Contrast Adaptation in a Nonadapting LGN Model

Sensory neurons appear to adapt their gain to match the variance of signals along the dimension they encode, a property we shall call “contrast normalization.” Contrast normalization has been the subject of extensive physiological and theoretical study. We previously found that neurons in the lateral geniculate nucleus (LGN) exhibit contrast normalization in their responses to full-field flickering white-noise stimuli, and that neurons with the strongest contrast normalization best preserved information transmission across a range of contrasts. We have also shown that both of these properties could be reproduced by nonadapting model cells. Here we present a detailed comparison of this nonadapting model to physiological data from the LGN. First, the model cells recapitulated other contrast dependencies of LGN responses: decreasing stimulus contrast resulted in an increase in spike-timing jitter and spike-number variability. Second, we find that the extent of contrast normalization in this model depends on model parameters related to refractoriness and to noise. Third, we show that the model cells exhibit rapid, transient changes in firing rate just after changes in contrast, and that this is sufficient to produce the transient changes in information transmission that have been reported in other neurons. It is known that intrinsic properties of neurons change during contrast adaptation. Nevertheless the model demonstrates that the spiking nonlinearity of neurons can produce many of the temporal aspects of contrast gain control, including normalization to input variance and transient effects of contrast change.