  Extracting a connectome from an electron microscopy (EM) data set requires
identification of neurons and determination of synapses between neurons. As
manual extraction of this information is very time-consuming, there has been
extensive research effort to automatically segment the neurons to help guide
and eventually replace manual tracing. Until recently, there has been
comparatively less research on automatically detecting the actual synapses
between neurons. This discrepancy can, in part, be attributed to several
factors: obtaining neuronal shapes is a prerequisite first step in extracting a
connectome, manual tracing is much more time-consuming than annotating
synapses, and neuronal contact area can be used as a proxy for synapses in
determining connections.
  However, recent research has demonstrated that contact area alone is not a
sufficient predictor of synaptic connection. Moreover, as segmentation has
improved, we have observed that synapse annotation is consuming a more
significant fraction of overall reconstruction time. This ratio will only get
worse as segmentation improves, gating overall possible speed-up. Therefore, we
address this problem by developing algorithms that automatically detect
pre-synaptic neurons and their post-synaptic partners. In particular,
pre-synaptic structures are detected using a Deep and Wide Multiscale Recursive
Network, and post-synaptic partners are detected using a MLP with features
conditioned on the local segmentation.
  This work is novel because it requires minimal amount of training, leverages
advances in image segmentation directly, and provides a complete solution for
polyadic synapse detection. We further introduce novel metrics to evaluate our
algorithm on connectomes of meaningful size. These metrics demonstrate that
complete automatic prediction can be used to effectively characterize most
connectivity correctly.
