von Ziegler Lukas, Sturman Oliver, Bohacek Johannes
Department of Health Sciences and Technology, ETH, Laboratory of Molecular and Behavioral Neuroscience, Institute for Neuroscience, Zurich, Switzerland.
Neuroscience Center Zurich, ETH Zurich and University of Zurich, Zurich, Switzerland.
Neuropsychopharmacology. 2021 Jan;46(1):33-44. doi: 10.1038/s41386-020-0751-7. Epub 2020 Jun 29.
The assessment of rodent behavior forms a cornerstone of preclinical assessment in neuroscience research. Nonetheless, the true and almost limitless potential of behavioral analysis has been inaccessible to scientists until very recently. Now, in the age of machine vision and deep learning, it is possible to extract and quantify almost infinite numbers of behavioral variables, to break behaviors down into subcategories and even into small behavioral units, syllables or motifs. However, the rapidly growing field of behavioral neuroethology is experiencing birthing pains. The community has not yet consolidated its methods, and new algorithms transfer poorly between labs. Benchmarking experiments as well as the large, well-annotated behavior datasets required are missing. Meanwhile, big data problems have started arising and we currently lack platforms for sharing large datasets-akin to sequencing repositories in genomics. Additionally, the average behavioral research lab does not have access to the latest tools to extract and analyze behavior, as their implementation requires advanced computational skills. Even so, the field is brimming with excitement and boundless opportunity. This review aims to highlight the potential of recent developments in the field of behavioral analysis, whilst trying to guide a consensus on practical issues concerning data collection and data sharing.
啮齿动物行为评估是神经科学研究临床前评估的基石。然而，直到最近，行为分析真正且几乎无限的潜力才为科学家所用。如今，在机器视觉和深度学习时代，提取和量化几乎无限数量的行为变量、将行为细分为子类别甚至细分为小的行为单元、音节或模式成为可能。然而，快速发展的行为神经行为学领域正在经历阵痛。该领域尚未整合其方法，新算法在不同实验室之间的移植性很差。基准实验以及所需的大量标注良好的行为数据集都很缺乏。与此同时，大数据问题开始出现，而我们目前缺乏类似于基因组学中的测序库那样用于共享大型数据集的平台。此外，普通的行为研究实验室无法使用最新的行为提取和分析工具，因为这些工具的应用需要先进的计算技能。即便如此，该领域仍充满兴奋和无限机遇。本综述旨在突出行为分析领域近期发展的潜力，同时尝试就数据收集和数据共享等实际问题达成共识。
