Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

TAFKAP: An improved method for probabilistic decoding of cortical activity

View ORCID ProfileR.S. van Bergen, View ORCID ProfileJ.F.M. Jehee
doi: https://doi.org/10.1101/2021.03.04.433946
R.S. van Bergen
1Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, the Netherlands
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for R.S. van Bergen
  • For correspondence: ruben.vanbergen@columbia.edu janneke.jehee@donders.ru.nl
J.F.M. Jehee
1Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, the Netherlands
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for J.F.M. Jehee
  • For correspondence: ruben.vanbergen@columbia.edu janneke.jehee@donders.ru.nl
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

Cortical activity can be difficult to interpret. Neural responses to the same stimulus vary between presentations, due to random noise and other sources of variability. This unreliable relationship to external stimuli renders any pattern of activity open to a multitude of plausible interpretations. We have previously shown that this uncertainty in cortical stimulus representations can be characterized using a probabilistic decoding algorithm, which inverts a generative model of stimulus-evoked cortical responses. Here, we improve upon this method in two important ways, which both target the precision with which the generative model can be estimated from limited, noisy training data. We show that these improvements lead to considerably better estimation of the presented stimulus and its associated uncertainty. Estimates of the presented stimulus are recovered with an accuracy that exceeds that of standard decoding methods (SVMs), and in some cases even approaches the behavioral accuracy of human observers. Moreover, the uncertainty in the decoded probability distributions better characterizes the precision of cortical stimulus information from trial to trial.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Added missing section on behavioral data + minor cosmetic changes and typo corrections.

  • https://github.com/jeheelab/TAFKAP

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted April 16, 2021.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
TAFKAP: An improved method for probabilistic decoding of cortical activity
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
TAFKAP: An improved method for probabilistic decoding of cortical activity
R.S. van Bergen, J.F.M. Jehee
bioRxiv 2021.03.04.433946; doi: https://doi.org/10.1101/2021.03.04.433946
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
TAFKAP: An improved method for probabilistic decoding of cortical activity
R.S. van Bergen, J.F.M. Jehee
bioRxiv 2021.03.04.433946; doi: https://doi.org/10.1101/2021.03.04.433946

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (4232)
  • Biochemistry (9128)
  • Bioengineering (6774)
  • Bioinformatics (23989)
  • Biophysics (12117)
  • Cancer Biology (9523)
  • Cell Biology (13772)
  • Clinical Trials (138)
  • Developmental Biology (7627)
  • Ecology (11686)
  • Epidemiology (2066)
  • Evolutionary Biology (15504)
  • Genetics (10638)
  • Genomics (14322)
  • Immunology (9477)
  • Microbiology (22832)
  • Molecular Biology (9089)
  • Neuroscience (48957)
  • Paleontology (355)
  • Pathology (1480)
  • Pharmacology and Toxicology (2568)
  • Physiology (3844)
  • Plant Biology (8327)
  • Scientific Communication and Education (1471)
  • Synthetic Biology (2296)
  • Systems Biology (6186)
  • Zoology (1300)