English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A central problem in neuroscience is reconstructing neuronal circuits on thesynapse level. Due to a wide range of scales in brain architecture suchreconstruction requires imaging that is both high-resolution andhigh-throughput. Existing electron microscopy (EM) techniques possess requiredresolution in the lateral plane and either high-throughput or high depthresolution but not both. Here, we exploit recent advances in unsupervisedlearning and signal processing to obtain high depth-resolution EM imagescomputationally without sacrificing throughput. First, we show that the braintissue can be represented as a sparse linear combination of localized basisfunctions that are learned using high-resolution datasets. We then developcompressive sensing-inspired techniques that can reconstruct the brain tissuefrom very few (typically 5) tomographic views of each section. This enablestracing of neuronal processes and, hence, high throughput reconstruction ofneural circuits on the level of individual synapses.

Super-resolution using Sparse Representations over Learned Dictionaries: Reconstruction of Brain Structure using Electron Microscopy