Skip to main content

Umbrella menu

  • SfN.org
  • eNeuro
  • The Journal of Neuroscience
  • Neuronline
  • BrainFacts.org

Main menu

  • HOME
  • CONTENT
    • Early Release
    • Featured
    • Current Issue
    • Issue Archive
    • Collections
  • ALERTS
  • FOR AUTHORS
    • Preparing a Manuscript
    • Submission Guidelines
    • Fees
    • Journal Club
    • eLetters
    • Submit
  • EDITORIAL BOARD
  • ABOUT
    • Overview
    • Advertise
    • For the Media
    • Rights and Permissions
    • Privacy Policy
    • Feedback
  • SUBSCRIBE
  • SfN.org
  • eNeuro
  • The Journal of Neuroscience
  • Neuronline
  • BrainFacts.org

User menu

  • Log in
  • Subscribe
  • My alerts

Search

  • Advanced search
Journal of Neuroscience
  • Log in
  • Subscribe
  • My alerts
Journal of Neuroscience

Advanced Search

Submit a Manuscript
  • HOME
  • CONTENT
    • Early Release
    • Featured
    • Current Issue
    • Issue Archive
    • Collections
  • ALERTS
  • FOR AUTHORS
    • Preparing a Manuscript
    • Submission Guidelines
    • Fees
    • Journal Club
    • eLetters
    • Submit
  • EDITORIAL BOARD
  • ABOUT
    • Overview
    • Advertise
    • For the Media
    • Rights and Permissions
    • Privacy Policy
    • Feedback
  • SUBSCRIBE
PreviousNext
Research Articles, Systems/Circuits

Information limiting correlations in large neural populations

Ramon Bartolo, Richard C. Saunders, Andrew R. Mitz and Bruno B. Averbeck
Journal of Neuroscience 15 January 2020, 2072-19; DOI: https://doi.org/10.1523/JNEUROSCI.2072-19.2019
Ramon Bartolo
Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda MD.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Richard C. Saunders
Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda MD.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Andrew R. Mitz
Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda MD.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Bruno B. Averbeck
Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda MD.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Info & Metrics
  • eLetters
  • PDF
Loading

Abstract

Understanding the neural code requires understanding how populations of neurons code information. Theoretical models predict that information may be limited by correlated noise in large neural populations. Nevertheless, analyses based on tens of neurons have failed to find evidence of saturation. Moreover, some studies have shown that noise correlations can be very small, and therefore may not affect information coding. To determine whether information-limiting correlations exist, we implanted 8 Utah arrays in prefrontal cortex (PFC; area 46) of two male macaque monkeys, recording >500 neurons simultaneously. We estimated information in PFC about saccades as a function of ensemble size. Noise correlations were, on average, small (∼10-3). However, information scaled strongly sub-linearly with ensemble size. After shuffling trials, destroying noise correlations, information becomes a linear function of ensemble size. Thus, we provide evidence for the existence of information-limiting noise correlations in large populations of PFC neurons.

SIGNIFICANCE STATEMENT

Recent theoretical work has shown that even small correlations can limit information if they are “differential correlations”, which are difficult to measure directly. However, they can be detected through decoding analyses on recordings from a large number of neurons over a large number of trials. We have achieved both by collecting neural activity in dorsal-lateral prefrontal cortex of macaques using 8 microelectrode arrays (768 electrodes), from which we were able to compute accurate information estimates. We show, for the first time, strong evidence for information-limiting correlations. Despite pair-wise correlations being small (on the order of 10-3) they affect information coding in populations on the order of 100s of neurons.

Footnotes

  • The authors declare no competing financial interests.

  • To perform the analyses described in this paper we made use of the computational resources of the NIH/HPC Biowulf cluster (http://hpc.nih.gov). This work was supported by the Intramural Research Program, National Institute of Mental Health/NIH (ZIA MH002928-01).

Member Log In

Sign in with your SFN login

If you have an SfN.org account and DO NOT know
your username and/or password

If you DO NOT have an SfN membership

Log in through your institution

If your organization uses OpenAthens, you can log in using your OpenAthens username and password. To check if your institution is supported, please see this list. Contact your library for more details.

Pay Per Article - You may access this article (from the computer you are currently using) for 1 day for US$35.00

Regain Access - You can regain access to a recent Pay per Article purchase if your access period has not yet expired.

Back to top
Email

Thank you for sharing this Journal of Neuroscience article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
Information limiting correlations in large neural populations
(Your Name) has forwarded a page to you from Journal of Neuroscience
(Your Name) thought you would be interested in this article in Journal of Neuroscience.
View Full Page PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Citation Tools
Information limiting correlations in large neural populations
Ramon Bartolo, Richard C. Saunders, Andrew R. Mitz, Bruno B. Averbeck
Journal of Neuroscience 15 January 2020, 2072-19; DOI: 10.1523/JNEUROSCI.2072-19.2019

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Respond to this article
Request Permissions
Share
Information limiting correlations in large neural populations
Ramon Bartolo, Richard C. Saunders, Andrew R. Mitz, Bruno B. Averbeck
Journal of Neuroscience 15 January 2020, 2072-19; DOI: 10.1523/JNEUROSCI.2072-19.2019
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
  • Info & Metrics
  • eLetters
  • PDF

Responses to this article

Respond to this article

Jump to comment:

No eLetters have been published for this article.

Related Articles

Cited By...

More in this TOC Section

Research Articles

  • Cortical and Subcortical Effects of Transcutaneous Spinal Cord Stimulation in Humans with Tetraplegia
  • Robust Rate-Place Coding of Resolved Components in Harmonic and Inharmonic Complex Tones in Auditory Midbrain
  • Context-dependent coding of temporal distance between cinematic events in the human precuneus
Show more Research Articles

Systems/Circuits

  • Robust Rate-Place Coding of Resolved Components in Harmonic and Inharmonic Complex Tones in Auditory Midbrain
  • Computational Mechanisms for Perceptual Stability using Disparity and Motion Parallax
  • Taste Quality Representation in the Human Brain
Show more Systems/Circuits
  • Home
  • Alerts
  • Visit Society for Neuroscience on Facebook
  • Follow Society for Neuroscience on Twitter
  • Follow Society for Neuroscience on LinkedIn
  • Visit Society for Neuroscience on Youtube
  • Follow our RSS feeds

Content

  • Early Release
  • Current Issue
  • Issue Archive
  • Collections

Information

  • For Authors
  • For the Media
  • For Subscribers

About

  • About the Journal
  • Editorial Board
  • Privacy Policy
  • Contact
  • Feedback
(JNeurosci logo)
(SfN logo)

Copyright © 2020 by the Society for Neuroscience.
JNeurosci   Print ISSN: 0270-6474   Online ISSN: 1529-2401

The ideas and opinions expressed in JNeurosci do not necessarily reflect those of SfN or the JNeurosci Editorial Board. Publication of an advertisement or other product mention in JNeurosci should not be construed as an endorsement of the manufacturer’s claims. SfN does not assume any responsibility for any injury and/or damage to persons or property arising from or related to any use of any material contained in JNeurosci.