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Divisive normalization is a canonical mechanism that can explain a variety of sensory phenomena. While normalization models have been used to explain spiking activity in response to different stimulus/behavioral conditions in multiple brain areas, it is unclear whether similar models can also explain modulation in population-level neural measures such as power at various frequencies in local field potentials (LFPs) or steady-state visually evoked potential (SSVEP) that is produced by flickering stimuli and popular in electroencephalogram studies. To address this, we manipulated normalization strength by presenting static as well as flickering orthogonal superimposed gratings (plaids) at varying contrasts to 2 female monkeys while recording multiunit activity (MUA) and LFP from the primary visual cortex and quantified the modulation in MUA, gamma (32–80 Hz), high-gamma (104–248 Hz) power, as well as SSVEP. Even under similar stimulus conditions, normalization strength was different for the 4 measures and increased as: spikes, high-gamma, SSVEP, and gamma. However, these results could be explained using a normalization model that was modified for population responses, by varying the tuned normalization parameter and semisaturation constant. Our results show that different neural measures can reflect the effect of stimulus normalization in different ways, which can be modeled by a simple normalization model.

Effect of Cross-Orientation Normalization on Different Neural Measures in Macaque Primary Visual Cortex