  We describe a neural attention model with a learnable retinal sampling
lattice. The model is trained on a visual search task requiring the
classification of an object embedded in a visual scene amidst background
distractors using the smallest number of fixations. We explore the tiling
properties that emerge in the model's retinal sampling lattice after training.
Specifically, we show that this lattice resembles the eccentricity dependent
sampling lattice of the primate retina, with a high resolution region in the
fovea surrounded by a low resolution periphery. Furthermore, we find conditions
where these emergent properties are amplified or eliminated providing clues to
their function.
