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  • 1.
    Djurfeldt, Mikael
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC.
    The Connection-set Algebra-A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models2012In: Neuroinformatics, ISSN 1539-2791, E-ISSN 1559-0089, Vol. 10, no 3, p. 287-304Article in journal (Refereed)
    Abstract [en]

    The connection-set algebra (CSA) is a novel and general formalism for the description of connectivity in neuronal network models, from small-scale to large-scale structure. The algebra provides operators to form more complex sets of connections from simpler ones and also provides parameterization of such sets. CSA is expressive enough to describe a wide range of connection patterns, including multiple types of random and/or geometrically dependent connectivity, and can serve as a concise notation for network structure in scientific writing. CSA implementations allow for scalable and efficient representation of connectivity in parallel neuronal network simulators and could even allow for avoiding explicit representation of connections in computer memory. The expressiveness of CSA makes prototyping of network structure easy. A C+ + version of the algebra has been implemented and used in a large-scale neuronal network simulation (Djurfeldt et al., IBM J Res Dev 52(1/2):31-42, 2008b) and an implementation in Python has been publicly released.

  • 2.
    Djurfeldt, Mikael
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hjorth, Johannes
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Eppler, Jochen
    Honda Research Institute.
    Dudani, Niraj
    Helias, Moritz
    University of Freiburg, Germany.
    Potjans, Tobias
    Bhalla, Upinder
    Diesmann, Markus
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Ekeberg, Örjan
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework2010In: Neuroinformatics, ISSN 1539-2791, E-ISSN 1559-0089, Vol. 8, no 1, p. 43-60Article in journal (Refereed)
    Abstract [en]

    MUSIC is an API allowing large scale neuron simulators using MPI internally to exchange data during runtime. We provide experiences from the adaptation of two neuronal network simulators of different kinds, NEST and MOOSE, to this API. A multi-simulation of a cortico-striatal network model involving both simulators is performed, demonstrating how MUSIC can promote inter-operability between models written for different simulators and how these can be re-used to build a larger model system. We conclude that MUSIC fulfills the design goals of being portable and simple to adapt to existing simulators. In addition, since the MUSIC API enforces independence between the applications, the multi-simulationcould be built from pluggable component modules without adaptation of the components to each other in terms of simulation time-step or topology of connections between the modules.

  • 3.
    Gutierrez-Arenas, Omar
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Handling and Analyzing Meshed Rendering of Segmented Structures From 3D Image Stacks in Blender2015In: Neuroinformatics, ISSN 1539-2791, E-ISSN 1559-0089, Vol. 13, no 2, p. 151-152Article in journal (Other academic)
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