Searching for motifs in the behaviour of larval Drosophila melanogaster and Caenorhabditis elegans reveals continuity between behavioural states

Abstract

We present a novel method for the unsupervised discovery of behavioural motifs in larval Drosophila melanogaster and Caenorhabditis elegans. A motif is defined as a particular sequence of postures that recurs frequently. The animal's changing posture is represented by an eigenshape time series, and we look for motifs in this time series. To find motifs, the eigenshape time series is segmented, and the segments clustered using spline regression. Unlike previous approaches, our method can classify sequences of unequal duration as the same motif. The behavioural motifs are used as the basis of a probabilistic behavioural annotator, the eigenshape annotator (ESA). Probabilistic annotation avoids rigid threshold values and allows classification uncertainty to be quantified. We apply eigenshape annotation to both larval Drosophila and C. elegans and produce a good match to hand annotation of behavioural states. However, we find many behavioural events cannot be unambiguously classified. By comparing the results with ESA of an artificial agent's behaviour, we argue that the ambiguity is due to greater continuity between behavioural states than is generally assumed for these organisms.

Continuous lateral oscillations as a core mechanism for taxis in Drosophila larvae

“…However, behavioural observation shows rather strong similarities in the behavioural modulations resulting from apparently unrelated conditions, such as odour-tastant associative learning and variation of stimulus concentration (Schleyer et al, 2015a), which simultaneously modulate both the klinokinetic and klinotactic responses (weathervaning was not assessed in this study). Also, a recent attempt to categorise larval behavioural states using an unsupervised method based on the animal’s posture suggests the existence of a continuum rather than clear-cut categories (Szigeti et al, 2015). …”

Systematic exploration of unsupervised methods for mapping behavior

“…Vogelstein et al (2014) used unsupervised structure learning to infer a hierarchical organization of larval behaviors based on eight time varying measures of posture and motion. Most recently an 'eigenlarva' analysis revealed that many behavioral events defy easy assignment to discrete clusters, suggesting that, at least in larvae, behavior may vary rather continuously (Szigeti et al 2015).…”

Quantifying behavior to solve sensorimotor transformations: advances from worms and flies

“…How the animal’s posture changes in each of these dimensions is sufficient to explain a wide variety of behaviors, from foraging to thermotaxis[12,13], and has led to the discovery of new behaviors [14] as well as identified novel roles for genes in behavior[15]. Similar approaches that use automated tools to more closely quantify posture have been used to characterize larval zebrafish [16] and larval Drosophila [17] behavior.…”

“…Converting postures (e.g., ‘straight forearm’ or ‘extended elbow’) into actions (e.g., ‘reaching’) requires some way of clustering together stereotyped sequences of postures that are repeatedly seen with only minor variations. Methods to accomplish this range from embedding postures into a low-dimensional space to find ‘clumps’ of similar sequences[18] to identifying when one posture is predictable from previous postures[19], though other methods exist[15,17,20,21]. What is exciting about these ‘unsupervised’ algorithms is that they offer not only ever-more-precise quantification of what an animal is doing at each moment in time, but also reveal the underlying structure of behavior (e.g., which behaviors are sub-programs of other behaviors or which behaviors co-occur), and offer the potential for discovering completely new behaviors.…”