According to the current standard model, neurons in lateral geniculate nucleus (LGN) operate linearly. There is, however, ample evidence that LGN responses are nonlinear. To account for nonlinearities we propose that neurons have a linear receptive field, and a nonlinear, divisive suppressive field. The suppressive field computes a measure of local contrast. The model synthesizes more than 30 years of research in the field. To test it we recorded responses from LGN of anesthetized paralyzed cats. We estimate model parameters from a basic set of measurements and show that the model can accurately predict responses to novel stimuli. The model might serve as the new standard model of LGN responses. It specifies how visual processing in LGN involves both linear filtering and divisive gain control. The latter resembles histogram normalization in image processing.