Retinal circuits for precise coding

我们建议研究视网膜中相关视觉信号处理的编码精度 然后通过神经节细胞传递到大脑。视网膜回路的性能有限 因为视觉信号具有大的（10个对数单位）动态范围， 随机事件，如囊泡释放、通道开放和尖峰。因此视网膜 视觉环境的相关特征的优势，例如扩展的对象，速度或方向， 运动编码这些功能与特定的电路，提高他们的信号/噪声比。但视网膜 电路实现这一点是未知的。一个标准的理论是突触释放和电压产生的噪音- 通过在延长的时间上积分来去除门控通道。然而，非线性的存在， 视网膜回路表明编码更为复杂。例如，所有无长突细胞和双极细胞 细胞含有电压门控通道，可以放大和提供适应，它们还含有间隙 检测相关信号并去除噪声的结。许多神经节细胞的树突是活跃的 并且可以非线性地增强突触后电位以产生可靠的信号。我们假设这些 神经元件准备专门放大快速的空间相关信号， 使视觉信号显著的探测器。我们建议通过应用一个理想的 观察者对真实的和模型神经元的响应。理想的观察者是一个计算机程序， 使用对一对刺激的响应之间的似然规则来区分，以测量精度 神经元用其发出例如运动或对比度的信号。该分析提供了灰度级的数量， 信息能力的基本衡量标准。我们将记录活体双极细胞，无长突细胞和神经节细胞， 构建这些神经元及其电路的真实计算机模型，并测量真实的 神经元和模型与理想的观察者。跟踪瞬态、持续和定向的精度 选择性的视觉信号从一层到下一层，我们将发现在视觉通路的信息 丢失和保存，并更好地了解信息是如何编码的。这项工作将有助于 了解眼睛的功能，这些知识将帮助临床研究人员确定什么是眼睛的功能。 在许多类型的眼病中，如夜盲症和其他视网膜营养不良。

We propose to study the precision of coding in the retina where correlated visual signals are processed before being passed to ganglion cells for transmission to the brain. Performance of retinal circuits is limited by noise because the visual signal has a large (10 log unit) dynamic range but is carried by discrete stochastic events such as vesicle release, channel opening, and spikes. Therefore the retina takes advantage of correlated features of the visual environment such as extended objects, velocity, or direction of motion to code these features with specific circuits, improving their signal/noise ratio. But exactly how retinal circuits accomplish this is unknown. One standard theory is that noise from synaptic release and voltage- gated channels is removed by integrating over an extended time. However, the presence of nonlinearities in retinal circuitry suggests that encoding is more complex. For example, the All amacrine cells and bipolar cells contain voltage-gated channels that may amplify and provide adaptation, and they also contain gap junctions that detect correlated signals and remove noise. The dendrites of many ganglion cells are active and may boost postsynaptic potentials nonlinearly to generate a reliable signal. We hypothesize that these neural elements are poised to specifically amplify fast spatially-correlated signals, creating a coincidence detector that imparts salience to visual signals. We propose to test this hypothesis by applying an ideal observer to the responses of real and model neurons. The ideal observer is a computer program that discriminates using the likelihood rule between the responses to a pair of stimuli to measure the precision with which a neuron signals e.g. motion or contrast. This analysis provides the number of gray levels, a fundamental measure of information capacity. We will record from live bipolar, amacrine, and ganglion cells, construct realistic computer models of these neurons and their circuits, and measure the precision of real neurons and model with the ideal observer. Tracking the precision of transient, sustained, and directional selective visual signals from one layer to the next, we will discover where in the visual pathway information is lost and preserved, and gain a better understanding of how information is coded. This work will help to understand how the eye functions, and this knowledge will help clinical researchers determine what has gone wrong in many types of eye disease such as night blindness and other retinal dystrophies.