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Reconciling persistent and dynamic hypotheses of working memory coding in prefrontal cortex

Author

Listed:
  • Sean E. Cavanagh

    (University College London)

  • John P. Towers

    (University College London)

  • Joni D. Wallis

    (University of California at Berkeley
    Helen Wills Neuroscience Institute, University of California at Berkeley)

  • Laurence T. Hunt

    (University College London
    University College London
    University of Oxford)

  • Steven W. Kennerley

    (University College London
    University of California at Berkeley
    Helen Wills Neuroscience Institute, University of California at Berkeley)

Abstract

Competing accounts propose that working memory (WM) is subserved either by persistent activity in single neurons or by dynamic (time-varying) activity across a neural population. Here, we compare these hypotheses across four regions of prefrontal cortex (PFC) in an oculomotor-delayed-response task, where an intervening cue indicated the reward available for a correct saccade. WM representations were strongest in ventrolateral PFC neurons with higher intrinsic temporal stability (time-constant). At the population-level, although a stable mnemonic state was reached during the delay, this tuning geometry was reversed relative to cue-period selectivity, and was disrupted by the reward cue. Single-neuron analysis revealed many neurons switched to coding reward, rather than maintaining task-relevant spatial selectivity until saccade. These results imply WM is fulfilled by dynamic, population-level activity within high time-constant neurons. Rather than persistent activity supporting stable mnemonic representations that bridge subsequent salient stimuli, PFC neurons may stabilise a dynamic population-level process supporting WM.

Suggested Citation

  • Sean E. Cavanagh & John P. Towers & Joni D. Wallis & Laurence T. Hunt & Steven W. Kennerley, 2018. "Reconciling persistent and dynamic hypotheses of working memory coding in prefrontal cortex," Nature Communications, Nature, vol. 9(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05873-3
    DOI: 10.1038/s41467-018-05873-3
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    Cited by:

    1. Torben Ott & Anna Marlina Stein & Andreas Nieder, 2023. "Dopamine receptor activation regulates reward expectancy signals during cognitive control in primate prefrontal neurons," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. 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.
    3. 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.
    4. Jake Gavenas & Ueli Rutishauser & Aaron Schurger & Uri Maoz, 2024. "Slow ramping emerges from spontaneous fluctuations in spiking neural networks," Nature Communications, Nature, vol. 15(1), pages 1-16, December.

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