AUTHOR=Yegenoglu Alper , Subramoney Anand , Hater Thorsten , Jimenez-Romero Cristian , Klijn Wouter , Pérez Martín Aarón , van der Vlag Michiel , Herty Michael , Morrison Abigail , Diaz-Pier Sandra 

TITLE=Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn

JOURNAL=Frontiers in Computational Neuroscience

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2022.885207

DOI=10.3389/fncom.2022.885207

ISSN=1662-5188

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 parameter and hyper-parameter space exploration of neuroscience models on HPC infrastructure.L2L  is  an  implementation  of  the  learning  to  learn  concept  written  in  Python.  This  open-source software allows several instances of an optimization target to be executed with different parameters in a embarrassingly parallel fashion on HPC. L2L provides a set of built-in optimizer algorithms which makes adaptive and efficient exploration of parameter spaces possible. Different from other optimization toolboxes, L2L provides maximum flexibility for the way the optimization target can be executed. In this paper we show a variety of examples of neuroscience models being optimized within the L2L framework to execute different types of tasks. The tasks used to illustrate the concept go from reproducing empirical data to learning how to solve a problem in a dynamic environment. We particularly focus on simulations with models ranging from the single cell to the whole brain and using a variety of simulation engines like NEST, Arbor,TVB, OpenAIGym and NetLogo.