Biomedical Simulations and Imaging Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
Comput Biol Med. 2019 Oct;113:103399. doi: 10.1016/j.compbiomed.2019.103399. Epub 2019 Aug 23.
Retinal Prosthesis (RP) is an approach to restore vision, using an implanted device to electrically stimulate the retina. A fundamental problem in RP is to translate the visual scene to retina neural spike patterns, mimicking the computations normally done by retina neural circuits. Towards the perspective of improved RP interventions, we propose a Computer Vision (CV) image preprocessing method based on Retinal Ganglion Cells functions and then use the method to reproduce retina output with a standard Generalized Integrate & Fire (GIF) neuron model. "Virtual Retina" simulation software is used to provide the stimulus-retina response data to train and test our model. We use a sequence of natural images as model input and show that models using the proposed CV image preprocessing outperform models using raw image intensity (interspike-interval distance 0.17 vs 0.27). This result is aligned with our hypothesis that raw image intensity is an improper image representation for Retinal Ganglion Cells response prediction.
视网膜假体（RP）是一种通过植入设备电刺激视网膜来恢复视力的方法。RP 的一个基本问题是将视觉场景转换为视网膜神经尖峰模式，模拟视网膜神经回路通常进行的计算。为了提高 RP 干预的效果，我们提出了一种基于视网膜神经节细胞功能的计算机视觉（CV）图像预处理方法，然后使用该方法通过标准的广义积分和点火（GIF）神经元模型再现视网膜输出。“虚拟视网膜”仿真软件用于提供刺激-视网膜反应数据来训练和测试我们的模型。我们使用一系列自然图像作为模型输入，并表明使用所提出的 CV 图像预处理的模型优于使用原始图像强度的模型（尖峰间隔 0.17 与 0.27）。这一结果与我们的假设一致，即原始图像强度是预测视网膜神经节细胞反应的一种不适当的图像表示。
