A Deep Learning-based in silico Framework for Optimization on Retinal Prosthetic Stimulation

• URL: http://arxiv.org/abs/2302.03570v1
• Date: Tue, 7 Feb 2023 16:32:05 GMT
• Title: A Deep Learning-based in silico Framework for Optimization on Retinal Prosthetic Stimulation
• Authors: Yuli Wu, Ivan Karetic, Johannes Stegmaier, Peter Walter, Dorit Merhof
• Abstract summary: We propose a neural network-based framework to optimize the perceptions simulated by the in silico retinal implant model pulse2percept. The pipeline consists of a trainable encoder, a pre-trained retinal implant model and a pre-trained evaluator.
• Score: 3.870538485112487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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