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.

  • News & Views
  • Published:

COMPUTATIONAL NEUROSCIENCE

Untangling network information flow

Nature Computational Science volume 2, pages 475–476 (2022)Cite this article

Subjects

Determining how information flows throughout a network of interconnected components is a challenging task in many scientific domains. A framework is presented to deconstruct the flow of signals that are transmitted across any two areas (such as brain areas) and define how each area represents these signals.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: DLAG computational flow.

References

  1. Gokcen, E. et al. Nat. Comput. Sci. https://doi.org/10.1038/s43588-022-00282-5 (2022).

  2. Siegel, M., Buschman, T. J. & Miller, E. K. Science 348, 1352–1355 (2015).

    Article  Google Scholar 

  3. Chen, M. et al. Neuron 82, 682–694 (2014).

    Article  Google Scholar 

  4. Issa, E. B., Cadieu, C. F. & DiCarlo, J. J. eLife 7, e42870 (2018).

    Article  Google Scholar 

  5. Siegle, J. H. Nature 592, 86–92 (2021).

    Google Scholar 

  6. Oemisch, M., Westendorff, S., Everling, S. & Womelsdorf, T. J. Neurosci. 35, 13076–13089 (2015).

    Article  Google Scholar 

  7. Zandvakili, A. & Kohn, A. Neuron 87, 827–839 (2015).

    Article  Google Scholar 

  8. Steinmetz, N. A., Zatka-Haas, P., Carandini, M. & Harris, K. D. Nature 576, 266–273 (2019).

    Article  Google Scholar 

  9. Inagaki, H. K. et al. Cell 185, 1065–1081 (2022).

    Article  Google Scholar 

  10. Zhuang, X., Zhengshi, Y. & Cordes, D. Hum. Brain Mapp. 41, 3807–3833 (2020).

    Article  Google Scholar 

  11. Kim, T. D., Luo, T. Z., Pillow, J. W. & Brody, C. D. Inferring latent dynamics underlying neural population activity via neural differential equations. In Proc. 38th Int. Conf. Machine Learning (PMLR, 2021).

Download references

Author information

Authors and Affiliations

  1. University of Washington, Seattle, WA, USA

    Stefano Recanatesi

Authors
  1. Stefano Recanatesi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stefano Recanatesi.

Ethics declarations

Competing interests

The author declares no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Recanatesi, S. Untangling network information flow. Nat Comput Sci 2, 475–476 (2022). https://doi.org/10.1038/s43588-022-00284-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43588-022-00284-3

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