A higher level for neuron reconstruction

更高水平的神经元重建

• 批准号: 273233945
• 负责人: Professor Dr. Fred A. Hamprecht
• 金额: --
• 依托单位: Universität Zürich (UZH)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2018-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/273233945?language=en
• 关键词: higher; level; neuron; reconstruction

To better understand the brain, the substrate of our cognition, we require detailed knowledge of its structure, in particular its "wiring diagram". Unfortunately, an exact diagram of even the tiniest part of a brain is not available to date! Thanks to great advances in volume electron microscopy, human experts can now reliably trace individual neurons. Unfortunately, the effort for a dense tracing of even a tiny part of the brain is in the order of multiple decades. Automated methods still break down in spots where the image quality is poor. Unlike humans, such automated methods cannot leverage knowledge on shape and context yet. The present proposal will develop higher order models that can learn about shape and structure. Such learning will rely on new error metrics that are meaningful in this application domain, also to be developed here, and on estimators for local uncertainty. Such not quite local properties will result in difficult optimization problems which we are confident to solve thanks to the efficient generation of multiple locally plausible interpretations of the raw data. In summary, this project will contribute to the automated extraction of parts of the wiring diagram of a brain, with an accuracy that shall enable the asking and answering of new biological questions.

为了更好地理解大脑，我们认知的基底，我们需要详细了解它的结构，特别是它的“接线图”。不幸的是，至今还没有一张精确的大脑图，哪怕是大脑最微小的部分。由于体积电子显微镜的巨大进步，人类专家现在可以可靠地追踪单个神经元。不幸的是，即使是对大脑的一小部分进行密集追踪的努力也需要几十年的时间。自动化方法在图像质量差的地方仍然会失败。与人类不同，这种自动化方法还不能利用形状和背景知识。目前的建议将开发更高阶的模型，可以了解形状和结构。这样的学习将依赖于新的误差度量，这是有意义的，在这个应用领域，也要在这里开发，并估计当地的不确定性。这种不太局部的属性将导致困难的优化问题，我们有信心解决由于多个本地合理的解释的原始数据的有效生成。总之，该项目将有助于自动提取大脑接线图的部分，其准确性应能够提出和回答新的生物学问题。