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

Frontiers in Computational Neuroscience, vol. 16, pp. 885207

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. Today, high performance computing (HPC) can provide a powerful infrastructure to speed up explorations and increase our general understanding of the behavior of the model in reasonable times. Learning to learn (L2L) is a well-known concept in machine learning (ML) 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. Learning to learn is an implementation of the L2L concept written in Python. This open-source software allows several instances of an optimization target to be executed with different parameters in an embarrassingly parallel fashion on HPC. L2L provides a set of built-in optimizer algorithms, which make 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.

Authors 10

1. Alper Yegenoglu corresponding Aachen Department of Mathematics Institute of Geometry and Applied Mathematics

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · Jülich Supercomputing Centre · RWTH Aachen University

   Affiliation as printed

   Department of Mathematics, Institute of Geometry and Applied Mathematics, RWTH Aachen University, Aachen, Germany

   Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany

2. Anand Subramoney

   Ruhr University Bochum

   Affiliation as printed

   Institute of Neural Computation, Ruhr University Bochum, Bochum, Germany

3. Thorsten Hater

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · Jülich Supercomputing Centre

   Affiliation as printed

   Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany

4. Cristian Jimenez-Romero

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · Jülich Supercomputing Centre

   Affiliation as printed

   Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany

5. Wouter Klijn

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · Jülich Supercomputing Centre

   Affiliation as printed

   Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany

6. Aarón Pérez Martín

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · Jülich Supercomputing Centre

   Affiliation as printed

   Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany

7. Michiel van der Vlag

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · Jülich Supercomputing Centre

   Affiliation as printed

   Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany

8. Michaël Herty Aachen Department of Mathematics Institute of Geometry and Applied Mathematics

   RWTH Aachen University

   Affiliation as printed

   Department of Mathematics, Institute of Geometry and Applied Mathematics, RWTH Aachen University, Aachen, Germany

9. Abigail Morrison Aachen Computer Science 3 - Software Engineering

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · Jülich Supercomputing Centre · RWTH Aachen University

   Affiliation as printed

   Computer Science 3-Software Engineering, RWTH Aachen University, Aachen, Germany

   Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany

   Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany

10. Sandra Diaz-Pier

    Forschungszentrum Jülich · Jülich Aachen Research Alliance · Jülich Supercomputing Centre

    Affiliation as printed

    Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany