In the first chapter, we bring together previous accounts - in both the LGN and in V1 - of shifts in spatial frequency tuning with image contrast. We use a common stimulus set comprised of sinusoidal gratings that vary in spatial frequency and contrast. Fitting canonical, mechanistic models which capture our understanding of each area's receptive field structure, we show that the tuning shifts in V1 are larger than those in the LGN. This result suggests that shifts in LGN selectivity are inherited in V1, but further intracortical processing contributes to the more pronounced tuning shifts.

In the second and third chapters, we turn our focus to stimuli of intermediate complexity. We used superimposed mixtures of gratings as well as a more direct masking experiment to measure the tuning of spatial frequency suppression. In the second chapter, we report stronger spatial frequency-dependent suppression in V1 than in LGN, and find that suppression is typically strongest for frequencies at or below the cell's preference. These stimulus sets were also designed to evoke a broad range of responses which help constrain our computational model of spatial frequency selectivity. In the third chapter, we fit this model to the observed neuronal responses. The model implements divisive normalization, a canonical computation in cortical processing that accounts for a wide variety of observed neural activity. In the standard model of normalization, the response of a given neuron is normalized by the activity of nearby neurons that are selective across a wide range of stimulus values and features. We show that introducing a spatial-frequency tuned weighting of the normalization signal can preserve gain control while also better accounting for shifts in spatial frequency tuning and the observed suppression to complex stimuli. The tuning of the normalization was typically found to be stronger for frequencies below the cell’s peak, highlighting the role of low frequency suppression in shaping selectivity.