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. However, oscillations in this model are infinitely long, unlike physiological gamma that occurs in short bursts. Further, unlike the model, gamma is faster after stimulus onset and slows down over time. Here, we first characterized gamma burst duration in local field potential data recorded from two monkeys as they viewed full screen iso-luminant hues. We then added different types of noise in the inputs to the WC model and tested how that affected duration and temporal dynamics of gamma. While the model failed with the often-used Poisson noise, Ornstein-Uhlenbeck noise applied to both the excitatory and the inhibitory populations replicated the duration and slowing of gamma and replicated the shape and stimulus dependencies. Thus, the temporal dynamics of gamma oscillations put constraints on the type and properties of underlying neural noise.