Guo Tianruo, Tsai David, Bai Siwei, Morley John W, Suaning Gregg J, Lovell Nigel H, Dokos Socrates
Graduate School of Biomedical Engineering, UNSW Australia, Sydney, NSW 2052, Australia.
Graduate School of Biomedical Engineering, UNSW Australia, Sydney, NSW 2052, Australia; Howard Hughes Medical Institute, Biological Sciences, Bioelectronic Systems Lab, Electrical Engineering, Columbia University, New York, NY.
Crit Rev Biomed Eng. 2014;42(5):419-36. doi: 10.1615/critrevbiomedeng.2014011732.
The vertebrate retina is a clearly organized signal-processing system. It contains more than 60 different types of neurons, arranged in three distinct neural layers. Each cell type is believed to serve unique role(s) in encoding visual information. While we now have a relatively good understanding of the constituent cell types in the retina and some general ideas of their connectivity, with few exceptions, how the retinal circuitry performs computation remains poorly understood. Computational modeling has been commonly used to study the retina from the single cell to the network level. In this article, we begin by reviewing retinal modeling strategies and existing models. We then discuss in detail the significance and limitations of these models, and finally, we provide suggestions for the future development of retinal neural modeling.
脊椎动物的视网膜是一个组织清晰的信号处理系统。它包含60多种不同类型的神经元，排列在三个不同的神经层中。据信每种细胞类型在编码视觉信息中都发挥着独特的作用。虽然我们现在对视网膜中的组成细胞类型以及它们的连接方式有了相对较好的理解，但除了少数例外情况，视网膜回路如何进行计算仍然知之甚少。计算建模已被广泛用于从单细胞到网络层面研究视网膜。在本文中，我们首先回顾视网膜建模策略和现有模型。然后详细讨论这些模型的意义和局限性，最后，我们为视网膜神经建模的未来发展提供建议。
