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

视网膜内层突触相互作用导致视网膜神经节细胞反应多样性的出现——双光子群体成像和计算建模的组合方法

• 批准号: 260009071
• 负责人: Professor Dr. Philipp Berens, since 6/2016
• 金额: --
• 依托单位: School of Life Sciences
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2014
• 资助国家: 德国
• 起止时间: 2013-12-31 至 2016-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/260009071?language=en
• 关键词: Emergence; retinal; ganglion; cell; response

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.

为了处理信息，神经元整合来自多个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 个并行表征之一，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双极细胞 (BC) 和无长突细胞 (AC) 的突触输入。这种情况发生在视网膜交换机（内丛状层）中。尽管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。我们将结合在三个连续水平（突触前双极细胞末梢、突触后 RGC 树突/胞体）进行的光学记录和计算模型，以确定内部视网膜中的突触连接如何产生 RGC 响应特异性。第一步，我们将生成 BC 输出通道的完整功能指纹。此前，我们在小鼠视网膜中区分出了 8 种功能不同的 BC 类型。然而，在解剖学上已鉴定出 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 BC 突触末端中光驱动的钙变化，以响应标准化的刺激和聚类技术。利用这些数据以及从 RGC 体细胞获取的 10,000 多个光学记录的现有数据，我们将使用线性非线性级联模型中的贝叶斯推理来映射 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，并且还允许推断从 AC 到 RGC 的功能输入。我们将首先关注三个特定的 RGC 电路，然后将该方法扩展到其余的 RGC。最后，我们将确定不同的 RGC 类型如何沿着树突的长度整合 BC 和 AC 突触输入。为此，我们将记录所选 RGC 类型的不同树突片段的光诱发钙信号，并将这些测量结果与来自体细胞和药理操作的膜片钳记录相结合。将这些数据与基于选定 RGC 类型形态重建的生物物理模型相结合，将能够理清突触前输入、形态和主动树突计算对创建 RGC 响应特异性的贡献。该项目将帮助我们理解不同 BC 类型提供的响应构建块如何与每种 RGC 类型组合，以产生观察到的视网膜输出多样性。我们的结果将为从功能级别到电路实现级别的一组神经计算提供独特的视角。此外，它们可以作为更好地了解视网膜退行性疾病的突触基础的起点。

项目成果

Connectomics of synaptic microcircuits: lessons from the outer retina

突触微电路的连接组学：来自外视网膜的教训

• DOI: 10.1113/jp273671
• 发表时间: 2017
• 期刊: The Journal of Physiology
• 作者: Rogerson;Behrens;Berens;Schubert
• 通讯作者: Schubert

Zebrafish differentially process colour across visual space to match natural scenes

斑马鱼在视觉空间中差异化地处理颜色以匹配自然场景

• DOI: 10.1101/230144
• 发表时间: 2017
• 期刊: bioRxiv
• 作者: Zimmermann;M. J. Y;Nevala;Yosimatsu;Osorio;Nilsson;Berens
• 通讯作者: Berens

Community-based benchmarking improves spike inference from two-photon calcium imaging data

• DOI: 10.1101/177956
• 发表时间: 2017-08
• 期刊: bioRxiv
• 作者: Philipp Berens;Jeremy Freeman;Thomas Deneux;Nicolay Chenkov;Thomas McColgan;Artur Speiser;J. Macke;Srinivas C. Turaga;Patrick J. Mineault;Peter Rupprecht;S. Gerhard;R. Friedrich;Johannes Friedrich;L. Paninski;Marius Pachitariu;K. Harris;Ben Bolte;Timothy A. Machado;D. Ringach;Jasmine Stone;L. Rogerson;N. Sofroniew;Jacob Reimer;E. Froudarakis;Thomas Euler;M. Rosón;Lucas Theis;A. Tolias;M. Bethge