1. Neuroscience

Hyperalignment: Modeling shared information encoded in idiosyncratic cortical topographies

  1. James V Haxby  Is a corresponding author
  2. J Swaroop Guntupalli
  3. Samuel A Nastase
  4. Ma Feilong
  1. Center for Cognitive Neuroscience, Dartmouth College, United States
  2. Vicarious AI, United States
  3. Princeton Neuroscience Institute, United States
https://doi.org/10.7554/eLife.56601
Share this article
Cite this article
  1. James V Haxby
  2. J Swaroop Guntupalli
  3. Samuel A Nastase
  4. Ma Feilong
(2020)
Hyperalignment: Modeling shared information encoded in idiosyncratic cortical topographies
eLife 9:e56601.
https://doi.org/10.7554/eLife.56601
  2. Download BibTeX
  3. Download .RIS

Peer review process

This article was accepted for publication as part of eLife's original publishing model.

History

  1. Version of Record published
  2. Accepted
  3. Received

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. James V Haxby
  2. J Swaroop Guntupalli
  3. Samuel A Nastase
  4. Ma Feilong
(2020)
Hyperalignment: Modeling shared information encoded in idiosyncratic cortical topographies
eLife 9:e56601.
https://doi.org/10.7554/eLife.56601

Share this article

https://doi.org/10.7554/eLife.56601