Retinal circuits for precise coding

我们建议研究视网膜中编码的精确度，在那里相关的视觉信号被处理 在被传递到神经节细胞并传递到大脑之前。视网膜电路的性能有限 由于视觉信号具有很大的动态范围(10个对数单位)，但由离散的 随机事件，如囊泡释放、通道打开和尖峰。因此，视网膜需要 视觉环境的相关特征的优势，例如延伸的对象、速度或方向 使用特定电路对这些功能进行编码，以提高其信噪比。但是视网膜到底有多大 电路实现这一点是未知的。一种标准的理论是，来自突触释放和电压的噪音- 通过在较长时间内进行积分来移除选通通道。然而，非线性的存在在 视网膜回路表明，编码更复杂。例如，所有的无长突细胞和双极细胞 细胞含有可放大和提供适应的电压门控通道，它们也含有间隙 检测相关信号并去除噪声的结点。许多神经节细胞的树突是活跃的。 并且可以非线性地提高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，创造一种巧合 使视觉信号突出的探测器。我们建议通过应用一个理想来检验这一假设 观察真实神经元和模型神经元的反应。理想的观察者是一个计算机程序，它 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 神经元用它来发出信号，例如运动或对比。该分析提供了灰度级的数量、 信息能力的基本衡量标准。我们将从活的双极细胞、无长突细胞和神经节细胞中记录， 构建这些神经元及其电路的真实计算机模型，并测量REAL的精度 神经元和模型具有理想的观测器。跟踪瞬变、持续和定向的精确度 选择性视觉信号从一层到下一层，我们将发现视觉通路信息中的哪里 丢失和保存，并更好地理解信息是如何编码的。这项工作将有助于 了解眼睛是如何工作的，这一知识将帮助临床研究人员确定 在许多类型的眼病中出了问题，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.