STRUCT/FUNCT OF RETINAL CIRCUIT FOR SCOTOPIC LUMINANCE

暗视亮度视网膜电路的结构/功能

• 批准号: 2248011
• 负责人: Robert G Smith
• 金额: $ 24.56万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-09-30 至 1995-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2248011
• 关键词: alternatives to animals in research; amacrine cells; biological signal transduction; cats; computational neuroscience; computer simulation; confocal scanning microscopy; dark adaptation; electron microscopy; electrophysiology; gap junctions; horizontal cell; immunocytochemistry; light intensity; membrane potentials; neural information processing; noise biological effect; retina; retinal bipolar neuron; retinal ganglion; rod cell; sensory mechanism; stainings; synapses; visual feedback; visual pathways

The long term goal is to determine how neural circuits in mammalian retina solve problems of signal processing. The present project concerns the circuit for night vision which is well defined. The input range spans 3-4 log units. At the low end each quantal event evokes a burst of 2-3 spikes in a ganglion cell (up to 20 events/second); above this level gain is controlled and varies inversely with mean luminance. The circuit's feedforward structure is known (1500 rods -> 100 rod bipolar -> 5 AII amacrine -> 4 b1 bipolar -> beta ganglion cell), and so are three of its feedback loops. This project addresses two questions: 1) By what mechanism does the circuit protect a quantal signal against noise? Lacking such a mechanism, the continuous dark noise from 1500 rods would tend to accumulate in the ganglion cell (as 1500) and obliterate the tiny signal. Noise might be removed by "thresholding" mechanisms at the first two stages of the circuit (where most convergence occurs). Candidate neurons to accomplish thresholding are, respectively, the rod horizontal cell and the A17 amacrine cell. 2) What is the mechanism for gain control? Candidate neurons for gain control are the rod horizontal and the interplexiform cells. To investigate these questions the project will: 1) Gather additional structural data regarding the feedback loops (measure fine features of the horizontal cell, quantitate gap junctions between AII amacrine cells, determine synaptic connections of the interplexiform cell). 2) Construct a compartmental model of each stage of the circuit (constrained by known structure and physiology). A model includes on the order of 103 neurons (104 compartments) and is constructed using a high-level language (based on "C") invented for this purpose. 3) Simulate the response of each stage at different light intensities to explore the dynamics and determine whether the mechanisms proposed for noise removal and gain control are plausible. 4) Simulate the overall circuit (constrained by results from individual stages and the known physiology of the ganglion cell) to explore whether the models of separate stages are compatible. Simulation of this multi-stage circuit, plus its several layers of feedback, should advance basic knowledge regarding the mechanisms of night vision, possibly identifying which sites in the circuit are most vulnerable to deterioration. Simulation should also help understand mechanisms that maintain stability (i.e., oppose seizures) in complex neural circuits. Also, simulating a realistic circuit with 103 neurons provides a start toward the larger scale simulations that will ultimately be needed to understand the brain.

长期目标是确定哺乳动物视网膜中的神经回路 解决信号处理问题。 目前的项目涉及 夜视的电路，定义得很好。 输入范围跨越3-4 日志单元。 在低端，每个量化事件都会唤起2-3个尖峰 在神经节细胞中（最多20个事件/秒）；高于此级别的收益是 受控并随平均亮度呈面而变化。 电路的 前馈结构已知（1500杆 - > 100杆双极 - > 5 AII amacrine-> 4 b1双极 - >β神经节细胞），其中三个也是如此 反馈循环。 该项目解决了两个问题：1）通过什么机制 电路保护数量信号免受噪声？ 缺乏这种机制， 1500杆的连续黑噪声会倾向于在 神经节细胞（AS 1500）并抑制微小的信号。 噪音可能是 通过电路前两个阶段的“阈值”机制去除 （大多数收敛发生的地方）。 候选神经元要完成 阈值分别为杆水平细胞和A17 无长熟细胞。 2）收益控制的机制是什么？ 候选人 获得增益控制的神经元是杆的水平和链链。 细胞。 为了调查这些问题，该项目将：1）收集其他 有关反馈回路的结构数据（测量 水平细胞，定量AII无链氨酸细胞之间的间隙连接， 确定跨膜间形细胞的突触连接）。 2）构造 电路每个阶段的隔室模型（受已知的限制 结构和生理学）。 一个模型包括103个神经元的阶 （104个隔间），并使用高级语言构造（基于 “ C”）是为此目的发明的。 3）模拟每个阶段的响应 不同的光强度来探索动态并确定是否 提出的去除噪声和增益控制的机制是合理的。 4）模拟整个电路（受个人的结果约束 阶段和神经节细胞的已知生理学），以探讨是否是否 单独阶段的模型兼容。 模拟此多阶段电路，以及其几层 反馈，应提高有关夜晚机制的基本知识 视觉，可能确定电路中的哪些站点最脆弱 恶化。 模拟还应有助于了解机制 保持复杂的神经回路中的稳定性（即相反的癫痫发作）。 此外，模拟使用103个神经元的现实电路提供了一个开始 迈向最终需要的大规模模拟 了解大脑。