567 RWTH Publication No: 849258        2022       
TITLE Exploring hyper-parameter spaces of neuroscience models on high performance computers with Learning to Learn
AUTHORS Alper Yegenoglu, Anand Subramoney, Thorsten Hater, Cristian Jimenez-Romero, Wouter Klijn, Aaron Perez Martin, Michiel van der Vlag, Michael Herty, Abigail Morrison, Sandra Diaz-Pier
ABSTRACT Neuroscience models commonly have a high number of degrees of freedom and only specific regions within the parameter space are able to produce dynamics of interest. This makes the development of tools and strategies to efficiently find these regions of high importance to advance brain research. Exploring the high dimensional parameter space using numerical simulations has been a frequently used technique in the last years in many areas of computational neuroscience. High performance computing (HPC) can provide today a powerful infrastructure to speed up explorations and increase our general understanding of the model's behavior in reasonable times.
KEYWORDS simulation, meta learning, hyper-parameter optimization, high performance computing, connectivity generation, parameter exploration
DOI 10.3389/fncom.2022.885207
PUBLICATION Frontiers in computational neuroscience 16
article no. 885207