The study of naturalistic social behavior requires quantification of animals’ interactions. This is generally done through manual annotation—a highly time-consuming and tedious process. Recent advances in computer vision enable tracking the pose (posture) of freely behaving animals. However, automatically and accurately classifying complex social behaviors remains technically challenging. We introduce the Mouse Action Recognition System (MARS), an automated pipeline for pose estimation and behavior quantification in pairs of freely interacting mice. We compare MARS’s annotations to human annotations and find that MARS’s pose estimation and behavior classification achieve human-level performance. We also release the pose and annotation datasets used to train MARS to serve as community benchmarks and resources. Finally, we introduce the Behavior Ensemble and Neural Trajectory Observatory (BENTO), a graphical user interface for analysis of multimodal neuroscience datasets. Together, MARS and BENTO provide an end-to-end pipeline for behavior data extraction and analysis in a package that is user-friendly and easily modifiable.

The study of social behavior requires scoring the animals’ interactions. This is generally done by hand— a time consuming, subjective, and expensive process. Recent advances in computer vision enable tracking the pose (posture) of freely-behaving laboratory animals automatically. However, classifying complex social behaviors such as mounting and attack remains technically challenging. Furthermore, the extent to which expert annotators, possibly from different labs, agree on the definitions of these behaviors varies. There is a shortage in the neuroscience community of benchmark datasets that can be used to evaluate the performance and reliability of both pose estimation tools and manual and automated behavior scoring.We introduce the Mouse Action Recognition System (MARS), an automated pipeline for pose estimation and behavior quantification in pairs of freely behaving mice. We compare MARS’s annotations to human annotations and find that MARS’s pose estimation and behavior classification achieve human-level performance. As a by-product we characterize the inter-expert variability in behavior scoring. The two novel datasets used to train MARS were collected from ongoing experiments in social behavior, and identify the main sources of disagreement between annotators. They comprise 30,000 frames of manual annotated mouse poses and over 14 hours of manually annotated behavioral recordings in a variety of experimental preparations. We are releasing this dataset alongside MARS to serve as community benchmarks for pose and behavior systems. Finally, we introduce the Behavior Ensemble and Neural Trajectory Observatory (Bento), a graphical interface that allows users to quickly browse, annotate, and analyze datasets including behavior videos, pose estimates, behavior annotations, audio, and neural recording data. We demonstrate the utility of MARS and Bento in two use cases: a high-throughput behavioral phenotyping study, and exploration of a novel imaging dataset. Together, MARS and Bento provide an end-to-end pipeline for behavior data extraction and analysis, in a package that is user-friendly and easily modifiable.

We develop approximation algorithms for set-selection problems with deterministic constraints, but random objective values, i.e., stochastic probing problems. When the goal is to maximize the objective, approximation algorithms for probing problems are well-studied. On the other hand, few techniques are known for minimizing the objective, especially in the adaptive setting, where information about the random objective is revealed during the set-selection process and allowed to influence it. For minimization problems in particular, incorporating adaptivity can have a considerable effect on performance. In this work, we seek approximation algorithms that compare well to the optimal adaptive policy.We develop new techniques for adaptive minimization, applying them to a few problems of interest. The core technique we develop here is an approximate reduction from an adaptive expectation minimization problem to a set of adaptive probability minimization problems which we call threshold problems. By providing near-optimal solutions to these threshold problems, we obtain bicriteria adaptive policies.We apply this method to obtain an adaptive approximation algorithm for the M E problem, where the goal is to adaptively pick random variables to minimize the expected minimum value seen among them, subject to a knapsack constraint. This partially resolves an open problem raised in Goel et al. [2010]. We further consider three extensions on the M E problem, where our objective is the sum of the smallest element-weights, or the weight of the min-weight basis of a given matroid, or where the constraint is not given by a knapsack but by a matroid constraint. For all three of the variations we explore, we develop adaptive approximation algorithms for their corresponding threshold problems, and prove their near-optimality via coupling arguments.

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