INCF Working Group on Standardized Representations of Network Structures
Padraig Gleeson, UCL
The motivation for this Working Group is the ongoing development of complex data-driven models of neuronal networks, as well as the emergence of general purpose software packages and standardised formats to make it easier to build, specify and share such networks. To encourage researchers to use these common tools and formats (and ultimately create and disseminate higher quality models) the group gathers together developers creating these packages to share code and ideas, to encourage interoperability and have a forum for users and developers to find out about the state of the art and to contribute to a better ecosystem of tools and standards in this area.
Meetings at relevant conferences and workshops. Organizing workshops. An overview of the tasks which are ongoing and planned related to the work of this SIG can be found here (GitHub).
Anton Arkhipov, Allen Institute
Tom Close, Monash University
Sharon Crook, Arizona State University
Kael Dai, Allen Institute
Andrew Davison, UNIC & CNRS
Lia Domide, Codemart & Aix-Marseille University
Salvador Durá-Bernal, SUNY DMC
Viktor Jirsa, Aix-Marseille University
John Griffiths, RRI & Baycrest
Padraig Gleeson, UCL
Sacha van Albada, Jülich Research Centre
Marmaduke Woodman, Aix-Marseille University
This Working Group deals with the various tools and formats for creating and sharing representations of biological neuronal networks, and will work towards ensuring these are as interoperable and usable as possible for computational neuroscientists.
- Converting TVB models to equivalents in NeuroML/LEMS (expected)
- Shared JSON based notation for network specification/generation (expected)
- Extend NeuroML to incorporate rate based/neural mass models (expected)
- Convert multi area cortical model to standardised format (expected)
- Whole brain connectivity datasets in standardised formats (expected)
- Incorporate NetPyNE UI into OSB (expected)
- Improve interoperability of NeuroML/NineML (expected)