---
_id: '60900'
abstract:
- lang: eng
text: 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.
article_number: '885207'
author:
- first_name: Alper
full_name: Yegenoglu, Alper
id: '117951'
last_name: Yegenoglu
orcid: 0000-0001-8869-215X
- first_name: Anand
full_name: Subramoney, Anand
last_name: Subramoney
- first_name: Thorsten
full_name: Hater, Thorsten
last_name: Hater
- first_name: Cristian
full_name: Jimenez-Romero, Cristian
last_name: Jimenez-Romero
- first_name: Wouter
full_name: Klijn, Wouter
last_name: Klijn
- first_name: Aarón
full_name: Pérez Martín, Aarón
last_name: Pérez Martín
- first_name: Michiel
full_name: van der Vlag, Michiel
last_name: van der Vlag
- first_name: Michael
full_name: Herty, Michael
last_name: Herty
- first_name: Abigail
full_name: Morrison, Abigail
last_name: Morrison
- first_name: Sandra
full_name: Diaz-Pier, Sandra
last_name: Diaz-Pier
citation:
ama: Yegenoglu A, Subramoney A, Hater T, et al. Exploring Parameter and Hyper-Parameter
Spaces of Neuroscience Models on High Performance Computers With Learning to Learn.
Frontiers in Computational Neuroscience. 2022;16. doi:10.3389/fncom.2022.885207
apa: Yegenoglu, A., Subramoney, A., Hater, T., Jimenez-Romero, C., Klijn, W., Pérez
Martín, A., van der Vlag, M., Herty, M., Morrison, A., & Diaz-Pier, S. (2022).
Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High
Performance Computers With Learning to Learn. Frontiers in Computational Neuroscience,
16, Article 885207. https://doi.org/10.3389/fncom.2022.885207
bibtex: '@article{Yegenoglu_Subramoney_Hater_Jimenez-Romero_Klijn_Pérez Martín_van
der Vlag_Herty_Morrison_Diaz-Pier_2022, title={Exploring Parameter and Hyper-Parameter
Spaces of Neuroscience Models on High Performance Computers With Learning to Learn},
volume={16}, DOI={10.3389/fncom.2022.885207},
number={885207}, journal={Frontiers in Computational Neuroscience}, publisher={Frontiers
Media SA}, author={Yegenoglu, Alper and Subramoney, Anand and Hater, Thorsten
and Jimenez-Romero, Cristian and Klijn, Wouter and Pérez Martín, Aarón and van
der Vlag, Michiel and Herty, Michael and Morrison, Abigail and Diaz-Pier, Sandra},
year={2022} }'
chicago: Yegenoglu, Alper, Anand Subramoney, Thorsten Hater, Cristian Jimenez-Romero,
Wouter Klijn, Aarón Pérez Martín, Michiel van der Vlag, Michael Herty, Abigail
Morrison, and Sandra Diaz-Pier. “Exploring Parameter and Hyper-Parameter Spaces
of Neuroscience Models on High Performance Computers With Learning to Learn.”
Frontiers in Computational Neuroscience 16 (2022). https://doi.org/10.3389/fncom.2022.885207.
ieee: 'A. Yegenoglu et al., “Exploring Parameter and Hyper-Parameter Spaces
of Neuroscience Models on High Performance Computers With Learning to Learn,”
Frontiers in Computational Neuroscience, vol. 16, Art. no. 885207, 2022,
doi: 10.3389/fncom.2022.885207.'
mla: Yegenoglu, Alper, et al. “Exploring Parameter and Hyper-Parameter Spaces of
Neuroscience Models on High Performance Computers With Learning to Learn.” Frontiers
in Computational Neuroscience, vol. 16, 885207, Frontiers Media SA, 2022,
doi:10.3389/fncom.2022.885207.
short: A. Yegenoglu, A. Subramoney, T. Hater, C. Jimenez-Romero, W. Klijn, A. Pérez
Martín, M. van der Vlag, M. Herty, A. Morrison, S. Diaz-Pier, Frontiers in Computational
Neuroscience 16 (2022).
date_created: 2025-08-06T15:02:30Z
date_updated: 2025-08-08T11:40:08Z
doi: 10.3389/fncom.2022.885207
intvolume: ' 16'
language:
- iso: eng
publication: Frontiers in Computational Neuroscience
publication_identifier:
issn:
- 1662-5188
publication_status: published
publisher: Frontiers Media SA
status: public
title: Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High
Performance Computers With Learning to Learn
type: journal_article
user_id: '117951'
volume: 16
year: '2022'
...
---
_id: '60903'
article_number: '41'
author:
- first_name: Pietro
full_name: Quaglio, Pietro
last_name: Quaglio
- first_name: Alper
full_name: Yegenoglu, Alper
id: '117951'
last_name: Yegenoglu
orcid: 0000-0001-8869-215X
- first_name: Emiliano
full_name: Torre, Emiliano
last_name: Torre
- first_name: Dominik M.
full_name: Endres, Dominik M.
last_name: Endres
- first_name: Sonja
full_name: Grün, Sonja
last_name: Grün
citation:
ama: Quaglio P, Yegenoglu A, Torre E, Endres DM, Grün S. Detection and Evaluation
of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with
SPADE. Frontiers in Computational Neuroscience. 2017;11. doi:10.3389/fncom.2017.00041
apa: Quaglio, P., Yegenoglu, A., Torre, E., Endres, D. M., & Grün, S. (2017).
Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel
Spike Train Data with SPADE. Frontiers in Computational Neuroscience, 11,
Article 41. https://doi.org/10.3389/fncom.2017.00041
bibtex: '@article{Quaglio_Yegenoglu_Torre_Endres_Grün_2017, title={Detection and
Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train
Data with SPADE}, volume={11}, DOI={10.3389/fncom.2017.00041},
number={41}, journal={Frontiers in Computational Neuroscience}, publisher={Frontiers
Media SA}, author={Quaglio, Pietro and Yegenoglu, Alper and Torre, Emiliano and
Endres, Dominik M. and Grün, Sonja}, year={2017} }'
chicago: Quaglio, Pietro, Alper Yegenoglu, Emiliano Torre, Dominik M. Endres, and
Sonja Grün. “Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively
Parallel Spike Train Data with SPADE.” Frontiers in Computational Neuroscience
11 (2017). https://doi.org/10.3389/fncom.2017.00041.
ieee: 'P. Quaglio, A. Yegenoglu, E. Torre, D. M. Endres, and S. Grün, “Detection
and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train
Data with SPADE,” Frontiers in Computational Neuroscience, vol. 11, Art.
no. 41, 2017, doi: 10.3389/fncom.2017.00041.'
mla: Quaglio, Pietro, et al. “Detection and Evaluation of Spatio-Temporal Spike
Patterns in Massively Parallel Spike Train Data with SPADE.” Frontiers in Computational
Neuroscience, vol. 11, 41, Frontiers Media SA, 2017, doi:10.3389/fncom.2017.00041.
short: P. Quaglio, A. Yegenoglu, E. Torre, D.M. Endres, S. Grün, Frontiers in Computational
Neuroscience 11 (2017).
date_created: 2025-08-06T15:02:58Z
date_updated: 2025-08-08T11:39:26Z
doi: 10.3389/fncom.2017.00041
intvolume: ' 11'
language:
- iso: eng
publication: Frontiers in Computational Neuroscience
publication_identifier:
issn:
- 1662-5188
publication_status: published
publisher: Frontiers Media SA
status: public
title: Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel
Spike Train Data with SPADE
type: journal_article
user_id: '117951'
volume: 11
year: '2017'
...
---
_id: '17236'
abstract:
- lang: eng
text: 'The behavior for a humanoid robot is often modeled in accordance with human
behavior. Current research suggests that analyzing infant behavior as a basis
for designing the robot behavior can guide us to a natural robot interface. Based
on this idea many researchers support saliency systems as a bottom-up inspired
way to simulate infant-like gazing behavior. In the field of saliency systems
many different approaches have proposed and quantified in terms of speed, quality
and other technical issues. But so far, no one compared and quantified them in
terms of natural infant tutor interaction. The question we would like to address
in this paper is: Can state-of-the-art saliency systems model infant gazing behavior
in tutoring situations? By addressing these issues we want to take a step towards
an autonomous robot system, which could be used more natural interaction experiments
in future.'
author:
- first_name: Vikram
full_name: Narayan, Vikram
last_name: Narayan
- first_name: Katrin Solveig
full_name: Lohan, Katrin Solveig
last_name: Lohan
- first_name: Marko
full_name: Tscherepanow, Marko
last_name: Tscherepanow
- first_name: Katharina
full_name: Rohlfing, Katharina
id: '50352'
last_name: Rohlfing
- first_name: Britta
full_name: Wrede, Britta
last_name: Wrede
citation:
ama: Narayan V, Lohan KS, Tscherepanow M, Rohlfing K, Wrede B. Can state-of-the-art
saliency systems model infant gazing behavior in tutoring situations? Frontiers
in Computational Neuroscience. 2011;5(35). doi:10.3389/conf.fncom.2011.52.00035
apa: Narayan, V., Lohan, K. S., Tscherepanow, M., Rohlfing, K., & Wrede, B.
(2011). Can state-of-the-art saliency systems model infant gazing behavior in
tutoring situations? Frontiers in Computational Neuroscience, 5(35).
https://doi.org/10.3389/conf.fncom.2011.52.00035
bibtex: '@article{Narayan_Lohan_Tscherepanow_Rohlfing_Wrede_2011, title={Can state-of-the-art
saliency systems model infant gazing behavior in tutoring situations?}, volume={5},
DOI={10.3389/conf.fncom.2011.52.00035},
number={35}, journal={Frontiers in Computational Neuroscience}, publisher={Frontiers
Media SA}, author={Narayan, Vikram and Lohan, Katrin Solveig and Tscherepanow,
Marko and Rohlfing, Katharina and Wrede, Britta}, year={2011} }'
chicago: Narayan, Vikram, Katrin Solveig Lohan, Marko Tscherepanow, Katharina Rohlfing,
and Britta Wrede. “Can State-of-the-Art Saliency Systems Model Infant Gazing Behavior
in Tutoring Situations?” Frontiers in Computational Neuroscience 5, no.
35 (2011). https://doi.org/10.3389/conf.fncom.2011.52.00035.
ieee: 'V. Narayan, K. S. Lohan, M. Tscherepanow, K. Rohlfing, and B. Wrede, “Can
state-of-the-art saliency systems model infant gazing behavior in tutoring situations?,”
Frontiers in Computational Neuroscience, vol. 5, no. 35, 2011, doi: 10.3389/conf.fncom.2011.52.00035.'
mla: Narayan, Vikram, et al. “Can State-of-the-Art Saliency Systems Model Infant
Gazing Behavior in Tutoring Situations?” Frontiers in Computational Neuroscience,
vol. 5, no. 35, Frontiers Media SA, 2011, doi:10.3389/conf.fncom.2011.52.00035.
short: V. Narayan, K.S. Lohan, M. Tscherepanow, K. Rohlfing, B. Wrede, Frontiers
in Computational Neuroscience 5 (2011).
date_created: 2020-06-24T13:02:00Z
date_updated: 2023-02-01T12:57:14Z
department:
- _id: '749'
doi: 10.3389/conf.fncom.2011.52.00035
intvolume: ' 5'
issue: '35'
keyword:
- child gazing behavior
- computer vision
- saliency
- development
language:
- iso: eng
publication: Frontiers in Computational Neuroscience
publication_identifier:
issn:
- 1662-5188
publisher: Frontiers Media SA
status: public
title: Can state-of-the-art saliency systems model infant gazing behavior in tutoring
situations?
type: journal_article
user_id: '14931'
volume: 5
year: '2011'
...
---
_id: '17245'
author:
- first_name: Lars
full_name: Schillingmann, Lars
last_name: Schillingmann
- first_name: Petra
full_name: Wagner, Petra
last_name: Wagner
- first_name: Christian
full_name: Munier, Christian
last_name: Munier
- first_name: Britta
full_name: Wrede, Britta
last_name: Wrede
- first_name: Katharina
full_name: Rohlfing, Katharina
id: '50352'
last_name: Rohlfing
citation:
ama: 'Schillingmann L, Wagner P, Munier C, Wrede B, Rohlfing K. Acoustic Packaging
and the Learning of Words. In: ; 2011. doi:10.3389/conf.fncom.2011.52.00020'
apa: Schillingmann, L., Wagner, P., Munier, C., Wrede, B., & Rohlfing, K. (2011).
Acoustic Packaging and the Learning of Words. https://doi.org/10.3389/conf.fncom.2011.52.00020
bibtex: '@inproceedings{Schillingmann_Wagner_Munier_Wrede_Rohlfing_2011, title={Acoustic
Packaging and the Learning of Words}, DOI={10.3389/conf.fncom.2011.52.00020},
author={Schillingmann, Lars and Wagner, Petra and Munier, Christian and Wrede,
Britta and Rohlfing, Katharina}, year={2011} }'
chicago: Schillingmann, Lars, Petra Wagner, Christian Munier, Britta Wrede, and
Katharina Rohlfing. “Acoustic Packaging and the Learning of Words,” 2011. https://doi.org/10.3389/conf.fncom.2011.52.00020.
ieee: 'L. Schillingmann, P. Wagner, C. Munier, B. Wrede, and K. Rohlfing, “Acoustic
Packaging and the Learning of Words,” 2011, doi: 10.3389/conf.fncom.2011.52.00020.'
mla: Schillingmann, Lars, et al. Acoustic Packaging and the Learning of Words.
2011, doi:10.3389/conf.fncom.2011.52.00020.
short: 'L. Schillingmann, P. Wagner, C. Munier, B. Wrede, K. Rohlfing, in: 2011.'
date_created: 2020-06-24T13:02:11Z
date_updated: 2023-02-01T12:54:16Z
department:
- _id: '749'
doi: 10.3389/conf.fncom.2011.52.00020
keyword:
- Prominence
- Multimodal Action Segmentation
- Feedback
- Color Saliency
- Human Robot Interaction
language:
- iso: eng
publication_identifier:
issn:
- 1662-5188
status: public
title: Acoustic Packaging and the Learning of Words
type: conference
user_id: '14931'
year: '2011'
...