  We propose a neural network-based framework to optimize the perceptions
simulated by the in silico retinal implant model pulse2percept. The overall
pipeline consists of a trainable encoder, a pre-trained retinal implant model
and a pre-trained evaluator. The encoder is a U-Net, which takes the original
image and outputs the stimulus. The pre-trained retinal implant model is also a
U-Net, which is trained to mimic the biomimetic perceptual model implemented in
pulse2percept. The evaluator is a shallow VGG classifier, which is trained with
original images. Based on 10,000 test images from the MNIST dataset, we show
that the convolutional neural network-based encoder performs significantly
better than the trivial downsampling approach, yielding a boost in the weighted
F1-Score by 36.17% in the pre-trained classifier with 6x10 electrodes. With
this fully neural network-based encoder, the quality of the downstream
perceptions can be fine-tuned using gradient descent in an end-to-end fashion.
