Rather than predicting perceptual distortions, one needs to solve the inverse problem: What is the best stimulus to generate a desired visual percept?

We propose a perceptual stimulus encoder based on convolutional neural networks that is trained in an end-to-end fashion to predict the electrode activation patterns required to produce a desired visual percept.