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

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

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. Expl...

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Bibliographic Details
Main Authors: Alper Yegenoglu, Anand Subramoney, Thorsten Hater, Cristian Jimenez-Romero, Wouter Klijn, Aarón Pérez Martín, Michiel van der Vlag, Michael Herty, Abigail Morrison, Sandra Diaz-Pier
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-05-01
Series:Frontiers in Computational Neuroscience
Subjects:
simulation
meta learning
hyper-parameter optimization
high performance computing
connectivity generation
parameter exploration
Online Access:https://www.frontiersin.org/articles/10.3389/fncom.2022.885207/full

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https://www.frontiersin.org/articles/10.3389/fncom.2022.885207/full

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