2012/10/01 by Tao Hu, Hu, Tao, Juan Nunez-Iglesias +18 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Materials Science · Mathematics · #Advanced Electron Microscopy Techniques and Applications #Advanced Fluorescence Microscopy Techniques #Computer Vision and Pattern Recognition (cs.CV) #Electron and X-Ray Spectroscopy Techniques #FOS: Biological sciences #FOS: Computer and information sciences #Integrated Circuits and Semiconductor Failure Analysis #Machine Learning (stat.ML) #Neurons and Cognition (q-bio.NC) #cs.CV #q-bio.NC #stat.ML
paper · pdf · doi:10.48550/arxiv.1210.0564
12 pages, 11 figures
arxiv created 2012/10/01 · openalex publication_date 2012/10/01 · arxiv updated 2012/10/03 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
A central problem in neuroscience is reconstructing neuronal circuits on the synapse level. Due to a wide range of scales in brain architecture such reconstruction requires imaging that is both high-resolution and high-throughput. Existing electron microscopy (EM) techniques possess required resolution in the lateral plane and either high-throughput or high depth resolution but not both. Here, we exploit recent advances in unsupervised learning and signal processing to obtain high depth-resolution EM images computationally without sacrificing throughput. First, we show that the brain tissue can be represented as a sparse linear combination of localized basis functions that are learned using high-resolution datasets. We then develop compressive sensing-inspired techniques that can reconstruct the brain tissue from very few (typically 5) tomographic views of each section. This enables tracing of neuronal processes and, hence, high throughput reconstruction of neural circuits on the level of individual synapses.