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Backbone spiking sequence as a basis for preplay, replay, and default states in human cortex

Author

Listed:
  • Alex P. Vaz

    (University of Pennsylvania
    NINDS, National Institutes of Health)

  • John H. Wittig

    (NINDS, National Institutes of Health)

  • Sara K. Inati

    (NINDS, National Institutes of Health)

  • Kareem A. Zaghloul

    (NINDS, National Institutes of Health)

Abstract

Sequences of spiking activity have been heavily implicated as potential substrates of memory formation and retrieval across many species. A parallel line of recent evidence also asserts that sequential activity may arise from and be constrained by pre-existing network structure. Here we reconcile these two lines of research in the human brain by measuring single unit spiking sequences in the temporal lobe cortex as participants perform an episodic memory task. We find the presence of an average backbone spiking sequence identified during pre-task rest that is stable over time and different cognitive states. We further demonstrate that these backbone sequences are composed of both rigid and flexible sequence elements, and that flexible elements within these sequences serve to promote memory specificity when forming and retrieving new memories. These results support the hypothesis that pre-existing network dynamics serve as a scaffold for ongoing neural activity in the human cortex.

Suggested Citation

  • Alex P. Vaz & John H. Wittig & Sara K. Inati & Kareem A. Zaghloul, 2023. "Backbone spiking sequence as a basis for preplay, replay, and default states in human cortex," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40440-5
    DOI: 10.1038/s41467-023-40440-5
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    References listed on IDEAS

    as
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    2. David J. Foster & Matthew A. Wilson, 2006. "Reverse replay of behavioural sequences in hippocampal place cells during the awake state," Nature, Nature, vol. 440(7084), pages 680-683, March.
    3. Michael A. Long & Dezhe Z. Jin & Michale S. Fee, 2010. "Support for a synaptic chain model of neuronal sequence generation," Nature, Nature, vol. 468(7322), pages 394-399, November.
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