A higher level for neuron reconstruction

点击翻译按钮获取中文摘要

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.