Revealing nonlinear neural decoding by analyzing choices

Simple item page
dc.citation.articleNumber6557en_US
dc.citation.journalTitleNature Communicationsen_US
dc.citation.volumeNumber12en_US
dc.contributor.authorYang, Qianlien_US
dc.contributor.authorWalker, Edgaren_US
dc.contributor.authorCotton, R. Jamesen_US
dc.contributor.authorTolias, Andreas S.en_US
dc.contributor.authorPitkow, Xaqen_US
dc.date.accessioned2021-12-02T15:09:27Zen_US
dc.date.available2021-12-02T15:09:27Zen_US
dc.date.issued2021en_US
dc.description.abstractSensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. The neurons that encode these relevant signals typically constitute a nonlinear population code. Here we present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information. Our theory obeys fundamental mathematical limitations on information content inherited from the sensory periphery, describing redundant codes when there are many more cortical neurons than primary sensory neurons. The theory predicts that if the brain uses its nonlinear population codes optimally, then more informative patterns should be more correlated with choices. More specifically, the theory predicts a simple, easily computed quantitative relationship between fluctuating neural activity and behavioral choices that reveals the decoding efficiency. This relationship holds for optimal feedforward networks of modest complexity, when experiments are performed under natural nuisance variation. We analyze recordings from primary visual cortex of monkeys discriminating the distribution from which oriented stimuli were drawn, and find these data are consistent with the hypothesis of near-optimal nonlinear decoding.en_US
dc.identifier.citationYang, Qianli, Walker, Edgar, Cotton, R. James, et al.. "Revealing nonlinear neural decoding by analyzing choices." <i>Nature Communications,</i> 12, (2021) Springer Nature: https://doi.org/10.1038/s41467-021-26793-9.en_US
dc.identifier.digitals41467-021-26793-9en_US
dc.identifier.doihttps://doi.org/10.1038/s41467-021-26793-9en_US
dc.identifier.urihttps://hdl.handle.net/1911/111730en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleRevealing nonlinear neural decoding by analyzing choicesen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
s41467-021-26793-9.pdf
Size:
1.72 MB
Format:
Adobe Portable Document Format
Download
Collections
Faculty Publications
ECE Publications