Exploring hyper-parameter spaces of neuroscience models on high performance computers with Learning to Learn
Neural and Evolutionary Computing
2022-12-19 v1
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.
Cite
@article{arxiv.2202.13822,
title = {Exploring hyper-parameter spaces of neuroscience models on high performance computers with Learning to Learn},
author = {Alper Yegenoglu and Anand Subramoney and Thorsten Hater and Cristian Jimenez-Romero and Wouter Klijn and Aaron Perez Martin and Michiel van der Vlag and Michael Herty and Abigail Morrison and Sandra Diaz-Pier},
journal= {arXiv preprint arXiv:2202.13822},
year = {2022}
}