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Strength of Gamma Rhythm Depends on Normalization

Author

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  • 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
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    References listed on IDEAS

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    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.
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    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.

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