In the last decade simulation models have become more prevalent in a variety of application areas. Each of these models define a set of input parameters which determine the features of the output. With enough of these outputs we can begin to perform what is known as reconstructing the black box in the field of parameter space analysis (Konyha et al.). One method to do this is through trial and error, where a researcher sets the input parameters to specific values based upon their experience, and collects the outputs as sample points to investigate later on (Sedlmair et al.). This is a computationally expensive method for exploring parameter space which limits the effectiveness of the program overall.

This project represents an effort to update the Parameter Space Exploration (PSE) tool so that it can be used effectively for exploring the parameter space associated with each included large-scale brain model. This achieved would effectively reconstruct the black box of the model which is vital for our understanding of its validity, and supports ongoing work to better understand the original basis for our model, the human brain.