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Neuronal baseline shifts underlying boundary setting during free recall

Author

Listed:
  • Yitzhak Norman

    (Weizmann Institute of Science)

  • Erin M. Yeagle

    (Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, and Feinstein Institute for Medical Research)

  • Michal Harel

    (Weizmann Institute of Science)

  • Ashesh D. Mehta

    (Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, and Feinstein Institute for Medical Research)

  • Rafael Malach

    (Weizmann Institute of Science)

Abstract

Asked to freely recall items from a predefined set (e.g., animals), we rarely recall a wrong exemplar (e.g., a vegetable). This capability is so powerful and effortless that it is essentially taken for granted, yet, surprisingly, the underlying neuronal mechanisms are unknown. Here we investigate this boundary setting mechanism using intracranial recordings (ECoG), in 12 patients undergoing epilepsy monitoring engaged in episodic free recall. After viewing vivid photographs from two categories (famous faces and places), patients were asked to freely recall these items, targeting each category in separate blocks. Our results reveal a rapid and sustained rise in neuronal activity (“baseline shift”) in high-order visual areas that persists throughout the free recall period and reflects the targeted category. We further show a more transient reactivation linked to individual recall events. The results point to baseline shift as a flexible top−down mechanism that biases spontaneous recall to remain within the required categorical boundaries.

Suggested Citation

  • Yitzhak Norman & Erin M. Yeagle & Michal Harel & Ashesh D. Mehta & Rafael Malach, 2017. "Neuronal baseline shifts underlying boundary setting during free recall," Nature Communications, Nature, vol. 8(1), pages 1-17, December.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-01184-1
    DOI: 10.1038/s41467-017-01184-1
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    Cited by:

    1. 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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