The Connection-set Algebra---a novel formalism for the representation of connectivity structure in neuronal network models
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1
INCF and KTH, PDC, Sweden
The connection-set algebra is a novel notation for the description of connectivity in neuronal network models. Both the connection structure, that is which connections exist, and parameters associated with connections can be described. An elementary set of connection patterns, connection-sets, are provided as well as a set of rules for how to compute new connection-sets from elementary ones. The design goals are to provide a description which is close to how we think about the connection pattern, is expressive enough to describe a wide range of connectivities, and, is still possible to implement in a neuronal network simulator with reasonable time and memory complexity. Special consideration is given to parallel simulators. A C++ version of the algebra has been implemented and used in a large-scale neuronal network simulation.
References
1. Djurfeldt M, Lundqvist M, Johansson C, Rehn M, Ekeberg Ö, Lansner A (2008) Brain-scale simulation of the neocortex on the Blue Gene/L supercomputer. IBM J Research and Development 52(1/2):31-42
Conference:
Neuroinformatics 2010 , Kobe, Japan, 30 Aug - 1 Sep, 2010.
Presentation Type:
Poster Presentation
Topic:
Computational Neuroscience
Citation:
Djurfeldt
M
(2010). The Connection-set Algebra---a novel formalism for the representation of connectivity structure in neuronal network models.
Front. Neurosci.
Conference Abstract:
Neuroinformatics 2010 .
doi: 10.3389/conf.fnins.2010.13.00086
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Received:
14 Jun 2010;
Published Online:
14 Jun 2010.
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Correspondence:
Mikael Djurfeldt, INCF and KTH, PDC, Stockholm, Sweden, mikael@djurfeldt.com