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Intrinsic neuronal dynamics predict distinct functional roles during working memory

Author

Listed:
  • D. F. Wasmuht

    (University of Oxford
    University of Oxford)

  • E. Spaak

    (University of Oxford
    University of Oxford)

  • T. J. Buschman

    (Princeton Neuroscience Institute and Department of Psychology)

  • E. K. Miller

    (Massachusetts Institute of Technology)

  • M. G. Stokes

    (University of Oxford
    University of Oxford)

Abstract

Working memory (WM) is characterized by the ability to maintain stable representations over time; however, neural activity associated with WM maintenance can be highly dynamic. We explore whether complex population coding dynamics during WM relate to the intrinsic temporal properties of single neurons in lateral prefrontal cortex (lPFC), the frontal eye fields (FEF), and lateral intraparietal cortex (LIP) of two monkeys (Macaca mulatta). We find that cells with short timescales carry memory information relatively early during memory encoding in lPFC; whereas long-timescale cells play a greater role later during processing, dominating coding in the delay period. We also observe a link between functional connectivity at rest and the intrinsic timescale in FEF and LIP. Our results indicate that individual differences in the temporal processing capacity predict complex neuronal dynamics during WM, ranging from rapid dynamic encoding of stimuli to slower, but stable, maintenance of mnemonic information.

Suggested Citation

  • D. F. Wasmuht & E. Spaak & T. J. Buschman & E. K. Miller & M. G. Stokes, 2018. "Intrinsic neuronal dynamics predict distinct functional roles during working memory," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05961-4
    DOI: 10.1038/s41467-018-05961-4
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    Cited by:

    1. Ana M. G. Manea & David J.-N. Maisson & Benjamin Voloh & Anna Zilverstand & Benjamin Hayden & Jan Zimmermann, 2024. "Neural timescales reflect behavioral demands in freely moving rhesus macaques," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    2. 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.
    3. Roxana Zeraati & Yan-Liang Shi & Nicholas A. Steinmetz & Marc A. Gieselmann & Alexander Thiele & Tirin Moore & Anna Levina & Tatiana A. Engel, 2023. "Intrinsic timescales in the visual cortex change with selective attention and reflect spatial connectivity," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    4. Lucas Rudelt & Daniel González Marx & Michael Wibral & Viola Priesemann, 2021. "Embedding optimization reveals long-lasting history dependence in neural spiking activity," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-51, June.
    5. Haoxin Zhang & Ivan Skelin & Shiting Ma & Michelle Paff & Lilit Mnatsakanyan & Michael A. Yassa & Robert T. Knight & Jack J. Lin, 2024. "Awake ripples enhance emotional memory encoding in the human brain," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    6. F P Spitzner & J Dehning & J Wilting & A Hagemann & J P. Neto & J Zierenberg & V Priesemann, 2021. "MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-21, April.
    7. Tal Seidel Malkinson & Dimitri J. Bayle & Brigitte C. Kaufmann & Jianghao Liu & Alexia Bourgeois & Katia Lehongre & Sara Fernandez-Vidal & Vincent Navarro & Virginie Lambrecq & Claude Adam & Daniel S., 2024. "Intracortical recordings reveal vision-to-action cortical gradients driving human exogenous attention," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

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