IDEAS home Printed from https://ideas.repec.org/a/plo/pbio00/3000625.html
   My bibliography  Save this article

Drifting codes within a stable coding scheme for working memory

Author

Listed:
  • Michael J Wolff
  • Janina Jochim
  • Elkan G Akyürek
  • Timothy J Buschman
  • Mark G Stokes

Abstract

Working memory (WM) is important to maintain information over short time periods to provide some stability in a constantly changing environment. However, brain activity is inherently dynamic, raising a challenge for maintaining stable mental states. To investigate the relationship between WM stability and neural dynamics, we used electroencephalography to measure the neural response to impulse stimuli during a WM delay. Multivariate pattern analysis revealed representations were both stable and dynamic: there was a clear difference in neural states between time-specific impulse responses, reflecting dynamic changes, yet the coding scheme for memorised orientations was stable. This suggests that a stable subcomponent in WM enables stable maintenance within a dynamic system. A stable coding scheme simplifies readout for WM-guided behaviour, whereas the low-dimensional dynamic component could provide additional temporal information. Despite having a stable subspace, WM is clearly not perfect—memory performance still degrades over time. Indeed, we find that even within the stable coding scheme, memories drift during maintenance. When averaged across trials, such drift contributes to the width of the error distribution.Working memory degrades over time. This study suggests that the neural coding scheme is stable, despite other time-varying dynamics. Error in performance is likely due to drift in the mnemonic representation within the otherwise stable coding scheme.

Suggested Citation

  • Michael J Wolff & Janina Jochim & Elkan G Akyürek & Timothy J Buschman & Mark G Stokes, 2020. "Drifting codes within a stable coding scheme for working memory," PLOS Biology, Public Library of Science, vol. 18(3), pages 1-19, March.
  • Handle: RePEc:plo:pbio00:3000625
    DOI: 10.1371/journal.pbio.3000625
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000625
    Download Restriction: no

    File URL: https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.3000625&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pbio.3000625?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
    ---><---

    References listed on IDEAS

    as
    1. Richard H. R. Hahnloser & Alexay A. Kozhevnikov & Michale S. Fee, 2002. "An ultra-sparse code underliesthe generation of neural sequences in a songbird," Nature, Nature, vol. 419(6902), pages 65-70, September.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Fabian Heim & Ezequiel Mendoza & Avani Koparkar & Daniela Vallentin, 2024. "Disinhibition enables vocal repertoire expansion after a critical period," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    3. 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.
    4. Jian K Liu & Zhen-Su She, 2009. "A Spike-Timing Pattern Based Neural Network Model for the Study of Memory Dynamics," PLOS ONE, Public Library of Science, vol. 4(7), pages 1-8, July.
    5. 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.
    6. John Palmer & Adam Keane & Pulin Gong, 2017. "Learning and executing goal-directed choices by internally generated sequences in spiking neural circuits," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-23, July.
    7. Chong Guo & Vincent Huson & Evan Z. Macosko & Wade G. Regehr, 2021. "Graded heterogeneity of metabotropic signaling underlies a continuum of cell-intrinsic temporal responses in unipolar brush cells," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    8. Yang Yiling & Katharine Shapcott & Alina Peter & Johanna Klon-Lipok & Huang Xuhui & Andreea Lazar & Wolf Singer, 2023. "Robust encoding of natural stimuli by neuronal response sequences in monkey visual cortex," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    9. David Kappel & Bernhard Nessler & Wolfgang Maass, 2014. "STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning," PLOS Computational Biology, Public Library of Science, vol. 10(3), pages 1-22, March.
    10. Benjamin M. Zemel & Alexander A. Nevue & Andre Dagostin & Peter V. Lovell & Claudio V. Mello & Henrique Gersdorff, 2021. "Resurgent Na+ currents promote ultrafast spiking in projection neurons that drive fine motor control," Nature Communications, Nature, vol. 12(1), pages 1-23, December.
    11. Morgan A. Brown & Kara M. Zappitelli & Loveprit Singh & Rachel C. Yuan & Melissa Bemrose & Valerie Brogden & David J. Miller & Matthew C. Smear & Stuart F. Cogan & Timothy J. Gardner, 2023. "Direct laser writing of 3D electrodes on flexible substrates," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    12. 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.
    13. 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.
    14. Alpha Renner & Forrest Sheldon & Anatoly Zlotnik & Louis Tao & Andrew Sornborger, 2024. "The backpropagation algorithm implemented on spiking neuromorphic hardware," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    15. Asaph Zylbertal & Anat Kahan & Yoram Ben-Shaul & Yosef Yarom & Shlomo Wagner, 2015. "Prolonged Intracellular Na+ Dynamics Govern Electrical Activity in Accessory Olfactory Bulb Mitral Cells," PLOS Biology, Public Library of Science, vol. 13(12), pages 1-25, December.
    16. 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.
    17. Sarah E Maguire & Marc F Schmidt & David J White, 2013. "Social Brains in Context: Lesions Targeted to the Song Control System in Female Cowbirds Affect Their Social Network," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-8, May.
    18. Linda Bistere & Carlos M. Gomez-Guzman & Yirong Xiong & Daniela Vallentin, 2024. "Female calls promote song learning in male juvenile zebra finches," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    19. 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.
    20. Izzet B Yildiz & Stefan J Kiebel, 2011. "A Hierarchical Neuronal Model for Generation and Online Recognition of Birdsongs," PLOS Computational Biology, Public Library of Science, vol. 7(12), pages 1-18, 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:plo:pbio00:3000625. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: plosbiology (email available below). General contact details of provider: https://journals.plos.org/plosbiology/ .

    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.