Leveraging In Situ Data Analysis to Enable Computational Steering of Brain's Neocortex Simulations with GENESIS

To investigate the origin and functional role of the brain's electrical activity in specific frequency ranges, scientists have employed biologically accurate computational models of the brain. The onset of extreme-scale computing brings the promise of allowing neuroscientists the ability to examine the brain in silico at unprecedented fidelity and resolution that is not possible using traditional methods such as the electroencephalogram. The large amount of data produced by high fidelity simulations are becoming increasingly difficult to save and transform into scientific insights for runtime simulations. One attractive solution is to modify the data analysis workflow from one that is accomplished post-simulation to one that is completed in situ at the cost of confining the analysis to the level of local data rather than a single global view. The missing global view of data has the potential to introduce inaccuracies in the simulation's findings. In this paper, we present the integration of in situ analysis into a simulation of the electrical activity of a brain's neuronal network to enable computational steering of the simulation itself at runtime (i.e., without stopping, performing post-simulation analysis, and restarting the simulation). We evaluate the accuracy of our in situ analysis versus the traditional post-simulation analysis, showing how we can gain meaningful global insights from local data. We demonstrate the integration's effectiveness in steering the electrical activity of a simulated neuronal network at runtime from electroencephalograph's (EEG) alpha frequency band (8-13Hz) to the beta frequency band (13-40Hz).