Towards a connectomics-based predictive model of the inner retina

建立基于连接组学的内视网膜预测模型

• 批准号: 346384612
• 负责人: Professor Dr. Philipp Berens
• 金额: --
• 依托单位: Max-Planck-Institut für Neurobiologie des Verhaltens - caesar
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/346384612?language=en
• 关键词: Towards; connectomics; based; predictive; model

Visual processing starts in the retina, where at least 40 distinct features are extracted and sent through parallel channels to higher visual centres in the brain. One of the biggest remaining challenges in retinal research is to understand how these diverse representations arise within the retinal circuits. So far, only a few of these are well understood; in each case, understanding the role of a specific type of interneuron, part of the largely inhibitory retinal cell class collectively called amacrine cells (ACs), was key to revealing the computational mechanisms. Despite the key role of ACs in retinal computations, surprisingly little is known about the great majority of the 60+ genetic types of ACs and their intricate networks in the inner retina. In the first phase of the Priority Program 2041 “Computational Connectomics”, we aimed to further dissect the functional roles of AC circuits for image processing. We developed a new two-photon imaging paradigm and combined functional data recorded with a wide range of visual stimuli with contact-based connectivity data to develop a well-performing predictive model for temporal processing in the inner retina, including AC circuits. In the next phase, we will extend our model to include spatial processing and base it on new electron microscopy reconstructions with synapse-level connectivity and dual-imaging functional data, allowing us to constrain the extended model in unprecedented ways. First, we will generate a new, functionally-annotated connectomics dataset of the mouse retina with synapse resolution. We will use a novel multibeam scanning electron microscope that enables large tissue volumes to be collected in weeks instead of months. Then, we will develop an automatic pipeline to classify the reconstructed cells into types and align them with functional data. We will use 2-photon imaging with axial scans to simultaneously measure glutamate release from bipolar cell (BC) axon terminals – the excitatory input to the inner retina – and activity in AC dendrites through the entire depth of the inner plexiform layer. Next, we will integrate both data modalities by setting up a model for spatio-temporal processing based on the connectivity data between BC and AC types and inferring its parameters based on the functional data. Through iteration, we will improve the model by successively adding more details on connectivity and by fitting it with functional data gained from experiments designed based on modelling results from previous rounds. Together, this will allow us to advance our knowledge of the ACs’ roles in retinal computations.

视觉处理开始于视网膜，在那里至少有40个不同的特征被提取出来，并通过平行通道发送到大脑中更高的视觉中心。视网膜研究中最大的挑战之一是了解这些不同的表征是如何在视网膜回路中产生的。到目前为止，只有少数这些被很好地理解;在每种情况下，理解一种特定类型的中间神经元的作用，这是统称为无长突细胞（AC）的主要抑制性视网膜细胞类别的一部分，是揭示计算机制的关键。尽管AC在视网膜计算中起着关键作用，但令人惊讶的是，人们对60多种遗传类型的AC中的绝大多数及其在内视网膜中复杂的网络知之甚少。在优先计划2041“计算连接组学”的第一阶段，我们的目标是进一步剖析AC电路在图像处理中的功能作用。我们开发了一种新的双光子成像范例，并将记录的各种视觉刺激的功能数据与基于接触的连接数据相结合，以开发一种性能良好的预测模型，用于内层视网膜的时间处理，包括AC电路。在下一阶段，我们将扩展我们的模型，以包括空间处理，并将其基于新的电子显微镜重建与突触水平的连接和双成像功能数据，使我们能够以前所未有的方式约束扩展模型。首先，我们将生成一个新的，具有突触分辨率的小鼠视网膜的功能注释的连接组学数据集。我们将使用一种新型的多束扫描电子显微镜，它可以在几周而不是几个月内收集大量组织。然后，我们将开发一个自动管道来将重建的细胞分类为类型，并将它们与功能数据对齐。我们将使用双光子成像与轴向扫描，同时测量谷氨酸释放从双极细胞（BC）轴突终端-兴奋性输入到内部视网膜-和活性AC树突通过整个深度的内部网状层。接下来，我们将通过建立基于BC和AC类型之间的连接数据的时空处理模型并基于功能数据推断其参数来整合这两种数据模态。通过迭代，我们将通过连续添加更多关于连通性的细节并将其与基于前几轮建模结果设计的实验中获得的功能数据进行拟合来改进模型。总之，这将使我们能够推进我们对AC在视网膜计算中的作用的了解。