HUB-DT: A tool for unsupervised behavioural discovery and analysis

There has been an expansion in the diversity of tools used to measure various aspects of brain function in behaving animals. While these tools have great potential to transform our understanding of brain function, they are of little value if the behavior of interest is poorly defined or quantified. Traditional methods of behavioural labelling focus on easily quantified gross measure, such as velocity, gate crossing, nosepokes, etc. While these measures are specific and reproducible, they are crude descriptions of behaviour at best. Manually defined behaviours, while providing increased granularity and descriptive power over specific gross measures, suffer from being inexact and somewhat arbitrary. Consistent labelling between human observers is often difficult, and even if manually defined behaviours are subsequently labelled in an automated fashion (via a supervised learning algorithm) these behaviours need to be defined ahead of time, possibly biasing the range of behaviours of interest for a given task. Here we present HUB-DT, a behavioural discovery pipeline built on the frameworks of several existing tools and methods in the space of behavioural categorisation, the specifics of which will be highlighted in this report, and designed to address the requirements of behavioral discovery.