Overview
The "wiring diagram" of essentially all nervous systems remain unknown due to the extreme difficulty of measuring detailed patterns of synaptic connectivity of entire neural circuits. At this point, the major bottleneck is in the analysis of tera or peta-voxel 3d electron microscopy image data in which neuronal processes need to be traced and synapses localized in order for connectivity information to be inferred. This presents an opportunity for machine learning and machine perception to have a fundamental impact on advances in neurobiology. However, it also presents a major challenge, as existing machine learning methods fall short of solving the problem.
The goal of this workshop is to bring together researchers in machine learning and neuroscience to discuss progress and remaining challenges in this exciting and rapidly growing field. We aim to attract machine learning and computer vision specialists interested in learning about a new problem, as well as computational neuroscientists at NIPS who may be interested in modeling connectivity data. We will discuss the release of public datasets and competitions that may facilitate further activity in this area. We expect the workshop to result in a significant increase in the scope of ideas and people engaged in this field.
The workshop solicits presentations on any related subjects; for example:- methods for reconstruction of neural connectivity from microscopy or other sources;
- theoretical and empirical analysis of artificial and biological connectivity data;
- applications of wiring diagram data to novel engineering or scientific applications.