Consequently, great efforts are devoted to studying brain network structure at multiple scales: from the detailed connectivity of local neural circuits comprising small numbers of neurons to the large scale connection patterns between entire brain areas, comprising hundreds of millions of nerve cells. The multi-scale network structure is studied directly both at the anatomical or structural level and at the functional level defined by the activity patterns of neural elements. Since both anatomical and functional connectivity patterns change across different time scales, the dynamics of brain connectivity and its relation to learning and adaptation are also of great importance.

Recent progress in experimental techniques as well as continuous advances in information technology have led to improved reconstructions of small circuits at a scale of millimeters and large-scale wiring patterns between entire brain areas in different species. High throughput approaches promise to produce data sets of unprecedented size at unprecedented speed.

But just as deciphering the genome hasn’t meant that we now understand genetic networks, charting the brain’s wiring diagram will not mean that we will understand its function. To fully capitalize on the new technological developments in obtaining wiring diagrams of the brain, the refinement of experimental techniques must be accompanied by corresponding computational and theoretical developments. Specifically, as experimental techniques are maturing, there is a growing need to develop new computational approaches to facilitate the automated reconstruction of connectivity from experimental approaches, to support the curation and open-access distribution of large-scale data sets, to undertake systematic analysis of complex connectivity networks, as well as computational modeling, and ultimately understanding of these data sets. The Computational Connectomics SPP addresses this growing need.