IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v399y1999i6735d10.1038_20939.html
   My bibliography  Save this article

Neuronal correlates of parametric working memory in the prefrontal cortex

Author

Listed:
  • Ranulfo Romo

    (Instituto de Fisiología Celular, Universidad Nacional Autonáma de México)

  • Carlos D. Brody

    (Instituto de Fisiología Celular, Universidad Nacional Autonáma de México)

  • Adrián Hernández

    (Instituto de Fisiología Celular, Universidad Nacional Autonáma de México)

  • Luis Lemus

    (Instituto de Fisiología Celular, Universidad Nacional Autonáma de México)

Abstract

Humans and monkeys have similar abilities to discriminate the difference in frequency between two mechanical vibrations applied sequentially to the fingertips1,2,3. A key component of this sensory task is that the second stimulus is compared with the trace left by the first (base) stimulus, which must involve working memory. Where and how is this trace held in the brain? This question was investigated by recording from single neurons in the prefrontal cortex of monkeys while they performed the somatosensory discrimination task. Here we describe neurons in the inferior convexity of the prefrontal cortex whose discharge rates varied, during the delay period between the two stimuli, as a monotonic function of the base stimulus frequency. We describe this as ‘monotonic stimulus encoding’, and we suggest that the result may generalize: monotonic stimulus encoding may be the basic representation of one-dimensional sensory stimulus quantities in working memory. Thus we predict that other behavioural tasks that require ordinal comparisons between scalar analogue stimuli would give rise to monotonic responses similar to those reported here.

Suggested Citation

  • Ranulfo Romo & Carlos D. Brody & Adrián Hernández & Luis Lemus, 1999. "Neuronal correlates of parametric working memory in the prefrontal cortex," Nature, Nature, vol. 399(6735), pages 470-473, June.
    • Handle: RePEc:nat:nature:v:399:y:1999:i:6735:d:10.1038_20939
    DOI: 10.1038/20939
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/20939
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1038/20939?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kaushik J. Lakshminarasimhan & Eric Avila & Xaq Pitkow & Dora E. Angelaki, 2023. "Dynamical latent state computation in the male macaque posterior parietal cortex," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    2. Juan Linde-Domingo & Bernhard Spitzer, 2024. "Geometry of visuospatial working memory information in miniature gaze patterns," Nature Human Behaviour, Nature, vol. 8(2), pages 336-348, February.
    3. Sacha Jennifer van Albada & Moritz Helias & Markus Diesmann, 2015. "Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations," PLOS Computational Biology, Public Library of Science, vol. 11(9), pages 1-37, September.
    4. Brian DePasquale & Christopher J Cueva & Kanaka Rajan & G Sean Escola & L F Abbott, 2018. "full-FORCE: A target-based method for training recurrent networks," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-18, February.
    5. Francesco Ceccarelli & Lorenzo Ferrucci & Fabrizio Londei & Surabhi Ramawat & Emiliano Brunamonti & Aldo Genovesio, 2023. "Static and dynamic coding in distinct cell types during associative learning in the prefrontal cortex," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    6. Xin Wei Chia & Jian Kwang Tan & Lee Fang Ang & Tsukasa Kamigaki & Hiroshi Makino, 2023. "Emergence of cortical network motifs for short-term memory during learning," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    7. Gabriel D Puccini & Maria V Sanchez-Vives & Albert Compte, 2007. "Integrated Mechanisms of Anticipation and Rate-of-Change Computations in Cortical Circuits," PLOS Computational Biology, Public Library of Science, vol. 3(5), pages 1-13, May.
    8. Yue Liu & Xiao-Jing Wang, 2024. "Flexible gating between subspaces in a neural network model of internally guided task switching," Nature Communications, Nature, vol. 15(1), pages 1-20, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:399:y:1999:i:6735:d:10.1038_20939. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.