Identification of C. elegans Strains Using a Fully Convolutional Neural Network on Behavioural Dynamics

  • Avelino JaverEmail author
  • André E. X. Brown
  • Iasonas Kokkinos
  • Jens Rittscher
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11134)


The nematode C. elegans is a promising model organism to understand the genetic basis of behaviour due to its anatomical simplicity. In this work, we present a deep learning model capable of discerning genetically diverse strains based only on their recorded spontaneous activity, and explore how its performance changes as different embeddings are used as input. The model outperforms hand-crafted features on strain classification when trained directly on time series of worm postures.


Behavioural phenotyping Classification Deep learning 



This work was supported by grants EPSRC SeeBiByte Programme EP/M013774/1 to JR and Medical Research Council MC-A658-5TY30 to AEXB. AJ benefited from both grants.


  1. 1.
    Brown, A.E.X., de Bivort, B.: Ethology as a physical science. Nat. Phys. 14(7), 653–657 (2018). Scholar
  2. 2.
    Brown, A.E., Yemini, E.I., Grundy, L.J., Jucikas, T., Schafer, W.R.: A dictionary of behavioral motifs reveals clusters of genes affecting caenorhabditis elegans locomotion. Proc. Natl. Acad. Sci. 110(2), 791–796 (2013)CrossRefGoogle Scholar
  3. 3.
    Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4724–4733. IEEE (2017)Google Scholar
  4. 4.
    Cook, D.E., Zdraljevic, S., Roberts, J.P., Andersen, E.C.: Cendr, the caenorhabditis elegans natural diversity resource. Nucleic Acids Res. 45(D1), D650–D657 (2016)CrossRefGoogle Scholar
  5. 5.
    Fay, D.S.: Classical genetic methods. WormBook: the online review of C. elegans biology, pp. 1–58 (2013)Google Scholar
  6. 6.
    Hall, S.S.: Neuroscience: as the worm turns. Nature 494(7437), 296–299 (2013). Scholar
  7. 7.
    Javer, A., et al.: An open source platform for analyzing and sharing worm behavior data. bioRxiv (2018).,
  8. 8.
    Jhuang, H., et al.: Automated home-cage behavioural phenotyping of mice. Nat. Commun. 1, 68 (2010)CrossRefGoogle Scholar
  9. 9.
    Kabra, M., Robie, A.A., Rivera-Alba, M., Branson, S., Branson, K.: Jaaba: interactive machine learning for automatic annotation of animal behavior. Nat. Methods 10(1), 64 (2013)CrossRefGoogle Scholar
  10. 10.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  11. 11.
    Lehtinen, J., et al.: Noise2Noise: learning image restoration without clean data. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 2965–2974. PMLR, Stockholmsmässan, Stockholm, Sweden, 10–15 July 2018.
  12. 12.
    Li, K., Javer, A., Keaveny, E.E., Brown, A.E.: Recurrent neural networks with interpretable cells predict and classify worm behaviour. bioRxiv p. 222208 (2017)Google Scholar
  13. 13.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  14. 14.
    Rahmani, H., Mian, A., Shah, M.: Learning a deep model for human action recognition from novel viewpoints. IEEE Trans. Pattern Anal. Mach. Intell. 99(1), 1–1 (2017)Google Scholar
  15. 15.
    Schindler, K., Van Gool, L.: Action snippets: How many frames does human action recognition require? In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)Google Scholar
  16. 16.
    Schwarz, R.F., Branicky, R., Grundy, L.J., Schafer, W.R., Brown, A.E.: Changes in postural syntax characterize sensory modulation and natural variation of C. elegans locomotion. PLoS Comput. Biol. 11(8), e1004322 (2015)CrossRefGoogle Scholar
  17. 17.
    Sengupta, P., Samuel, A.D.: Caenorhabditis elegans: a model system for systems neuroscience. Curr. Opin. Neurobiol. 19(6), 637–643 (2009)CrossRefGoogle Scholar
  18. 18.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  19. 19.
    Stephens, G.J., Johnson-Kerner, B., Bialek, W., Ryu, W.S.: Dimensionality and dynamics in the behavior of C. elegans. PLoS Comput. Biol. 4(4), e1000028 (2008)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Wählby, C., et al.: An image analysis toolbox for high-throughput C. elegans assays. Nat. Methods 9(7), 714 (2012)CrossRefGoogle Scholar
  21. 21.
    Yan, G., et al.: Network control principles predict neuron function in the caenorhabditis elegans connectome. Nature 550(7677), 519 (2017)CrossRefGoogle Scholar
  22. 22.
    Yemini, E., Jucikas, T., Grundy, L.J., Brown, A.E., Schafer, W.R.: A database of caenorhabditis elegans behavioral phenotypes. Nat. Methods 10(9), 877 (2013)CrossRefGoogle Scholar
  23. 23.
    Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: Computer Vision and Pattern Recognition, vol. 1, p. 2 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Avelino Javer
    • 1
    • 2
    Email author
  • André E. X. Brown
    • 3
    • 4
  • Iasonas Kokkinos
    • 5
  • Jens Rittscher
    • 1
    • 2
  1. 1.Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
  2. 2.Big Data InstituteUniversity of OxfordOxfordUK
  3. 3.MRC London Institute of Medical SciencesLondonUK
  4. 4.Institute of Clinical SciencesImperial College LondonLondonUK
  5. 5.Facebook AI ResearchParisFrance

Personalised recommendations