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是如何 解决信号处理中的问题。本项目涉及 定义明确的夜视电路。输入范围为3-4 原木单位。在低端，每个量子事件都会引起2-3个尖峰的爆发。 在神经节细胞中(高达20次/秒)；高于此级别的增益为 受控制，与平均亮度成反比变化。这条赛道 已知前馈结构(1500棒-100棒双极-5所有 无长突细胞-4个b1双极-β神经节细胞)，以及它的三个 反馈循环。 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干扰？缺乏这样一种机制， 来自1500棒的连续暗噪声将倾向于累积在 神经节细胞(AS 1500)，并消除微小信号。噪音可能是 在电路的前两个阶段通过“门限”机制去除 (发生最大趋同的地方)。待完成的候选神经元 阈值分别为杆状水平细胞和A17 无长突细胞。2)增益控制的机制是什么？侯选人 控制增益的神经元是视杆水平核和神经丛间。 细胞。 为了调查这些问题，该项目将：1)收集其他 关于反馈环路的结构数据(测量 水平细胞，定量所有无长突细胞之间的缝隙连接， 确定丛间细胞的突触连接)。2)构造 电路的每一级的分隔模型(受已知约束 结构和生理)。一个模型包括大约103个神经元 (104个隔间)，并使用高级语言(基于 “C”)为此目的而发明。3)模拟每个阶段的响应 不同的光强度来探索动力学并确定 提出的去除噪声和控制增益的机制是合理的。 4)对整个电路进行仿真(受个人结果的限制 阶段和已知的神经节细胞生理学)，以探索 不同阶段的模型是兼容的。 这个多级电路的仿真，加上它的几层 反馈，应该促进关于夜间机制的基本知识 视觉，可能识别电路中最易受攻击的位置 变坏了。模拟也应该有助于理解 在复杂的神经回路中保持稳定(即反对癫痫发作)。 此外，模拟一个有103个神经元的真实电路也是一个开始 走向最终需要的更大规模的模拟 了解大脑。

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.