论文标题
混合神经自动编码器，用于编码视觉和其他感觉神经pho的刺激
Hybrid Neural Autoencoders for Stimulus Encoding in Visual and Other Sensory Neuroprostheses
论文作者
论文摘要
感官神经假体正在成为一种有前途的技术，可以恢复失去的感觉功能或增强人类能力。但是，当前设备引起的感觉通常是人造和扭曲的。尽管当前的模型可以预测对电刺激的神经或感知反应，但最佳刺激策略可以解决反问题：产生所需响应的必需刺激是什么？在这里，我们将其视为一个端到端优化问题，其中对深度神经网络刺激编码器进行了训练，以倒入近似基础生物系统的已知和固定的正向模型。作为概念的证明，我们证明了这种混合神经自动编码器（HNA）在视觉神经植物中的有效性。我们发现，HNA产生高保真的患者特异性刺激，代表了手写数字和日常物体的分段图像，并且在所有模拟患者中的常规编码策略都显着胜过。总体而言，这是朝着长期挑战的重要一步，即恢复无法治愈的失明的人，并可能证明是各种神经假体技术的有前途的解决方案。
Sensory neuroprostheses are emerging as a promising technology to restore lost sensory function or augment human capabilities. However, sensations elicited by current devices often appear artificial and distorted. Although current models can predict the neural or perceptual response to an electrical stimulus, an optimal stimulation strategy solves the inverse problem: what is the required stimulus to produce a desired response? Here, we frame this as an end-to-end optimization problem, where a deep neural network stimulus encoder is trained to invert a known and fixed forward model that approximates the underlying biological system. As a proof of concept, we demonstrate the effectiveness of this Hybrid Neural Autoencoder (HNA) in visual neuroprostheses. We find that HNA produces high-fidelity patient-specific stimuli representing handwritten digits and segmented images of everyday objects, and significantly outperforms conventional encoding strategies across all simulated patients. Overall this is an important step towards the long-standing challenge of restoring high-quality vision to people living with incurable blindness and may prove a promising solution for a variety of neuroprosthetic technologies.
