IEEE Trans Neural Netw Learn Syst. 2018 May;29(5):1796-1808. doi: 10.1109/TNNLS.2017.2690139. Epub 2017 Apr 12.
The processing capabilities of biological vision systems are still vastly superior to artificial vision, even though this has been an active area of research for over half a century. Current artificial vision techniques integrate many insights from biology yet they remain far-off the capabilities of animals and humans in terms of speed, power, and performance. A key aspect to modeling the human visual system is the ability to accurately model the behavior and computation within the retina. In particular, we focus on modeling the retinal ganglion cells (RGCs) as they convey the accumulated data of real world images as action potentials onto the visual cortex via the optic nerve. Computational models that approximate the processing that occurs within RGCs can be derived by quantitatively fitting the sets of physiological data using an input-output analysis where the input is a known stimulus and the output is neuronal recordings. Currently, these input-output responses are modeled using computational combinations of linear and nonlinear models that are generally complex and lack any relevance to the underlying biophysics. In this paper, we illustrate how system identification techniques, which take inspiration from biological systems, can accurately model retinal ganglion cell behavior, and are a viable alternative to traditional linear-nonlinear approaches.
生物视觉系统的处理能力仍然远远超过人工视觉，尽管这已经是半个多世纪以来的一个活跃研究领域。目前的人工视觉技术整合了许多来自生物学的见解，但在速度、功率和性能方面，它们仍然远远落后于动物和人类的能力。模拟人类视觉系统的一个关键方面是能够准确地模拟视网膜内的行为和计算。特别是，我们专注于模拟视网膜神经节细胞（RGC），因为它们通过视神经将真实世界图像的累积数据作为动作电位传送到视觉皮层。可以通过使用输入-输出分析来定量拟合生理数据集来推导出近似于 RGC 中发生的处理的计算模型，其中输入是已知的刺激，输出是神经元记录。目前，这些输入-输出响应使用线性和非线性模型的计算组合来建模，这些模型通常很复杂，与基础生物物理学无关。在本文中，我们说明了如何从生物系统中获得灵感的系统识别技术可以准确地模拟视网膜神经节细胞的行为，并且是传统的线性-非线性方法的可行替代方案。
