Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

NEUROSCIENCE

Translational neuroscience applications for automated detection of rodent grooming with deep learning

A Publisher Correction to this article was published on 16 September 2021

This article has been updated

Rodent grooming is an important behaviour that is commonly used to characterize preclinical models of human brain disorders. A new paper has leveraged deep learning to develop a precise, high throughput and automated grooming classifier to facilitate mechanistic neuroscience research on grooming.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Schematic of grooming microstructure and different potenital patterns that may not be detected by the grooming classifier developed by Geuther et al.

Change history

References

  1. 1.

    Kalueff, A. V. et al. Nat. Rev. Neurosci. 17, 45–59, https://doi.org/10.1038/nrn.2015.8 (2016).

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Geuther, B. Q. et al. eLife 10, e63207, https://doi.org/10.7554/eLife.63207 (2021).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P. Proceedings of the IEEE 86, 2278–2324, https://doi.org/10.1109/5.726791 (1998).

    Article  Google Scholar 

  4. 4.

    Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 1106–1114 http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf (2012).

  5. 5.

    Mathis, A. et al. Nat. Neurosci. 21, 1281–1289, https://doi.org/10.1038/s41593-018-0209-y (2018).

    CAS  Article  Google Scholar 

  6. 6.

    Pereira, T. D. et al. Nat. Method 16, 117–125, https://doi.org/10.1038/s41592-018-0234-5 (2019).

    CAS  Article  Google Scholar 

  7. 7.

    Kabra, M., Robie, A. A., Rivera-Alba, M., Branson, S. & Branson, K. Nat. Method 10, 64–67, https://doi.org/10.1038/nmeth.2281 (2013).

    CAS  Article  Google Scholar 

  8. 8.

    Berridge, K. C. Behaviour 113, 21–56, https://doi.org/10.1163/156853990X00428 (1990).

    Article  Google Scholar 

  9. 9.

    Kalueff, A. V., Aldridge, J. W., LaPorte, J. L., Murphy, D. L. & Tuohimaa, P. Nat. Protoc. 2, 2538–2544, https://doi.org/10.1038/nprot.2007.367 (2007).

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Aldridge, J. W., Berridge, K. C. & Rosen, A. R. Can. J. Physiol. Pharmacol. 82, 732–739, https://doi.org/10.1139/y04-061 (2004).

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Berridge, K. C., Aldridge, J. W., Houchard, K. R. & Zhuang, X. BMC Biol. 3, 4, https://doi.org/10.1186/1741-7007-3-4 (2005).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Hsu, A. I. & Yttri, E. A. bioRxiv https://doi.org/10.1101/770271 (2019).

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Elizbeth E. Manning.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Burton, N.J., Borne, L. & Manning, E.E. Translational neuroscience applications for automated detection of rodent grooming with deep learning. Lab Anim 50, 244–245 (2021). https://doi.org/10.1038/s41684-021-00830-y

Download citation

Search

Quick links