@article{60900,
  abstract     = {{<jats:p>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.</jats:p>}},
  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}},
  issn         = {{1662-5188}},
  journal      = {{Frontiers in Computational Neuroscience}},
  publisher    = {{Frontiers Media SA}},
  title        = {{{Exploring Parameter and Hyper-Parameter Spaces of Neuroscience Models on High Performance Computers With Learning to Learn}}},
  doi          = {{10.3389/fncom.2022.885207}},
  volume       = {{16}},
  year         = {{2022}},
}

@article{60903,
  author       = {{Quaglio, Pietro and Yegenoglu, Alper and Torre, Emiliano and Endres, Dominik M. and Grün, Sonja}},
  issn         = {{1662-5188}},
  journal      = {{Frontiers in Computational Neuroscience}},
  publisher    = {{Frontiers Media SA}},
  title        = {{{Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE}}},
  doi          = {{10.3389/fncom.2017.00041}},
  volume       = {{11}},
  year         = {{2017}},
}

@article{17236,
  abstract     = {{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       = {{Narayan, Vikram and Lohan, Katrin Solveig and Tscherepanow, Marko and Rohlfing, Katharina and Wrede, Britta}},
  issn         = {{1662-5188}},
  journal      = {{Frontiers in Computational Neuroscience}},
  keywords     = {{child gazing behavior, computer vision, saliency, development}},
  number       = {{35}},
  publisher    = {{Frontiers Media SA}},
  title        = {{{Can state-of-the-art saliency systems model infant gazing behavior in tutoring situations?}}},
  doi          = {{10.3389/conf.fncom.2011.52.00035}},
  volume       = {{5}},
  year         = {{2011}},
}

@inproceedings{17245,
  author       = {{Schillingmann, Lars and Wagner, Petra and Munier, Christian and Wrede, Britta and Rohlfing, Katharina}},
  issn         = {{1662-5188}},
  keywords     = {{Prominence, Multimodal Action Segmentation, Feedback, Color Saliency, Human Robot Interaction}},
  title        = {{{Acoustic Packaging and the Learning of Words}}},
  doi          = {{10.3389/conf.fncom.2011.52.00020}},
  year         = {{2011}},
}