STRUCT/FUNCT OF RETINAL CIRCUIT FOR SCOTOPIC LUMINANCE

长期目标是确定哺乳动物视网膜中的神经回路 解决信号处理的问题。 本项目涉及 夜视电路，它是明确的。 输入范围为3 - 4 对数单位。 在低端，每个量子事件引起2 - 3个尖峰的突发 在神经节细胞中（高达20个事件/秒）;高于此水平的增益是 控制并与平均亮度成反比变化。 电路的 前馈结构是已知的（1500杆-> 100杆双极-> 5 AII 无长突-> 4 b1双极-> β神经节细胞），其三个 反馈回路 这个项目解决了两个问题：1）通过什么机制， 电路保护量子信号不受噪声影响？ 如果没有这样的机制， 来自1500个棒的连续的暗噪声将倾向于积累在 神经节细胞（如1500），并消除微小的信号。 噪音可能是 在电路的前两级通过"阈值"机制去除 （大部分的融合发生在这里）。 候选神经元完成 阈值分别是视杆细胞水平细胞和A17细胞水平细胞。 无长突细胞 2)增益控制的机制是什么？ 候选 控制增益的神经元有水平的视杆神经元和网间神经元 细胞 为了调查这些问题，该项目将：1）收集更多的 关于反馈回路的结构数据（测量 水平细胞，定量AII无长突细胞之间缝隙连接， 确定网间细胞的突触连接）。 2)构建体 电路的每一级的隔室模型（由已知的 结构和生理学）。 一个模型包括大约103个神经元 (104隔室），并使用高级语言（基于 “C”就是为了这个目的而发明的。 3)模拟每个阶段的响应， 不同的光照强度，以探索动态，并确定是否 所提出的用于噪声去除和增益控制的机制是合理的。 4)模拟整个电路（受单个电路的结果约束 阶段和神经节细胞的已知生理学），以探索是否 各个阶段的模型是兼容的。 仿真这个多级电路，加上它的几层 反馈，应该推进关于夜间机制的基本知识 视觉，可能识别电路中哪些部位最脆弱 恶化。 模拟还有助于理解 保持稳定性（即，对抗癫痫发作）。 此外，模拟具有103个神经元的现实电路提供了一个开始 最终需要进行更大规模的模拟， 理解大脑。

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.