CRCNS: Complete functional characterization of a population of retinal ganglion c

描述（由申请人提供）：拟议研究的目的是了解整个视网膜神经元群体如何将视觉信息传递给大脑，视网膜网络活动的活动如何从其元素中产生，以及网络功能如何服务于生物体的需求。到目前为止，这些基本问题的进展一直受到以下方面的限制：(a)对完整神经元群活动的直接实验访问，以及(b)多神经元反应模型，使我们能够理解完整神经元群如何相互作用以表示信息。我们最近开发了一种技术，用于记录孤立猕猴视网膜上的视网膜神经节细胞（RGCs）的完整种群，以及对这些反应进行建模的方法，这些方法为理解整个网络如何编码视觉场景提供了很大的希望。我们将结合这些强大的新技术来解决以下目标：(1)如何将感官输入、非线性、噪声和相互连接结合起来确定大型rgc集合中的详细尖峰模式？(2)基于RGCs的集合放电模式对视觉刺激的解码效率如何？刺激的可判别性如何依赖于脉冲序列的精细时间结构？(3)集合RGC活动对视觉刺激的哪些方面进行了最有效的编码，这些方面在多大程度上反映了人类的感知能力和自然视觉环境的结构？

DESCRIPTION (provided by applicant): The objective of the proposed research is to understand how entire populations of retinal neurons convey visual information to the brain, how the activity of the retinal network activity emerges from its elements, and how network function serves the needs of the organism. Until now, progress on these basic problems has been limited by lack of: (a) direct experimental access to the activity of a complete population of neurons, and (b) models of multineuronal responses that allow us to understand how complete populations of neurons interact to represent information. We have recently developed techniques for recording from complete populations of retinal ganglion cells (RGCs) in isolated macaque monkey retina, and approaches to modeling these responses that provide great promise in understanding how the entire network encodes the visual scene. We will combine these powerful new techniques to address the following aims: (1) How do sensory inputs, nonlinearities, noise, and inter-connections combine to determine the detailed spiking patterns in large ensembles of RGCs? (2) How effectively can visual stimuli be decoded based on the ensemble firing patterns of RGCs, and how does stimulus discriminability depend on the fine temporal structure of spike trains? (3) What aspects of the visual stimulus are most effectively encoded by ensemble RGC activity, and to what degree do these reflect the perceptual abilities of humans and the structure of the natural visual environment? Relevance: Because retinal ganglion cells transmit all visual information to the brain, understanding how they collectively encode visual information is a fundamental aspect of understanding vision, in health and in disease. The proposed work will for the first time allow us to explain the spike trains of a complete population of cells in a single framework that incorporates their response properties, sources of physiological noise, and network connectivity. Each of these ultimately contribute to the healthy function of the visual system, while disruption of each will degrade the visual signals transmitted by the RGC population to the brain in ways that may be predicted and understood with the proposed approach. Furthermore, prosthetic devices to replace retinal function, which are now being tested in humans, will eventually need to reproduce the normal patterns of spiking activity in order to provide natural visual signals to the brain. Therefore, our recent experiments using electrical stimulation with multi-electrode arrays for prosthetic design will benefit greatly from the proposed work. In summary, knowing how the entire retinal network encodes the visual scene in spike trains is a key element in understanding the healthy visual system and designing prosthetic treatments for retinas damaged by disease.