Mapping the stereotyped behaviour of freely moving fruit flies

By Gordon J. Berman, Daniel M. Choi, William Bialek, Joshua W. Shaevitz. 2013

543 citations recorded by the source corpus.

A frequent assumption in behavioural science is that most of an animal's activities can be described in terms of a small set of stereotyped motifs. Here, we introduce a method for mapping an animal's actions, relying only upon the underlying structure of postural movement data to organize and classify behaviours. Applying this method to the ground-based behaviour of the fruit fly, Drosophila melanogaster, we find that flies perform stereotyped actions roughly 50% of the time, discovering over 100 distinguishable, stereotyped behavioural states. These include multiple modes of locomotion and grooming. We use the resulting measurements as the basis for identifying subtle sex-specific behavioural differences and revealing the low-dimensional nature of animal motions.

Subfields: Ecology, Evolution, Behavior and Systematics, Genetics

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Citations in the corpus

Using DeepLabCut for 3D markerless pose estimation across species and behaviors (2018, methodology): “The outputs from DeepLabCut can also interfaced with behavioral clustering tools such as JAABA [43], MotionMapper [44], MoSeq [45], or other clustering approaches such as iterative denoising tree (IDT) methods [46, 47], and more [48].”
Computational neuroethology: a call to action (2019, methodology): “Similar work in C. elegans using a variety of methods (including clustering and MotionMapper) has quantified behavioral responses to the optogenetic activation of a nociceptive neuron, and to characterize the innate behaviors expressed by thousands of individual worms during optogenetic stimulation ”
SLEAP: A deep learning system for multi-animal pose tracking (2022, background): “The ‘fly32’ dataset is a single-animal dataset that has been previously described and annotated with poses 6,29 .”
BEEtag: A Low-Cost, Image-Based Tracking System for the Study of Animal Behavior and Locomotion (2015, background): “In ethology, automated tracking techniques have allowed for the collection of vast, highly-accurate behavioral datasets [6–8], which can be used, for example, in detailed quantitative analysis of animal behavior [9,10].”
Revealing the vectors of cellular identity with single-cell genomics (2016, methodology): “A post hoc procedure to ACCENSE can embed the remaining cells in the t-SNE space 116 to verify that they do not change the density map substantially.”
Mapping Sub-Second Structure in Mouse Behavior. (2015, background): “…behavioral modules and their associated transition probabilities can now be discovered systematically through automated machine vision, clustering, and classification algorithms (Berman et al., 2014; Croll, 1975; Garrity et al., 2010; Ste- Ne phens et al., 2008, 2010; Vogelstein et al., 2014).”
Fast animal pose estimation using deep neural networks (2018, methodology): “11 .”
Deep neuroethology of a virtual rodent (2019, methodology): “…combined postural and dynamical features, the later on intermediate timescales, to yield a 60 dimensional set of ‘behavioral features’ that we used to map the animal’s behavior using tSNE (Figure 4C) (Berman et al., 2014). tSNEs were made using the Barnes-Hut approximation with a perplexity of 30.”
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets (2015, methodology): “The remaining 36,145 single-cell libraries (<900 genes detected) were next projected on this t-SNE map, based on their representation within the PC-subspace of the training set (Berman et al., 2014; Shekhar et al., 2014).”