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种平行表征中的一种，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（BC）和无突细胞（AC）的突触输入。这种情况发生在视网膜的开关层，也就是内丛状层。尽管这种高度结构化的组织片在解剖学上有很好的特征，但每个BC/AC/RGC微电路中的功能连接和集成规则在很大程度上是未知的。我们将结合三个连续水平的光学记录（突触前双极细胞终端，突触后RGC树突/体细胞）和计算模型来确定内视网膜的突触连接如何引起RGC反应特异性。作为第一步，我们将生成BC输出通道的完整功能指纹。在此之前，我们已经在小鼠视网膜中分化出8种功能不同的BC类型。然而，在解剖学上已经确定了12种类型。为了确定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BCs突触末端的光驱动钙变化，以响应标准化的刺激和聚类技术。利用这些数据以及从RGC体细胞中获取的超过10,000个光学记录的现有数据，我们将使用贝叶斯推理在线性-非线性-级联模型中映射BC和RGC类型之间的功能连接，并根据我们的实验数据选择灵活的非线性。该方法将仅限于解剖学上合理的连接，并将允许推断从ACs到rgc的功能输入。我们会先把重点放在三个特定的研究资助局回路，然后再扩展到其他的研究资助局回路。最后，我们将确定不同类型的RGC如何沿其树突长度整合BC和AC突触输入。为此，我们将在选定的RGC类型的不同树突段记录光诱发钙信号，并将这些测量结果与来自体细胞的膜片钳记录和药物操作相结合。将这些数据与基于所选RGC类型形态学重建的生物物理模型相结合，将能够理清突触前输入、形态学和活动树突计算对创建RGC响应特异性的贡献。这个项目将帮助我们理解不同BC类型提供的响应构建块是如何由每个RGC类型组合在一起的，从而产生观察到的视网膜输出的多样性。我们的研究结果将从功能层面到电路层面提供一组神经计算的独特视角。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.