We present an approach to low-level vision that combines two main ideas: the use of
convolutional networks as an image processing architecture and an unsupervised learning …

The neural circuits responsible for animal behavior remain largely unknown. We summarize
new methods and present the circuitry of a large fraction of the brain of the fruit fly …

We acquired a rapidly preserved human surgical sample from the temporal lobe of the cerebral
cortex. We stained a 1 mm 3 volume with heavy metals, embedded it in resin, cut more …

Reconstruction of neural circuits from volume electron microscopy data requires the tracing
of cells in their entirety, including all their neurites. Automated approaches have been …

Large scientific projects in genomics and astronomy are influential not because they answer
any single question but because they enable investigation of continuously arising new …

Comprehensive high-resolution structural maps are central to functional exploration and
understanding in biology. For the nervous system, in which high resolution and large spatial …

Many image segmentation algorithms first generate an affinity graph and then partition it. We
present a machine learning approach to computing an affinity graph using a convolutional …

We describe a methodology for rapid experimentation in statistical machine translation which
we use to add a large number of features to a baseline system exploiting features from a …

Convolutional networks have achieved a great deal of success in high-level vision problems
such as object recognition. Here we show that they can also be used as a general method …

For the past decade, convolutional networks have been used for 3D reconstruction of
neurons from electron microscopic (EM) brain images. Recent years have seen great …