Delayed accumulation of inhibitory input explains gamma frequency variation with changing contrast in an Inhibition Stabilized Network.

Gamma rhythm (30-70 Hz), thought to represent the interactions between excitatory and inhibitory populations, can be induced by presenting achromatic gratings in the primary visual cortex (V1) and is sensitive to stimulus properties such as size and contrast. In addition, gamma occurs in short bursts, and shows a "frequency-falloff" effect where its peak frequency is high after stimulus onset and slowly decreases to a steady state. Recently, these size-contrast properties and temporal characteristics were replicated in a self-oscillating Wilson-Cowan (WC) model operating as an Inhibition stabilized network (ISN), stimulated by Ornstein-Uhlenbeck (OU)-type inputs.

Temporal characteristics of gamma rhythm constrain properties of noise in an inhibition-stabilized network model.

Gamma rhythm refers to oscillatory neural activity between 30 and 80 Hz, induced in visual cortex by stimuli such as iso-luminant hues or gratings. The power and peak frequency of gamma depend on the properties of the stimulus such as size and contrast. Gamma waveform is typically arch-shaped, with narrow troughs and broad peaks, and can be replicated in a self-oscillating Wilson-Cowan (WC) model operating in an appropriate regime.

Shape analysis of gamma rhythm supports a superlinear inhibitory regime in an inhibition-stabilized network.

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.