Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3378-3381. doi: 10.1109/EMBC46164.2021.9629869.
Retinal models are needed to simulate the translation of visual percepts to Retinal Ganglion Cells (RGCs) neural spike trains, through which visual information is transmitted to the brain. Restoring vision through neural prostheses motivates the development of accurate retinal models. We integrate biologically-inspired image features to RGC models. We trained Linear-Nonlinear models using response data from biological retinae. We show that augmenting raw image input with retina-inspired image features leads to performance improvements: in a smaller (30sec. of retina recordings) set integration of features leads to improved models in approximately $\frac{2}{3}$ of the modeled RGCS; in a larger (4min. recording) we show that utilizing Spike Triggered Average analysis to localize RGCs in input images and extract features in a cell-based manner leads to improved models in all (except two) of the modeled RGCs.
视网膜模型用于模拟视觉感知到视网膜神经节细胞（RGC）神经尖峰的转换，通过这个转换，视觉信息被传输到大脑。通过神经假体恢复视力激发了对精确视网膜模型的开发。我们将受生物启发的图像特征整合到 RGC 模型中。我们使用来自生物视网膜的响应数据训练线性-非线性模型。我们表明，通过向原始图像输入添加受视网膜启发的图像特征，可以提高性能：在较小的（30 秒视网膜记录）集合中，特征的集成导致约 $\frac{2}{3}$ 的模拟 RGC 中模型的改进；在较大的（4 分钟记录）中，我们表明，利用尖峰触发平均分析在输入图像中定位 RGC 并以细胞为基础的方式提取特征，可导致所有（除了两个）模拟 RGC 中模型的改进。
