IDEAS home Printed from https://ideas.repec.org/a/plo/pbio00/1001477.html
   My bibliography  Save this article

Strength of Gamma Rhythm Depends on Normalization

Author

Listed:
  • Supratim Ray
  • Amy M Ni
  • John H R Maunsell

Abstract

Manipulating a divisive normalization mechanism independently of attention in monkeys suggests that gamma power reflects excitation-inhibition interactions rather than plays a functional role in attentional processing. Neuronal assemblies often exhibit stimulus-induced rhythmic activity in the gamma range (30–80 Hz), whose magnitude depends on the attentional load. This has led to the suggestion that gamma rhythms form dynamic communication channels across cortical areas processing the features of behaviorally relevant stimuli. Recently, attention has been linked to a normalization mechanism, in which the response of a neuron is suppressed (normalized) by the overall activity of a large pool of neighboring neurons. In this model, attention increases the excitatory drive received by the neuron, which in turn also increases the strength of normalization, thereby changing the balance of excitation and inhibition. Recent studies have shown that gamma power also depends on such excitatory–inhibitory interactions. Could modulation in gamma power during an attention task be a reflection of the changes in the underlying excitation–inhibition interactions? By manipulating the normalization strength independent of attentional load in macaque monkeys, we show that gamma power increases with increasing normalization, even when the attentional load is fixed. Further, manipulations of attention that increase normalization increase gamma power, even when they decrease the firing rate. Thus, gamma rhythms could be a reflection of changes in the relative strengths of excitation and normalization rather than playing a functional role in communication or control. Author Summary: Brain signals often show a stimulus-induced rhythm in the “gamma” band (30–80 Hz) whose magnitude depends on attentional load, leading to suggestions that gamma rhythm plays a functional role in routing signals across cortical areas. However, gamma power also depends on simple stimulus features such as size or contrast, which suggests that gamma could arise from basic cortical processes involving excitation–inhibition interactions. One such process is divisive normalization, a mechanism that suppresses the response of a neuron by the overall activity of a large pool of neighboring neurons. Recent studies have shown that attention increases the strength of both excitation and normalization. We hypothesized that the increase in gamma power in an attention task is due to the effect of attention on excitation and normalization. By manipulating the normalization strength independent of attentional load in macaque monkeys, we show that gamma power increases with increasing normalization, even when attentional load is held fixed. Thus, gamma rhythms could be a reflection of changes in the relative strengths of excitation and normalization rather than playing a functional role in communication or control.

Suggested Citation

  • Supratim Ray & Amy M Ni & John H R Maunsell, 2013. "Strength of Gamma Rhythm Depends on Normalization," PLOS Biology, Public Library of Science, vol. 11(2), pages 1-12, February.
  • Handle: RePEc:plo:pbio00:1001477
    DOI: 10.1371/journal.pbio.1001477
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001477
    Download Restriction: no

    File URL: https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.1001477&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pbio.1001477?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. Jessica A. Cardin & Marie Carlén & Konstantinos Meletis & Ulf Knoblich & Feng Zhang & Karl Deisseroth & Li-Huei Tsai & Christopher I. Moore, 2009. "Driving fast-spiking cells induces gamma rhythm and controls sensory responses," Nature, Nature, vol. 459(7247), pages 663-667, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xilin Zhang & Shruti Japee & Zaid Safiullah & Nicole Mlynaryk & Leslie G Ungerleider, 2016. "A Normalization Framework for Emotional Attention," PLOS Biology, Public Library of Science, vol. 14(11), pages 1-25, November.
    2. Ayoung Yoon & Andrea Copeland, 2020. "Toward community‐inclusive data ecosystems: Challenges and opportunities of open data for community‐based organizations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(12), pages 1439-1454, December.

    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. Hironobu Osaki & Moeko Kanaya & Yoshifumi Ueta & Mariko Miyata, 2022. "Distinct nociception processing in the dysgranular and barrel regions of the mouse somatosensory cortex," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Sorinel A Oprisan & Xandre Clementsmith & Tamas Tompa & Antonieta Lavin, 2019. "Dopamine receptor antagonists effects on low-dimensional attractors of local field potentials in optogenetic mice," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-39, October.
    3. Nozomu H. Nakamura & Hidemasa Furue & Kenta Kobayashi & Yoshitaka Oku, 2023. "Hippocampal ensemble dynamics and memory performance are modulated by respiration during encoding," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    4. Kai Zhou & Wei Wei & Dan Yang & Hui Zhang & Wei Yang & Yunpeng Zhang & Yingnan Nie & Mingming Hao & Pengcheng Wang & Hang Ruan & Ting Zhang & Shouyan Wang & Yaobo Liu, 2024. "Dual electrical stimulation at spinal-muscular interface reconstructs spinal sensorimotor circuits after spinal cord injury," Nature Communications, Nature, vol. 15(1), pages 1-26, December.
    5. Federico Rocchi & Carola Canella & Shahryar Noei & Daniel Gutierrez-Barragan & Ludovico Coletta & Alberto Galbusera & Alexia Stuefer & Stefano Vassanelli & Massimo Pasqualetti & Giuliano Iurilli & Ste, 2022. "Increased fMRI connectivity upon chemogenetic inhibition of the mouse prefrontal cortex," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    6. Yang, Pengbo & Shang, Pengjian & Lin, Aijing, 2017. "Financial time series analysis based on effective phase transfer entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 398-408.
    7. Xin Fu & Eric Teboul & Grant L. Weiss & Pantelis Antonoudiou & Chandrashekhar D. Borkar & Jonathan P. Fadok & Jamie Maguire & Jeffrey G. Tasker, 2022. "Gq neuromodulation of BLA parvalbumin interneurons induces burst firing and mediates fear-associated network and behavioral state transition in mice," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    8. Eric Lowet & Krishnakanth Kondabolu & Samuel Zhou & Rebecca A. Mount & Yangyang Wang & Cara R. Ravasio & Xue Han, 2022. "Deep brain stimulation creates informational lesion through membrane depolarization in mouse hippocampus," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    9. Vadivel, R. & Hammachukiattikul, P. & Gunasekaran, Nallappan & Saravanakumar, R. & Dutta, Hemen, 2021. "Strict dissipativity synchronization for delayed static neural networks: An event-triggered scheme," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    10. Li, Jiajia & Zhang, Xuan & Du, Mengmeng & Wu, Ying, 2022. "Switching behavior of the gamma power in the neuronal network modulated by the astrocytes," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).

    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:pbio00:1001477. 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.