Connection-set Algebra (CSA) for the representation of connectivity in NN models (Djurfeldt 2012)

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Accession:144455
"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 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."
References:
1 . Djurfeldt M (2012) The Connection-set Algebra-A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models Neuroinformatics 10(3):287-304 [PubMed]
2 . Djurfeldt M, Lundqvist M, Johansson C, Rehn M, Ekeberg O, Lansner A (2008b) Brain-scale simulation of the neocortex on the Blue Gene/L supercomputer IBM Journal of Research and Development 52(1/2):31-42
Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: C or C++ program; Python;
Model Concept(s): Methods;
Implementer(s): Djurfeldt M;
  
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