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 AND VIEWS

Brain-inspired computing boosted by new concept of completeness

Hardware modelled on the brain could revolutionize computing, but implementing algorithms on such systems is a challenge. A proposed conceptual framework could simplify implementation, accelerating research in this field.
  1. Oliver Rhodes

    1. Oliver Rhodes is in the Department of Computer Science, University of Manchester, Manchester M13 9PL, UK.

    View author publications

    You can also search for this author in PubMed  Google Scholar

The next generation of high-performance, low-power computer systems might be inspired by the brain. However, as designers move away from conventional computer technology towards brain-inspired (neuromorphic) systems, they must also move away from the established formal hierarchy that underpins conventional machines — that is, the abstract framework that broadly defines how software is processed by a digital computer and converted into operations that run on the machine’s hardware. This hierarchy has helped enable the rapid growth in computer performance. Writing in Nature, Zhang et al.1 define a new hierarchy that formalizes the requirements of algorithms and their implementation on a range of neuromorphic systems, thereby laying the foundations for a structured approach to research in which algorithms and hardware for brain-inspired computers can be designed separately.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

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

Nature 586, 364-366 (2020)

doi: https://doi.org/10.1038/d41586-020-02829-w

References

  1. Zhang, Y. et al. Nature 586, 378–384 (2020).

    Article  Google Scholar 

  2. Roy, K., Jaiswal, A. & Panda, P. Nature 575, 607–617 (2019).

    Article  PubMed  Google Scholar 

  3. Gerstner, W., Kistler, W. M., Naud, R. & Paninski, L. Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition (Cambridge Univ. Press, 2014).

    Google Scholar 

  4. Pei, J. et al. Nature 572, 106–111 (2019).

    Article  PubMed  Google Scholar 

Download references

Subjects

Latest on:

Nature Careers

Jobs

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

Search

Advanced search

Quick links