IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v593y2021i7859d10.1038_s41586-021-03452-z.html
   My bibliography  Save this article

Mouse prefrontal cortex represents learned rules for categorization

Author

Listed:
  • Sandra Reinert

    (Max Planck Institute of Neurobiology
    Ludwig-Maximilians-Universität München)

  • Mark Hübener

    (Max Planck Institute of Neurobiology)

  • Tobias Bonhoeffer

    (Max Planck Institute of Neurobiology)

  • Pieter M. Goltstein

    (Max Planck Institute of Neurobiology)

Abstract

The ability to categorize sensory stimuli is crucial for an animal’s survival in a complex environment. Memorizing categories instead of individual exemplars enables greater behavioural flexibility and is computationally advantageous. Neurons that show category selectivity have been found in several areas of the mammalian neocortex1–4, but the prefrontal cortex seems to have a prominent role4,5 in this context. Specifically, in primates that are extensively trained on a categorization task, neurons in the prefrontal cortex rapidly and flexibly represent learned categories6,7. However, how these representations first emerge in naive animals remains unexplored, leaving it unclear whether flexible representations are gradually built up as part of semantic memory or assigned more or less instantly during task execution8,9. Here we investigate the formation of a neuronal category representation throughout the entire learning process by repeatedly imaging individual cells in the mouse medial prefrontal cortex. We show that mice readily learn rule-based categorization and generalize to novel stimuli. Over the course of learning, neurons in the prefrontal cortex display distinct dynamics in acquiring category selectivity and are differentially engaged during a later switch in rules. A subset of neurons selectively and uniquely respond to categories and reflect generalization behaviour. Thus, a category representation in the mouse prefrontal cortex is gradually acquired during learning rather than recruited ad hoc. This gradual process suggests that neurons in the medial prefrontal cortex are part of a specific semantic memory for visual categories.

Suggested Citation

  • Sandra Reinert & Mark Hübener & Tobias Bonhoeffer & Pieter M. Goltstein, 2021. "Mouse prefrontal cortex represents learned rules for categorization," Nature, Nature, vol. 593(7859), pages 411-417, May.
  • Handle: RePEc:nat:nature:v:593:y:2021:i:7859:d:10.1038_s41586-021-03452-z
    DOI: 10.1038/s41586-021-03452-z
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41586-021-03452-z
    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/s41586-021-03452-z?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. Nicholas Cole & Matthew Harvey & Dylan Myers-Joseph & Aditya Gilra & Adil G. Khan, 2024. "Prediction-error signals in anterior cingulate cortex drive task-switching," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    2. Matthias Fritsche & Antara Majumdar & Lauren Strickland & Samuel Liebana Garcia & Rafal Bogacz & Armin Lak, 2024. "Temporal regularities shape perceptual decisions and striatal dopamine signals," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    3. Joao Barbosa & Rémi Proville & Chris C. Rodgers & Michael R. DeWeese & Srdjan Ostojic & Yves Boubenec, 2023. "Early selection of task-relevant features through population gating," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Srinivasan, Aditya & Srinivasan, Arvind & Goodman, Michael R. & Riceberg, Justin S. & Guise, Kevin G. & Shapiro, Matthew L., 2023. "Hippocampal and Medial Prefrontal Cortex Fractal Spiking Patterns Encode Episodes and Rules," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    5. Shinichiro Kira & Houman Safaai & Ari S. Morcos & Stefano Panzeri & Christopher D. Harvey, 2023. "A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions," Nature Communications, Nature, vol. 14(1), pages 1-28, 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:593:y:2021:i:7859:d:10.1038_s41586-021-03452-z. 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.