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We describe a neural attention model with a learnable retinal samplinglattice. The model is trained on a visual search task requiring theclassification of an object embedded in a visual scene amidst backgrounddistractors using the smallest number of fixations. We explore the tilingproperties that emerge in the model's retinal sampling lattice after training.Specifically, we show that this lattice resembles the eccentricity dependentsampling lattice of the primate retina, with a high resolution region in thefovea surrounded by a low resolution periphery. Furthermore, we find conditionswhere these emergent properties are amplified or eliminated providing clues totheir function.

Emergence of foveal image sampling from learning to attend in visual scenes