Abstract

Visual inspection of stimulus-induced gamma oscillations (30-70 Hz) often reveals a non-sinusoidal shape. Such distortions are a hallmark of non-linear systems and are also observed in mean-field models of gamma oscillations. A thorough characterization of the shape of the gamma cycle can therefore provide additional constraints on the operating regime of such models. However, the gamma waveform has not been quantitatively characterized, partially because the first harmonic of gamma, which arises because of the non-sinusoidal nature of the signal, is typically weak and gets masked due to a broadband increase in power related to spiking. To address this, we recorded local field potential (LFP) from the primary visual cortex (V1) of two awake female macaques while presenting full-field gratings or iso-luminant chromatic hues that produced huge gamma oscillations with prominent peaks at harmonic frequencies in the power spectra. We found that gamma and its first harmonic always maintained a specific phase relationship, resulting in a distinctive shape with a sharp trough and a shallow peak. Interestingly, a Wilson-Cowan (WC) model operating in an inhibition stabilized mode could replicate this shape, but only when the inhibitory population operated in the super-linear regime, as predicted recently. However, another recently developed model of gamma that operates in a linear regime driven by stochastic noise failed to produce salient harmonics or the observed shape. Our results impose additional constraints on models that generate gamma oscillations and their operating regimes.

Highlights

  • Gamma rhythm refers to oscillatory neural activity in the 30–70 Hz range that changes in response to different stimuli and cognitive states [1]

  • We use harmonic phase analysis to describe these waveforms quantitatively and show that gamma rhythm recorded from the primary visual cortex of macaques has a signature arch shaped waveform, with a sharp trough and a shallow peak, when visual stimuli such as full-screen plain hues and achromatic gratings are presented

  • Jadi and Sejnowski (JS) [19] used a Wilson and Cowan (WC) model with a non-linear activation function and showed that constraining the model to operate in an inhibition stabilized mode [20] and the inhibitory population to operate in superlinear domain can reproduce the size and contrast dependence of gamma rhythm

Read more

Summary

Introduction

Gamma rhythm refers to oscillatory neural activity in the 30–70 Hz range that changes in response to different stimuli and cognitive states [1]. Chromatic stimuli have been explored, which, for low-wavelength (reddish) hues, can generate huge gamma oscillations that are an order of magnitude stronger than gamma produced by achromatic gratings [7,8,9]. Gamma oscillations are thought to reflect the push-pull activity of interconnected excitatory and inhibitory neurons, which has been demonstrated in different spiking network models [10,11,12,13]. Since such large-scale network models have several parameters to be tuned and can become hard to interpret, simplified population rate models are sometimes used. More complex rate models that explain stimulus dependencies and potentially the gamma shape are elaborated in the Discussion section

Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call