  In this work, we propose a learning framework for identifying synapses using
a deep and wide multi-scale recursive (DAWMR) network, previously considered in
image segmentation applications. We apply this approach on electron microscopy
data from invertebrate fly brain tissue. By learning features directly from the
data, we are able to achieve considerable improvements over existing techniques
that rely on a small set of hand-designed features. We show that this system
can reduce the amount of manual annotation required, in both acquisition of
training data as well as verification of inferred detections.
