Uncovering the retinal circuits that underlie visual feature detection

突触连接的复杂模式将神经元连接在一起，形成电路，其计算对于我们的各种感觉，运动和认知行为至关重要。然而，这种计算和底层电路模式之间的明确联系尚不清楚。我的研究目标是了解神经电路如何产生计算，如刺激检测，信息存储和预测。为了实现这一目标，我们将绘制这些视网膜回路的连接，以了解它们如何创建视网膜的主要计算：检测显著的视觉特征。大约有30种类型的视网膜回路分析视觉场景，每一种都被调整以检测特定的特征，如运动、边缘、颜色等;每一种回路都以视网膜神经节细胞（RGC）结束，它将特征信号发送到大脑进行进一步分析。目前的模型是，RGC通过它们从特定类型的视网膜中间神经元（称为无长突细胞和双极细胞）接收的连接而被赋予其特征偏好。然而，缺乏这种电路的清晰地图及其与特征检测的联系。为了了解更多信息，我们将设计方法，利用遗传编码钙指标（gcamp 6 f）和光遗传学的最新进展快速绘制RGC，AC和BC之间的连接。有了这项技术，我们将映射功能连接从遗传定义的interneurons到所有RGC类型的平行（目标#1），并获得有价值的信息interneurons连接模式。与此同时，我们将测量中间神经元的视觉反应，以了解它们的信号如何用于创建RGC特征信号（图2）。中间神经元对一系列刺激的反应，包括方向，方向和对比度选择性的测试，将用gcamp 6 f测量，并与由相同刺激诱发的RGC的反应相关;这将使我们能够定义从光子到发生在中间神经元RGC电路中的特征的转换。最后，我们寻求中间神经元输入和RGC特征偏好之间的因果关系，并将采用遗传方法来消融或沉默特定的中间神经元类型，同时成像RGC反应（目标#3）。这项研究将导致高素质人才的培训，谁将离开我的实验室武装与尖端的生命和神经科学研究方法的组合。这项培训是本提案的核心目标，我们的计划使我的人员成为加拿大科学和创新的领导者。人们在我的实验室接受的培训将使他们对研究，生物技术，基金会和其他领域的雇主极具吸引力。我们共同学习的内容将揭示视网膜中电路模式和电路功能之间的联系，并为将这些联系推广到更高的中心提供巨大的潜力。这项工作中的试剂、基本见解和工具提供了一种在大脑中剖析这一问题的方法

Complex patterns of synaptic wiring unite neurons together to form circuits whose computations are critical for a wide range of our sensory, motor and cognitive behaviours. Yet, clear links between such computations and underlying circuit patterns are unclear. The goal of my research is to learn how neural circuitry gives rise to computations such as stimulus detection, information storage, and prediction. To meet this goal, we will map the connections of these retinal circuits to understand how they create the retina's primary computation: to detect salient visual features. Approximately 30 types of retinal circuits analyze the visual scene, each tuned to detect a particular feature such as motion, edges, colour, and so on; each circuit ends with a retinal ganglion cell (RGC), which sends feature signals to the brain for further analysis. The current model is that the RGCs are endowed with their feature preference via the connections they receive from particular types of retinal interneurons, called amacrine and bipolar cells. However, clear maps of such circuitry and its links to feature detection are lacking. To learn more, we will devise methods to rapidly map connections among RGCs, ACs, and BCs using recent advances in genetically encoded calcium indicators (gcamp6f) and optogenetics. Armed with this technique, we will map functional connections from genetically defined interneurons to all RGC types in parallel (Objective #1) and obtain valuable information about the patterns of interneuron connectivity. In parallel, we will measure the visual responses of interneurons to understand how their signals could be used to create RGC feature signals (Objective#2). Interneuron responses to a battery of stimuli that include tests for direction, orientation, and contrast-selectivity will be measured with gcamp6f and related to responses from RGCs evoked by the same stimuli; this will allow us to define transformation from photons to features that occurs in interneuron-RGC circuitry. Finally, we seek causal links between interneuron input and RGC feature preference and will employ genetic methods to ablate or silence particular interneuron types while imaging RGC responses (Objective #3). This research will lead to the training of Highly Qualified Personnel, who will leave my lab armed with a combination of cutting-edge life- and neuroscience research methods. This training is a core objective of this proposal and our plan poises my personnel to become leaders in science and innovation in Canada. The training people receive in my lab will make them highly attractive to employers in research, biotech, foundations and beyond. What we learn together will uncover links between circuit-patterns and circuit function in the retina and offer immense potential to generilize these links to higher centers. The reagents, basic insights and tools from this work offer a way to dissect this issue in the brain