Exploring hyper-parameter spaces of neuroscience models on high performance computers with Learning to Learn

arXiv (Cornell University)

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.

Authors 10

1. Alper Yegenoglu Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen

2. Anand Subramoney
3. Thorsten Hater
4. Cristian Jimenez-Romero Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen

5. Wouter Klijn Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen

6. Aarón Pérez Martín Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen

7. Michiel van der Vlag Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen

8. Michaël Herty Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen

9. Abigail Morrison Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen

10. Sandra Diaz-Pier Aachen

    RWTH Aachen University

    Affiliation as printed

    RWTH Aachen