LAUSR

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 behavior 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.