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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

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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.

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

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References

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

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Correspondence to Elizbeth E. Manning.

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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

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