Emergence of retinal ganglion cell response diversity from synaptic interactions in the inner retina - a combined approach of two-photon population imaging and computational modeling

为了处理信息，神经元整合来自大量突触前伙伴的突触输入。在视网膜中，每个视网膜神经节细胞（RGC）从视觉场景中提取约20个平行表征中的一个并将其发送到大脑。为了达到其独特的反应模式，它整合了来自特定类型的双极细胞（BC）和无长突细胞（AC）的突触输入。这发生在视网膜交换板，内部网状层。虽然这种高度结构化的组织片在解剖学上具有良好的特征，但每个BC/AC/RGC微电路内的功能连接和集成规则在很大程度上是未知的。我们将结合联合收割机在三个连续的水平（突触前双极细胞终端，突触后RGC树突/胞体）和计算模型，以建立如何在视网膜内层的突触连接引起RGC反应特异性的光学记录。作为第一步，我们将生成BC输出通道的完整功能指纹。以前，我们已经区分了8个功能不同的BC类型在小鼠视网膜。然而，在解剖学上已经确定了12种类型。为了识别每种解剖类型的功能特征，我们将使用双光子成像来记录单个BC突触末端中光驱动的钙变化，以响应标准化的刺激电池和聚类技术。使用这些数据和现有的数据超过10,000光学记录从RGC胞体，我们将映射BC和RGC类型之间的功能连接使用贝叶斯推理的线性非线性级联模型与灵活的非线性选择基于我们的实验数据。该方法将被限制到解剖学上合理的连接，并将允许推断功能输入从AC到RGC以及。我们会先集中研究三个特定的研资局范畴，然后再把研究方法扩展至研资局的其他范畴。最后，我们将确定不同的RGC类型如何整合BC和AC突触输入沿着其树突的长度。为此，我们将记录光诱发的钙信号在不同的树突状节段选择RGC类型和联合收割机这些测量与膜片钳记录从索马和药理学操作。将这些数据与基于所选RGC类型的形态重建的生物物理模型相结合，将允许解开突触前输入、形态和主动树突计算对创建RGC反应特异性的贡献。这个项目将帮助我们了解不同BC类型提供的反应构建模块如何被每种RGC类型组合，以产生视网膜输出中观察到的多样性。我们的研究结果将提供一个独特的观点，从功能层面的神经计算到电路层面的实现。此外，它们可以作为更好地理解视网膜退行性疾病的突触基础的起点。

To process information, neurons integrate synaptic inputs from a multitude of presynaptic partners. In the retina, each retinal ganglion cell (RGC) extracts one of ~20 parallel representations from the visual scene and sends it to the brain. To arrive at its unique response pattern, it integrates synaptic inputs from specific types of bipolar (BC) and amacrine cells (AC). This happens in the retinal switchboard, the inner plexiform layer. Although this highly structured sheet of tissue is anatomically well-characterized, the functional connections and the integration rules within each BC/AC/RGC microcircuit are largely unknown. We will combine optical recordings taken at three consecutive levels (presynaptic bipolar cell terminals, postsynaptic RGC dendrites/somata) and computational models to establish how synaptic connectivity in the inner retina gives rise to RGC response specificity. As a first step, we will generate a complete functional fingerprint of BC output channels. Previously, we have differentiated 8 functionally distinct BC types in the mouse retina. However, 12 types have been identified anatomically. To identify a functional signature for each anatomical type, we will use two-photon imaging to record light-driven calcium changes in the synaptic terminals of individual BCs in response to a standardized battery of stimuli and clustering techniques. Using this data and existing data on more than 10,000 optical recordings taken from RGC somata, we will map the functional connections between BC and RGC types using Bayesian inference in linear-nonlinear-cascade models with flexible non-linearities chosen based on our experimental data. The approach will be restricted to anatomically plausible connections and will allow inferring functional inputs from ACs onto RGCs as well. We will focus initially on three specific RGC circuits before expanding the approach to the remainder of RGCs. Finally, we will determine how different RGC types integrate BC and AC synaptic inputs along the length of their dendrites. To this end, we will record light evoked calcium signals at different dendritic segments of selected RGC types and combine these measurements with patch clamp recordings from the soma and pharmacological manipulation. Combining this data with biophysical models based on morphological reconstructions of selected RGC types, will allows to disentangle the contributions of presynaptic input, morphology and active dendritic computation for creating RGC response specificity. This project will aid our understanding of how the response building blocks provided by the different BC types are combined by each RGC type to yield the observed diversity in the retinal output. Our results will offer a unique view on a set of neural computations from the functional level to the circuit level of implementation. In addition, they can serve as a starting point for a better understanding of the synaptic basis underlying retinal degenerative diseases.