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Delayed Accumulation of Inhibitory Input Explains Gamma Frequency Variation with Changing Contrast in an Inhibition

R Krishnakumaran1, Abhimanyu Pavuluri2, Supratim Ray3,2

  • 1IISc Mathematics Initiative, Department of Mathematics, Indian Institute of Science, Bangalore 560012, India.

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
|December 10, 2024
PubMed
Summary
This summary is machine-generated.

Gamma rhythm (30-70 Hz) in the visual cortex is sensitive to stimulus contrast. Preceding a stimulus with a higher contrast one can abolish or reverse the typical frequency falloff, as confirmed in monkeys.

Keywords:
Wilson–Cowan modeldelayed inhibitiongammamultitapernoisy inputtime–frequency spectrumtransient

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Visual Cortex Research

Background:

  • Gamma rhythm (30-70 Hz) in the primary visual cortex (V1) reflects neural population interactions.
  • Gamma rhythm is sensitive to stimulus properties like size and contrast, exhibiting a 'frequency falloff' over time.
  • Previous models, like the Wilson-Cowan (WC) model as an inhibition-stabilized network (ISN), have replicated some gamma rhythm characteristics.

Purpose of the Study:

  • To test the hypothesis that preceding a visual stimulus with a higher contrast stimulus can alter or reverse gamma rhythm frequency falloff.
  • To investigate the role of inhibition dynamics and adaptation in gamma rhythm responses to time-varying contrasts.
  • To validate computational models (ISN) in replicating observed gamma rhythm phenomena in V1.

Main Methods:

  • Presented consecutive gratings of varying contrasts to two female monkeys.
  • Recorded gamma rhythm activity in V1 using microelectrode arrays.
  • Utilized self-oscillating Wilson-Cowan (WC) models (ISN) with Ornstein-Uhlenbeck inputs and models with adapted feedforward input for simulation.

Main Results:

  • The prediction was confirmed: preceding a stimulus with a higher contrast stimulus abolished or reversed the gamma frequency falloff.
  • The ISN model successfully replicated this phenomenon, attributing frequency falloff to delayed inhibitory inputs.
  • The model also replicated gamma frequency modulation to counter-phasing stimuli and responses to adapted feedforward input.

Conclusions:

  • Gamma rhythm frequency responses are significantly influenced by the history of stimulus contrast.
  • Inhibition-stabilized network models, incorporating delayed surround inhibition or adapted feedforward input, accurately predict gamma frequency dynamics.
  • These findings provide insights into the neural mechanisms underlying visual processing and gamma rhythm generation.