EBRAINS Modelling Services

What we do

The work package manages the overall architecture integrating scientific software into EBRAINS. It comprises the development and advancement of applications for brain modelling and simulation, analysis of neural activity data, model validation, and visualization. The deployed services will combine the applications into comprehensive workflows, they will enable multiscale co-simulation of brain processes and closed-loop neuroscience experiments /in silico/, which in turn support R&D on embodied AI and robotics. The work package delivers the e-infrastructure for operating all services of the project on the computing infrastructure allocated to them.

How we're organized

WP5 comprises SC3 - Brain modeling and simulation workflows: integrated tools to create and investigate models of the brain, SC4 - Closed loop AI and robotics workflows: design, test and implement robotic and AI solutions tasks, and tasks that will deliver the necessary e-infrastructure for all service categories of the project.

Publication highlights

  • Einevoll GT, Destexhe A, Diesmann M, Grün S, Jirsa V, de Kamps M, Migliore M, Ness TV, Plesser HE, Schürmann F (2019). The Scientific Case for Brain Simulations. Neuron, 102, 735–744. DOI: 10.1016/j.neuron.2019.03.027
  • Galindo SE, Toharia P, Robles ÓD, Pastor L (2016). ViSimpl: Multi-View Visual Analysis of Brain Simulation Data. Frontiers in Neuroinformatics, 10. DOI: 10.3389/fninf.2016.00044, ISSN=1662-5196
  • Galindo SE, Toharia P, Robles ÓD, Ros E, Pastor L, Garrido JA (2020). Simulation, visualization and analysis tools for pattern recognition assessment with spiking neuronal networks. Neurocomputing, 400:309-321. DOI: 10.1016/j.neucom.2020.02.114.
  • Garcia-Cantero JJ, Brito JP, Mata S, Bayona S, Pastor L (2017). NeuroTessMesh: A Tool for the Generation and Visualization of Neuron Meshes and Adaptive On-the-Fly Refinement. Frontiers in Neuroinformatics, 11. DOI: 10.3389/fninf.2017.00038, ISSN=1662-5196
  • Gutzen R, von Papen M, Trensch G, Quaglio P, Grün S, and Denker M (2018). Reproducible Neural Network Simulations: Statistical Methods for Model Validation on the Level of Network Activity Data. Frontiers in Neuroinformatics 12, 90. DOI: 10.3389/fninf.2018.00090
  • Jordan J, Ippen T, Helias M, Kitayama I, Sato M, Igarashi J, Diesmann M, Kunkel S (2018). Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers. Frontiers in Neuroinformatics, 12(2). DOI: 10.3389/fninf.2018.00002
  • Jordan J, Helias M, Diesmann M and Kunkel S (2020). Efficient Communication in Distributed Simulations of Spiking Neuronal Networks With Gap Junctions. Front. Neuroinform. 14:12. DOI: 10.3389/fninf.2020.00012
  • Kumbhar P, Hines M, Fouriaux J, Ovcharenko A, King J, Delalondre F, Schürmann F (2019). CoreNEURON: An optimized compute engine for the neuron simulator. Front. Neuroinform. 13, 63. DOI: 10.3389/fninf.2019.00063
  • Migliore R, Lupascu CA, Bologna LL, Romani A, Courcol JD, Antonel S, Van Geit WAH, Thomson AM, Mercer A, Lange S, Falck J, Roessert CA, Freund TF, Kali S, Muller EB, Schürmann F, Markram H, Migliore M (2018). The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow. PLOS Computational Biology, 14(9). DOI: 10.1371/journal.pcbi.1006423.
  • Schmidt M, Bakker R, Shen K, Bezgin G, Diesmann M, van Albada SJ (2018). A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas. PLOS Computational Biology, 14(10). DOI: 10.1371/journal.pcbi.1006359
  • van Albada SJ, Rowley AG, Senk J, Hopkins M, Schmidt M, Stokes AB, Lester DR, Diesmann M, Furber SB (2018). Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model. Frontiers in Neuroscience, 12(291). DOI: 10.3389/fnins.2018.00291