Connectivity generation, exploration and visualization for large scale neural networks
Connectivity is an essential component for defining the functionality at all organizational levels of the brain. There is no standardized way to represent, visualize, explore, and generate connectivity for simulation or analysis. Generating connectivity from experimental data is still in its infancy. The goal of this workshop is to discuss different aspects of interacting with the connectivity of large neural networks, as well as representing, visualizing, and ultimately generating these large connectomes.
This workshop offers the interested neuroscientist presentations on the state of the connectivity challenge: powerful abstract representations using mathematical language, current advances in the visual representation and exploration of connections at multiple scale. We will also discuss highly performant library standards for generation of connectivity at the simulator level, and the relationship of these tools and analyses to experimental data.
Which connectivity functions are essential for characterizing higher level organization is still unknown, limiting our ability to port the extremely complex data acquired by experiments into simplified models of large scale neural networks for simulation and analysis. This workshop will promote discussion regarding how to best represent connectivity and port connectivity data produced in experiments to simulations which can accurately reproduce activity at different scales.
With the arrival of high performance computing we are now able to simulate large scale neural networks but if we are not able to port, create and explore connectivity in them, it will be difficult to use them as a tool to better understand the relationships between connectivity and function. As a result, it is becoming of essence for the computational neuroscience community to agree on how to represent, generate and port connectivity. It is time to combine the efforts on all relevant disciplines to assure a coherent flux of data among experiments and simulations so that a better understanding of the structure and function of the brain can be achieved.