Mah Kar Men, Torres-Espín Abel, Hallworth Ben W, Bixby John L, Lemmon Vance P, Fouad Karim, Fenrich Keith K
Department of Neurological Surgery, The Miami Project to Cure Paralysis, University of Miami, Miami, FL 33136, USA.
Brain and Spinal Injury Center, Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA.
Exp Neurol. 2021 Jun;340:113647. doi: 10.1016/j.expneurol.2021.113647. Epub 2021 Feb 15.
Testing and training animals in motor and related tasks is a cornerstone of pre-clinical behavioural and rehabilitative neuroscience. Yet manually testing and training animals in these tasks is time consuming and analyses are often subjective. Consequently, there have been many recent advances in automating both the administration and analyses of animal behavioural training and testing. This review is an in-depth appraisal of the history of, and recent developments in, the automation of animal behavioural assays used in neuroscience. We describe the use of common locomotor and non-locomotor tasks used for motor training and testing before and after nervous system injury. This includes a discussion of how these tasks help us to understand the underlying mechanisms of neurological repair and the utility of some tasks for the delivery of rehabilitative training to enhance recovery. We propose two general approaches to automation: automating the physical administration of behavioural tasks (i.e., devices used to facilitate task training, rehabilitative training, and motor testing) and leveraging the use of machine learning in behaviour analysis to generate large volumes of unbiased and comprehensive data. The advantages and disadvantages of automating various motor tasks as well as the limitations of machine learning analyses are examined. In closing, we provide a critical appraisal of the current state of automation in animal behavioural neuroscience and a prospective on some of the advances in machine learning we believe will dramatically enhance the usefulness of these approaches for behavioural neuroscientists.
在运动及相关任务中对动物进行测试和训练是临床前行为神经科学和康复神经科学的基石。然而，手动对动物进行这些任务的测试和训练非常耗时，而且分析往往具有主观性。因此，在动物行为训练和测试的管理及分析自动化方面，近年来已经取得了许多进展。这篇综述深入评估了神经科学中使用的动物行为检测自动化的历史和最新进展。我们描述了在神经系统损伤前后用于运动训练和测试的常见运动和非运动任务的使用情况。这包括讨论这些任务如何帮助我们理解神经修复的潜在机制，以及一些任务在提供康复训练以促进恢复方面的效用。我们提出了两种自动化的一般方法：行为任务物理管理的自动化（即用于促进任务训练、康复训练和运动测试的设备），以及在行为分析中利用机器学习来生成大量无偏且全面的数据。我们研究了各种运动任务自动化的优缺点以及机器学习分析的局限性。最后，我们对动物行为神经科学中自动化的当前状态进行了批判性评估，并展望了我们认为将极大提高这些方法对行为神经科学家有用性的一些机器学习进展。
