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 AND MACHINE LEARNING

Bursting potentiates the neuro–AI connection

Nature Neuroscience volume 24, pages 905–906 (2021)Cite this article

Subjects

For decades, researchers have wondered whether algorithms used by artificial neural networks might be implemented by biological networks. Payeur et al. have strengthened the connection between neuroscience and artificial intelligence by showing that biologically plausible mechanisms can approximate key features of an essential artificial intelligence learning algorithm.

For many researchers, the connection between neuroscience and artificial intelligence (AI) is a natural one. Over the past several years, artificial neural networks, or ANNs—so-named because of their resemblance to brains—have been trained to perform specific tasks with superhuman performance. For example, artificial systems now routinely beat the best human players at complex games such as Go1 and Dota2, and they can solve enormously complex problems such as protein folding3. Neuroscientists therefore wonder whether these sophisticated systems can teach us something about the mechanisms of cognition in humans and other animals. On the other hand, AI systems are, for the most part, highly specialized. A system designed and trained to play games cannot fold proteins, and vice versa. In the domain of general intelligence, humans still outperform machines. Thus, practitioners of AI wonder whether neuroscience can offer clues as to how to achieve more general artificial intelligence.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

Buy

All prices are NET prices.

Fig. 1: Does the brain solve the credit assignment problem using learning algorithms akin to back-propagation?.

References

  1. Silver, D. et al. Nature 529, 484–489 (2016).

    Article  CAS  Google Scholar 

  2. OpenAI et al. Preprint at arXiv https://arxiv.org/abs/1912.06680 (2019).

  3. Senior, A. W. et al. Nature 577, 706–710 (2020).

    Article  CAS  Google Scholar 

  4. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Nature 323, 533–536 (1986).

    Article  Google Scholar 

  5. Payeur et al. Nat. Neurosci. https://doi.org/10.1038/s41593-021-00857-x (2021).

  6. Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J. & Hinton, G. Nat. Rev. Neurosci. 21, 335–346 (2020).

    Article  CAS  Google Scholar 

  7. Larkum, M. E., Zhu, J. J. & Sakmann, B. Nature 398, 338–341 (1999).

    Article  CAS  Google Scholar 

  8. Letzkus, J. J., Kampa, B. M. & Stuart, G. J. J. Neurosci. 26, 10420–10429 (2006).

    Article  CAS  Google Scholar 

  9. Sjöström, P. J., Turrigiano, G. G. & Nelson, S. B. Neuron 32, 1149–1164 (2001).

    Article  Google Scholar 

  10. Grossberg, S. Cog. Sci. 11, 23–63 (1987).

    Article  Google Scholar 

  11. Kolen, J. F. & Pollack, J. B. in Proc. 1994 IEEE International Conference on Neural Networks (ICNN’94) https://doi.org/10.1109/ICNN.1994.374486 (1994).

  12. Akrout, M., Wilson, C., Humphreys, P. C., Lillicrap, T. & Tweed, D. Adv. Neural Inf. Process. Syst. {https://proceedings.neurips.cc/paper/2019/file/f387624df552cea2f369918c5e1e12bc-Paper.pdf (2020).

  13. Song, S., Sjöström, P. J., Reigl, M., Nelson, S. & Chklovskii, D. B. PLOS Biol. 3, e68 (2005).

    Article  Google Scholar 

  14. Cossell, L. et al. Nature 518, 399–403 (2015).

    Article  CAS  Google Scholar 

  15. Richards, B. A. et al. Nat. Neurosci. 22, 1761–1770 (2019).

    Article  CAS  Google Scholar 

Download references

Author information

Author notes

  1. These authors contributed equally: Weinan Sun, Xinyu Zhao.

Authors and Affiliations

  1. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA

    Weinan Sun, Xinyu Zhao & Nelson Spruston

Authors
  1. Weinan Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinyu Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nelson Spruston
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nelson Spruston.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sun, W., Zhao, X. & Spruston, N. Bursting potentiates the neuro–AI connection. Nat Neurosci 24, 905–906 (2021). https://doi.org/10.1038/s41593-021-00844-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-021-00844-2

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing