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Related Experiment Video

Updated: Sep 7, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Published on: March 2, 2015

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

Alper Yegenoglu^1,2, Anand Subramoney³, Thorsten Hater¹

¹Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany.

Frontiers in Computational Neuroscience

|June 20, 2022

Summary

This study introduces Learning to Learn (L2L), a Python framework for efficiently exploring complex neuroscience model parameters using high-performance computing (HPC). L2L accelerates the discovery of critical model behaviors for advancing brain research.

Keywords:

connectivity generation high performance computing hyper-parameter optimization meta learning parameter exploration simulation

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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Area of Science:

Computational Neuroscience
Machine Learning
High-Performance Computing

Background:

Neuroscience models often have numerous parameters, with only specific regions yielding relevant dynamics.
Efficiently navigating these high-dimensional parameter spaces is crucial for advancing brain research.

Purpose of the Study:

To present Learning to Learn (L2L) as a flexible framework for parameter and hyper-parameter space exploration of neuroscience models.
To leverage high-performance computing (HPC) infrastructure for accelerated model analysis.

Main Methods:

Developed an open-source Python framework implementing the Learning to Learn (L2L) concept.
Utilized embarrassingly parallel execution on HPC for optimizing various neuroscience models.
Integrated built-in adaptive and efficient optimizer algorithms.

Main Results:

Demonstrated L2L's versatility across diverse neuroscience models, from single cells to whole-brain simulations.
Showcased L2L's ability to perform tasks such as reproducing empirical data and dynamic environment problem-solving.
Validated L2L with simulation engines including NEST, Arbor, TVB, OpenAIGym, and NetLogo.

Conclusions:

L2L provides an accessible and adaptable tool for efficient parameter space exploration in computational neuroscience.
The framework facilitates a deeper understanding of complex neural system dynamics on HPC infrastructure.