  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.
