Retinal circuits for precise coding

描述(申请人提供)：我们建议研究视网膜编码的精确度，相关的视觉信号在被传递到神经节细胞传输到大脑之前被处理。视网膜电路的性能受到噪声的限制，因为视觉信号具有大的(10对数单位)动态范围，但由离散的随机事件携带，例如囊泡释放、通道开放和尖峰。因此，视网膜利用视觉环境的相关特征，如延伸的物体、速度或运动方向，用特定的电路对这些特征进行编码，从而提高它们的信噪比。但视网膜回路到底是如何做到这一点的还不得而知。一种标准的理论是，突触释放和电压门控通道的噪音可以通过延长时间的积分来消除。然而，视网膜回路中非线性的存在表明编码更加复杂。例如，所有的无长突细胞和双极细胞都包含可以放大和提供适应的电压门控通道，它们还包含检测相关信号和消除噪声的缝隙连接。许多神经节细胞的树突是活跃的，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件准备专门放大快速的空间相关信号，创造出一种给予视觉信号突出的符合探测器。我们建议通过将理想的观测器应用于真实神经元和模型神经元的响应来检验这一假设。理想的观察者是一种计算机程序，它使用对一对刺激的反应之间的似然规则来区分，以测量神经元发出信号的精度，例如运动或对比。这一分析提供了灰度级的数量，这是信息能力的基本衡量标准。我们将记录活的双极细胞、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，并用理想的观察者测量真实神经元和模型的精度。跟踪从一层到下一层的瞬时、持续和定向选择性视觉信号的精度，我们将发现视觉路径中信息丢失和保留的位置，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，这一知识将帮助临床研究人员确定许多类型的眼病，如夜盲和其他视网膜营养不良症，出了什么问题。

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.