The impact of machine learning on ethological neuroscience

Machine learning is revolutionizing behavioral neuroscience by enabling the study of animal behavior with greater ecological validity while maintaining experimental rigor. Traditional manual observation methods in ethology are constrained by subjectivity, costs, and low throughput, whereas modern machine learning algorithms now provide quantitative tools to investigate natural behavior with unprecedented precision. This mini review surveys recent advances in machine learning for behavioral neuroscience, focusing on markerless pose estimation and unsupervised behavioral clustering, and discusses their roles along the typical research pipeline, from tracking and detection to classification and integration of behavioral and neural data. Open-source platforms using deep learning-based image processing have turned video cameras into high-resolution measurement devices, while unsupervised methods extend inference across large-scale behavioral recordings. In laboratory settings, machine learning enables fine-scale analysis of animal kinematics and their relationship to neural activity, while in field studies it enhances longitudinal data collection through drone and satellite imaging. These approaches expand ethological research by quantifying movement, segmenting behavior into meaningful units, detecting transient events often missed by human observers, and bridging behavior with brain activity via joint latent spaces and closed-loop paradigms. Although challenges remain in handling high-dimensional datasets, machine learning offers powerful opportunities for more comprehensive neuroscientific insights. By bridging the controlled precision of the laboratory with the complexity of real-world environments, these methods advance our understanding of animal behavior and its neural underpinnings, providing experimentalists with practical tools to design, implement, and interpret more naturalistic studies in the field of ethological neuroscience.