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

TL;DR

This paper discusses leveraging high performance computing combined with learning strategies to efficiently explore complex parameter spaces in neuroscience models, aiming to identify regions that produce meaningful dynamics.

Contribution

It introduces a novel approach that integrates learning techniques with HPC to improve the exploration of high-dimensional neuroscience models.

Findings

Enhanced efficiency in exploring parameter spaces

Identification of key regions producing desired dynamics

Potential for accelerating brain research

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.