Abstract
Animals display characteristic behavioural patterns when performing a task, such as the spiraling of a soaring bird or the surge-and-cast of a male moth searching for a female. Identifying such recurring sequences occurring rarely in noisy behavioural data is key to understanding the behavioural response to a distributed stimulus in unrestrained animals. Existing models seek to describe the dynamics of behaviour or segment individual locomotor episodes rather than to identify the rare and transient sequences of locomotor episodes that make up the behavioural response. To fill this gap, we develop a lexical, hierarchical model of behaviour. We designed an unsupervised algorithm called "BASS" to efficiently identify and segment recurring behavioural action sequences transiently occurring in long behavioural recordings. When applied to navigating larval zebrafish, BASS extracts a dictionary of remarkably long, non-Markovian sequences consisting of repeats and mixtures of slow forward and turn bouts. Applied to a novel chemotaxis assay, BASS uncovers chemotactic strategies deployed by zebrafish to avoid aversive cues consisting of sequences of fast large-angle turns and burst swims. In a simulated dataset of soaring gliders climbing thermals, BASS finds the spiraling patterns characteristic of soaring behaviour. In both cases, BASS succeeds in identifying rare action sequences in the behaviour deployed by freely moving animals. BASS can be easily incorporated into the pipelines of existing behavioural analyses across diverse species, and even more broadly used as a generic algorithm for pattern recognition in low-dimensional sequential data.
Highlights
Animal behaviour is extremely diverse and context-dependent
Motifs and transitions between motifs arguably contain more meaning in the context of a behavioural algorithm, much like in language. In his seminal paper, Lashley [22, 23] rejects the reflex chain theory, which posits, in current terminology, a Markov model for the dynamics of movements in favor of a model based on noisy motifs
To discover and construct dictionaries of recurring action sequences, referred to as ‘motifs’ [3], we developed a lexical model of behaviour and the unsupervised method BASS (Behavioural Action Sequence Segmentation)
Summary
Animal behaviour is extremely diverse and context-dependent. Yet, animals often exhibit certain characteristic patterns in their behaviour, when executing a task or in response to a stimulus. One increasingly common computational approach to quantitatively describe behaviour is to leverage recent developments in the automated tracking of postural dynamics [1,2,3,4,5,6,7,8]. Motifs and transitions between motifs arguably contain more meaning in the context of a behavioural algorithm, much like in language In his seminal paper, Lashley [22, 23] rejects the reflex chain theory, which posits, in current terminology, a Markov model for the dynamics of movements in favor of a model based on noisy motifs
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