@article {69526, title = {LarvaTagger: Manual and automatic tagging of drosophila larval behaviour.}, journal = {Bioinformatics}, year = {2024}, month = {2024 Jul 05}, abstract = {

MOTIVATION: As more behavioural assays are carried out in large-scale experiments on Drosophila larvae, the definitions of the archetypal actions of a larva are regularly refined. In addition, video recording and tracking technologies constantly evolve. Consequently, automatic tagging tools for Drosophila larval behaviour must be retrained to learn new representations from new data. However, existing tools cannot transfer knowledge from large amounts of previously accumulated data.We introduce LarvaTagger, a piece of software that combines a pre-trained deep neural network, providing a continuous latent representation of larva actions for stereotypical behaviour identification, with a graphical user interface to manually tag the behaviour and train new automatic taggers with the updated ground truth.

RESULTS: We reproduced results from an automatic tagger with high accuracy, and we demonstrated that pre-training on large databases accelerates the training of a new tagger, achieving similar prediction accuracy using less data.

AVAILABILITY: All the code is free and open source. Docker images are also available. See gitlab.pasteur.fr/nyx/LarvaTagger.jl.

SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btae441}, author = {Laurent, Francois and Blanc, Alexandre and May, Lilly and G{\'a}ndara, Lautaro and Cocanougher, Benjamin T and Jones, Benjamin M W and Hague, Peter and Barre, Chloe and Vestergaard, Christian L and Crocker, Justin and Zlatic, Marta and Jovanic, Tihana and Masson, Jean-Baptiste} } @article {65301, title = {Identifying neural substrates of competitive interactions and sequence transitions during mechanosensory responses in Drosophila.}, journal = {PLoS Genetics}, volume = {16}, year = {2020}, month = {2020 Feb 14}, pages = {e1008589}, abstract = {

Nervous systems have the ability to select appropriate actions and action sequences in response to sensory cues. The circuit mechanisms by which nervous systems achieve choice, stability and transitions between behaviors are still incompletely understood. To identify neurons and brain areas involved in controlling these processes, we combined a large-scale neuronal inactivation screen with automated action detection in response to a mechanosensory cue in Drosophila larva. We analyzed behaviors from 2.9x105 larvae and identified 66 candidate lines for mechanosensory responses out of which 25 for competitive interactions between actions. We further characterize in detail the neurons in these lines and analyzed their connectivity using electron microscopy. We found the neurons in the mechanosensory network are located in different regions of the nervous system consistent with a distributed model of sensorimotor decision-making. These findings provide the basis for understanding how selection and transition between behaviors are controlled by the nervous system.

}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1008589}, author = {Masson, Jean-Baptiste and Laurent, Francois and Cardona, Albert and Barre, Chloe and Skatchkovsky, Nicolas and Zlatic, Marta and Jovanic, Tihana} }