Exploring hyper-parameter spaces of neuroscience models on high performance computers with Learning to Learn
This work addresses a bottleneck in computational neuroscience by improving parameter exploration efficiency, though it appears incremental as it applies existing methods to new infrastructure.
The paper tackles the challenge of efficiently exploring high-dimensional parameter spaces in neuroscience models to find regions that produce interesting dynamics, proposing the use of Learning to Learn on high-performance computers to speed up this process and enhance understanding of model behavior.
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.