论文标题
基于深度学习的感知刺激编码器
Deep Learning-Based Perceptual Stimulus Encoder for Bionic Vision
论文作者
论文摘要
视网膜植入物具有治疗无法治愈的失明的潜力，但是它们产生的人造视力的质量仍然是基本的。一个杰出的挑战是确定导致可理解视觉感知（磷光元素）的电极激活模式。在这里，我们提出了一个基于CNN的PSE，该PSE以端到端方式进行了训练，以预测产生所需视觉感知所需的电极激活模式。我们使用针对个体视网膜植入物使用者量身定制的经过心理验证的磷酸模型来证明编码器对MNIST的有效性。目前的工作构成了提高视网膜植入物提供的人造视力质量的重要第一步。
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 PSE based on CNN 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.
