Retinal circuits for precise coding

描述（由申请人提供）：我们拟研究视网膜中编码的精度，在视网膜中，相关视觉信号在传递到神经节细胞并传递到大脑之前被处理。视网膜电路的性能受到噪声的限制，因为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）携带。因此，视网膜利用视觉环境的相关特征，如扩展的物体、速度或运动方向，用特定的电路对这些特征进行编码，提高它们的信噪比。但视网膜回路究竟是如何做到这一点的还不得而知。一种标准理论认为，突触释放和电压门控通道产生的噪声通过长时间积分消除。然而，视网膜电路中非线性的存在表明编码更为复杂。例如，All - amacrine细胞和双极细胞含有电压门控通道，可以放大和提供适应，它们也含有间隙连接，检测相关信号和去除噪声。许多神经节细胞的树突是活跃的，并可能非线性地提高突触后电位以产生可靠的信号。我们假设，这些神经元素已经准备好专门放大快速空间相关信号，创造一个巧合探测器，赋予视觉信号显著性。我们建议通过对真实和模型神经元的反应应用理想观测器来验证这一假设。理想的观察者是一个计算机程序，它使用对一对刺激的反应之间的似然规则来区分，以测量神经元信号的精度，例如运动或对比。这种分析提供了灰度等级的数量，这是信息容量的基本衡量标准。我们将记录活的双极、无突和神经节细胞，构建这些神经元及其电路的真实计算机模型，并测量真实神经元的精度，并与理想的观察者建立模型。跟踪瞬态、持续和定向选择性视觉信号从一层到下一层的精度，我们将发现视觉通路中信息丢失和保存的位置，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的功能，这些知识将帮助临床研究人员确定许多类型的眼病，如夜盲症和其他视网膜营养不良症的问题。

DESCRIPTION (provided by applicant): 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.