14th Fenix Infrastructure Webinar "EBRAINS services deployed on ICEI: Embodied Large Scale Spiking Neural Networks in the Neurorobotics Platform

17 Feb 2022
Place: Online

The 14th Fenix Infrastructure Webinar "EBRAINS services deployed on ICEI: Embodied Large Scale Spiking Neural Networks in the Neurorobotics Platform" takes place on Tuesday 15 February 2021 at 15:00 CET. 

Date and Time: Thursday 17 February 2022, 15:00-16:00 CET

Cost: Free of charge

Speakers: Benedikt Feldotto (Technische Universität München)

Description: This webinar will present the deployment of the Neurorobotics Platform (NRP) on Fenix Research Infrastructure exploiting HPC compute node parallelization for embodied large scale spiking neural network simulations. The webinar will introduce the architecture that builds up on persistent virtual machines as well as requested compute nodes and presents benchmark results showcasing the benefits of a highly distributed simulation setup. You will learn about a browser based Graphical User Interface for managing simulation jobs and see a use case of an embodied million neuron rodent brain simulation running interactively in the NRP.

Who should attend?

  • Neuroscientists
  • Roboticists with focus on biologically derived learning
  • Researchers in Embodied AI
  • HPC infrastructure users
  • EBRAINS service developers
  • Other platform service developers

Main takeaways

  • How the NRP is deployed on HPC infrastructure for large scale simulations
  • How we interface virtual machines, compute nodes and a Graphical User Interface for an interactive, easily accessible simulation service
  • How scientific tool developers can make their services available using ICEI/Fenix Research Infrastructure

Agenda

  • Architecture and Interfaces
  • Multi-Node Brain Parallelization
  • Graphical User Interface
  • Embodied Million Neuron Rodent Brain Simulation
  • Q&A

The webinar has been recorded and the full recording and presentation slides are available on the Fenix Webinars page.

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Fenix has received funding from the European Union's Horizon 2020 research and innovation programme through the ICEI project under the grant agreement No. 800858.