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. 2026 Jan 9;19:1745658. doi: 10.3389/fnbeh.2025.1745658

FIGURE 1.

Diagram showing the study of behavioral repertoires, organized into three sections. On the left, a circle labeled “Behavioral Repertoire” with images of various animals representing wild, species-specific, conditioned, social behaviors. The middle section, “Data Recording,” includes video, sensory, physiological, and neuronal data. The right section, “Data Analysis and Explaining Behavior,” features pose estimation, feature extraction, classification methods like SVM and KNN, and explanations of behavior through levels, movement trajectories, and joint latent spaces.

Integrative framework for ethological neuroscience using machine learning. This schematic summarizes stages for quantification and interpretation of animal behavior with contemporary computational tools. Left: behavioral repertoires range from naturalistic and species-typical actions to conditioned and socially coordinated behaviors, illustrating ecological and cognitive diversity across animal models. Center: multimodal data recording integrates multi-camera video capture, sensory environment monitoring (e.g., sound, odor), physiological measures (e.g., pupil dynamics) and neuronal recordings (electrophysiology, imaging). Middle–right: data analysis pipelines apply pose estimation and feature extraction to video and physiological signals, followed by classifiers and pattern-discovery algorithms (e.g., SVM, k-NN, CNNs) to identify behavioral units and trajectories. Right: the final column explicitly links analysis outputs to explanatory levels, from body kinematics, through actions and movement trajectories, to neural activity. Showing how machine learning uncovers behavioral sequences, hierarchical structure, and joint latent spaces that bridge behavior and brain activity.