Inhibition is a component of nearly every neural system, and increasingly prevalent component in theoretical network models. However, its role in sensory processing is often difficult to directly measure and/or infer. Using a nonlinear modeling framework that can infer the presence and stimulus tuning of inhibition using extracellular and intracellular recordings, I will both describe different forms of inferred inhibition (subtractive and multiplicative), and suggest multiple roles in sensory processing. I will primarily refer to studies in the retina, where it likely contributes to contrast adaptation, the generation of precise timing, and also to diversity of computation among different retinal ganglion cell types. I will also describe roles of shaping sensory processing in other areas, including the auditory areas and the visual cortex. Understanding the role of inhibition in neural processing both can inform a richer view of how single neuron processing can contribute to network behavior, as well as provide tools to validate network models using neural data.