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Cortical feedback loops bind distributed representations of working memory

Author

Listed:
  • Ivan Voitov

    (University College London
    University of Basel)

  • Thomas D. Mrsic-Flogel

    (University College London)

Abstract

Working memory—the brain’s ability to internalize information and use it flexibly to guide behaviour—is an essential component of cognition. Although activity related to working memory has been observed in several brain regions1–3, how neural populations actually represent working memory4–7 and the mechanisms by which this activity is maintained8–12 remain unclear13–15. Here we describe the neural implementation of visual working memory in mice alternating between a delayed non-match-to-sample task and a simple discrimination task that does not require working memory but has identical stimulus, movement and reward statistics. Transient optogenetic inactivations revealed that distributed areas of the neocortex were required selectively for the maintenance of working memory. Population activity in visual area AM and premotor area M2 during the delay period was dominated by orderly low-dimensional dynamics16,17 that were, however, independent of working memory. Instead, working memory representations were embedded in high-dimensional population activity, present in both cortical areas, persisted throughout the inter-stimulus delay period, and predicted behavioural responses during the working memory task. To test whether the distributed nature of working memory was dependent on reciprocal interactions between cortical regions18–20, we silenced one cortical area (AM or M2) while recording the feedback it received from the other. Transient inactivation of either area led to the selective disruption of inter-areal communication of working memory. Therefore, reciprocally interconnected cortical areas maintain bound high-dimensional representations of working memory.

Suggested Citation

  • Ivan Voitov & Thomas D. Mrsic-Flogel, 2022. "Cortical feedback loops bind distributed representations of working memory," Nature, Nature, vol. 608(7922), pages 381-389, August.
  • Handle: RePEc:nat:nature:v:608:y:2022:i:7922:d:10.1038_s41586-022-05014-3
    DOI: 10.1038/s41586-022-05014-3
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    Cited by:

    1. Yuan, Guoyong & Liu, Pengwei & Shi, Jifang & Wang, Guangrui, 2023. "Dynamics and control of spiral waves under feedback derived from a moving measuring point," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    2. Kotaro Ishizu & Shosuke Nishimoto & Yutaro Ueoka & Akihiro Funamizu, 2024. "Localized and global representation of prior value, sensory evidence, and choice in male mouse cerebral cortex," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    3. Huee Ru Chong & Yadollah Ranjbar-Slamloo & Malcolm Zheng Hao Ho & Xuan Ouyang & Tsukasa Kamigaki, 2023. "Functional alterations of the prefrontal circuit underlying cognitive aging in mice," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    4. Frank Gelens & Juho Äijälä & Louis Roberts & Misako Komatsu & Cem Uran & Michael A. Jensen & Kai J. Miller & Robin A. A. Ince & Max Garagnani & Martin Vinck & Andres Canales-Johnson, 2024. "Distributed representations of prediction error signals across the cortical hierarchy are synergistic," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    5. Antonino Greco & Julia Moser & Hubert Preissl & Markus Siegel, 2024. "Predictive learning shapes the representational geometry of the human brain," Nature Communications, Nature, vol. 15(1), pages 1-12, 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.

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