DeepAction: a MATLAB toolbox for automated classification of animal behavior in video

Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience

The study of complex behaviors is often challenging when using manual annotation due to the absence of quantifiable behavioral definitions and the subjective nature of behavioral annotation. Integration of supervised machine learning approaches mitigates some of these issues through the inclusion of accessible and explainable model interpretation. To decrease barriers to access, and with an emphasis on accessible model explainability, we developed the open-source Simple Behavioral Analysis (SimBA) platform for behavioral neuroscientists. SimBA introduces several machine learning interpretability tools, including SHapley Additive exPlanation (SHAP) scores, that aid in creating explainable and transparent behavioral classifiers. Here we show how the addition of explainability metrics allows for quantifiable comparisons of aggressive social behavior across research groups and species, reconceptualizing behavior as a sharable reagent and providing an open-source framework. We provide an open-source, graphical user interface (GUI)-driven, well-documented package to facilitate the movement toward improved automation and sharing of behavioral classification tools across laboratories.

ARBUR, a machine learning-based analysis system for relating behaviors and ultrasonic vocalizations of rats

Deciphering how different behaviors and ultrasonic vocalizations (USVs) of rats interact can yield insights into the neural basis of social interaction. However, the behavior-vocalization interplay of rats remains elusive because of the challenges of relating the two communication media in complex social contexts. Here, we propose a machine learning-based analysis system (ARBUR) that can cluster without bias both non-step (continuous) and step USVs, hierarchically detect eight types of behavior of two freely behaving rats with high accuracy, and locate the vocal rat in 3-D space. ARBUR reveals that rats communicate via distinct USVs during different behaviors. Moreover, we show that ARBUR can indicate findings that are long neglected by former manual analysis, especially regarding the non-continuous USVs during easy-to-confuse social behaviors. This work could help mechanistically understand the behavior-vocalization interplay of rats and highlights the potential of machine learning algorithms in automatic animal behavioral and acoustic analysis.

SaLSa: A Combinatory Approach of Semi-Automatic Labeling and Long Short-Term Memory to Classify Behavioral Syllables

Accurately and quantitatively describing mouse behavior is an important area. Although advances in machine learning have made it possible to track their behaviors accurately, reliable classification of behavioral sequences or syllables remains a challenge. In this study, we present a novel machine learning approach, called SaLSa (a combination of semi-automatic labeling and long short-term memory-based classification), to classify behavioral syllables of mice exploring an open field. This approach consists of two major steps. First, after tracking multiple body parts, spatial and temporal features of their egocentric coordinates are extracted. A fully automated unsupervised process identifies candidates for behavioral syllables, followed by manual labeling of behavioral syllables using a graphical user interface (GUI). Second, a long short-term memory (LSTM) classifier is trained with the labeled data. We found that the classification performance was marked over 97%. It provides a performance equivalent to a state-of-the-art model while classifying some of the syllables. We applied this approach to examine how hyperactivity in a mouse model of Alzheimer's disease develops with age. When the proportion of each behavioral syllable was compared between genotypes and sexes, we found that the characteristic hyperlocomotion of female Alzheimer's disease mice emerges between four and eight months. In contrast, age-related reduction in rearing is common regardless of genotype and sex. Overall, SaLSa enables detailed characterization of mouse behavior.