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

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.Learning to learn is a well known concept in machine learning and a specific method for acquiring constraints to improve learning performance. This concept can be decomposed into a two loop optimization process where the target of optimization can consist of any program such as an artificial neural network, a spiking network, a single cell model or a whole brain simulation. In this work we present L2L as an easy to use and flexible framework to perform hyper-parameter space exploration of neuroscience models on HPC infrastructure.