Topic: behavioral syllables

Quantitative methods that cluster videos of animal behavior into repeated syllables (sometimes called “motifs") have become fundamental tools for systems neuroscientists and neuroethologists. A popular approach is to use autoregressive hidden Markov models (ARHMMs) to identify behavioral syllables in the absence of labeled training data. This model, called MoSeq, has been shown to successfully segment depth-camera videos of mouse behavior into syllables that are familiar to a human observer. However, one issue with MoSeq is that it sorts behaviors which appear very similar to the human eye (e.

Individuality is an intrinsic and essential aspect of mammalian behavior that emerges even in genetically identical organisms experiencing the same environmental conditions1. In the International Brain Laboratory, over one hundred mice were trained on a visual decision-making task with the explicit goal of establishing a rigorously standardized experimental protocol. This effort led to an automated pipeline that produced trained mice whose behavior was indistinguishable across seven different labs, when considering trial-level descriptors of behavior. Nevertheless, substantial inter-individual

Investigation of social behavior in mice is increasingly relevant to understand associated brain circuits and thereby develop treatments for psychiatric symptoms such as social withdrawal. Importantly, a data-driven description of social behavior and how the behavioral repertoire of mice differs in solitary vs. social context is lacking. Here, we aim to identify, in a data-driven manner, recurring behavioral patterns (“syllables”) that are unique to social and solitary contexts.We recorded videos of male C57BL/6j mice, in two epochs of 90 minutes duration, either in a social context (n=4) or i

Behavior analysis typically relies on trial-based repetitions of constrained tasks. We aimed to uncover how age, pain, and itch influence the patterns of behavioral sequences and their transitions in freely behaving animals using MoSeq, a machine learning network analysis tool developed by the Datta lab. Mice of different ages were treated with chloroquine (itch) or formalin (pain) and placed in an open field arena and recorded for 30 minutes via a high-speed color camera and infrared depth camera. A trained autoregressive hidden Markov model classified the mouse movements into sub-second syll