program, Blacksburg, VA 24061 USA
[2] Virginia Tech, Dept Math, Blacksburg, VA 24061 USA
[3] Virginia Tech, Dept Philosophy, Blacksburg, VA 24061 USA
来源:
基金:
美国国家科学基金会;
关键词:
DENSITY;
D O I:
10.1063/5.0146161
中图分类号:
O29 [应用数学];
学科分类号:
070104 ;
摘要:
Understanding emergent collective phenomena in biological systems is a complex challenge due to the high dimensionality of state variables and the inability to directly probe agent-based interaction rules. Therefore, if one wants to model a system for which the underpinnings of the collective process are unknown, common approaches such as using mathematical models to validate experimental data may be misguided. Even more so, if one lacks the ability to experimentally measure all the salient state variables that drive the collective phenomena, a modeling approach may not correctly capture the behavior. This problem motivates the need for model-free methods to characterize or classify observed behavior to glean biological insights for meaningful models. Furthermore, such methods must be robust to low dimensional or lossy data, which are often the only feasible measurements for large collectives. In this paper, we show that a model-free and unsupervised clustering of high dimensional swarming behavior in midges (Chironomus riparius), based on dynamical similarity, can be performed using only two-dimensional video data where the animals are not individually tracked. Moreover, the results of the classification are physically meaningful. This work demonstrates that low dimensional video data of collective motion experiments can be equivalently characterized, which has the potential for wide applications to data describing animal group motion acquired in both the laboratory and the field.
引用
收藏
页数:9
相关论文