Gris Katsiaryna V, Coutu Jean-Philippe, Gris Denis
Gris Lab of Neuroimmunology, Pediatrics, University of SherbrookeSherbrooke, QC, Canada.
Front Behav Neurosci. 2017 Jul 28;11:141. doi: 10.3389/fnbeh.2017.00141. eCollection 2017.
Quantifying behavior is a challenge for scientists studying neuroscience, ethology, psychology, pathology, etc. Until now, behavior was mostly considered as qualitative descriptions of postures or labor intensive counting of bouts of individual movements. Many prominent behavioral scientists conducted studies describing postures of mice and rats, depicting step by step eating, grooming, courting, and other behaviors. Automated video assessment technologies permit scientists to quantify daily behavioral patterns/routines, social interactions, and postural changes in an unbiased manner. Here, we extensively reviewed published research on the topic of the structural blocks of behavior and proposed a structure of behavior based on the latest publications. We discuss the importance of defining a clear structure of behavior to allow professionals to write viable algorithms. We presented a discussion of technologies that are used in automated video assessment of behavior in mice and rats. We considered advantages and limitations of supervised and unsupervised learning. We presented the latest scientific discoveries that were made using automated video assessment. In conclusion, we proposed that the automated quantitative approach to evaluating animal behavior is the future of understanding the effect of brain signaling, pathologies, genetic content, and environment on behavior.
对研究神经科学、动物行为学、心理学、病理学等领域的科学家来说，量化行为是一项挑战。到目前为止，行为大多被视为对姿势的定性描述，或是对个体运动片段进行劳动密集型的计数。许多杰出的行为科学家进行了研究，描述小鼠和大鼠的姿势，一步步描绘进食、梳理毛发、求偶及其他行为。自动化视频评估技术使科学家能够以无偏差的方式量化日常行为模式/惯例、社交互动和姿势变化。在此，我们广泛回顾了已发表的关于行为结构组成部分这一主题的研究，并基于最新出版物提出了一种行为结构。我们讨论了定义清晰的行为结构对于专业人员编写可行算法的重要性。我们阐述了用于小鼠和大鼠行为自动化视频评估的技术。我们考量了监督学习和无监督学习的优缺点。我们展示了利用自动化视频评估取得的最新科学发现。总之，我们提出，评估动物行为的自动化定量方法是理解大脑信号、病理学、基因组成和环境对行为影响的未来方向。
