Progress on Computational Ethology

Abstract

Animal behavior, mostly controlled by the central brain, has been studied in natural environments and controlled laboratory settings. In the early 20th century, researchers studied behavior in natural environments to reveal how it is built from components and organized over time in response to stimuli. In the laboratory settings, researchers study the ability of brain to generate behaviors in response to rewards and punishments. However, there are limitations in quantifying animal behaviors in these two approaches. Recent advances and breakthroughs in computer science provide an important opportunity for overcoming these limitations of behavioral studies. In this review, we focus on an emerging new discipline called “Computational Ethology”, which uses a wide variety of techniques, including computer vision and machine learning, to measure and analyze the patterns of animal behaviors. Computational Ethology allows quantitative analyses of animal behaviors with high efficiency, and has made significant progress in recent years towards a better understanding of behaviors as well as their underlying neural mechanism. Over the last decade, with the application of artificial intelligence in animal behavior study, numerous methods have been developed to automatically quantify animal behavior, including automatic tracking of movements. Classical computer vision methods estimate centroids and ellipses of animals, which reflect the locomotion and orientation, respectively. This estimation was then extended to multiple animals. Nevertheless, non-locomotion information of animal pose cannot be reliably captured. To solve this problem, deep learning-based pose estimation of behaviors in the laboratory setting has been developed for various animal species. In this review, we present a pipeline of collecting and analyzing behavioral data. High-dimensional raw behavioral data are first subjected to dimension reduction. The low-dimensional data are then segmented and analyzed by supervised classification or unsupervised clustering in order to produce behavioral modules. After this, transition probabilities between behavioral modules over time are calculated to elucidate the pattern of behaviors. We also review the application of artificial intelligence to analyze behaviors for diagnosing and evaluating neuropsychiatric diseases and discuss the opportunities and challenges in Computational Ethology. Computational Ethology, a result of substantial interdisciplinary research of neuroscience, psychology, physics, computer science, and ethology has great potential towards a deeper understanding of the nature of animal behaviors. Nevertheless, it is still in its infancy and many questions remain to be explored. This review provides a summary as well as a reference resource for this new yet rapidly advancing discipline. We hope that this review will be informative and useful for a wide interdisciplinary scientific community studying animal behaviors and artificial intelligence.