Fenix Infrastructure presented at the Neuroinformatics 2019 conference

31 Aug 2019

Participants of the Neuroinformatics 2019 conference had the opportunity to learn about Fenix from Michele Migliore (National Research Council, Italy), who gave an overview of the Fenix Infrastructure and presented the available resources during the "Simulating the brain with the Brain Simulation Platform (BSP)" workshop.

The workshop was held on 31 August, just before the official start of the annual INCF Congress (Neuroinformatics 2019) at the Department of Physics, University of Warsaw, Poland. This short course consisted of 5 plenary lectures and 4 hands-on tutorials and its aim was to introduce the students to the Brain Simulation Platform (BSP) of the Human Brain Project (HBP).

Michele Migliore stated that “this workshop was an excellent opportunity to introduce the Fenix resources and services to users from different communities in the field of Computational Neuroscience” and he continued saying that “the attendees were interested in learning about what the Fenix Infrastructure has to offer they could use for their research projects”.

After the course, students were able to use the BSP and access HPC systems to configure and run a simulation, to visualize and analyse simulation results, and to form collaborative groups interested in exploring scientific issues of common interest.

About Neuroinformatics 2019

The annual INCF Congress (Neuroinformatics 2019) took place on 1-2 September 2019 at the Department of Physics, University of Warsaw, Poland. It provided a forum for researchers in all fields of neuroinformatics and for infrastructure providers and developers of tools, standards, and best practices to help define the capabilities of the global network and to receive training on how to participate in the Open, FAIR and Citable global network. The conference was also a great opportunity to introduce new participants to the field of neuroinformatics and to highlight new and innovative tools and approaches.

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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.