@inproceedings{10.1145/3519391.3524034,
    author = {Relic, Lucas and Zhang, Bowen and Tuan, Yi-Lin and Beyeler, Michael},
    title = {Deep Learning–Based Perceptual Stimulus Encoder for Bionic Vision},
    year = {2022},
    isbn = {9781450396325},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3519391.3524034},
    doi = {10.1145/3519391.3524034},
    abstract = {Retinal implants have the potential to treat incurable blindness, yet the quality of the artificial vision they produce is still rudimentary. An outstanding challenge is identifying electrode activation patterns that lead to intelligible visual percepts (phosphenes). Here we propose a perceptual stimulus encoder (PSE) based on convolutional neural networks (CNNs) that is trained in an end-to-end fashion to predict the electrode activation patterns required to produce a desired visual percept. We demonstrate the effectiveness of the encoder on MNIST using a psychophysically validated phosphene model tailored to individual retinal implant users. The present work constitutes an essential first step towards improving the quality of the artificial vision provided by retinal implants.},
    booktitle = {Proceedings of the Augmented Humans International Conference 2022},
    pages = {323–325},
    numpages = {3},
    keywords = {stimulus optimization, retinal implants, deep learning},
    location = {Kashiwa, Chiba, Japan},
    series = {AHs '22}
}