  Perceptual capabilities of artificial systems have come a long way since the
advent of deep learning. These methods have proven to be effective, however
they are not as efficient as their biological counterparts. Visual attention is
a set of mechanisms that are employed in biological visual systems to ease
computational load by only processing pertinent parts of the stimuli. This
paper addresses the implementation of top-down, feature-based attention in an
artificial neural network by use of externally controlled neuron gating. Our
results showed a 5% increase in classification accuracy on the CIFAR-10 dataset
versus a non-gated version, while adding very few parameters. Our gated model
also produces more reasonable errors in predictions by drastically reducing
prediction of classes that belong to a different category to the true class.
